SCODiA Report by MLBI Lab

Comprehensive Single-Cell Analysis of Colorectal Cancer Reveals Key Cellular and Molecular Alterations in the Tumor Microenvironment

This report details a single-cell RNA-seq analysis of human colon tissue, delineating profound changes in cellular composition, genomic integrity, and intercellular communication within colorectal cancer. UMAP visualizations show clear segregation of tumor versus adjacent normal cells and a strong association of aneuploidy with malignant Intestinal Epithelial cells. The tumor microenvironment is characterized by shifts in immune cell populations, notably an increase in immunosuppressive T cell subsets and specific pro-tumorigenic macrophage populations, along with extensive cell-cell interaction reprogramming. Key signaling pathways and surface markers identified in tumor cells, macrophages, and fibroblasts provide novel insights into disease progression and potential therapeutic avenues.

Contents

  1. Dataset overview
  2. UMAP Visualization of Colon Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy
  3. UMAP Visualization of Major Cell Type Scores, Ploidy Status, and Cell Type Annotations
  4. Marker Expression Dot Plot for Cell Subsets
  5. Genomic Copy Number Variation Analysis in Colorectal Intestinal Epithelial Cells
  6. CNV-Aware UMAP Visualization of Cell Type, Ploidy, Condition, and Sample Distribution in Colon Tissue
  7. Minor Cell Type Population Bar Plot Across Conditions
  8. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Colorectal Cancer
  9. T Cell Subset Population Dynamics in Colorectal Cancer
  10. Macrophage Subset Population Dynamics in Colorectal Cancer
  11. Differential Macrophage Subpopulation Proportions in Colorectal Cancer
  12. Ploidy Status of Intestinal Epithelial Cells in Colorectal Cancer
  13. Analysis of Cell-Cell Interaction Patterns in Colorectal Cancer
  14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Colorectal Cancer
  15. Condition-Specific Cell-Cell Interaction Patterns in Colorectal Cancer
  16. Surfaceome Marker Expression in Intestinal Epithelial Cells of Colorectal Cancer
  17. Macrophage Condition-Specific Surfaceome Markers in Colorectal Cancer
  18. Fibroblast Condition-Specific Surfaceome Marker Expression in Colorectal Cancer
  19. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Colorectal Cancer
  20. Analysis of Cell Cycle Pathway Gene Expression in Colorectal Cancer Intestinal Epithelial Cells
  21. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colorectal Cancer
  22. Gene Set Enrichment Analysis Across Cell Types in Colorectal Cancer
  23. Discussion
  24. Query List

0. Dataset overview

데이터셋 요약

세포 유형:

염색체 이수성 (CNV 및 Ploidy):

사전 계산된 분석 결과

추론된 생물학적 맥락

1. UMAP Visualization of Colon Single-Cell RNA-seq Data by Condition, Sample, Cell Type, and Ploidy

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) plots for single-cell RNA-sequencing data from colon tissue. These visualizations are colored by various metadata features: condition (Tumor vs. Adj_normal), sample (individual patient samples), celltype_major, celltype_minor, ploidy_dec (Aneuploid, Diploid, Unclear), and celltype_subset. The purpose is to evaluate the overall data structure, assess the quality of cell clustering and annotation, and reveal the distribution of different biological features across the cell landscape. The data is derived from human colon tissue, and the context strongly suggests the study of Colorectal Cancer.

Visual Summary

Condition

The UMAP colored by condition (Tumor vs. Adj_normal) reveals a striking separation between cells from tumor and adjacent normal tissues. The plot shows distinct clusters predominantly composed of either "Adj_normal" (e.g., the large cluster on the right) or "Tumor" cells (e.g., the large cluster in the top-middle/left). This clear segregation indicates significant transcriptional differences between cells originating from the tumor microenvironment compared to the healthy adjacent tissue.

Sample

When colored by individual sample, the UMAP displays a diverse distribution of colors across most major clusters. This intermingling of samples suggests that batch effects or sample-specific variations do not heavily dominate the overarching transcriptional landscape, indicating effective integration across different patient samples. While some small regions may show localized enrichment for specific samples, the overall impression is one of robust sample mixing, which is crucial for generalizing findings.

Cell Type (Major, Minor, Subset)

The UMAPs colored by celltype_major, celltype_minor, and celltype_subset demonstrate a hierarchical and biologically coherent clustering structure.

Ploidy Status

The UMAP colored by ploidy_dec provides critical insights into the genomic stability of the cells. "Diploid" cells (yellow) are widely distributed across the manifold, particularly enriched in regions corresponding to "Adj_normal" tissue and "Intestinal Epithelial cell" clusters. In stark contrast, "Aneuploid" cells (maroon) primarily localize to specific clusters that largely overlap with the "Tumor" condition and specific "Intestinal Epithelial cell" populations. This strong association of aneuploidy with the tumor compartment is a hallmark of malignancy. "Unclear" cells are sparsely scattered.

Biological Interpretation

The UMAP plots provide a foundational overview of the cellular heterogeneity in colon tissue, particularly highlighting the dramatic changes associated with colorectal cancer.

Annotation Notes

2. UMAP Visualization of Major Cell Type Scores, Ploidy Status, and Cell Type Annotations

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents several UMAP (Uniform Manifold Approximation and Projection) plots, which visualize the single-cell RNA-seq data in a lower-dimensional space. The plots display:

  1. Major Cell Type Scores: Individual plots show the confidence score (HiCAT_major_score) for each cell belonging to a specific major cell type (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Enteric neuron, Intestinal Epithelial cell). Higher scores (yellow/light green) indicate stronger confidence in the cell's assignment to that type.
  2. Ploidy Status: A UMAP colored by ploidy_dec (ploidy decision) categorizes cells as Aneuploid, Diploid, or Unclear. Aneuploidy is a hallmark of cancer cells.
  3. Major Cell Type Annotation: A final UMAP colored by the celltype_major annotation, showing the assigned identity for each cell.

The purpose of these visualizations is to assess the quality of cell type annotation, confirm distinct clustering of cell populations, and investigate the spatial relationship between specific cell types (especially Intestinal Epithelial cells) and aneuploid status within the UMAP embedding.

Visual Summary

Major Cell Type Score UMAPs:

Ploidy Status UMAP (ploidy_dec):

Major Cell Type Annotation UMAP (celltype_major):

Biological Interpretation

The UMAP visualizations provide a clear landscape of the cellular heterogeneity within the human colon tissue samples, differentiating between Tumor and Adj_normal conditions, which is consistent with the inferred Colorectal Cancer context.

  1. Robust Cell Type Identification: The distinct clustering and high confidence scores for major cell types like T cells, B cells, Myeloid cells, Endothelial cells, Stromal cells, and Intestinal Epithelial cells suggest a robust and accurate cell type annotation. Each major cell type forms a largely contiguous region in the UMAP, indicating good separation of transcriptomically distinct populations. This robust annotation is crucial for downstream differential expression and pathway analyses.
  2. Identification of Malignant Epithelial Cells: The ploidy_dec UMAP shows a prominent cluster of aneuploid cells specifically localized to the region occupied by Intestinal Epithelial cells. This observation strongly supports the resolution of "Intestinal Epithelial cell" as the tumor-origin cell type, as cancer cells are frequently characterized by aneuploidy. The clear segregation of aneuploid cells within the epithelial compartment helps delineate the tumor epithelial cells from non-malignant epithelial cells (potentially from adjacent normal tissue). This segregation is a critical step in isolating and characterizing the true tumor compartment.
  3. Composition of the Tumor Microenvironment: The presence of diverse immune cells (T cells, B cells, Myeloid cells, Mast cells), stromal cells (Fibroblasts, Smooth muscle cells implied by 'Stromal cell' major type), and Endothelial cells, alongside the Intestinal Epithelial cells, highlights the complexity of the colorectal cancer tumor microenvironment. Each of these cell types plays a critical role in tumor progression, immune response, and therapeutic resistance. For example, Myeloid cells, particularly Macrophages, are known to be highly plastic and contribute to pro- or anti-tumor immunity in CRC, depending on their polarization (e.g., M1 vs. M2).
  4. Minor Cell Populations/Unassigned Cells: The low scores for 'Enteric neuron' suggest this population is either very rare in these samples or its transcriptomic profile is not strongly represented by the chosen markers, leading to its absence as a major identified cluster. The 'unassigned' cluster might represent cells with ambiguous profiles, technical artifacts, or rare cell types not well-captured by the major categories.

Annotation Notes

3. Marker Expression Dot Plot for Cell Subsets

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a dot plot illustrating the expression of marker genes across different cell subsets (celltype_subset) identified in the single-cell RNA-seq data from human colon tissue. The plot serves as a critical step in validating cell type annotations by visualizing the specificity and expression levels of known or computationally derived marker genes within each cellular population. This helps confirm whether the assigned cell identities are supported by characteristic gene expression profiles.

Visual Summary

The dot plot displays cell types (celltype_subset) on the y-axis and marker genes on the x-axis. Each dot represents the expression of a specific gene within a particular cell type:

Biological Interpretation

The dot plot reveals distinct and largely specific marker gene expression patterns across the various celltype_subset categories, providing strong support for the current cell type annotations.

Key observations by cell type group include:

Intestinal Epithelial cells:

Endothelial cells:

T cells:

Overall, the observed marker gene expression patterns are highly consistent with the known biology of the assigned celltype_subset annotations for colon tissue, reinforcing the quality and reliability of the single-cell clustering and labeling.

Annotation Notes

The dot plot strongly validates the current celltype_subset annotations. The identified marker genes largely correspond to well-established markers for these cell types in the human colon, confirming the accuracy of the cell identity assignments. The distinct expression profiles for each subset, characterized by high specificity and robust expression levels (dark, large dots), suggest that the resolution of cell types is appropriate and biologically meaningful. No significant inconsistencies or misattributions were immediately apparent from this marker analysis.

---

References:

[1] PROX1 (PubMed search): https://pubmed.ncbi.nlm.nih.gov/?term=PROX1+lymphatic+endothelial+marker

[2] CTLA4 and TNFRSF18 (GITR) in T regulatory cells (PubMed search): https://pubmed.ncbi.nlm.nih.gov/?term=CTLA4+TNFRSF18+Treg

4. Genomic Copy Number Variation Analysis in Colorectal Intestinal Epithelial Cells

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates Copy Number Variations (CNVs) in Intestinal Epithelial cells from human colon tissue, comparing 'Tumor' and 'Adj_normal' conditions. The plot_cnv_heatmap tool was used to visualize log2(Copy Number Ratio) across genomic spots for individual cell groups, clustered by sample. A summary heatmap of significantly amplified regions and their frequency across tumor samples was also generated. The primary goal is to identify and characterize genomic alterations characteristic of colorectal cancer in the presumed tumor-origin cell type.

Visual Summary

CNV Heatmap (log2(CNR))

The main heatmap displays log2(Copy Number Ratio) values, where red signifies amplification, blue signifies deletion, and yellow/white indicates a near-diploid state.

CNV Amplification Frequency Summary Heatmap

This heatmap summarizes the frequency of significantly amplified regions at the cytogenetic band level across individual tumor samples. Darker blue indicates higher frequency of amplification for a given region in a given sample.

Biological Interpretation

The CNV analysis of Intestinal Epithelial cells strongly supports the inference of colorectal cancer pathology.

Clinical or Translational Implications

5. CNV-Aware UMAP Visualization of Cell Type, Ploidy, Condition, and Sample Distribution in Colon Tissue

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP visualizations of single cells, where the embedding space ('X_cnv_umap1', 'X_cnv_umap2') is derived using copy number variation (CNV) patterns. These plots illustrate how major cell types, minor cell types, ploidy status, biological condition (Tumor vs. Adj_normal), and individual samples distribute within this CNV-informed manifold. The primary goal is to assess the separation and clustering of cells based on their genomic integrity, and to evaluate the consistency of cell type and ploidy annotations with the observed CNV landscape.

Visual Summary

The UMAPs reveal a clear structural separation of cells into distinct populations based on CNV profiles.

Cell Type Distribution (celltype_major, celltype_minor):

Ploidy Status Distribution (ploidy_dec):

Condition Distribution (condition):

Sample Distribution (sample):

Biological Interpretation

The CNV-aware UMAP provides strong evidence for a distinct population of malignant cells within the colon tissue samples.

  1. Identification of Malignant Cells: The most striking finding is the clear separation of 'Aneuploid' cells from 'Diploid' cells based on CNV patterns. This aneuploid population is predominantly identified as 'Intestinal Epithelial cell', which is consistent with the 'Resolved tumor-origin cell type' and the known biology of colorectal adenocarcinoma, where malignant cells typically originate from epithelial lineage and often exhibit aneuploidy.
  2. Tumor Microenvironment: The presence of a substantial number of 'Diploid' cells within the 'Tumor' condition, intermingled with cells from 'Adj_normal' tissue, confirms the diverse cellular composition of the tumor microenvironment. This includes various immune (T cells, B cells, Myeloid cells) and stromal cells (Fibroblasts, Endothelial cells) that infiltrate or reside within the tumor and are typically diploid.
  3. Ploidy as a Marker for Malignancy: The strong co-localization of 'Aneuploid' status with 'Tumor' condition and 'Intestinal Epithelial cell' identity within a unique UMAP region reinforces the utility of CNV analysis and ploidy estimation in single-cell data for distinguishing malignant cells from the surrounding non-malignant cells.
  4. Inter-Patient Heterogeneity: While the UMAP shows a general clustering of aneuploid cells, the subtle sample-specific patterns within this cluster suggest that the precise CNV landscapes of malignant cells can vary between individual patients, reflecting the genomic heterogeneity of colorectal cancer.

Annotation Notes

The UMAP visualizations demonstrate that the celltype_major, celltype_minor, ploidy_dec, and condition annotations are highly congruent with the underlying CNV patterns of the cells. The distinct separation of aneuploid, epithelial cells from the broader diploid, non-epithelial populations strongly supports the accuracy and biological relevance of these annotations within the context of colorectal cancer. The embedding successfully captures the fundamental genomic differences between malignant and non-malignant cells, providing a robust basis for downstream analyses.

6. Minor Cell Type Population Bar Plot Across Conditions

Report figure

[Analysis Visualization Results]...

Analysis Overview

This visualization displays the relative proportions of different minor cell types within individual samples, distinguishing between 'Adj_normal' (adjacent normal) and 'Tumor' conditions derived from human colon tissue. Each bar represents a single sample, and the stacked segments within each bar show the percentage contribution of each identified minor cell type. This analysis helps to understand the cellular composition and heterogeneity across samples and conditions, providing insights into changes associated with the colorectal cancer tumor microenvironment.

Visual Summary

The bar plot provides a comprehensive overview of the minor cell type composition for each sample, grouped by 'Adj_normal' and 'Tumor' conditions.

Biological Interpretation

The observed shifts in cell type populations between adjacent normal and tumor tissues provide important biological insights into colorectal cancer development and its microenvironment.

Altered Immune Microenvironment:

Clinical or Translational Implications

Understanding the cellular composition of colorectal tumors and adjacent normal tissue has several clinical and translational implications:

7. T Cell and Innate Lymphoid Cell Subpopulation Analysis in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a stacked bar plot visualizing the proportional distribution of various T cell subsets and related innate lymphoid cell (ILC) populations across individual samples, comparing 'Adj_normal' (adjacent normal colon tissue) and 'Tumor' (colorectal tumor tissue) conditions. Each bar represents a single sample, and the segments within the bar show the relative abundance of each cell subset within the broader T cell/ILC compartment. This analysis aims to highlight potential shifts in the immune microenvironment associated with colorectal cancer development.

Visual Summary

The stacked bar plots display the relative proportions of 18 different cell subsets, including various T helper (Th) cell types, cytotoxic T cells, naive T cells, regulatory T cells (Tregs), NK cells, and several ILC subsets. Samples are clearly demarcated by 'Adj_normal' and 'Tumor' conditions.

Key observations:

Biological Interpretation

The observed shifts in T cell and ILC populations provide insights into the immune response in the colorectal cancer microenvironment.

Clinical or Translational Implications

The distinct alterations in T cell and ILC populations, particularly the expansion of Tregs in the tumor microenvironment, have several clinical implications for colorectal cancer:

8. T Cell Subset Population Dynamics in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of various T cell subsets between 'Adj_normal' (adjacent normal tissue) and 'Tumor' conditions within human colon samples, as derived from single-cell RNA sequencing data. Box plots illustrate the distribution of cell type proportions across individual samples for each condition, with statistical significance indicated by p-values. The objective is to identify T cell subsets whose proportions are significantly altered in the colorectal tumor microenvironment, providing insights into immune shifts associated with cancer progression.

Visual Summary

The box plots reveal distinct shifts in the proportions of several T cell subsets when comparing tumor tissue to adjacent normal tissue.

Increased Proportions in Tumor Tissue (relative to Adjacent Normal):

Decreased Proportions in Tumor Tissue (relative to Adjacent Normal):

Biological Interpretation

The observed changes in T cell subset proportions reflect significant remodeling of the immune landscape within the colorectal cancer microenvironment.

  1. Immune Suppression and Tolerance: The most striking finding is the highly significant increase in Treg cells. Regulatory T cells are key mediators of immune suppression, maintaining self-tolerance and preventing excessive immune responses. Their enrichment in the tumor microenvironment is a well-established mechanism by which tumors evade anti-tumor immunity by inhibiting effector T cell functions (PubMed search: Treg cancer immune evasion). Similarly, the increase in ILCreg (regulatory innate lymphoid cells) could suggest an additional layer of innate immune regulation contributing to an immunosuppressive environment.
  2. Pro-tumorigenic Inflammation: The significant increases in Th17, Th22, and Th9 cells point towards a complex inflammatory milieu within the tumor.
    • Th17 cells have a context-dependent role in cancer, but in colorectal cancer, they are often associated with promoting tumor growth, angiogenesis, and metastasis, potentially by inducing pro-inflammatory cytokines and recruiting other immune cells (PubMed search: Th17 colorectal cancer).
    • Th22 cells can contribute to tumor progression in various cancers, including CRC, by driving chronic inflammation and influencing epithelial cell proliferation and survival (PubMed search: Th22 colorectal cancer).
    • The role of Th9 cells in CRC is less clearly defined but an increase suggests their involvement in shaping the tumor immune landscape, potentially contributing to either pro- or anti-tumor effects depending on the specific context and cytokine production.
  3. Loss of Naive T cells: The significant decrease in T_Naive cell proportions in tumor tissue suggests a shift from an unprimed to an activated or exhausted T cell state. This could indicate either successful antigen-driven activation and differentiation of T cells within the tumor microenvironment or, conversely, a lack of recruitment of new naive T cells into the tumor, possibly due to chemokine gradients or stromal barriers.
  4. Altered Th2 Response: The marginal decrease in Th2 cell proportions, while less dramatic, might reflect a de-emphasis of a Th2-mediated immune response, which is typically associated with humoral immunity and allergic reactions, not always effective against solid tumors. Shifts away from Th2 dominance might indicate a different effector profile being favored in the tumor.

Collectively, these findings are consistent with the inferred biological context of Colorectal Cancer, where a complex and often immune-suppressive microenvironment characterized by specific T cell subset expansions (Treg, Th17, Th22, Th9) and reduction of naive cells supports tumor growth and immune evasion.

Clinical or Translational Implications

The observed shifts in T cell subset populations in colorectal cancer have several potential clinical and translational implications:

  1. Biomarkers for Prognosis and Prediction: The proportions of specific T cell subsets, particularly the elevated Tregs and Th17 cells, could serve as prognostic biomarkers for disease progression or indicators of response to therapy in colorectal cancer patients. Higher Treg infiltration, for example, is often associated with poorer outcomes in various cancers.
  2. Immunotherapeutic Targets: The enrichment of immunosuppressive cells like Tregs represents a clear target for immunotherapy. Strategies aimed at depleting Tregs or inhibiting their function could enhance anti-tumor immune responses. Similarly, targeting pathways that promote the differentiation or function of pro-tumorigenic Th17 and Th22 cells could be explored to remodel the tumor microenvironment towards an anti-tumor state.
  3. Monitoring Immune Responses: Quantifying these T cell subsets in patient biopsies or peripheral blood could provide a means to monitor the efficacy of immunotherapies, track disease progression, or identify patients who might benefit from specific immune-modulating treatments.

9. Macrophage Subset Population Dynamics in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of different macrophage subsets within the total macrophage population across individual adjacent normal and colorectal tumor tissue samples. The macrophage subsets identified in this dataset include Macrophage (M1), Macrophage (M2A), Macrophage (M2B), Macrophage (M2C), and Macrophage (M2D). Each bar in the plot corresponds to a distinct patient sample, and the colored segments within each bar represent the fractional abundance of each macrophage subset. The samples are categorized and grouped by their tissue condition: 'Adj_normal' (adjacent normal colon tissue) and 'Tumor' (colorectal tumor tissue). For both conditions, samples are visually arranged such that the proportion of Macrophage (M1) generally increases from left to right, facilitating trend observation.

Visual Summary

The visualization clearly highlights distinct compositional differences in macrophage subsets when comparing adjacent normal and tumor samples from the colon.

Biological Interpretation

The observed shift towards a predominant Macrophage (M1) phenotype within the colorectal tumor microenvironment is a significant finding that warrants detailed consideration.

Clinical or Translational Implications

These findings regarding the distinct macrophage subset composition in colorectal cancer offer several potential clinical and translational implications:

---

References:

  1. Macrophage (M1) Information: GeneCards - M1 Macrophage
  2. Macrophage (M2) Information: GeneCards - M2 Macrophage
  3. Macrophages in Colorectal Cancer (M1/M2): PubMed Search: colorectal cancer macrophage M1 M2

10. Differential Macrophage Subpopulation Proportions in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of specific macrophage subsets within colon tissue, comparing samples derived from tumor tissue ("Tumor") with those from adjacent normal tissue ("Adj_normal"). The goal is to identify macrophage subpopulations that exhibit statistically significant differences in their relative abundance, providing insights into the immune microenvironment in colorectal cancer. The analysis was performed on single-cell RNA-seq data from human colon tissue, and box plots were generated to visualize cell type proportions, with statistical significance determined using a p-value cutoff of 0.1.

Visual Summary

The box plots illustrate the distribution of celltype proportions for four macrophage subsets across "Adj_normal" and "Tumor" conditions, with individual data points overlaid as a stripplot. All four macrophage subsets displayed statistically significant differences (p < 0.1) in their proportions between the two conditions:

Overall, M2-like macrophage subsets (M2B, M2C) are enriched in tumor tissue, while other macrophage subsets (M2A, M1) are relatively diminished or less abundant.

Biological Interpretation

The observed shifts in macrophage proportions strongly suggest a significant remodeling of the immune microenvironment in colorectal cancer. Macrophages are highly plastic cells that can adopt various phenotypes, broadly categorized into M1 (pro-inflammatory, anti-tumor) and M2 (anti-inflammatory, pro-tumor) functional states, with M2 further sub-categorized (e.g., M2A, M2B, M2C, M2D).

The significant increase in Mac (M2B) and Mac (M2C) populations in the Tumor condition is particularly notable. M2-like macrophages, often referred to as Tumor-Associated Macrophages (TAMs), are known to play a crucial role in promoting tumor progression through various mechanisms, including:

[Reference: PubMed search for "M2 macrophages cancer progression"]

Conversely, the decrease in Mac (M2A) and Mac (M1) populations in the tumor microenvironment is also biologically significant. M1 macrophages are traditionally associated with anti-tumor activity, characterized by their ability to present antigens, secrete pro-inflammatory cytokines (e.g., TNF-α, IL-12), and directly kill tumor cells. A reduction in their relative proportion suggests a dampened anti-tumor immune response. While M2A macrophages are generally involved in wound healing and allergic responses, their reduction in tumor tissue might indicate a shift away from these specific M2-like phenotypes towards others that are more directly supportive of cancer. The collective shift towards M2B/M2C dominance further highlights a pro-tumorigenic polarization of the macrophage compartment in colorectal cancer.

Clinical or Translational Implications

These findings have important clinical and translational implications for colorectal cancer:

  1. Biomarker Potential: The altered proportions of macrophage subsets, particularly the enrichment of M2B and M2C, could serve as potential biomarkers for colorectal cancer progression, prognosis, or even response to therapy. Monitoring these populations might offer insights into disease status.
  2. Therapeutic Targets: Macrophages, particularly TAMs, are increasingly recognized as attractive targets for cancer immunotherapy. Strategies aimed at re-polarizing pro-tumor M2-like macrophages to an anti-tumor M1-like phenotype, or depleting specific pro-tumorigenic macrophage subsets (like M2B/M2C), could enhance anti-tumor immunity and improve treatment outcomes in colorectal cancer.

[Reference: PubMed search for "macrophage reprogramming cancer therapy"]

  1. Understanding Immune Evasion: The observed macrophage polarization pattern contributes to our understanding of how colorectal tumors evade immune surveillance and promote their growth. Therapeutic approaches that disrupt this pro-tumorigenic macrophage shift could be crucial for overcoming resistance to existing immunotherapies.

Further research focusing on the specific functions and molecular mechanisms driving the observed polarization of these macrophage subsets in colorectal cancer would be valuable for developing targeted immunotherapeutic strategies.

11. Ploidy Status of Intestinal Epithelial Cells in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the ploidy status (Aneuploid, Diploid, or Unclear) of Intestinal Epithelial cells, which have been identified as the tumor-origin cell type within the provided single-cell RNA-sequencing dataset. The ploidy status is evaluated across individual samples derived from both 'Adj_normal' (adjacent normal colon tissue) and 'Tumor' (colorectal tumor tissue) conditions. This comparison aims to highlight genomic stability differences characteristic of malignancy.

Visual Summary

The bar plots display the proportional distribution of ploidy populations within Intestinal Epithelial cells for each sample.

Biological Interpretation

The observed ploidy patterns strongly corroborate the inferred biological context of colorectal cancer.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for colorectal cancer.

12. Analysis of Cell-Cell Interaction Patterns in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using single-cell RNA-seq data from human colon tissue, comparing 'Adj_normal' (adjacent normal) and 'Tumor' conditions. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions between key cell types involved in colorectal cancer (CRC) pathophysiology, including Intestinal Epithelial cells (tumor-origin cells), Fibroblasts, Macrophages, and T cells (CD4+ and CD8+). The analysis specifically expanded Intestinal Epithelial cells based on ploidy, distinguishing 'Diploid Intestinal Epi' in normal tissue and 'Aneuploid Intestinal Epi' in tumor tissue. The goal is to identify active communication pathways that are altered in the tumor microenvironment.

Visual Summary

The provided dot plots illustrate cell-cell interaction strengths and significances for 'Adj_normal' and 'Tumor' conditions, respectively. The size of each dot represents the -log10(p-value) (significance), and the color intensity reflects the log2(mean) expression of the ligand-receptor pair (interaction strength).

Key Observations:

Specific Interaction Patterns in Adj_normal:

Specific Interaction Patterns in Tumor:

Biological Interpretation

The stark contrast in CCI patterns between adjacent normal and tumor tissue underscores the profound transformation of the cellular ecosystem in colorectal cancer.

  1. Remodeled Tumor Microenvironment (TME): The explosion of diverse and strong cell-cell interactions in the tumor is characteristic of a highly active and dysregulated TME. This complex network facilitates tumor growth, invasion, metastasis, and immune evasion.
  2. Tumor Cell-Intrinsic Signaling and Adhesion: The prominent homotypic interactions among 'Aneuploid Intestinal Epi' cells, particularly involving CEACAMs (CEACAM1, 5, 6) and various Integrins, highlight altered cell adhesion and potentially autonomous signaling that promotes tumor cell survival and proliferation. CEACAMs are known to be overexpressed in CRC and are implicated in cell adhesion, migration, and immune modulation [1, 2].
  3. Immune Evasion Mechanisms:
    • PVR-TIGIT Axis: The detection of PVR (CD155) on tumor cells interacting with TIGIT on T cells (CD4+ and CD8+) is a significant finding. TIGIT is an inhibitory receptor on T cells that, upon binding to PVR, suppresses T cell activation and effector functions, contributing to immune evasion [3, 4]. PVR-CD226 interactions also occur, with CD226 typically promoting T cell activation, suggesting a complex interplay between activating and inhibitory signals.
    • TGF-beta Signaling: The frequent observation of TGFB1-TGFBR interactions, particularly between tumor cells and immune cells (macrophages, T cells), points to a potent immunosuppressive microenvironment. TGF-beta is a pleiotropic cytokine that inhibits anti-tumor immunity, promotes fibrosis, and can induce epithelial-mesenchymal transition (EMT) [5].
  4. Pro-Tumorigenic Macrophage Functions: The strong interactions involving macrophages, especially with tumor cells (SPP1-CD44, VEGF-VEGFR) and T cells (SPP1-CD44, TGFB1-TGFBR), indicate their critical role in fostering tumor progression. SPP1 (Osteopontin) is known to promote tumor growth, angiogenesis, and macrophage polarization towards an M2 (pro-tumorigenic) phenotype [6]. VEGF-VEGFR signaling is crucial for angiogenesis, which supports tumor nutrient supply and waste removal, and can also directly suppress anti-tumor immunity.
  5. Extracellular Matrix (ECM) Remodeling and Invasion: The widespread involvement of various Integrin complexes across many cell-cell pairs in the tumor highlights extensive interactions with the ECM. Integrins mediate cell-ECM and cell-cell adhesion, and their altered expression and signaling are fundamental to tumor cell invasion, migration, and metastasis [7].

Clinical or Translational Implications

The identified cell-cell interaction patterns provide valuable insights for potential therapeutic strategies and biomarker discovery in colorectal cancer.

  1. Therapeutic Targeting of Immune Checkpoints: The prominence of the PVR-TIGIT axis involving tumor cells and T cells makes TIGIT a compelling immune checkpoint target. Blocking TIGIT could restore T cell anti-tumor activity, potentially in combination with other immunotherapies (e.g., anti-PD-1/PD-L1).
  2. Modulating Macrophage Activity: Given the extensive interactions involving macrophages, especially via SPP1-CD44 and VEGF-VEGFR, targeting these pathways could reprogram macrophages from a pro-tumorigenic M2-like state to an anti-tumorigenic M1-like state, or inhibit angiogenesis. Therapies targeting VEGF are already established in CRC, and targeting SPP1 is an emerging area.
  3. Inhibiting Pro-Tumorigenic Signaling: The ubiquitous presence of TGFB1-TGFBR signaling suggests that inhibiting the TGF-beta pathway could ameliorate immunosuppression, reduce fibrosis, and potentially slow tumor progression.
  4. Targeting Cell Adhesion and Invasion: The dysregulation of CEACAMs and Integrins highlights their potential as therapeutic targets to impede tumor cell adhesion, migration, and metastatic spread. For instance, CEACAM5 is a known target for antibody-drug conjugates (ADCs) in CRC [8].
  5. Biomarker Identification: Upregulated components of these key interaction pathways (e.g., expression levels of PVR, TIGIT, specific CEACAMs, SPP1, or TGFBRs) could serve as prognostic biomarkers or predictive markers for response to targeted therapies.
  6. Experimental Validation: The specific ligand-receptor pairs identified provide clear candidates for further functional studies in cell lines, organoids, or animal models to validate their mechanistic roles in CRC progression and to test the efficacy of therapeutic interventions.

---

References:

[1] GeneCards: CEACAM1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CEACAM1

[2] GeneCards: CEACAM5. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CEACAM5

[3] PubMed Search: PVR TIGIT immune checkpoint cancer. https://pubmed.ncbi.nlm.nih.gov/?term=PVR+TIGIT+immune+checkpoint+cancer

[4] UniProt: TIGIT. https://www.uniprot.org/uniprot/W5XG18

[5] PubMed Search: TGF-beta immune suppression cancer. https://pubmed.ncbi.nlm.nih.gov/?term=TGF-beta+immune+suppression+cancer

[6] GeneCards: SPP1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=SPP1

[7] PubMed Search: integrins cancer progression metastasis. https://pubmed.ncbi.nlm.nih.gov/?term=integrins+cancer+progression+metastasis

[8] PubMed Search: CEACAM5 antibody-drug conjugate colorectal cancer. https://pubmed.ncbi.nlm.nih.gov/?term=CEACAM5+antibody-drug+conjugate+colorectal+cancer

13. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Pathways in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCIs) involving a curated list of genes associated with immune checkpoint and cell cycle pathways in both tumor and adjacent normal colon tissues. Using CellPhoneDB, interactions between different cell types (celltype_minor level) were computed for these specific genes. The results are visualized as dot plots, where dot size represents the significance of the interaction (-log10(p-value)) and dot color indicates the interaction strength (log2(mean expression) of the interacting ligand-receptor pair). The primary goal is to identify active communication pathways related to tumor progression and immune modulation within the tumor microenvironment compared to normal tissue.

Visual Summary

The analysis generated two dot plots comparing CCIs in "Adj_normal" and "Tumor" conditions:

Prominent Interactions involving Aneuploid Intestinal Epithelial cells:

T Cell Interactions:

Biological Interpretation

The striking difference in CCI profiles between adjacent normal and tumor tissues highlights the profound reprogramming of intercellular communication within the colorectal cancer microenvironment.

Immune Cell Cross-talk:

Clinical or Translational Implications

The findings from this CCI analysis have several potential clinical and translational implications for colorectal cancer:

---

References:

[1] Moses, H. L., & Barcellos-Hoff, M. H. (2009). TGF-β in tumour suppression and cancer progression. *Nature Reviews Cancer*, 9(9), 665-675. PubMed Search: TGF-beta colorectal cancer

[2] Lobb, R. R., & Horizon Therapeutics. (2018). Integrin αvβ6 in cancer. *Cancers*, 10(11), 411. PubMed Search: Integrin alphaVbeta6 colorectal cancer

[3] Garrett, R. S., et al. (1989). Human heparin-binding EGF-like growth factor: cloning and biological properties. *Molecular and Cellular Biology*, 9(12), 5296-5301. GeneCards: HBEGF

14. Condition-Specific Cell-Cell Interaction Patterns in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies cell-cell interactions (CCIs) involving major immune and stromal cell types that show significant differences between colorectal tumor (Tumor) and adjacent normal (Adj_normal) colon tissues. The dot plot visualizes the strength and significance of these interactions across individual samples, providing insights into how the cellular communication landscape changes in the tumor microenvironment. The analysis specifically focused on interactions involving Myeloid cells, T cells, Mast cells, B cells, Endothelial cells, and Stromal cells (Fibroblasts).

Visual Summary

The dot plot, titled "Condition-specific CCI pattern," clearly illustrates distinct cell-cell interaction profiles between Adj_normal and Tumor samples.

In summary, the visualization strongly suggests a profound reorganization and activation of numerous cell-cell communication pathways in the colorectal tumor microenvironment, particularly those related to ECM remodeling, angiogenesis, and immune cell modulation.

Biological Interpretation

The observed shifts in cell-cell interactions highlight key biological processes disrupted in colorectal cancer, involving critical communication axes between stromal and immune cells. Below, we detail representative interactions highly prominent in the Tumor condition and one from the Adjacent Normal condition, discussing their associated signaling pathways and mechanisms.

Interactions Upregulated in Tumor Condition

1. ECM-Integrin Interactions (e.g., COL1A1_integrin_a11b1_complex--Fib|Fib, FN1_integrin_a5b1_complex--Endo|Endo)

2. Angiogenesis/Lymphangiogenesis-related Interactions (e.g., VEGFC_KDR--Endo|Endo, EFNB2_EPHB4--Endo|Endo, JAG1_NOTCH1--Endo|Endo)

Ligand-Receptor & Interacting Cells:

These pathways collectively contribute to the formation of a dysfunctional tumor vasculature and lymphatic network, supporting tumor expansion and metastatic dissemination. PubMed search: tumor angiogenesis signaling pathways

3. Immune Cell Adhesion and Modulation (e.g., ICAM1_integrin_aLb2_complex--Mac|T CD4+)

4. TGF-beta Signaling (e.g., TGFB1_TGFBR3--Endo|Endo)

Interactions in Adjacent Normal Condition

1. Chemokine Signaling (e.g., CXCL14_CXCR4--Fib|T CD8+)

Clinical or Translational Implications

The significant upregulation of specific cell-cell interactions in colorectal tumor tissue compared to adjacent normal tissue highlights potential therapeutic vulnerabilities within the tumor microenvironment.

  1. Targeting ECM-Integrin Interactions:
    • Potential as a Therapeutic Target: Disrupting the widespread collagen/fibronectin-integrin interactions can inhibit tumor cell adhesion, migration, invasion, and metastasis. It can also reduce ECM stiffness and density, potentially improving drug delivery and sensitizing tumor cells to chemotherapy or immunotherapy. Targeting cancer-associated fibroblasts (CAFs) that drive ECM remodeling is also a strategy.
    • Relevant Therapeutic Agents: Integrin inhibitors, such as specific antibodies or small molecules that block collagen/fibronectin binding to integrins, are in development. For example, some integrin αvβ3/αvβ5 inhibitors (like Cilengitide) have been explored in various cancers. Strategies aimed at depleting or reprogramming CAFs are also actively being investigated.
  2. Targeting Angiogenesis and Lymphangiogenesis Pathways:
    • Potential as a Therapeutic Target: Inhibiting the VEGFC-KDR, EFNB2-EPHB4, and JAG1-NOTCH1 axes is a well-established strategy to suppress tumor growth and metastasis by starving the tumor of oxygen and nutrients and limiting lymphatic spread.
    • Relevant Therapeutic Agents: Anti-VEGF antibodies (e.g., Bevacizumab), multi-targeted receptor tyrosine kinase inhibitors (TKIs) that target VEGFR (e.g., Regorafenib, Sunitinib) are approved for various cancers, including colorectal cancer. Notch pathway inhibitors (e.g., Gamma-secretase inhibitors like Navicixizumab) and Ephrin/Eph receptor antagonists are also under active investigation. PubMed search: anti-angiogenic therapy colorectal cancer
  3. Modulating Immune Cell Adhesion:
    • Potential as a Therapeutic Target: The altered ICAM1-integrin αLβ2 interaction indicates potential dysregulation of immune cell recruitment and function in the TME. Modulating such adhesion molecules could enhance anti-tumor immune responses by facilitating T cell infiltration and activation or by preventing immune evasion mechanisms.
    • Relevant Therapeutic Agents: While direct targeting of ICAM1/LFA-1 is more common in inflammatory diseases (e.g., Efalizumab, although withdrawn), strategies to optimize immune cell trafficking and interaction within tumors are crucial for the success of immunotherapies. This could involve combining adhesion molecule modulators with checkpoint inhibitors or adoptive cell therapies.
  4. Targeting TGF-beta Signaling:
    • Potential as a Therapeutic Target: Inhibiting the TGFB1-TGFBR3 pathway, which is highly active in the tumor, can counteract its pro-tumorigenic effects, including immune suppression, fibrosis, and EMT, thereby making the tumor more susceptible to other therapies.
    • Relevant Therapeutic Agents: TGF-beta receptor kinase inhibitors (e.g., Galunisertib) and anti-TGF-beta antibodies (e.g., Fresolimumab) are in various stages of clinical development for different cancers. PubMed search: TGF beta inhibitor cancer therapy

In summary, the observed condition-specific CCI patterns in colorectal cancer point to a highly active and complex network of interactions within the TME that promotes tumor progression. Targeting these specific intercellular communication pathways offers promising avenues for novel therapeutic interventions.

15. Surfaceome Marker Expression in Intestinal Epithelial Cells of Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of surfaceome markers in Intestinal Epithelial cells across different conditions: 'Adj_normal' (adjacent normal tissue), 'Diploid Tumor' (tumor cells with diploid ploidy), and 'Tumor' (tumor cells with aneuploid or general tumor characteristics). The goal is to identify condition-specific surface markers, particularly those upregulated in tumor cells, which may represent established or potential therapeutic targets for colorectal cancer (CRC). The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for each sample.

Visual Summary

The dot plot clearly differentiates surfaceome marker profiles among the three conditions for Intestinal Epithelial cells.

Biological Interpretation

The observed differential expression patterns highlight the profound cellular changes that occur in Intestinal Epithelial cells during colorectal cancer progression. The loss of 'Adj_normal'-specific markers like PIGR and MUCA4, which are involved in mucosal immunity and epithelial differentiation respectively, suggests a dedifferentiation process or disruption of normal colon function in tumor cells.

Conversely, the widespread and robust upregulation of numerous surfaceome proteins in the 'Tumor' group points to altered cell adhesion, signaling, metabolism, and immune evasion mechanisms that are characteristic of malignancy. These tumor-enriched markers are often implicated in key cancer hallmarks, including sustained proliferative signaling, resistance to cell death, induction of angiogenesis, activation of invasion and metastasis, and evasion of immune destruction. The heterogeneity and intermediate profile of 'Diploid Tumor' cells may indicate the dynamic nature of tumor evolution, where cells acquire oncogenic features over time, possibly undergoing ploidy changes as part of this progression. The clear distinction between 'Adj_normal' and 'Tumor' populations based on these surface markers provides strong evidence for their potential utility in diagnosis, prognosis, and therapeutic intervention.

Clinical or Translational Implications

The identified tumor-specific surface markers offer compelling candidates for therapeutic targeting and diagnostic/prognostic applications in colorectal cancer. These markers can be broadly categorized into established therapeutic targets with available agents and less well-characterized markers with high potential for future therapeutic development.

Established Therapeutic Targets and Promising Candidates

Several highly expressed surface markers in Intestinal Epithelial tumor cells are either validated therapeutic targets in cancer or are actively being investigated:

CD47 (Cluster of Differentiation 47)

CD44 (Cluster of Differentiation 44)

MET (MET Proto-Oncogene, Receptor Tyrosine Kinase)

SLC1A5 (Solute Carrier Family 1 Member 5 / ASCT2)

Less Well-Characterized Markers with Potential as Therapeutic Targets

PODXL2 (Podocalyxin Like 2)

PIGR (Polymeric Immunoglobulin Receptor)

Experimental Validation Implications

The strong and distinct expression patterns observed for these markers suggest several avenues for experimental validation:

  1. Immunohistochemistry (IHC) / Immunofluorescence (IF): Confirm protein expression and localization of selected markers in primary CRC tissue sections versus adjacent normal tissue. This can validate the single-cell RNA-seq findings at the protein level and assess spatial heterogeneity.
  2. Flow Cytometry: Validate surface expression of key markers (e.g., CD47, CD44, CEACAM5) on dissociated tumor cells or organoids derived from CRC patients to identify and isolate specific cell populations.
  3. Functional Studies: For tumor-upregulated markers, perform *in vitro* (cell lines, organoids) and *in vivo* (xenograft models) studies to investigate the functional consequences of marker overexpression or knockdown/knockout on cell proliferation, migration, invasion, and drug sensitivity.
  4. Targeted Therapeutic Efficacy: Evaluate the efficacy of existing or novel therapeutic agents targeting the identified established markers (e.g., anti-CD47 antibodies, MET inhibitors) in CRC models. For potential targets, validate their therapeutic potential through antibody or small molecule development.
  5. Biomarker Utility: Assess the diagnostic and prognostic value of panels of these markers, particularly the 'Adj_normal'-specific markers whose loss could indicate early transformation, or 'Tumor'-specific markers for staging and recurrence risk.

In conclusion, this analysis provides a rich dataset of surfaceome markers that are highly differentially expressed in colorectal cancer Intestinal Epithelial cells, offering significant opportunities for developing new diagnostic tools and targeted therapies.

16. Macrophage Condition-Specific Surfaceome Markers in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome markers differentially expressed in Macrophages from Colorectal Cancer (CRC) tumor tissue compared to adjacent normal colon tissue. Utilizing single-cell RNA-seq data, a dot plot visualizes the mean expression and percentage of cells expressing specific surface markers across various patient samples within both 'Adj_normal' and 'Tumor' conditions. The objective is to highlight markers that distinguish tumor-associated macrophages (TAMs) from their counterparts in healthy tissue, offering insights into their functional states and potential as therapeutic targets in the tumor microenvironment (TME).

Visual Summary

The dot plot effectively illustrates distinct expression profiles of surfaceome markers in macrophages from adjacent normal versus tumor samples.

Biological Interpretation

The distinct surfaceome profiles reveal a profound reprogramming of macrophages within the colorectal cancer microenvironment, indicating a shift from a homeostatic or generally immune-active state in normal tissue to a highly specialized, often pro-tumoral, phenotype in the tumor.

Markers Enriched in Tumor-Associated Macrophages (TAMs)

The strong upregulation of these surface markers in tumor macrophages points towards their altered functions, involvement in tumor progression, and potential as therapeutic targets.

FOLR2 (Folate Receptor Beta)

MMP14 (Matrix Metallopeptidase 14)

MRC1 (CD206, Mannose Receptor C-Type 1)

TREM2 (Triggering Receptor Expressed on Myeloid Cells 2)

CD36

SIRPA (Signal Regulatory Protein Alpha)

NRP1 (Neuropilin 1)

Markers Enriched in Adjacent Normal Macrophages

These markers potentially reflect macrophages involved in immune surveillance, tissue homeostasis, or inflammatory responses distinct from the chronic, pro-tumoral inflammation seen in tumors.

CD40

SLC40A1 (Ferroportin)

CD83

Clinical or Translational Implications

The profound differences in surfaceome marker expression between macrophages in tumor versus adjacent normal tissue in colorectal cancer highlight several critical translational implications:

  1. Biomarker Discovery: The identified tumor-specific macrophage markers (e.g., FOLR2, MRC1, TREM2, CD36, SIRPA, NRP1) can serve as valuable biomarkers for:
    • Diagnosis and Prognosis: Quantifying TAMs expressing these markers in biopsies could offer prognostic insights or aid in diagnosis.
    • Patient Stratification: Identifying patients with specific TAM phenotypes could guide treatment decisions, particularly for immunotherapies.
  2. Therapeutic Targeting: The strong and prevalent expression of markers like FOLR2, MRC1, TREM2, CD36, SIRPA, and NRP1 on TAMs provides compelling rationale for developing targeted therapies.
    • Direct Depletion/Inhibition: Strategies like antibody-drug conjugates (ADCs) or direct inhibitors could selectively deplete pro-tumoral TAMs or block their pro-cancer functions. For example, blocking the SIRPA-CD47 axis is an active area of investigation to "release the brakes" on macrophage phagocytosis of tumor cells.
    • Immune Reprogramming: Therapies aimed at modulating the activity of these receptors (e.g., TREM2 modulators) could reprogram TAMs from an M2-like, immunosuppressive state to a more M1-like, anti-tumoral state, thus enhancing the efficacy of existing immunotherapies such as checkpoint inhibitors.
  3. Combination Therapies: Understanding the specific surface markers on TAMs allows for rational design of combination therapies. For instance, combining a targeted TAM therapy (e.g., anti-SIRPA) with a T-cell-centric immunotherapy (e.g., anti-PD-1) could synergistically enhance anti-tumor responses by simultaneously boosting effector T cells and removing immunosuppressive TAMs.
  4. Monitoring Treatment Response: Changes in the expression of these markers on circulating or tissue-resident macrophages could potentially serve as pharmacodynamic biomarkers to monitor the effectiveness of macrophage-targeting therapies.

Overall, the observed macrophage surfaceome profiles underscore the pivotal role of these immune cells in the colorectal cancer microenvironment and provide a rich reservoir of potential targets for developing next-generation cancer immunotherapies.

17. Fibroblast Condition-Specific Surfaceome Marker Expression in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome markers differentially expressed in fibroblasts from 'Adj_normal' (adjacent normal) and 'Tumor' tissues in colorectal cancer (CRC) patients. The dot plot visualizes the expression patterns of these markers across individual samples, showing both the mean expression level (color intensity) and the fraction of cells expressing the gene (dot size). The aim is to distinguish normal tissue-resident fibroblasts from cancer-associated fibroblasts (CAFs) based on their surface protein profiles, offering potential insights into their distinct roles in the tumor microenvironment and identifying candidate therapeutic targets.

Visual Summary

The dot plot clearly differentiates two main groups of fibroblast markers based on their expression patterns across conditions:

Biological Interpretation

The differential surfaceome marker expression observed in fibroblasts from 'Adj_normal' versus 'Tumor' tissues underscores the profound phenotypical changes undergone by fibroblasts in the colorectal cancer microenvironment. Cancer-associated fibroblasts (CAFs) are a critical component of the tumor stroma, playing diverse roles in promoting tumor growth, invasion, metastasis, angiogenesis, and immune evasion. The identified surfaceome markers provide molecular handles to understand these processes.

Markers Enriched in Adjacent Normal Fibroblasts

  1. PDGFRB (Platelet-Derived Growth Factor Receptor Beta)
    • Associated Signaling Pathways: PDGFRB is a receptor tyrosine kinase that binds PDGFs, activating downstream pathways such as PI3K/Akt, MAPK/ERK, and STAT. These pathways regulate cell proliferation, survival, migration, and differentiation.
    • Role in TME: In normal tissues, PDGFRB signaling is crucial for fibroblast proliferation, migration, and extracellular matrix (ECM) remodeling, contributing to tissue homeostasis and repair. Its reduced expression in tumor fibroblasts might indicate a shift in their responsiveness to PDGF or that alternative growth signals dominate within the tumor.
    • Therapeutic Potential: While PDGFRB is a known CAF marker in some cancers, its downregulation here suggests it might be more characteristic of quiescent or normal tissue-maintaining fibroblasts in this CRC context. Modulating PDGF signaling remains a therapeutic strategy in other contexts (e.g., GIST), but its role here suggests it may not be a *pro-tumorigenic* target on fibroblasts in CRC.
    • Relevant Therapeutic Agents: Imatinib (Gleevec), Sunitinib (Sutent) are multi-kinase inhibitors targeting PDGFR, among others.
    • Other Information: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=PDGFRB
  2. TGFBR3 (Transforming Growth Factor Beta Receptor 3 / Betaglycan)
    • Associated Signaling Pathways: TGFBR3 is a co-receptor for TGF-beta, modulating its signaling by presenting TGF-beta ligands to the signaling receptors (TGFBR1/TGFBR2) or by sequestering them. It can either enhance or inhibit TGF-beta signaling depending on the cellular context and co-expression of other receptors.
    • Role in TME: TGF-beta signaling is highly contextual in cancer, acting as a tumor suppressor in early stages and a tumor promoter later. Reduced TGFBR3 on tumor fibroblasts could alter their response to TGF-beta, potentially contributing to unchecked CAF activation and pro-tumorigenic functions.
    • Therapeutic Potential: As a modulator of the critical TGF-beta pathway, alterations in TGFBR3 expression could influence therapeutic responses to TGF-beta inhibitors.
    • Other Information: UniProt: https://www.uniprot.org/uniprotkb/P28374
  3. SCARA5 (Scavenger Receptor Class A Member 5)
    • Associated Signaling Pathways: Scavenger receptors are involved in diverse functions including host defense, apoptotic cell clearance, and lipid metabolism. Specific signaling pathways for SCARA5 in fibroblasts are not extensively characterized but likely involve endocytosis and intracellular signaling related to ligand binding.
    • Role in TME: SCARA5 has been implicated as a tumor suppressor in some cancers, potentially by inhibiting cell proliferation or promoting apoptosis. Its higher expression in normal fibroblasts might reflect a role in maintaining tissue integrity or a less activated state.
    • Therapeutic Potential: If SCARA5 indeed has tumor-suppressive functions, strategies to upregulate its expression or mimic its activity could be explored.
    • Other Information: PubMed search for "SCARA5 cancer": https://pubmed.ncbi.nlm.nih.gov/?term=SCARA5+cancer

Markers Enriched in Tumor Fibroblasts

  1. PMEPA1 (Prostate Transmembrane Protein Androgen Induced 1)
    • Associated Signaling Pathways: PMEPA1 is a transmembrane protein that acts as an intracellular regulator of TGF-beta signaling. It can degrade TGF-beta receptors, thereby attenuating TGF-beta signaling, or it can be involved in other cellular processes.
    • Role in TME: Upregulation of PMEPA1 in CAFs could contribute to altered TGF-beta responsiveness, potentially favoring certain pro-tumorigenic aspects of CAF activation or immune evasion. Dysregulation of TGF-beta signaling is central to CAF function.
    • Therapeutic Potential: Modulating PMEPA1 could indirectly influence TGF-beta signaling, offering a novel approach to target CAF activation.
    • Other Information: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=PMEPA1
  2. IL1R1 (Interleukin 1 Receptor Type 1)
    • Associated Signaling Pathways: IL1R1 is the primary receptor for the pro-inflammatory cytokine IL-1β. Its activation triggers downstream signaling cascades including NF-κB and MAPK pathways, leading to the expression of inflammatory genes, growth factors, and chemokines.
    • Role in TME: IL-1β is a key cytokine in the inflammatory tumor microenvironment. CAFs expressing high levels of IL1R1 are likely highly responsive to IL-1β, contributing to chronic inflammation, angiogenesis, and immune suppression, thereby promoting tumor progression.
    • Therapeutic Potential: Targeting IL1R1 or its ligand IL-1β with antibodies or small molecule inhibitors could dampen pro-inflammatory signaling in the TME, thereby inhibiting CAF activation and tumor growth.
    • Relevant Therapeutic Agents: Anakinra (IL-1R antagonist), Canakinumab (anti-IL-1β antibody).
    • Other Information: UniProt: https://www.uniprot.org/uniprotkb/P14778
  3. ITGA1 (Integrin Subunit Alpha 1)
    • Associated Signaling Pathways: ITGA1 combines with ITGB1 to form the VLA-1 integrin, which primarily binds to collagen and laminin in the ECM. Integrin signaling is bidirectional, connecting the ECM to the cytoskeleton and activating various intracellular pathways (e.g., FAK, Src, MAPK, PI3K/Akt) that regulate cell adhesion, migration, proliferation, and survival.
    • Role in TME: CAFs extensively remodel the ECM, and integrins like VLA-1 are critical for these interactions. Upregulation of ITGA1 suggests enhanced adhesion to specific ECM components, potentially facilitating CAF migration, invasion, and mechanical coupling within the stiff tumor stroma, contributing to tumor progression.
    • Therapeutic Potential: Inhibiting specific integrins involved in CAF-ECM interactions could disrupt CAF functions and reduce desmoplasia. Antibodies targeting ITGA1 or its associated integrin complex could be explored.
    • Other Information: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=ITGA1
  4. NECTIN2 (Nectin Cell Adhesion Molecule 2)
    • Associated Signaling Pathways: Nectin2 is a cell adhesion molecule involved in homotypic and heterotypic cell-cell adhesion. It can interact with other nectins and form complexes with cadherins. Nectin signaling influences cell proliferation, migration, and immune cell interactions.
    • Role in TME: Nectin2 is often upregulated in various cancers and can promote tumor cell growth and metastasis. Its expression on CAFs suggests a role in mediating CAF-tumor cell interactions or CAF-immune cell interactions within the TME, potentially contributing to immune evasion or tumor cell survival.
    • Therapeutic Potential: Targeting Nectin2 on CAFs could disrupt cell-cell communication crucial for tumor growth and potentially enhance immune cell infiltration or activity.
    • Other Information: PubMed search for "Nectin2 cancer": https://pubmed.ncbi.nlm.nih.gov/?term=NECTIN2+cancer
  5. SLC2A3 (Solute Carrier Family 2 Member 3 / GLUT3)
    • Associated Signaling Pathways: SLC2A3 is a glucose transporter responsible for facilitating glucose uptake into cells. While not a signaling molecule itself, its activity is critical for cellular metabolism and impacts metabolic signaling pathways.
    • Role in TME: CAFs are metabolically reprogrammed and can undergo aerobic glycolysis (Warburg effect) or provide metabolic support to tumor cells. Upregulation of GLUT3 indicates increased glucose demand by CAFs, fueling their energetic needs for proliferation, ECM production, and secretome activity, thus supporting tumor growth.
    • Therapeutic Potential: Inhibiting glucose uptake through GLUT3 in CAFs could selectively starve them, disrupting their pro-tumorigenic functions and sensitizing tumor cells to therapy.
    • Relevant Therapeutic Agents: Small molecule inhibitors targeting glucose transporters are under investigation in cancer therapy.
    • Other Information: GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=SLC2A3
  6. IFNGR2 (Interferon Gamma Receptor 2)
    • Associated Signaling Pathways: IFNGR2 is a component of the heterodimeric receptor for Interferon-gamma (IFN-γ), alongside IFNGR1. Upon IFN-γ binding, the receptor complex activates the JAK-STAT signaling pathway, leading to the transcription of IFN-γ inducible genes involved in immune responses, cell growth inhibition, and apoptosis.
    • Role in TME: CAFs can modulate immune responses. While IFN-γ typically has anti-tumor effects, prolonged exposure in the TME can lead to immune evasion mechanisms. Upregulation of IFNGR2 on CAFs suggests they are highly responsive to IFN-γ, which could influence their immunomodulatory functions, potentially leading to increased expression of immune checkpoint ligands or other immune suppressive factors.
    • Therapeutic Potential: Targeting IFNGR2, or pathways downstream of IFN-γ signaling in CAFs, could be considered in combination with immunotherapies to counteract potential immune suppressive effects or to re-sensitize CAFs to anti-tumor immunity.
    • Other Information: UniProt: https://www.uniprot.org/uniprotkb/P38484

Clinical or Translational Implications

The identification of distinct surfaceome marker profiles for fibroblasts in adjacent normal tissue versus tumor tissue has significant clinical and translational implications for colorectal cancer:

18. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the expression patterns of surfaceome markers in CD4+ T cells derived from both colorectal tumor and adjacent normal tissues. The primary objective is to identify markers that are differentially expressed between these two conditions, offering insights into the phenotypic state and potential therapeutic vulnerabilities of CD4+ T cells within the colorectal cancer (CRC) microenvironment. The provided dot plot visualizes the fraction of cells expressing each marker (dot size) and the mean expression level (color intensity) across individual patient samples, highlighting condition-specific trends.

Visual Summary

The dot plot clearly illustrates a distinct shift in the surfaceome profile of CD4+ T cells when comparing adjacent normal and tumor conditions.

Markers predominantly expressed in Adjacent Normal Tissues:

In summary, the visualization points towards a significant phenotypic adaptation of CD4+ T cells within the CRC TME, characterized by a reduction in naive/central memory markers and a marked increase in both co-stimulatory and co-inhibitory receptors.

Biological Interpretation

The observed differential expression of surfaceome markers in CD4+ T cells provides crucial insights into their functional roles and regulatory mechanisms within the colorectal cancer TME.

Markers Upregulated in Tumor CD4+ T cells

  1. CTLA4 (Cytotoxic T-Lymphocyte-Associated Protein 4)
    • Signaling Pathways: CTLA4 is an inhibitory receptor expressed on T cells that competes with CD28 for binding to CD80/CD86 on antigen-presenting cells (APCs). Its signaling curtails T cell activation and proliferation, acting as a crucial negative regulator of immune responses.
    • Role in TME: The increased expression of CTLA4 on tumor-infiltrating CD4+ T cells, particularly regulatory T cells (Tregs) and activated effector T cells, contributes to immune tolerance and suppresses effective anti-tumor immunity in the colorectal cancer TME.
    • Therapeutic Target Potential: CTLA4 is a well-established immune checkpoint target. Blocking CTLA4 can enhance anti-tumor immune responses by promoting T cell activation and attenuating Treg-mediated suppression.
    • Relevant Therapeutic Agents: Ipilimumab, an anti-CTLA4 monoclonal antibody, is approved for various cancers and is often used in combination with anti-PD-1 therapy to maximize therapeutic benefit PubMed search: Ipilimumab colorectal cancer.
  2. TIGIT (T cell immunoreceptor with Ig and ITIM domains)
    • Signaling Pathways: TIGIT is an inhibitory receptor found on T cells and NK cells. It binds to poliovirus receptor (PVR; CD155) and nectin-2 (CD112) on APCs and tumor cells, competing with the co-stimulatory receptor CD226 (DNAM-1). TIGIT engagement leads to suppressed T cell proliferation and cytokine production.
    • Role in TME: Elevated TIGIT expression on tumor-infiltrating CD4+ T cells contributes to T cell exhaustion and immunosuppression within the colorectal cancer TME, hindering robust anti-tumor responses.
    • Therapeutic Target Potential: TIGIT is an emerging immune checkpoint target. Blocking TIGIT can potentially reverse T cell exhaustion and enhance the efficacy of other immune checkpoint inhibitors.
    • Relevant Therapeutic Agents: Anti-TIGIT monoclonal antibodies, such as Tiragolumab and Vibostolimab, are currently in clinical trials for various solid tumors, including CRC, often in combination with anti-PD-1 agents PubMed search: TIGIT blockade colorectal cancer.
  3. TNFRSF18 (Tumor Necrosis Factor Receptor Superfamily Member 18, also known as GITR)
    • Signaling Pathways: GITR is a co-stimulatory receptor expressed on activated T cells, including effector and regulatory T cells. Its binding to GITRL (GITR Ligand) delivers a co-stimulatory signal that enhances effector T cell function and can attenuate Treg-mediated suppression.
    • Role in TME: The upregulation of GITR on tumor-infiltrating CD4+ T cells indicates T cell activation within the TME. Its precise role is context-dependent, as its activation can both bolster anti-tumor immunity by inhibiting Tregs and potentially influence effector T cell responses.
    • Therapeutic Target Potential: Agonistic anti-GITR antibodies are being explored as a strategy to boost anti-tumor immunity by stimulating effector T cells and inhibiting Tregs.
    • Relevant Therapeutic Agents: Several agonistic anti-GITR antibodies (e.g., TRX518, MEDI1873) are under clinical development for various cancers PubMed search: GITR agonist cancer therapy.
  4. ICOS (Inducible T cell Co-stimulator)
    • Signaling Pathways: ICOS is a co-stimulatory receptor on activated T cells, critical for the development and function of T follicular helper (Tfh) cells and certain regulatory T cells (Tregs). It binds to ICOSL on APCs, promoting T cell proliferation, survival, and cytokine production (e.g., IL-10, IL-21).
    • Role in TME: Elevated ICOS on tumor-infiltrating CD4+ T cells can support B cell responses and tertiary lymphoid structures. However, its expression on Tregs can enhance their suppressive function, potentially contributing to immune evasion in colorectal cancer.
    • Therapeutic Target Potential: Both agonistic and antagonistic anti-ICOS antibodies are under investigation. Agonists aim to enhance anti-tumor effector T cells, while antagonists might target ICOS+ Tregs to reduce immune suppression.
    • Relevant Therapeutic Agents: Anti-ICOS agonists (e.g., Vopalimab) and antagonists are in various phases of clinical trials for different malignancies PubMed search: ICOS antibody cancer therapy.
  5. HLA-DRB1 (Human Leukocyte Antigen - DRB1)
    • Signaling Pathways: HLA-DRB1 is an MHC class II molecule, typically expressed on professional APCs. Its presence on activated CD4+ T cells suggests a highly activated state, and these T cells may acquire the ability to present antigens, influencing other T cells.
    • Role in TME: The increased expression of HLA-DRB1 on tumor-infiltrating CD4+ T cells indicates chronic antigen exposure and T cell activation within the colorectal cancer TME, possibly reflecting an exhausted or terminally differentiated state.
    • Therapeutic Target Potential: While HLA-DRB1 itself is not a direct therapeutic target for modulating anti-tumor immunity, its expression is a valuable biomarker for T cell activation status and can correlate with responsiveness to immunotherapy.
    • Relevant Therapeutic Agents: Not a direct drug target, but its expression is important for assessing immune activity and potential for antigen presentation GeneCards: HLA-DRB1.
  6. ENTPD1 (Ectonucleoside Triphosphate Diphosphohydrolase 1, also known as CD39)
    • Signaling Pathways: CD39 is an ectoenzyme that hydrolyzes ATP and ADP to AMP. AMP is then further converted to adenosine by CD73. Adenosine is a potent immunosuppressive molecule in the TME, acting through adenosine receptors to inhibit effector T cell function.
    • Role in TME: High expression of CD39 on tumor-infiltrating CD4+ T cells, particularly Tregs and exhausted T cells, contributes to an immunosuppressive, adenosine-rich environment in the colorectal cancer TME, thus hindering anti-tumor responses.
    • Therapeutic Target Potential: Inhibiting CD39, or the broader adenosinergic pathway, is a promising strategy to counteract immune suppression in the TME and enhance anti-tumor immunity.
    • Relevant Therapeutic Agents: CD39 inhibitors (e.g., A0740, POM-1) and CD73 inhibitors are currently under investigation to improve the efficacy of various cancer immunotherapies PubMed search: CD39 inhibitor cancer.

Markers Downregulated in Tumor CD4+ T cells (Upregulated in Adjacent Normal)

  1. CCR7 (C-C Chemokine Receptor Type 7)
    • Signaling Pathways: CCR7 is a chemokine receptor that binds CCL19 and CCL21, orchestrating T cell homing to secondary lymphoid organs (SLOs) and promoting their recirculation. It is a key marker for naive and central memory T cells.
    • Role in TME: The decreased expression of CCR7 on CD4+ T cells within the colorectal tumor suggests a phenotypic shift away from naive or central memory states towards more differentiated, effector, or exhausted phenotypes typically found in chronic inflammatory or tumor environments. This indicates impaired egress from the tumor or homing to SLOs.
    • Therapeutic Target Potential: While not a direct therapeutic target for enhancing anti-tumor immunity in this context, CCR7 expression serves as an important biomarker for T cell differentiation, trafficking, and overall T cell repertoire diversity. Modulating CCR7 expression or function is relevant in adoptive cell therapies for improved cell trafficking.
    • Relevant Therapeutic Agents: No direct therapeutic agents targeting CCR7 for cancer treatment; however, it is a crucial marker for understanding T cell migration and homing GeneCards: CCR7.

Clinical or Translational Implications

The distinct surfaceome profiles of CD4+ T cells in colorectal tumor versus adjacent normal tissues present compelling clinical and translational opportunities:

The identified markers provide a rich foundation for further functional validation studies, drug development, and patient stratification efforts in the realm of colorectal cancer immunotherapy.

19. Analysis of Cell Cycle Pathway Gene Expression in Colorectal Cancer Intestinal Epithelial Cells

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a curated set of cell cycle pathway genes within Intestinal Epithelial cells, the identified tumor-origin cell type, comparing cells from colorectal tumor tissue to those from adjacent normal tissue. The aim was to identify genes with statistically significant expression differences between these two conditions, providing insight into the proliferative state of tumor cells. Box plots were generated to visualize the "expressing cell fraction (sample)" for each significant gene, illustrating the proportion of cells within each sample that express a given gene.

Visual Summary

The visualization displays box plots for 24 distinct cell cycle-related genes that exhibit statistically significant differences in their expressing cell fraction between tumor and adjacent normal conditions in Intestinal Epithelial cells.

Biological Interpretation

The observed widespread and highly significant upregulation of cell cycle pathway genes in Intestinal Epithelial cells within colorectal tumor samples provides strong biological evidence of increased cellular proliferation, a hallmark of cancer.

Clinical or Translational Implications

These findings have significant implications for understanding colorectal cancer pathogenesis and identifying potential therapeutic strategies.

---

References:

  1. MYC oncogene function: For an overview of MYC's role in cell growth and proliferation, refer to resources like GeneCards: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MYC
  2. CDK inhibitors in cancer therapy: PubMed search for "CDK inhibitors cancer therapy": https://pubmed.ncbi.nlm.nih.gov/?term=CDK+inhibitors+cancer+therapy
  3. MCM proteins as cancer targets: PubMed search for "MCM proteins cancer drug target": https://pubmed.ncbi.nlm.nih.gov/?term=MCM+proteins+cancer+drug+target

20. Gene Ontology (GSA) Analysis of Intestinal Epithelial Cells in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GSA) enrichment results for Intestinal Epithelial cells, a cell type identified as being of tumor origin. The results are visualized as bar plots, displaying significantly enriched pathways/terms (based on -log(p-val) and -log(q-val)) under three different comparison conditions: 'Adj_normal vs others', 'Diploid vs others', and 'Tumor vs others'. These comparisons aim to delineate the distinct biological processes active in normal, non-malignant (diploid), and cancerous (tumor) states of intestinal epithelial cells in the context of colorectal cancer.

Visual Summary

The provided bar plots illustrate the top enriched Gene Ontology terms, ranked by their statistical significance (-log(p-val) and -log(q-val)).

  1. GSA_up for Intestinal Epithelial cell: Adj_normal_vs_others
    • This plot shows pathways upregulated in adjacent normal intestinal epithelial cells compared to other conditions (likely tumor cells of the same type or other cell types).
    • Key enriched terms include "Non-alcoholic fatty liver disease," "Oxidative phosphorylation," "Diabetic cardiomyopathy," and various neurodegenerative disease pathways (e.g., "Parkinson's disease," "Alzheimer disease," "Prion disease," "Amyotrophic lateral sclerosis," "Pathways of neurodegeneration").
    • Metabolic pathways like "Fatty acid degradation" and "PPAR signaling pathway" are also prominent.
    • These terms show high significance, with -log(p-val) exceeding 10 for the top terms and -log(q-val) ranging up to ~7.
  2. GSA_up for Intestinal Epithelial cell: Diploid_vs_others
    • This plot highlights pathways upregulated in diploid Intestinal Epithelial cells (typically non-malignant) when compared to other cell populations (likely including aneuploid cells).
    • The most significantly enriched terms are "Ribosome" and "Coronavirus disease," followed by "Cell adhesion molecules," "Hematopoietic cell lineage," and "Pathogenic Escherichia coli infection."
    • Significance levels are very high for "Ribosome" and "Coronavirus disease," with -log(p-val) above 30 and -log(q-val) above 30.
  3. GSA_up for Intestinal Epithelial cell: Tumor_vs_others
    • This plot showcases pathways significantly upregulated in tumor-origin Intestinal Epithelial cells compared to other conditions (likely adjacent normal cells of the same type or other cell types).
    • A broad array of terms related to fundamental cellular processes and various diseases are enriched. Top terms include "Endocytosis," "Protein processing in endoplasmic reticulum," "Ubiquitin mediated proteolysis," "Cell cycle," "RNA transport," and "Ribosome."
    • Crucially, cancer-specific pathways such as "Colorectal cancer," "p53 signaling pathway," "mTOR signaling pathway," "Endometrial cancer," "Pancreatic cancer," and "Renal cell carcinoma" are highly enriched.
    • Numerous neurodegenerative disease pathways (e.g., "Amyotrophic lateral sclerosis," "Alzheimer disease," "Parkinson disease," "Huntington disease") also appear, similar to the 'Adj_normal' plot.
    • Infection-related pathways (e.g., "Salmonella infection," "Human T-cell leukemia virus 1 infection," "Shigellosis," "Human papillomavirus infection," "Epstein-Barr virus infection," "Hepatitis B," "Coronavirus disease") are also present.
    • The significance is very strong, with many terms reaching -log(p-val) values above 20 and -log(q-val) above 15.

Biological Interpretation

The Gene Ontology analysis reveals distinct functional profiles for Intestinal Epithelial cells under different conditions, providing insights into the biology of colorectal cancer.

Clinical or Translational Implications

The identified GO terms provide a molecular blueprint of colorectal cancer at the single-cell level, specifically within the tumor-origin Intestinal Epithelial cells.

21. Gene Set Enrichment Analysis Across Cell Types in Colorectal Cancer

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results as a dot plot, illustrating the enrichment of various biological pathways and gene sets across different cell types in colon tissue. The analysis compares gene expression profiles in cells from "Adj_normal" (adjacent normal) samples against all other cells, and "Tumor" samples against all other cells, for each identified celltype_minor (e.g., B cell, Macrophage, Intestinal Epithelial cell). The purpose is to identify differentially regulated biological processes within specific cell populations that characterize the colorectal cancer (CRC) tumor microenvironment compared to the adjacent normal tissue.

Visual Summary

The dot plot visualizes approximately 120 gene sets (y-axis) against various cell type and condition comparisons (x-axis). Each dot represents a specific gene set's enrichment in a given cell type and condition.

Biological Interpretation

The GSEA results highlight significant shifts in biological processes specific to cell types within colorectal tumor tissue and its adjacent normal counterpart.

Intestinal Epithelial Cells

As the primary cell type of tumor origin, Intestinal Epithelial cells from tumor samples ("Intestinal Epithelial cell: Tumor_vs_others") show robust and significant enrichment (large red dots) for numerous pathways critical in colorectal carcinogenesis:

Tumor Microenvironment Immune Cells

Immune cells within the tumor milieu display distinct functional states:

Stromal and Endothelial Cells

Adjacent Normal Tissue

Cells from "Adj_normal_vs_others" generally show distinct or inverse enrichment patterns compared to their tumor counterparts, often lacking enrichment for cancer-promoting pathways and potentially highlighting pathways associated with normal tissue homeostasis.

Clinical or Translational Implications

The comprehensive GSEA results provide several clinically relevant insights for colorectal cancer:

22. Discussion

The integrated single-cell analysis of colorectal cancer tissue and adjacent normal controls reveals a profoundly remodeled tumor microenvironment, driven by distinct molecular and cellular alterations. Aneuploid Intestinal Epithelial cells consistently emerge as the central malignant population, characterized by widespread genomic instability, including recurrent amplifications of oncogenes such such as EGFR, MYC, ERBB2, and highly activated cell cycle pathways (e.g., CCND1, CDK4, MCM genes). Gene Ontology and GSEA further confirm the enrichment of core cancer pathways like Wnt, PI3K-Akt, and ErbB signaling in these tumor cells, alongside significant metabolic reprogramming.

The immune landscape undergoes a dramatic shift. Within the T cell compartment, there is a statistically significant increase in the proportions of immunosuppressive regulatory T cells (Tregs) and pro-inflammatory Th17, Th22, and Th9 cells in tumors, coupled with a reduction in naive T cells. This suggests an orchestrated shift towards an immunosuppressive and potentially pro-tumorigenic T cell environment. Macrophage populations also show a nuanced but significant shift; while Macrophage (M1) cells might still constitute a large proportion, there is a statistically significant increase in pro-tumorigenic Macrophage (M2B) and Macrophage (M2C) subsets, accompanied by a decrease in Macrophage (M1) and Macrophage (M2A) populations in the tumor, highlighting a functional polarization towards tumor-promoting phenotypes. These tumor-associated macrophages (TAMs) are further characterized by a unique surfaceome, with notable upregulation of FOLR2, MMP14, MRC1, TREM2, CD36, SIRPA, and NRP1, indicative of immunosuppressive, pro-angiogenic, and ECM-remodeling functions. Tumor-infiltrating CD4+ T cells also exhibit increased expression of immune checkpoints (CTLA4, TIGIT) and co-stimulatory receptors (TNFRSF18/GITR, ICOS), along with molecules like ENTPD1 (CD39) involved in adenosine-mediated immunosuppression.

Intercellular communication is dramatically reprogrammed in the tumor microenvironment. Extensive cell-cell interactions are observed involving aneuploid Intestinal Epithelial cells, macrophages, and T cells. Key axes include the PVR-TIGIT immune checkpoint, widespread TGF-beta signaling (especially the TGFB1-integrin_avb6_complex), SPP1-CD44, VEGF-VEGFR, and various integrin-mediated interactions. Cancer-associated fibroblasts (CAFs) also acquire a distinct surfaceome (e.g., PMEPA1, IL1R1, ITGA1, SLC2A3), contributing to chronic inflammation, ECM remodeling, and metabolic support for tumor growth. A notable observation is the enrichment of 'neurodegenerative disease' pathways in both adjacent normal and tumor epithelial cells in GSA, which, while not directly cancer-specific, suggests underlying shared cellular stress responses or protein quality control mechanisms that may be perturbed or co-opted in the context of malignancy. These comprehensive findings provide a multi-faceted view of colorectal cancer progression, highlighting the intricate interplay between malignant cells and their supportive stromal and immune compartments, and revealing numerous potential targets for therapeutic intervention.

Hypotheses:

  1. The observed increase in regulatory T cells (Tregs), Th17, Th22, and Th9 cells, coupled with a decrease in naive T cells, drives an immunosuppressive and pro-inflammatory T cell microenvironment that promotes colorectal cancer progression.
  2. The proportional increase of Macrophage (M2B) and Macrophage (M2C) subsets in tumors, along with their distinct surfaceome expression (e.g., FOLR2, MRC1, TREM2, CD36, SIRPA, NRP1), indicates a strong pro-tumorigenic and immunosuppressive polarization of tumor-associated macrophages in colorectal cancer.
  3. Upregulation of the PVR-TIGIT axis and robust TGF-beta signaling between tumor cells and immune/stromal cells are central mechanisms of immune evasion and stromal remodeling that drive colorectal cancer progression.
  4. The widespread activation of cell cycle pathways (e.g., CCND1, MYC, MCM genes) and recurrent genomic amplifications (EGFR, MYC, ERBB2) in aneuploid Intestinal Epithelial cells are fundamental to the uncontrolled proliferation and genomic instability characteristic of colorectal cancer malignancy.
  5. Cancer-associated fibroblasts acquire a distinct surfaceome (e.g., PMEPA1, IL1R1, ITGA1, SLC2A3) that actively contributes to ECM remodeling, chronic inflammation, and metabolic support for tumor growth and immune evasion.

Potential therapeutic targets:

  1. CEACAM5 (Carcinoembryonic Antigen-related Cell Adhesion Molecule 5): CEACAM5 is highly expressed on aneuploid Intestinal Epithelial tumor cells and involved in cell adhesion, signal transduction, and promoting tumor cell proliferation and metastasis. Evidence: Strong upregulation in tumor epithelial cells observed in the marker expression dot plot (Section 15); implicated in prominent homotypic tumor cell interactions (Section 12); established diagnostic and prognostic biomarker for CRC. Validation: Immunohistochemistry (IHC) and immunofluorescence (IF) on primary CRC tissue sections to confirm protein expression and localization. Efficacy testing of CEACAM5-targeted antibody-drug conjugates (ADCs) in patient-derived xenograft or organoid models of CRC.
  2. CD47-SIRPA Axis: This axis mediates a 'don't eat me' signal, allowing cancer cells to evade phagocytosis by macrophages. CD47 (on tumor cells) and SIRPA (on macrophages) are both significantly upregulated in the tumor microenvironment. Evidence: CD47 is highly expressed on Intestinal Epithelial tumor cells (Section 15), and SIRPA is highly expressed on tumor-associated macrophages (Section 16). This interaction is a well-documented immune evasion mechanism in cancer. Validation: Functional *in vitro* phagocytosis assays using patient-derived tumor cells and macrophages with anti-CD47 or anti-SIRPA antibodies. *In vivo* efficacy studies of anti-CD47 or anti-SIRPA therapies, alone or in combination with other immunotherapies, in CRC animal models.
  3. TIGIT (on T cells) / PVR (on tumor cells): TIGIT is an inhibitory receptor on T cells that, upon binding to PVR on tumor cells, suppresses T cell activation and effector functions, contributing to immune evasion. Evidence: TIGIT is significantly upregulated in tumor-infiltrating CD4+ T cells (Section 18). The PVR-TIGIT axis is identified as a prominent tumor-immune cell interaction (Section 12), indicating its active role in the immunosuppressive tumor microenvironment. Validation: Evaluate the efficacy of anti-TIGIT monoclonal antibodies, as monotherapy or in combination with other immune checkpoint inhibitors (e.g., anti-PD-1), in preclinical CRC models. Assess T cell proliferation, cytokine production, and tumor regression.
  4. TGF-beta Signaling Pathway (e.g., via TGFB1, TGFBRs, Integrin αVβ6): TGF-beta is a pleiotropic cytokine that drives immunosuppression, fibrosis, epithelial-mesenchymal transition (EMT), and angiogenesis in the tumor microenvironment, promoting tumor progression. Evidence: Strong TGFB1-TGFBR interactions, particularly the TGFB1-integrin_avb6_complex, are prominently observed between macrophages and aneuploid Intestinal Epithelial cells in the tumor (Section 13). TGFB1-TGFBR3 interactions also occur among endothelial cells (Section 14). Validation: Test the therapeutic potential of TGF-beta signaling inhibitors (e.g., TGF-beta receptor kinase inhibitors or anti-TGF-beta antibodies) or Integrin αVβ6 inhibitors, in combination with immunotherapies, to counteract immunosuppression and fibrosis in CRC models.
  5. IL1R1 (on Fibroblasts) / IL-1β: IL1R1 is highly expressed on cancer-associated fibroblasts (CAFs) in the tumor, making them responsive to pro-inflammatory IL-1β. This signaling contributes to chronic inflammation, angiogenesis, and immune suppression within the tumor microenvironment. Evidence: IL1R1 is significantly upregulated in tumor fibroblasts compared to adjacent normal fibroblasts (Section 17). IL-1β signaling through IL1R1 is a known driver of pro-tumorigenic inflammation and CAF activation. Validation: Assess the anti-tumor effects of IL-1R antagonists (e.g., Anakinra) or anti-IL-1β antibodies (e.g., Canakinumab) in preclinical CRC models. Monitor their impact on CAF activation, cytokine secretion, and tumor growth, especially in combination with other therapies.

Follow-up validation ideas:

  1. Quantify and localize T cell subsets (e.g., Tregs, Th17) and macrophage subsets (e.g., M2B, M2C) in primary CRC tissue sections versus adjacent normal tissue using multiplex immunofluorescence or flow cytometry with specific surface markers (e.g., FOXP3, RORγt, FOLR2, MRC1).
  2. Perform spatial transcriptomics or proteomics to map the precise localization of key immune (Tregs, M2B/C macrophages), stromal (CAFs), and tumor epithelial cell populations, along with their interacting ligand-receptor pairs (e.g., TIGIT, PVR, TGFB1, CEACAM5, CD47), within the tumor microenvironment.
  3. Conduct *in vitro* co-culture experiments with patient-derived tumor organoids and autologous immune cells, assessing T cell proliferation, cytokine production, and cytotoxicity in the presence or absence of specific immune checkpoint blockade (e.g., anti-TIGIT, anti-CTLA4) or TGF-beta inhibitors.
  4. Investigate the impact of inhibiting CAF-specific surface markers (e.g., IL1R1, ITGA1, SLC2A3) or their associated signaling (e.g., IL-1β blockade, integrin inhibitors) on CAF activation, ECM production, and their ability to support tumor cell growth/invasion in 3D co-culture models or patient-derived xenografts.
  5. Utilize CRISPR/shRNA screens in CRC cell lines or organoids to functionally validate the roles of amplified oncogenes (EGFR, MYC, ERBB2) and upregulated cell cycle genes (CCND1, CDK4) in tumor cell proliferation and survival.
  6. Confirm specific genomic amplifications (e.g., EGFR, MYC, ERBB2) in malignant epithelial cells using fluorescence *in situ* hybridization (FISH) or comparative genomic hybridization (CGH) on tumor tissue sections to assess spatial heterogeneity and validate CNV calls.

Limitations:

This report is based on a single-cell RNA-seq dataset, providing a snapshot of cellular states and interactions at one time point, and may not fully capture dynamic changes over time or in response to therapy. Cell type and CNV inferences rely on computational algorithms and marker gene expression, requiring orthogonal validation. While UMAPs visualize clustering, comprehensive spatial organization within tissues is not fully represented. The observed patient-to-patient heterogeneity necessitates larger cohorts for generalizable findings and the establishment of robust prognostic or predictive biomarkers. All findings represent correlations, and their causal roles in colorectal cancer progression require further experimental validation.

23. Query List

  1. Show and save UMAPs for condition, sample, major cell type, minor cell type, ploidy_dec, and celltype_subset in two columns.
  2. Show major cell type scores on UMAP and save the result.
  3. Show and save a marker expression dot plot for celltype_subset. SET target_cell = None and var_group_rotation = 45. Leave all other arguments at their default values.
  4. Select Intestinal Epithelial cells (tumor-origin cells), show a CNV heatmap grouped by sample together with a summary of regions with significantly amplified copy numbers, and save the results.
  5. Show and save UMAPs of CNV patterns colored by major cell type, minor cell type, ploidy results, condition, and sample in two columns.
  6. Show and save a population bar plot of minor cell types.
  7. Show and save a population bar plot of T cell subsets.
  8. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
  9. Show and save a population bar plot of macrophage subsets.
  10. Show and save box plots of macrophage subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
  11. Select Intestinal Epithelial cells (tumor-origin cells), show their ploidy populations as a bar plot, and save the result.
  12. Show and save cell-cell interaction patterns involving Intestinal Epithelial cells (tumor-origin cells), fibroblasts, macrophages, T cells, and other relevant cell types. Select at most 80 cell-cell interactions per group.
  13. Select only genes associated with immune checkpoint and cell cycle pathways, show cell-cell interactions for these genes, and save the result.
  14. Find cell-cell interactions involving major immune and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms, its potential as a therapeutic target, and relevant therapeutic agents.
  15. Show marker expressions for Intestinal Epithelial cells (tumor-origin cells) in a dot plot, and save the result. Use only surfaceome markers, with at most 60 markers per condition. For the markers identified under each condition, distinguish between established therapeutic targets and less well-characterized markers with potential as therapeutic targets. For each marker, using the provided dataset context, describe the associated signaling pathways and mechanisms, relevant therapeutic agents, and supporting evidence.
  16. Show marker expressions for macrophages in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
  17. Show marker expressions for fibroblasts in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
  18. Show marker expressions for T cell CD4+ cells in a dot plot, and save the result. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways, the role of the corresponding cell type in the tumor microenvironment, the marker’s potential as a therapeutic target, relevant therapeutic agents, and other pertinent information.
  19. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Intestinal Epithelial cells (major disease-related cells), show box plots, and save the result. Set max_n_items_to_plot to 24 and choose ncols based on the total number of panels for an an overall width-to-height ratio of approximately 2:3.
  20. Show and save Gene Ontology (GSA) analysis results for Intestinal Epithelial cells (tumor-origin cells) as a bar plot.
  21. Show Gene Set Enrichment Analysis results as a dot plot, and save it. Set the color map to RdBu_r, n_pws_to_show to 120, and target_cells to None.
Top