SCODiA Report by MLBI Lab

Single-Cell Transcriptomic and Genomic Landscape of Renal Cell Carcinoma: Insights into Tumor Microenvironment and Therapeutic Vulnerabilities

This comprehensive single-cell analysis of kidney tissue reveals the distinct cellular and molecular landscape of Renal Cell Carcinoma (RCC). Tumor cells, identified as aneuploid Renal Epithelial cells, exhibit significant metabolic reprogramming and activated oncogenic pathways. The tumor microenvironment is characterized by substantial shifts in immune cell populations, particularly an increase in immunosuppressive macrophages and regulatory T cells, alongside extensive remodeling of the extracellular matrix and active angiogenesis. These findings highlight critical cell-cell communication pathways and specific surface markers that represent potential diagnostic and therapeutic targets for RCC.

Contents

  1. Dataset overview
  2. UMAP Visualization of Sample Metadata and Cell Annotations
  3. Major Cell Type Scores and Ploidy on UMAP
  4. Marker Expression Profile Across Kidney Cell Subsets
  5. Analysis of Copy Number Variations in Renal Epithelial Cells
  6. CNV-Informed UMAP Embedding of Kidney Cells by Cell Type, Ploidy, Condition, and Sample
  7. Minor Cell Type Population Analysis in Renal Cell Carcinoma Samples
  8. Population Analysis of T Cell Subsets in Kidney Tissue (Normal vs. Tumor)
  9. Analysis of Regulatory T cell (Treg) Proportions in Renal Cell Carcinoma
  10. Macrophage Subset Population Analysis in Renal Cell Carcinoma
  11. Differential Abundance of Macrophage M2C Subset in Renal Cell Carcinoma
  12. Ploidy Analysis of Renal Epithelial Cells in Normal and Tumor Conditions
  13. Cell-Cell Interaction Patterns in Renal Cell Carcinoma Tumor Microenvironment
  14. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Genes in Renal Cell Carcinoma
  15. Condition-Specific Cell-Cell Interaction Patterns in Renal Cell Carcinoma
  16. Renal Epithelial Cell Condition-Specific Surfaceome Markers in Kidney Tumor
  17. Macrophage Condition-Specific Surfaceome Markers in Renal Cell Carcinoma
  18. T cell CD4+ Condition-Specific Surfaceome Markers in Renal Cell Carcinoma
  19. Differential Expression Trends of Cell Cycle Pathway Genes in Renal Epithelial Cells
  20. Gene Ontology (GSA) Analysis for Renal Epithelial Cells in Kidney Tissue
  21. Gene Set Enrichment Analysis (GSEA) Across Renal Cell Carcinoma Cell Types
  22. Discussion
  23. Query List

0. Dataset overview

데이터셋 요약

1. UMAP Visualization of Sample Metadata and Cell Annotations

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a series of UMAP (Uniform Manifold Approximation and Projection) visualizations, depicting the relationships between individual cells based on their gene expression profiles. Each plot is colored by a different metadata attribute: experimental condition (tumor/normal), originating sample, major cell type, minor cell type, ploidy status, and cell type subsets. This comprehensive visualization allows for an assessment of dataset structure, the quality of cell annotations, and the distribution of biological variables across the single-cell landscape.

Visual Summary

Condition-based UMAP

The UMAP colored by condition shows a clear separation between tumor (purple) and normal (maroon) cells. Tumor cells largely occupy a distinct, interconnected region of the embedding space, while normal cells are primarily localized to separate, somewhat dispersed clusters. There are some areas of overlap or interface, indicating a mixture of cell types or regions where tumor and normal microenvironments might interact.

Sample-based UMAP

The sample UMAP reveals that cells from different samples are generally well-interspersed across the embedding, suggesting that the primary clustering is driven by biological variation rather than strong batch effects related to individual samples. While some clusters might show enrichment for specific samples, there is no single sample dominating entire large regions, which indicates good integration of the various samples.

Cell Type Hierarchy UMAPs (celltype_major, celltype_minor, celltype_subset)

These UMAPs progressively demonstrate the hierarchical organization of cell types from broad categories to fine-grained subsets:

Ploidy Status UMAP (ploidy_dec)

The ploidy_dec UMAP shows a striking pattern: Aneuploid cells (maroon) largely co-localize with the tumor clusters and, notably, with the large Renal Epithelial cell cluster. Conversely, Diploid cells (light yellow) predominantly occupy regions associated with normal conditions and non-epithelial cell types. Unclear cells (dark purple) are very few and scattered, suggesting a high confidence in ploidy assignments for most cells.

Biological Interpretation

The UMAP visualizations provide crucial insights into the cellular landscape of the kidney in the context of disease:

Annotation Notes

2. Major Cell Type Scores and Ploidy on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) embeddings of single-cell RNA-seq data from kidney tissue, comparing 'tumor' and 'normal' conditions. The UMAP plots visualize the distribution of cells, colored by major cell type scoring (HiCAT_major_score), ploidy status (ploidy_dec), and the final major cell type annotations (celltype_major). This allows for assessing the consistency of cell type assignments and their relationship with chromosomal ploidy, particularly in the context of the inferred Renal Cell Carcinoma.

Visual Summary

The UMAP projection effectively separates cells into distinct clusters, reflecting their underlying transcriptional profiles.

HiCAT Major Cell Type Scores

Ploidy Status (ploidy_dec)

Major Cell Type Annotation (celltype_major)

Biological Interpretation

The visualization provides strong evidence supporting the biological integrity of the dataset and the consistency of the cell type annotation pipeline.

  1. Robust Cell Type Identification: The clear segregation of cell populations by HiCAT_major_score and their excellent correspondence with the final celltype_major annotations confirm the accurate identification and clustering of major cell types within the kidney tissue. This indicates that the single-cell data accurately captures the distinct transcriptional profiles of these cell types.
  2. Identification of Tumor Cells: A critical finding is the strong co-localization of high Renal Epithelial cell scores, the Renal Epithelial cell annotation, and Aneuploid status within a single, prominent UMAP cluster. Given that the 'Resolved tumor-origin cell type' is Renal Epithelial cell and the dataset is from kidney tissue with 'tumor' conditions and a significant aneuploid fraction, this cluster represents the malignant tumor cell population (likely Renal Cell Carcinoma cells). Aneuploidy, the presence of an abnormal number of chromosomes, is a hallmark of many cancers, including renal cell carcinoma.
  3. Tumor Microenvironment and Normal Parenchyma: The remaining cell populations, primarily Diploid, consist of various immune cells (T cells, B cells, Myeloid cells, Mast cells) and stromal/endothelial cells. These cells represent components of the tumor microenvironment (e.g., tumor-infiltrating lymphocytes, tumor-associated macrophages) and/or normal kidney tissue, consistent with what would be expected in a kidney tumor sample. Their distinct separation from the aneuploid renal epithelial cells suggests clear boundaries between cancerous and non-cancerous cell compartments.

Annotation Notes

The consistency observed between the HiCAT_major_score plots, ploidy_dec, and celltype_major plots provides high confidence in the quality of the cell type annotations and the underlying dimensionality reduction. The distinct separation of aneuploid, tumor-origin renal epithelial cells from diploid, non-malignant cells is particularly well-resolved.

3. Marker Expression Profile Across Kidney Cell Subsets

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[Analysis Visualization Results]...

Analysis Overview

This dot plot visualizes the expression of marker genes across various celltype_subset populations identified in the single-cell RNA-seq dataset from kidney tissue. The purpose of this analysis is to evaluate the distinctiveness and consistency of the assigned cell type annotations based on the expression patterns of known and discovered marker genes. Each row represents a celltype_subset, and each column represents a marker gene. The size of each dot corresponds to the fraction of cells within that group expressing the gene, while the color intensity reflects the mean expression level of the gene in those cells. Red boxes highlight clusters of genes highly specific to particular cell subsets. The analysis was configured to identify and plot surfaceome-only markers, with a rotation of 45 degrees for gene labels for readability.

Visual Summary

The dot plot displays a clear diagonal pattern of strong and specific marker gene expression, indicated by larger, darker red dots. This pattern suggests that most celltype_subset annotations are well-supported by distinct transcriptional signatures.

Biological Interpretation

The marker expression patterns observed in the plot largely confirm the identity and annotation of the various celltype_subset populations, consistent with known kidney and immune cell biology.

Renal Epithelial Cells:

Immune Cells:

Stromal and Endothelial Cells:

The consistent and specific expression patterns of these markers across the different celltype_subset populations provide strong evidence for the accuracy and reliability of the cell type annotations derived from the single-cell RNA-seq data.

Annotation Notes

The marker expression dot plot effectively validates the celltype_subset annotations within the AnnData object. The clear, specific expression of known marker genes for each cell group, particularly those highlighted by the red boxes, indicates that the computational clustering and annotation process has successfully identified biologically meaningful and distinct cell populations. The surfaceome_only parameter for marker discovery is particularly useful as it prioritizes genes encoding cell surface proteins, which are critical for cell-cell interactions and can serve as targets for immunophenotyping or cell isolation. The overall quality of annotation appears high based on this marker analysis.

4. Analysis of Copy Number Variations in Renal Epithelial Cells

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Analysis Overview

This analysis investigates copy number variations (CNVs) in Renal Epithelial cells, identified as the tumor-origin cell type, across different samples. A heatmap visualizes the log2(Copy Number Ratio) (log2(CNR)) across the genome for each sample, allowing for the identification of regions of amplification (red) and deletion (blue). An accompanying summary highlights specific cytogenetic bands exhibiting significant recurrent amplifications, including their average log2(CNR) values and frequency across relevant samples.

Visual Summary

CNV Heatmap

The main heatmap displays log2(CNR) values for Renal Epithelial cells, grouped by individual samples.

Summary of Significantly Amplified Regions

The accompanying plots provide a focused summary of frequently amplified cytogenetic bands:

Biological Interpretation

The observed CNV patterns provide strong evidence of genomic instability within the Renal Epithelial cells of specific samples, consistent with the inferred context of Renal Cell Carcinoma.

Clinical or Translational Implications

5. CNV-Informed UMAP Embedding of Kidney Cells by Cell Type, Ploidy, Condition, and Sample

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Analysis Overview

This analysis presents five UMAP (Uniform Manifold Approximation and Projection) plots, where the embedding was specifically informed by Copy Number Variation (CNV) patterns (cnv=True). These plots visualize the single-cell RNA-sequencing data from kidney tissue, colored by major cell type, minor cell type, ploidy status, disease condition (tumor/normal), and individual sample. The primary goal is to assess how well these biological and experimental variables separate cells in a CNV-aware dimensionality reduction, particularly focusing on identifying tumor cell populations and associated microenvironments in the context of Renal Cell Carcinoma.

Visual Summary

The UMAPs display a multi-cluster structure with a large, diffuse central group of cells and several more compact, distinct peripheral clusters.

Biological Interpretation

The CNV-informed UMAP embedding effectively separates cell populations based on their genomic integrity, providing strong evidence for distinct normal and tumor compartments.

Annotation Notes

6. Minor Cell Type Population Analysis in Renal Cell Carcinoma Samples

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Analysis Overview

This analysis visualizes the population distribution of minor cell types across individual samples, distinguishing between 'normal' and 'tumor' conditions in kidney tissue. The objective is to identify shifts in cellular composition associated with the tumor microenvironment, particularly in the context of inferred Renal Cell Carcinoma (RCC). The AnnData context indicates the dataset comprises human kidney single-cell RNA-seq data, with a significant proportion of aneuploid cells and a resolved tumor-origin cell type as Renal Epithelial cell, reinforcing the RCC context.

Visual Summary

The bar plot displays the relative proportions of various minor cell types for each sample, grouped by 'normal' and 'tumor' conditions.

Normal Samples (Left Panel):

Tumor Samples (Right Panel):

Biological Interpretation

The observed cellular shifts provide significant biological insights into Renal Cell Carcinoma:

Clinical or Translational Implications

7. Population Analysis of T Cell Subsets in Kidney Tissue (Normal vs. Tumor)

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Analysis Overview

This analysis presents a stacked bar plot visualizing the relative proportions of T cell subsets across individual samples, comparing normal kidney tissue with renal cell carcinoma (tumor) tissue. The plot focuses specifically on cells classified under the 'T cell' major cell type, further dissecting them into finer 'subset' annotations. This provides insight into the compositional changes within the T cell compartment in the tumor microenvironment, which is crucial for understanding immune responses and potential immune evasion mechanisms in renal cell carcinoma.

Visual Summary

The visualization displays the distribution of various T cell subsets, as well as NK cells and ILCs (which are grouped under the 'T cell' major category for this analysis based on the dataset's annotation structure), in individual samples from both normal and tumor conditions.

Biological Interpretation

The observed shifts in the T cell subset landscape within the renal tumor microenvironment provide critical insights into the immune response against renal cell carcinoma.

  1. Dominance of Cytotoxic and Naive T Cells in Tumor: The striking relative abundance of T cell (Cytotoxic) cells in tumor samples suggests an attempt by the immune system to mount an anti-tumor response. Cytotoxic T lymphocytes (CTLs) are crucial for directly killing cancer cells PubMed Search: cytotoxic T cells cancer immunity. The concurrent high proportion of T cell (Naive) cells could indicate a continuous influx of new T cells into the tumor microenvironment, some of which may differentiate into effector cells.
  2. Reduced Helper T Cell Diversity: The general reduction in the relative proportions of various helper T cell subsets (Th1, Th17, Th2, Th22, Th9, Tfh) in tumor samples is notable. These subsets play critical roles in orchestrating adaptive immune responses, with Th1 cells driving anti-tumor immunity through IFN-γ production and Th17 cells being involved in both pro- and anti-tumor responses depending on context PubMed Search: helper T cell subsets cancer. A relative decrease in their diversity or proportion might impair a robust and multi-faceted immune response against the tumor.
  3. Diminished NK Cells and ILCs: The most striking observation is the significant reduction or near absence of NK cells and ILCs (ILC1, ILC2, ILC3 (NCR-), ILCreg, LTI) in the tumor samples, within this specific T cell major population grouping. NK cells and ILCs represent key components of the innate immune system that provide early and potent anti-tumor surveillance and response, often without prior sensitization PubMed Search: NK cells ILCs cancer immunity. Their relative depletion in the tumor microenvironment could represent a significant mechanism of immune evasion employed by renal cell carcinoma, allowing tumor progression by weakening crucial innate immune defenses.
  4. Treg Proportions: While Regulatory T cells (Treg) are known to suppress anti-tumor immunity and their infiltration often correlates with poor prognosis in RCC PubMed Search: regulatory T cells renal cell carcinoma, this plot shows a *relative reduction* in their proportion within the T cell compartment in tumor samples compared to some normal samples. This could imply a shift in the overall balance, where even if absolute numbers of Tregs increase in the tumor, other T cell populations (like cytotoxic T cells) increase even more dramatically, or their relative contribution is masked by the dominance of other subsets. Alternatively, it could suggest a specific immune landscape where Treg-mediated suppression might be less dominant at the T cell subset level, or that other suppressive mechanisms are at play.

Clinical or Translational Implications

The observed shifts in T cell subset populations, particularly the diminished presence of NK cells and ILCs and the altered helper T cell landscape in renal tumors, have important clinical implications for understanding disease progression and developing immunotherapeutic strategies for Renal Cell Carcinoma.

8. Analysis of Regulatory T cell (Treg) Proportions in Renal Cell Carcinoma

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Analysis Overview

This analysis investigates the proportional differences of various T cell subsets between 'tumor' and 'normal' kidney tissue conditions. The provided box plot specifically illustrates the proportion of Regulatory T cells (Tregs) in samples derived from tumor versus normal tissues. The comparison identifies statistically significant differences based on a p-value cutoff of 0.1, as defined in the analysis parameters.

Visual Summary

The box plot displays the celltype proportion of "Treg" cells across 'tumor' and 'normal' conditions.

Biological Interpretation

Regulatory T cells (Tregs) are a specialized subset of T lymphocytes crucial for maintaining immune tolerance and preventing autoimmunity. In the context of cancer, an elevated presence of Tregs in the tumor microenvironment (TME) is a well-established mechanism of immune evasion by cancer cells [1].

Clinical or Translational Implications

The increased proportion of Tregs in renal tumor tissue holds several clinical implications for Renal Cell Carcinoma:

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References

  1. Wing, K., & Sakaguchi, S. (2010). Regulatory T cells exert checks and balances on the immune system. *Nature Immunology*, 11(11), 984–991. PubMed Search: "Regulatory T cells cancer immune evasion"
  2. Ohue, Y., & Nishikawa, H. (2019). Regulatory T Cells in the Tumor Microenvironment: New Insights into Functions and Targets. *Cancer Science*, 110(5), 1590–1596. PubMed Search: "Tregs tumor microenvironment"
  3. Shen, X., Li, B., Lu, J., et al. (2020). Prognostic value of tumor-infiltrating regulatory T cells in renal cell carcinoma: A meta-analysis. *OncoTargets and Therapy*, 13, 11463–11475. PubMed Search: "Tregs renal cell carcinoma prognosis"
  4. Tanaka, A., & Sakaguchi, S. (2017). Regulatory T cells in cancer immunotherapy. *Cell Research*, 27(1), 109–118. PubMed Search: "Tregs cancer immunotherapy"

9. Macrophage Subset Population Analysis in Renal Cell Carcinoma

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Analysis Overview

This analysis presents a stacked bar plot illustrating the proportional distribution of different macrophage subsets (Macrophage M1, M2A, M2B, M2C, M2D) across individual normal and tumor kidney samples. The primary goal is to identify shifts in macrophage polarization that may be associated with the tumor microenvironment in Renal Cell Carcinoma (RCC).

Visual Summary

The plot displays macrophage subset composition for five normal kidney samples and eight tumor kidney samples. Each bar represents a single sample, normalized to 100% of total macrophages within that sample.

Key observations:

Biological Interpretation

Macrophages are critical components of the tumor microenvironment (TME) and play diverse roles in cancer progression, broadly categorized into M1-like (pro-inflammatory, anti-tumor) and M2-like (immunosuppressive, pro-tumor) phenotypes, though their polarization exists on a spectrum.

  1. Dominance of M1 Macrophages in Tumors: The observed consistent dominance of M1 macrophages in the tumor samples is intriguing. M1 macrophages are typically associated with classical activation, producing pro-inflammatory cytokines and mediating anti-tumor immunity. However, their persistent presence in the TME can also contribute to chronic inflammation, which has been implicated in tumor promotion. This pattern could suggest an ongoing, yet potentially overwhelmed or dysregulated, immune response against the RCC cells, or it might represent an M1-like phenotype with nuanced functions within the tumor context.
  2. Persistent M2 Subsets: Despite M1 dominance, the sustained presence of M2B and M2C macrophages in tumor samples is biologically significant. M2B macrophages are known for their immunomodulatory properties, while M2C macrophages are largely immunosuppressive, promoting tissue remodeling, fibrosis, and immune evasion (PubMed search: "M2C macrophages cancer" [PubMed Search]). The co-existence of M1 with these M2 subtypes highlights the complexity and heterogeneity of tumor-associated macrophages (TAMs) in RCC, where pro- and anti-tumor functions may operate simultaneously.
  3. Reduced M2A in Tumors: While M2A macrophages (associated with wound healing and angiogenesis) are prominent in some normal samples, their relative proportion appears reduced in many tumor samples compared to the M1 population. This might suggest a shift in the specific M2 programs activated within the RCC TME.
  4. Implications for Renal Cell Carcinoma: The inferred disease, Renal Cell Carcinoma, is often characterized by a highly immunosuppressive TME. While the M1 dominance might seem counterintuitive for an immunosuppressive environment, it underscores the plasticity of macrophages and the complex signaling within the kidney TME. The balance and specific activation states of these macrophage subsets are crucial determinants of disease progression and response to therapy in RCC (GeneCards: "M1 macrophage" [GeneCards]).

Clinical or Translational Implications

The distinct macrophage subset profiles observed between normal and tumor kidney tissues could have several clinical implications for Renal Cell Carcinoma:

10. Differential Abundance of Macrophage M2C Subset in Renal Cell Carcinoma

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Analysis Overview

This analysis investigates the proportional representation of macrophage subsets within kidney tissue, comparing 'normal' and 'tumor' conditions. Specifically, the provided box plot focuses on the Macrophage (M2C) subset. The aim is to identify macrophage populations with statistically significant differences in their proportions between the two conditions, based on a single-cell RNA sequencing dataset from human kidney samples where Renal Cell Carcinoma is the inferred disease context. The analysis utilized a p-value cutoff of 0.1 for statistical significance, and a log2 fold change cutoff of 0.1 was also applied.

Visual Summary

The box plot illustrates the celltype proportion of Macrophage (M2C) cells in normal versus tumor kidney samples.

Biological Interpretation

The observed increase in the proportion of Macrophage (M2C) cells in the tumor microenvironment is biologically significant in the context of Renal Cell Carcinoma (RCC). Macrophages are a heterogeneous population of immune cells that play diverse roles in cancer. The M2 subtype of macrophages, often termed tumor-associated macrophages (TAMs), is generally associated with pro-tumorigenic functions.

Clinical or Translational Implications

The significant enrichment of Macrophage (M2C) cells in Renal Cell Carcinoma tumors suggests several potential clinical and translational implications:

11. Ploidy Analysis of Renal Epithelial Cells in Normal and Tumor Conditions

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Analysis Overview

This analysis investigates the ploidy status (Diploid vs. Aneuploid) of Renal Epithelial cells, identified as the tumor-origin cell type, across different individual samples from both normal and tumor kidney tissues. The primary goal is to visualize the distribution of these ploidy populations within this specific cell type and observe any condition-associated shifts.

Visual Summary

The bar plots display the proportion of aneuploid (burgundy) and diploid (light yellow) cells within Renal Epithelial cells for each individual sample, grouped by 'normal' and 'tumor' conditions.

Biological Interpretation

The observed ploidy patterns strongly corroborate the inference of Renal Cell Carcinoma in the tumor samples.

  1. Maintenance of Diploidy in Normal Tissue: In healthy, non-cancerous kidney tissue, renal epithelial cells are expected to maintain a normal diploid chromosomal complement. The data perfectly reflects this, with normal samples showing virtually 100% diploidy among these cells. This serves as a critical baseline and validates the ploidy estimation.
  2. Aneuploidy as a Hallmark of Cancer: The striking prevalence of aneuploidy in Renal Epithelial cells from tumor samples is a well-established characteristic of malignancy and genomic instability in cancer, including Renal Cell Carcinoma. Aneuploidy, the condition of having an abnormal number of chromosomes, arises from errors during cell division and is a major driver of tumor evolution and heterogeneity [PubMed Search: "aneuploidy cancer" PubMed Search].
  3. Tumor Heterogeneity: The sample SI_18855 from the tumor group, which contains a significant fraction of diploid Renal Epithelial cells alongside aneuploid ones, suggests tumor heterogeneity. This could represent:
    • Admixture of normal renal epithelial cells within the tumor biopsy.
    • Presence of distinct tumor cell subclones, some of which might still be diploid or have recently undergone transformation but haven't fully committed to aneuploidy.
    • Contamination with stromal cells or immune cells from the tumor microenvironment that are inherently diploid (though the analysis was specifically for "Renal Epithelial cell").

This observation highlights the complex cellular landscape within tumors.

Clinical or Translational Implications

The findings have several important clinical and translational implications for Renal Cell Carcinoma:

12. Cell-Cell Interaction Patterns in Renal Cell Carcinoma Tumor Microenvironment

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Analysis Overview

This analysis investigates cell-cell interaction (CCI) patterns using CellPhoneDB results, comparing normal kidney tissue with renal cell carcinoma (RCC) tumor tissue. The analysis focuses on interactions involving key cell types implicated in cancer biology: Renal Epithelial cells (identified as the tumor-origin cell type), Fibroblasts, Macrophages, T cells (CD4+ and CD8+), Endothelial cells, and B cells. The plot_cci_dots tool was used to visualize significant ligand-receptor interactions, limited to the top 80 pairs per group based on p-value and mean expression cutoffs. The plots differentiate between normal and tumor conditions, highlighting cell-type specific interactions and the molecular pathways involved. Given the context of RCC, the analysis also differentiates between diploid and aneuploid renal epithelial cells.

Visual Summary

Two dot plots are provided, one for "CCI for normal" and another for "CCI for tumor".

CCI for Normal Tissue:

CCI for Tumor Tissue:

Biological Interpretation

The analysis reveals a dramatic shift in cellular communication patterns between normal kidney tissue and the RCC tumor microenvironment, consistent with the inferred disease context.

Tumor Growth and Angiogenesis:

The absence of Fibroblast, T cell, and B cell interactions in the top 80 pairs for both normal and tumor conditions implies that their interactions, while potentially present, are either less significant or less strong compared to the highlighted interactions involving epithelial cells, endothelial cells, and macrophages under the applied cutoffs and ranking.

Clinical or Translational Implications

The distinct and prominent cell-cell interaction patterns observed in the tumor microenvironment of RCC offer several compelling targets for therapeutic intervention and opportunities for experimental validation.

  1. Immunotherapeutic Targets: The strong presence of immune-modulatory interactions presents high-priority targets:
    • SIRPA-CD47 blockade: Interrupting this "don't eat me" signal could promote macrophage-mediated phagocytosis of tumor cells, synergizing with existing immunotherapies. PubMed Search: CD47 SIRPA cancer immunotherapy
    • LGALS9-HAVCR2 (TIM-3) pathway inhibition: Blocking this interaction could reverse T-cell exhaustion, enhancing anti-tumor immune responses. PubMed Search: LGALS9 HAVCR2 cancer therapy
    • IL-10 signaling inhibition: Targeting IL-10 or its receptor could reduce immunosuppression in the TME.
    • Macrophage Reprogramming: Given the extensive macrophage involvement, strategies to deplete TAMs, block their recruitment (e.g., via CCL3_CCR1/CCR5), or reprogram them from a pro-tumor to an anti-tumor phenotype could be highly effective.
  2. Targeting Tumor Growth and Angiogenesis:
    • EGFR/HER pathway inhibitors: The activation of HBEGF_EGFR/HERB2/HERB3 signaling reinforces the rationale for utilizing EGFR/HER2 inhibitors, which are established anticancer agents, potentially in combination therapies for RCC.
    • VEGFA-NRP2 blockade: Targeting the VEGFA_NRP2 axis could complement existing anti-VEGF therapies by inhibiting angiogenesis and reducing tumor blood supply. UniProt: P15692 (VEGFA_HUMAN)
  3. Metabolic Intervention: The identified lipid and steroid metabolism-related CCIs suggest that targeting these metabolic pathways, possibly through specific enzyme inhibitors or dietary interventions, could be explored as a novel therapeutic strategy in RCC.
  4. Inhibiting Metastasis: Targeting interactions like SPP1_CD44 or PLAUR_uPAR could potentially impede tumor cell migration, invasion, and metastasis, offering avenues for preventing disease progression.

These findings provide a basis for prioritizing specific ligand-receptor pairs for further experimental validation in in vitro and in vivo models of RCC, and for exploring their potential as biomarkers for disease progression or response to therapy.

13. Cell-Cell Interaction Analysis of Immune Checkpoint and Cell Cycle Genes in Renal Cell Carcinoma

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Analysis Overview

This analysis investigates significant cell-cell interactions (CCIs) involving a curated list of genes associated with immune checkpoint and cell cycle pathways, comparing interactions in 'normal' kidney tissue versus 'tumor' tissue. The data originates from single-cell RNA sequencing of human kidney samples, with an inferred disease context of Renal Cell Carcinoma, characterized by the presence of tumor-origin Renal Epithelial cells and aneuploidy. The plot_cci_dots tool was used to visualize these interactions, focusing on highly significant ligand-receptor pairs between specific cell types. The analysis aims to highlight key communication pathways that may be altered in the tumor microenvironment.

Visual Summary

The provided visualizations display selected highly significant cell-cell interactions for the target immune checkpoint and cell cycle genes, aggregated by 'normal' and 'tumor' conditions. The size of each dot represents the -log10(p-value) of the interaction (with larger dots indicating higher statistical significance), and the color represents the log2-transformed mean expression of the ligand-receptor pair (with yellow indicating higher mean expression and dark blue indicating lower mean expression).

  1. CCI for normal condition:
  1. CCI for tumor condition:

Biological Interpretation

The analysis reveals distinct and highly significant cell-cell interaction patterns in normal kidney tissue compared to the renal tumor microenvironment, specifically involving genes related to cell cycle and immune regulation.

Normal Tissue Homeostasis (Endo|Endo interactions):

Tumor Microenvironment Remodeling (Mac|Aneuploid Renal Epi interactions):

The shift in predominant ligand-receptor interactions from maintaining endothelial homeostasis in normal tissue to promoting tumor cell growth via macrophage-tumor cell crosstalk in the tumor environment underscores how the tumor hijacks and reshapes cellular communication pathways using genes critical for growth regulation.

Clinical or Translational Implications

The observed cell-cell interactions provide potential avenues for therapeutic intervention and biomarker development in Renal Cell Carcinoma.

14. Condition-Specific Cell-Cell Interaction Patterns in Renal Cell Carcinoma

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Analysis Overview

This analysis investigates cell-cell interactions (CCI) between major immune and stromal cell types (Myeloid cell, T cell, Endothelial cell, Stromal cell, Mast cell, B cell) that show significant differences between normal and tumor kidney tissues. The results are visualized as a dot plot, where dot size represents the significance of the interaction (-log10(p)) and dot color indicates the scaled log strength of the interaction. This helps identify key communicative pathways altered in the tumor microenvironment (TME) of Renal Cell Carcinoma (RCC).

Visual Summary

The dot plot, titled "Condition-specific CCI pattern," effectively highlights distinct cell-cell interaction landscapes between normal and tumor conditions.

Key Interaction Categories in Tumor:

Overall, the visualization clearly demonstrates a profound shift in cell-cell communication patterns from a relatively quiescent normal state to a highly interconnected and active tumor microenvironment, particularly emphasizing interactions critical for angiogenesis, ECM remodeling, and cell fate regulation.

Biological Interpretation

The observed surge in cell-cell interactions in the tumor condition reflects the complex and dynamic nature of the Renal Cell Carcinoma (RCC) tumor microenvironment (TME), which is critical for tumor progression.

Notch Signaling for Angiogenesis and Cell Fate:

Growth Factor Signaling in Angiogenesis and Stromal Support:

Mechanism:

Immune Cell Cross-talk and TME Modulation:

Clinical or Translational Implications

The identified cell-cell interactions represent promising targets for therapeutic intervention in Renal Cell Carcinoma.

Targeting Angiogenesis:

Inhibiting ECM Remodeling and Integrin Signaling:

Modulating Notch Signaling:

Reprogramming the Tumor Immune Microenvironment:

Biomarker Discovery and Combination Therapies:

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References:

  1. Integrin Signaling in Cancer:

PubMed Search: "integrin signaling cancer"

  1. Notch Signaling in Cancer Angiogenesis:

PubMed Search: "Notch signaling angiogenesis cancer"

  1. Angiopoietin-Tie2 System in Tumor Angiogenesis:

PubMed Search: "Angiopoietin Tie2 tumor angiogenesis"

  1. uPA-uPAR System in Cancer Progression:

PubMed Search: "uPA uPAR cancer progression"

  1. Targeted Therapies for Renal Cell Carcinoma:

PubMed Search: "targeted therapy renal cell carcinoma"

15. Renal Epithelial Cell Condition-Specific Surfaceome Markers in Kidney Tumor

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome markers specifically expressed in Renal Epithelial cells, comparing 'normal' and 'tumor' conditions within kidney tissue. The goal is to highlight potential diagnostic, prognostic, and therapeutic targets. The dot plot displays the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) across different samples classified as either 'normal' or 'tumor'. The Renal Epithelial cell type is designated as the tumor-origin cell type for the inferred Renal Cell Carcinoma context.

Visual Summary

The dot plot clearly differentiates two major groups of Renal Epithelial cells based on their surfaceome marker expression:

The observed patterns indicate a significant molecular rewiring of the cell surface proteome in Renal Epithelial cells transitioning from a normal to a tumor state, providing a robust set of differentially expressed surface markers.

Biological Interpretation

The distinct expression profiles of surfaceome markers in normal versus tumor Renal Epithelial cells align with the inferred diagnosis of Renal Cell Carcinoma (RCC). These markers offer insights into altered cellular functions, immune evasion, and potential vulnerabilities in tumor cells.

Markers Upregulated in Normal Renal Epithelial Cells

While the focus is often on tumor-specific targets, understanding markers lost in cancer can also provide insights into disease progression or identify subsets.

EPCAM (Epithelial Cell Adhesion Molecule)

TACSTD2 (TROP2)

Markers Upregulated in Tumor Renal Epithelial Cells

These markers are highly relevant as potential diagnostic, prognostic, and therapeutic targets in Renal Cell Carcinoma.

Established Therapeutic Targets

EGFR (Epidermal Growth Factor Receptor)

MET (Hepatocyte Growth Factor Receptor)

CD47 ("Don't Eat Me" Signal)

CA12 (Carbonic Anhydrase XII)

Less Well-Characterized Markers with Potential as Therapeutic Targets

CD151 (Tetraspanin-151)

VCAM1 (Vascular Cell Adhesion Molecule 1)

Clinical or Translational Implications

The identification of these condition-specific surfaceome markers in Renal Epithelial cells carries significant clinical and translational implications for Renal Cell Carcinoma:

  1. Diagnostic and Prognostic Biomarkers: Markers like EPCAM and TACSTD2 (if lost in tumor) or EGFR, MET, CD47, and CA12 (if gained in tumor) could serve as valuable biomarkers for early detection, differentiation of tumor from normal tissue, and potentially for monitoring disease progression or recurrence, especially when combined with imaging or liquid biopsy approaches.
  2. Therapeutic Target Identification:
    • Established Targets: The high expression of EGFR, MET, CD47, and CA12 in tumor cells highlights their potential as therapeutic targets in RCC. Existing drugs (e.g., MET inhibitors like cabozantinib, anti-CD47 antibodies) or those in advanced development could be directly applicable or explored further in RCC.
    • Novel/Emerging Targets: Markers like CD151 and VCAM1 represent promising novel targets. Their roles in cell adhesion, migration, and metastasis make them attractive candidates for developing new antibody-drug conjugates (ADCs) or specific blocking antibodies to inhibit tumor progression and spread.
  3. Immunotherapy Strategy Refinement: The upregulation of MHC Class II genes (HLA-DRA, etc.) on tumor cells provides an intriguing insight into tumor-immune interactions. While not direct drug targets, their presence might influence the efficacy of various immunotherapies by affecting antigen presentation to T cells. Further studies are warranted to understand if this expression is protective, permissive for immune evasion, or indicative of an immune-responsive tumor phenotype.
  4. Targeting Heterogeneity: The dot plot reveals sample-to-sample variability in marker expression. This underscores the importance of considering tumor heterogeneity in treatment strategies. A subset of markers might be highly expressed in certain patients but not others, suggesting the need for personalized medicine approaches based on individual tumor marker profiles.

Overall, this analysis provides a rich resource of surfaceome markers in Renal Epithelial cells that are differentially expressed between normal and tumor conditions, paving the way for further investigation into their roles in RCC biology and their potential translation into clinical applications.

16. Macrophage Condition-Specific Surfaceome Markers in Renal Cell Carcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies surfaceome marker expression in Macrophage cells, comparing 'normal' and 'tumor' conditions within the kidney tissue context. The goal is to uncover differentially expressed surface markers that could serve as biological insights into macrophage function in the tumor microenvironment (TME) of Renal Cell Carcinoma (RCC) and as potential therapeutic targets. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing the marker (dot size) for each gene across different samples clustered by condition. A maximum of 40 surfaceome markers per condition were selected for plotting.

Visual Summary

The dot plot clearly differentiates two major groups of macrophage surface markers based on their expression patterns in 'normal' versus 'tumor' conditions:

The overall pattern indicates a profound shift in the surfaceome landscape of macrophages when transitioning from a healthy kidney environment to the RCC TME.

Biological Interpretation

Macrophages are highly plastic immune cells and key players in the tumor microenvironment (TME), often adopting a pro-tumorigenic phenotype (Tumor-Associated Macrophages, TAMs) in various cancers, including Renal Cell Carcinoma (RCC). The observed differential expression of surface markers highlights this phenotypic reprogramming.

Macrophages in the Normal Kidney Microenvironment

Macrophages in the healthy kidney likely contribute to tissue homeostasis, immune surveillance, and debris clearance. The markers predominantly found in 'normal' macrophages reflect these functions:

The downregulation of these markers in tumor-associated macrophages suggests a suppression of their normal homeostatic and acute inflammatory roles.

Tumor-Associated Macrophages (TAMs) in Renal Cell Carcinoma TME

The extensive upregulation of numerous surface markers in 'tumor' macrophages signifies their distinct pro-tumorigenic phenotype, characteristic of TAMs in RCC. These markers collectively point towards roles in immunosuppression, angiogenesis, tumor growth, and metastasis.

Key tumor-associated macrophage markers and their biological implications:

Clinical or Translational Implications

The identification of numerous surface markers highly and specifically expressed on macrophages within the RCC tumor microenvironment opens several avenues for clinical and translational applications:

  1. Therapeutic Targeting of TAMs:
    • CSF1R: Inhibitors targeting CSF1R (e.g., pexidartinib, cabiralizumab, emactuzumab) are in various stages of clinical development for different cancers, aiming to deplete TAMs or reprogram them towards an anti-tumor phenotype. Given its strong upregulation, CSF1R is a prime candidate for TAM-directed therapy in RCC. PubMed search for CSF1R inhibitors cancer
    • AXL: AXL inhibitors (e.g., bemcentinib, BGB324) are being investigated, often in combination with chemotherapy or immunotherapy, to overcome resistance and suppress tumor progression by targeting both tumor cells and TAMs. PubMed search for AXL inhibitors cancer
    • BSG (CD147) and GPNMB: These are established targets in other cancers. Antibody-drug conjugates (ADCs) targeting CD147 (e.g., metuximab) or GPNMB (e.g., glembatumumab vedotin) could be explored for targeted delivery of cytotoxic agents to TAMs in RCC. PubMed search for CD147 cancer therapy, PubMed search for GPNMB cancer therapy
    • FOLR2: Its selective expression on M2-like TAMs makes FOLR2 an attractive target for delivering anti-cancer drugs, immunomodulators, or imaging agents specifically to the pro-tumor macrophage population, minimizing off-target effects. PubMed search for FOLR2 TAMs therapy
    • TREM2: Modulating TREM2 activity, for example, through blocking antibodies, could be a strategy to shift TAM polarization and enhance anti-tumor immunity. PubMed search for TREM2 cancer therapy
  2. Biomarkers for Disease Prognosis and Response to Therapy:
    • The expression levels of these TAM-specific markers could potentially serve as prognostic indicators in RCC patients, with higher expression of pro-tumor markers possibly correlating with poorer outcomes.
    • Monitoring the expression of these markers on circulating monocytes or tissue macrophages could assess the effectiveness of TAM-targeting therapies or immunotherapies.
  3. Combination Therapies:
    • Targeting TAMs by inhibiting key surface receptors like CSF1R or AXL could synergize with existing immunotherapies (e.g., checkpoint inhibitors) or conventional chemotherapies by alleviating the immunosuppressive TME and enhancing anti-tumor immune responses.

Further validation through in vitro functional studies, in vivo preclinical models of RCC, and analysis of human RCC patient samples using techniques like multiplex immunofluorescence or spatial transcriptomics would be crucial to confirm the functional significance of these markers and their therapeutic potential.

17. T cell CD4+ Condition-Specific Surfaceome Markers in Renal Cell Carcinoma

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes surfaceome marker expression in T cell CD4+ populations across different samples/conditions, distinguishing between "normal" and "tumor" associated phenotypes. The dot plot displays mean expression levels (color intensity) and the fraction of expressing cells (dot size) for a selection of surface markers. This approach helps to identify cell-type-specific markers that are differentially regulated in the tumor microenvironment (TME) of Renal Cell Carcinoma, providing insights into T cell function and potential therapeutic targets. The analysis specifically focused on surfaceome markers, with a maximum of 40 markers displayed per condition, as requested.

Visual Summary

The dot plot visualizes the expression of specific surface markers across three distinct samples/groups: SI_22369, SI_23459, and SI_22368. The markers are categorized under "normal" and "tumor" conditions, indicating their general association with either a non-diseased or cancerous state, respectively, within the CD4+ T cell population.

The distinct expression profiles highlight heterogeneity in CD4+ T cell states across different tumor samples, or between tumor and normal tissue contexts, particularly regarding markers associated with tumor progression or immune response.

Biological Interpretation

The observed differential expression of surface markers in T cell CD4+ cells between "normal" and "tumor"-associated contexts provides critical insights into their roles in the Renal Cell Carcinoma microenvironment. Focusing on markers highly expressed in tumor-associated samples (e.g., SI_23459) can reveal potential mechanisms of immune evasion, T cell activation/dysfunction, and therapeutic targets.

Key Markers Identified in T cell CD4+ Cells (Tumor-Associated Context):

  1. CD44
    • Associated Signaling Pathways: CD44 is a major cell adhesion molecule and a receptor for hyaluronan, involved in cell-cell and cell-matrix interactions. It interacts with various signaling molecules, including receptor tyrosine kinases, leading to activation of pathways like PI3K/Akt, MAPK, and Rho GTPases, influencing cell survival, proliferation, and migration. GeneCards: CD44
    • Role in the TME: In the TME, CD44 plays a multifaceted role. It is critical for T cell activation, differentiation, and homing to inflamed tissues and tumor sites. High CD44 expression is often associated with activated and memory T cells, as well as T regulatory cells (Tregs). It can enhance T cell-APC interactions and contribute to immune evasion by promoting cancer stemness and metastatic potential in tumor cells, but also mediating T cell anti-tumor responses.
    • Potential as a Therapeutic Target: Targeting CD44 in the TME is complex due to its diverse functions. However, its high expression on certain immune subsets (e.g., highly activated T cells) or in specific tumor contexts could make it a useful biomarker or a target for directing therapies, especially in conjunction with other targets.
    • Relevant Therapeutic Agents: Monoclonal antibodies targeting CD44 are under investigation in preclinical and early clinical studies, often aimed at inhibiting tumor growth or metastasis. Some strategies focus on blocking CD44-hyaluronan interactions.
  2. HLA-DRA (and other MHC Class II genes like HLA-DPB1, HLA-DRB1)
    • Associated Signaling Pathways: HLA-DRA encodes an alpha chain of the Major Histocompatibility Complex (MHC) class II, essential for presenting antigenic peptides to CD4+ T cells. Its signaling involves T cell receptor (TCR) activation upon antigen binding, leading to downstream cascades like Zap70, LAT, and activation of NFAT and NF-κB pathways, culminating in T cell activation and cytokine production. GeneCards: HLA-DRA
    • Role in the TME: While classically expressed by professional antigen-presenting cells (APCs), CD4+ T cells can upregulate MHC Class II molecules upon activation or in certain inflammatory/tumor contexts. This "ectopic" expression on T cells could signify highly activated T cells with some capacity for antigen presentation (e.g., to other T cells, or antigen "re-presentation") or may reflect a specific T cell subset like follicular helper T cells (Tfh) or highly activated effector T cells. In RCC, robust MHC Class II expression on CD4+ T cells in tumor samples suggests an active, potentially anti-tumor, immune response, or alternatively, a highly plastic regulatory phenotype.
    • Potential as a Therapeutic Target: Directly targeting MHC Class II on T cells is not a common therapeutic strategy, as it's crucial for immune recognition. However, its expression level serves as an excellent biomarker for T cell activation status and engagement within the TME. Modulating the signals that induce or maintain MHC Class II expression on T cells could be part of broader immunotherapeutic strategies aimed at enhancing antigen presentation and T cell activation.
    • Other Pertinent Information: High expression of MHC Class II molecules on tumor-infiltrating lymphocytes (TILs) often correlates with better prognosis in some cancers, indicating a more robust anti-tumor immune response.
  3. BSG (CD147)
    • Associated Signaling Pathways: BSG (Basigin, CD147, EMMPRIN) is a transmembrane glycoprotein involved in cell adhesion, migration, and extracellular matrix (ECM) remodeling. It activates various signaling pathways, including MAPK, PI3K/Akt, and NF-κB, through interactions with cyclophilins and other surface molecules. GeneCards: BSG
    • Role in the TME: In the TME, CD147 is known to promote tumor growth, invasion, and metastasis by inducing matrix metalloproteinase (MMP) production in surrounding stromal cells. On immune cells, including T cells, CD147 plays roles in cell adhesion, migration, and immune cell activation. Its expression on tumor-infiltrating T cells could contribute to their motility within the tumor or influence their interaction with tumor cells and stromal components.
    • Potential as a Therapeutic Target: CD147 is considered a promising therapeutic target in various cancers due to its pro-tumorigenic roles. Inhibiting CD147 could reduce tumor invasion, angiogenesis, and potentially modulate immune cell function within the TME.
    • Relevant Therapeutic Agents: Monoclonal antibodies and small molecule inhibitors targeting CD147 are under investigation for cancer therapy. For example, Meplazumab, an anti-CD147 antibody, has been explored in clinical trials for certain malignancies. PubMed search: CD147 cancer therapy
  4. CD52
    • Associated Signaling Pathways: CD52 is a glycosylphosphatidylinositol (GPI)-anchored glycoprotein highly expressed on lymphocytes, monocytes, and other immune cells. Its exact signaling pathways are not fully elucidated, but it is thought to play a role in cell adhesion, costimulation, and apoptosis. UniProt: P31358 (CD52)
    • Role in the TME: CD52 is broadly expressed on most leukocytes, including CD4+ T cells. Its high expression in the tumor context indicates the presence of these immune cells.
    • Potential as a Therapeutic Target: CD52 is a well-established therapeutic target in lymphoproliferative disorders and autoimmune diseases. Its widespread expression on lymphocytes makes it an effective target for inducing immunosuppression or lymphodepletion.
    • Relevant Therapeutic Agents: Alemtuzumab (anti-CD52 monoclonal antibody) is a widely used drug for conditions like chronic lymphocytic leukemia (CLL) and multiple sclerosis. In oncology, its use is primarily for lymphodepletion prior to other cell therapies or in specific leukemias. Its high expression in tumor-infiltrating CD4+ T cells suggests a potential strategy to deplete specific T cell subsets if they are deemed pathogenic or immunosuppressive.
  5. CCR7
    • Associated Signaling Pathways: CCR7 is a G-protein coupled receptor (GPCR) that binds chemokines CCL19 and CCL21. Activation of CCR7 by its ligands initiates signaling pathways involving Gαi proteins, leading to activation of PI3K/Akt, MAPK, and Rho GTPases, which regulate cell migration, survival, and proliferation. GeneCards: CCR7
    • Role in the TME: CCR7 is crucial for lymphocyte homing to secondary lymphoid organs (SLOs) like lymph nodes and spleen, and for their migration within these organs. Naive T cells and central memory T cells typically express high levels of CCR7. In the TME, its expression on CD4+ T cells can indicate their migratory potential, particularly towards lymphatic vessels or regional lymph nodes, which might be relevant for anti-tumor immunity (e.g., priming in lymph nodes) or for immune escape mechanisms (e.g., egress from the tumor microenvironment). Upregulation in a tumor-associated sample like SI_23459 could suggest a particular subset of T cells that are actively trafficking.
    • Potential as a Therapeutic Target: Modulating CCR7 signaling could be a strategy to control lymphocyte trafficking in cancer. For instance, blocking CCR7 might prevent regulatory T cells (Tregs) from entering lymph nodes where they could suppress anti-tumor responses, or enhance the retention of effector T cells within the tumor. Conversely, enhancing CCR7 signaling could improve the homing of engineered T cells to desired sites.
    • Other Pertinent Information: Dysregulation of CCR7 expression and its ligands in the TME is associated with altered immune cell trafficking and can impact clinical outcomes.

Normal-Associated Markers (in SI_22369):

Clinical or Translational Implications

The differential expression of surface markers on CD4+ T cells in Renal Cell Carcinoma samples highlights potential avenues for diagnostic, prognostic, and therapeutic intervention:

Therapeutic Targeting:

18. Differential Expression Trends of Cell Cycle Pathway Genes in Renal Epithelial Cells

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to investigate the expression differences of a predefined set of cell cycle pathway genes in Renal Epithelial cells, comparing tumor tissue samples to normal tissue samples. Renal Epithelial cells were selected as they are the inferred cell of origin for Renal Cell Carcinoma (RCC) in this dataset. The plot_box_for_gene_expression_with_signif_difference tool was used to visualize the fraction of cells expressing each gene per sample (rather than mean expression) and to evaluate statistical significance using a t-test with a p-value cutoff of 0.05.

Visual Summary

The box plots display the "Expressing cell fraction (sample)" for six cell cycle-related genes across tumor and normal conditions within Renal Epithelial cells.

Genes with higher expressing cell fraction in tumor (trends):

Genes with lower expressing cell fraction in tumor (trends):

It is important to note that none of the displayed genes met the strict statistical significance threshold of p < 0.05. However, several genes show p-values marginally above this cutoff, suggesting a potential biological trend that warrants further investigation.

Biological Interpretation

The analysis, though not yielding strictly significant results (p < 0.05) for individual genes in this visualization, reveals compelling trends in cell cycle gene expression within Renal Epithelial cells, consistent with the inferred context of Renal Cell Carcinoma. Cancer is fundamentally characterized by dysregulated cell proliferation and genomic instability, both of which are intimately linked to cell cycle control and DNA repair mechanisms.

Drivers of Proliferation and DNA Damage Response in Tumor Cells:

Cell Cycle Inhibitors and Chromosome Dynamics:

Overall, these trends suggest a complex dysregulation of cell cycle control, DNA damage response, and genomic maintenance mechanisms in Renal Epithelial cells from tumor tissues, even if individual gene differences did not reach stringent statistical significance in this particular analysis. The shift in expressing cell fractions implies changes in the proportion of cells actively engaging in these pathways within the tumor microenvironment.

Clinical or Translational Implications

The observed trends, particularly for genes involved in cell cycle progression and DNA damage response, have potential clinical and translational relevance for Renal Cell Carcinoma, despite not reaching strict statistical significance here.

19. Gene Ontology (GSA) Analysis for Renal Epithelial Cells in Kidney Tissue

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GO) enrichment results, specifically Gene Set Analysis (GSA), for Renal Epithelial cells. The results are displayed as bar plots comparing gene sets enriched (upregulated) in two conditions: "normal_vs_others" (normal renal epithelial cells compared to all other cells) and "tumor_vs_others" (tumor renal epithelial cells compared to all other cells). This aims to identify distinct biological processes and pathways characteristic of healthy versus cancerous renal epithelial cells within the kidney tissue context, aiding in understanding the molecular shifts associated with Renal Cell Carcinoma.

Visual Summary

The provided bar plots illustrate the top enriched GO terms, ranked by statistical significance, for Renal Epithelial cells in normal and tumor conditions. The x-axis represents the negative logarithm of the p-value and FDR-adjusted q-value, with longer bars indicating higher statistical significance of enrichment.

Renal Epithelial cell: normal_vs_others

Renal Epithelial cell: tumor_vs_others

Biological Interpretation

The GSA results reveal a profound shift in the biological landscape of Renal Epithelial cells when transitioning from a normal to a tumor state, consistent with the inferred context of Renal Cell Carcinoma.

  1. Metabolic Reprogramming in Normal vs. Tumor Cells:
    • Normal Renal Epithelial Cells: The strong enrichment of oxidative phosphorylation, fatty acid metabolism, and various amino acid metabolism pathways reflects the high metabolic demand and specialized functions of healthy kidney epithelial cells, which are crucial for active transport, reabsorption, and energy generation. This suggests a reliance on oxidative metabolism for energy production [1].
    • Tumor Renal Epithelial Cells: The absence of these oxidative metabolic pathways in the tumor context, coupled with the enrichment of pathways related to rapid protein synthesis (ER processing, spliceosome, ribosome), suggests a metabolic reprogramming often observed in cancer cells (e.g., Warburg effect, though not directly shown here) that prioritizes biomass accumulation for proliferation over efficient energy production [2]. The high activity of the spliceosome, for instance, is increasingly recognized as a dependency in many cancers.
  2. Oncogenic Signaling and Cell Cycle Dysregulation:
    • The enrichment of "Pathways in cancer", "HIF-1 signaling pathway", "p53 signaling pathway", "PI3K-AKT signaling pathway", and "MAPK signaling pathway" in tumor cells directly points to the activation of well-known oncogenic signaling cascades that drive cell proliferation, survival, and angiogenesis in cancer [3, 4]. HIF-1 activation is particularly relevant in clear cell RCC, where loss of VHL often leads to constitutive HIF activation.
    • Pathways related to apoptosis, necroptosis, and cellular senescence, which are crucial for maintaining tissue homeostasis, are also implicated, suggesting dysregulation of programmed cell death and cell cycle control in tumor cells.
  3. Immune Evasion and Inflammation:
    • The striking enrichment of numerous viral and bacterial infection pathways in tumor cells is a significant finding. While not directly implying active infection in all tumor cells, it could indicate:
      • Altered Immune Microenvironment: Tumor cells often create an immunosuppressive microenvironment, making them more susceptible to opportunistic infections or altering their response to pathogens [5].
      • Oncovirus Link: Some of the listed viruses (e.g., Epstein-Barr virus, Human T-cell leukemia virus 1, Kaposi sarcoma-associated herpesvirus, Human papillomavirus, Hepatitis B) are known oncoviruses, and their pathways might be activated even without active infection, potentially through viral proteins or epigenetic mechanisms contributing to oncogenesis [6].
      • Inflammatory Response: The activation of "TNF signaling pathway" and "NF-kappa B signaling pathway" points to chronic inflammation, a known driver of tumor progression and metastasis. "Antigen processing and presentation" also indicates changes in how tumor cells interact with the immune system, potentially in an attempt to evade detection.

Clinical or Translational Implications

The distinct GO term enrichments between normal and tumor Renal Epithelial cells offer several clinical and translational insights:

References

  1. Metabolism in kidney: PubMed Search: "renal epithelial cell metabolism"

https://pubmed.ncbi.nlm.nih.gov/?term=renal+epithelial+cell+metabolism

  1. Cancer metabolism: PubMed Search: "cancer metabolic reprogramming"

https://pubmed.ncbi.nlm.nih.gov/?term=cancer+metabolic+reprogramming

  1. HIF-1 pathway in RCC: GeneCards: HIF1A

https://www.genecards.org/cgi-bin/carddisp.pl?gene=HIF1A

  1. PI3K-AKT-MAPK pathways in cancer: PubMed Search: "PI3K AKT MAPK cancer signaling"

https://pubmed.ncbi.nlm.nih.gov/?term=PI3K+AKT+MAPK+cancer+signaling

  1. Tumor immune evasion: PubMed Search: "tumor immune microenvironment evasion"

https://pubmed.ncbi.nlm.nih.gov/?term=tumor+immune+microenvironment+evasion

  1. Oncoviruses: PubMed Search: "oncoviruses cancer"

https://pubmed.ncbi.nlm.nih.gov/?term=oncoviruses+cancer

  1. Targeting PI3K-AKT-mTOR in RCC: PubMed Search: "PI3K AKT mTOR inhibitors renal cell carcinoma"

https://pubmed.ncbi.nlm.nih.gov/?term=PI3K+AKT+mTOR+inhibitors+renal+cell+carcinoma

20. Gene Set Enrichment Analysis (GSEA) Across Renal Cell Carcinoma Cell Types

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The plot displays the Normalized Enrichment Score (NES) and statistical significance (-log(p-value)) for 120 gene sets across various cell types found in the kidney, comparing 'normal' and 'tumor' conditions. The inferred disease context is Renal Cell Carcinoma (RCC), given the kidney tissue, 'tumor' vs 'normal' conditions, and the identification of 'Renal Epithelial cell' as the resolved tumor-origin cell type, alongside significant aneuploidy.

For each cell type (Endothelial cell, Macrophage, Renal Epithelial cell, Smooth muscle cell, T cell CD4+, T cell CD8+), two comparisons are shown:

  1. normal_vs_others: Genes in cells of this type from 'normal' condition are compared against all other conditions (presumably 'tumor' in this two-condition context).
  2. tumor_vs_others: Genes in cells of this type from 'tumor' condition are compared against all other conditions (presumably 'normal' in this two-condition context).

Visual Summary

The dot plot uses color to represent the Normalized Enrichment Score (NES), where red indicates positive enrichment (upregulation of the pathway/genes) and blue indicates negative enrichment (downregulation). The intensity of the color reflects the magnitude of the NES. The size of each dot corresponds to the statistical significance, specifically -log(p-value), with larger dots indicating more significant enrichment or depletion.

Key visual patterns include:

Biological Interpretation

The GSEA results provide insight into the functional shifts occurring in different cell populations within the Renal Cell Carcinoma microenvironment.

1. Metabolic Reprogramming in Tumor Epithelial Cells

2. Proliferation and Oncogenic Signaling in Tumor Epithelial Cells

3. Immune Microenvironment Modulation

4. Stromal Remodeling in the Tumor Microenvironment

Clinical or Translational Implications

The GSEA results offer several insights with potential clinical and translational relevance for Renal Cell Carcinoma:

21. Discussion

The single-cell RNA sequencing analysis of kidney tissue provides a high-resolution view of the Renal Cell Carcinoma (RCC) tumor microenvironment (TME) and the malignant renal epithelial cells. A central finding is the clear distinction between normal and tumor cells, with aneuploid Renal Epithelial cells forming the core of the malignant population, exhibiting profound genomic instability and distinct transcriptomic profiles.

The malignant renal epithelial cells undergo significant metabolic reprogramming, shifting from the oxidative phosphorylation and diverse metabolic pathways characteristic of normal kidney cells towards increased glycolysis, HIF-1 signaling, and robust protein synthesis machinery (ER processing, spliceosome). This metabolic rewiring supports rapid proliferation, a hallmark of cancer, and underscores a potential vulnerability that could be targeted therapeutically. Furthermore, these tumor cells activate key oncogenic pathways, including PI3K-AKT and MAPK signaling, which drive cell survival and growth.

The TME in RCC is extensively remodeled, with substantial alterations in immune and stromal cell compositions and interactions. A notable shift includes the increased proportion of Macrophage (M2C) subsets and Regulatory T cells (Tregs) in tumor samples, both known contributors to immunosuppression. Concurrently, there is a marked reduction in NK cells and various ILC subsets in the tumor, suggesting a weakened innate anti-tumor immune surveillance. These changes establish an immunosuppressive milieu that facilitates tumor escape from immune destruction.

Cell-cell interaction (CCI) analyses further delineate the intricate communication networks driving tumor progression. Tumor-associated macrophages (TAMs) emerge as central players, engaging in pro-tumorigenic crosstalk with aneuploid renal epithelial cells. Key interactions involve the HBEGF-EGFR axis, promoting tumor cell proliferation, and the SIRPA-CD47 "don't eat me" signal, enabling immune evasion. Significant interactions related to extracellular matrix remodeling (collagen/fibronectin-integrin axes), angiogenesis (VEGFA, ANGPT2, PDGFB signaling), and Notch pathway activation are highly prevalent between endothelial cells and smooth muscle cells in tumor samples, collectively fostering tumor growth and metastasis.

The distinct surfaceome marker profiles observed on both tumor-origin Renal Epithelial cells and TAMs provide actionable insights. Tumor epithelial cells upregulate known oncogenic receptors like EGFR and MET, alongside immune evasion molecules like CD47 and metabolism-associated markers like CA12. TAMs, in turn, highly express pro-tumorigenic receptors such as CSF1R and AXL, further emphasizing their role in tumor sustenance and immune modulation.

Collectively, these findings paint a comprehensive picture of RCC pathogenesis, driven by intrinsic tumor cell dysregulation, active metabolic reprogramming, and a highly conducive, immunosuppressive TME shaped by extensive cell-cell interactions. The identified molecular targets and pathways offer a strong foundation for developing or refining diagnostic tools and therapeutic strategies.

Hypotheses:

  1. Renal Cell Carcinoma cells undergo a metabolic shift characterized by increased reliance on glycolysis and active protein synthesis, driven by HIF-1 signaling pathway activation.
  2. The tumor microenvironment in Renal Cell Carcinoma promotes immune evasion through the enrichment of immunosuppressive Macrophage (M2C) cells and Regulatory T cells (Tregs), and the downregulation of innate immune cells like NK and ILCs.
  3. The HBEGF-EGFR signaling axis between tumor-associated macrophages and aneuploid renal epithelial cells is a critical driver of tumor cell proliferation and survival in Renal Cell Carcinoma.
  4. Tumor cells evade phagocytosis by macrophages through the upregulation of CD47, which interacts with SIRPα on myeloid cells.
  5. Extensive extracellular matrix remodeling, angiogenesis, and Notch signaling pathways are hyperactive in the Renal Cell Carcinoma tumor microenvironment, primarily driven by endothelial-stromal cell interactions.
  6. The specific surfaceome signatures of tumor renal epithelial cells (e.g., high EGFR, MET, CD47, CA12) and tumor-associated macrophages (e.g., high CSF1R, AXL, GPNMB, FOLR2) are predictive of patient outcomes and therapeutic response in Renal Cell Carcinoma.

Potential therapeutic targets:

  1. EGFR (Epidermal Growth Factor Receptor): Upregulated and highly active HBEGF-EGFR interactions between tumor-associated macrophages and aneuploid Renal Epithelial cells, along with high EGFR expression on tumor cells, strongly indicate its role in promoting tumor cell proliferation and survival. This is a common oncogenic driver in many cancers. Evidence: Section 13 highlights HBEGF_EGFR interaction as highly significant in tumor. Section 15 shows EGFR as a highly expressed surfaceome marker on tumor Renal Epithelial cells. GSEA (Section 20) also shows enrichment in "Pathways in cancer" which include EGFR signaling. Validation: In vitro experiments with EGFR inhibitors (e.g., erlotinib, gefitinib) on RCC cell lines and co-cultures to assess impact on proliferation and survival. In vivo testing in RCC patient-derived xenograft models.
  2. MET (Hepatocyte Growth Factor Receptor): High expression of MET on tumor Renal Epithelial cells indicates dysregulation of the HGF/MET pathway, a known driver of proliferation, invasion, and metastasis in RCC, contributing to aggressive disease. Evidence: Section 15 identifies MET as a highly expressed surfaceome marker on tumor Renal Epithelial cells. Validation: Functional assays using MET inhibitors (e.g., cabozantinib, tepotinib) on RCC cell lines to assess impact on proliferation, migration, and survival. Efficacy testing in RCC animal models.
  3. CD47 ("Don't Eat Me" Signal): Upregulation of CD47 on tumor Renal Epithelial cells suggests a mechanism of immune evasion by preventing phagocytosis by macrophages. Blocking this interaction could re-enable innate anti-tumor immunity. Evidence: Section 12 identifies SIRPA_CD47 as a prominent interaction in tumor tissue. Section 15 shows CD47 as a highly expressed surfaceome marker on tumor Renal Epithelial cells. Validation: In vitro macrophage-tumor cell phagocytosis assays with anti-CD47 antibodies. In vivo studies combining anti-CD47 agents with existing immunotherapies in RCC models.
  4. CSF1R (Colony Stimulating Factor 1 Receptor): CSF1R is highly expressed on tumor-associated macrophages (TAMs), driving their survival, proliferation, and pro-tumorigenic phenotype. Inhibiting CSF1R can deplete or reprogram TAMs, reducing immunosuppression and promoting anti-tumor responses. Evidence: Section 16 identifies CSF1R as a highly expressed surfaceome marker on tumor Macrophage cells. CCI analyses (Section 12) show extensive macrophage involvement. Validation: Testing CSF1R inhibitors (e.g., pexidartinib, cabiralizumab) in RCC models to assess macrophage infiltration, polarization, and tumor growth.
  5. AXL (AXL Receptor Tyrosine Kinase): High expression of AXL on tumor-associated macrophages (TAMs) suggests its role in promoting an immunosuppressive, M2-like phenotype and contributing to resistance mechanisms in the tumor microenvironment. Evidence: Section 16 identifies AXL as a highly expressed surfaceome marker on tumor Macrophage cells. Validation: Evaluate AXL inhibitors (e.g., bemcentinib) in RCC models, particularly in combination with other agents, to overcome resistance and modulate TAM function.
  6. PD-1/PD-L1 Immune Checkpoint Pathway: GSEA reveals significant enrichment of the "PD-L1 expression and PD-1 checkpoint pathway in cancer" across multiple immune cell types in the tumor microenvironment. This indicates active immune evasion via this pathway, which is a known vulnerability in RCC. Evidence: Section 20 (GSEA) shows strong enrichment of "PD-L1 expression and PD-1 checkpoint pathway in cancer" in Macrophage, T cell CD4+, and T cell CD8+ from tumor samples. Validation: Clinical validation is already established for anti-PD-1/PD-L1 therapies in RCC. Preclinical validation could involve exploring novel combination strategies with other identified targets (e.g., CSF1R, AXL inhibitors).

Follow-up validation ideas:

  1. Targeted qPCR or Western Blot: Validate the differential expression of key genes (e.g., HIF1A, CCND1, CHEK1, EGFR, MET, CSF1R, AXL, CD47, CA12) at the mRNA and protein level in bulk tumor versus normal kidney tissue samples, and potentially in isolated cell populations using flow cytometry or cell sorting.
  2. Immunohistochemistry/Immunofluorescence: Spatially validate the expression patterns of specific surface markers (e.g., EGFR, MET, CD47, CA12 on tumor cells; CSF1R, AXL, FOLR2 on macrophages) and cell type proportions (e.g., Treg, M2C macrophage infiltration) in human RCC tissue sections, correlating with clinical outcomes.
  3. Flow Cytometry: Quantify the proportions of T cell subsets (e.g., Tregs, NK cells), macrophage subsets (M1/M2C), and the expression of surface markers (e.g., CD47, CD151, VCAM1, CSF1R) on these populations in dissociated tumor and normal kidney samples.
  4. In vitro Perturbation Assays: Use RCC cell lines and co-culture systems with macrophages or other stromal cells to functionally validate key cell-cell interactions (e.g., HBEGF-EGFR, SIRPA-CD47). This could involve blocking ligand/receptor activity with antibodies or genetic knockdowns/overexpressions and observing effects on proliferation, survival, or phagocytosis.
  5. In vivo Preclinical Models: Test the efficacy of targeting identified pathways (e.g., EGFR, MET, CD47, CSF1R, AXL) using specific inhibitors or antibodies in patient-derived xenograft (PDX) or syngeneic mouse models of RCC, assessing tumor growth, metastasis, and immune cell infiltration.
  6. Spatial Transcriptomics/Proteomics: Investigate the spatial distribution and co-localization of key cell types and their interacting ligand-receptor pairs within the tumor microenvironment to understand their precise roles in tumor progression.
  7. Validation Cohorts: Confirm identified biomarkers (e.g., gene expression signatures, cell type proportions) and therapeutic targets in independent, larger cohorts of RCC patients, correlating with prognosis and response to therapy.
  8. CRISPR/Cas9 Screening: Perform in vitro or in vivo CRISPR screens to identify novel genes or pathways essential for tumor cell survival or immune evasion in RCC.

Limitations:

This report, derived from single-cell RNA-seq data, provides robust insights into the cellular and molecular landscape of Renal Cell Carcinoma. However, it has several limitations. The inference of Renal Cell Carcinoma is provisional and not a clinical diagnosis. While CNV data supports malignancy, the specific grade, stage, or molecular subtype of RCC cannot be determined. All findings are correlative, and direct causality cannot be inferred without functional experimental validation. The "unassigned" cell population, particularly prominent in tumor samples, likely includes malignant epithelial cells but could also encompass novel or poorly characterized cell states, requiring further detailed characterization. The analysis focuses on predefined cell types and pathways, and other important interactions or markers might exist beyond the scope of the precomputed analyses. Finally, single-cell RNA-seq provides a snapshot of gene expression and cannot fully capture dynamic cellular processes, protein activity, or post-translational modifications.

22. 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 Renal 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 Renal 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 Renal Epithelial cells (tumor-origin cells), Fibroblasts, Macrophages, T cells, Endothelial cells, and B cells. 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 Renal 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 Macrophage 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.
  17. 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.
  18. Among cell cycle pathway genes, select those with statistically significant expression differences between conditions in Renal Epithelial 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 overall width-to-height ratio of approximately 2:3.
  19. Show and save Gene Ontology (GSA) analysis results for Renal Epithelial cells (tumor-origin cells) as a bar plot.
  20. 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.
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