ARHGAP22 as a Potential Prognostic Biomarker in Clear Cell Renal Cell Carcinoma: Insights into Tumor Immunity and Co-Expression Networks

Summary

This study evaluates ARHGAP22 expression in clear cell renal cell carcinoma and its associations with prognosis, clinicopathological features, the tumor immune microenvironment, and computationally predicted drug sensitivity.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is characterized by substantial clinical heterogeneity, highlighting the need for reliable prognostic biomarkers. This study evaluated the expression pattern, prognostic relevance, and immune-related associations of ARHGAP22 in ccRCC using transcriptomic and clinical data from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) cohort, together with external validation data and protein-expression information from the Human Protein Atlas (HPA). ARHGAP22 expression was compared between tumor and adjacent normal tissues, and its associations with overall survival, clinicopathological characteristics, tumor microenvironment scores, and estimated immune-cell fractions were assessed. Co-expression and functional-enrichment analyses were also performed to characterize potential biological associations. ARHGAP22 was significantly upregulated in ccRCC tissues at the transcriptomic level, with corresponding differences observed in immunohistochemical images. High ARHGAP22 expression was associated with shorter overall survival, advanced clinicopathological features, and higher ImmuneScore, StromalScore, and ESTIMATEScore values. CIBERSORT-based analysis showed that the high-expression group had higher estimated fractions of M2 macrophages and regulatory T cells and lower estimated fractions of naïve B cells, resting mast cells, and activated dendritic cells after false discovery rate correction. Functional-enrichment analyses linked ARHGAP22-associated genes to immune-related processes, cell migration, and chemokine- and cytokine-mediated signaling pathways. These findings suggest that ARHGAP22 may represent a potential prognostic and immune-related biomarker in ccRCC, although further independent clinical and experimental validation is required.

Introduction

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma, accounting for approximately 70%–80% of cases and contributing substantially to renal cancer–related mortality¹˒². The incidence of renal cell carcinoma has increased overall in recent years, with marked epidemiological variation across regions. Established risk factors include smoking, obesity, hypertension, and chronic kidney disease³˒⁴. Clear cell renal cell carcinoma is characterized by pronounced aggressiveness and molecular heterogeneity. A su....

Protocol

This study used publicly available, de-identified data from TCGA, HPA, and other open-access databases and did not involve new human participant recruitment, animal experiments, or identifiable private information. Therefore, additional institutional ethics approval and informed consent were not required. Detailed information on the tools used in the protocol is provided in the Table of Materials.

1. Public datasets and bioinformatics analysis
Publicly available datasets were used, and no direct research involving human participants or animals was conducted. Transcriptomic and clinical data were obtai....

Results

Pan-cancer expression profiling of ARHGAP22
Pan-cancer transcriptional profiling showed marked heterogeneity in ARHGAP22 expression across tumor types and corresponding normal tissues (Figure 1). An overall trend toward increased ARHGAP22 expression was observed in several solid tumors. ARHGAP22 expression was significantly higher in tumor tissues than in corresponding normal tissues in breast invasive carcinoma, cholangiocarcinoma, head and neck squamous cell carcinoma,.......

Discussion

Renal cell carcinoma (RCC) is one of the most common malignancies of the urinary system, and clear cell renal cell carcinoma (ccRCC) is the predominant histological subtype and a major contributor to RCC-related mortality. The incidence of RCC has increased overall in recent years, with notable geographic variation. Established risk factors include smoking, obesity, hypertension, and chronic kidney disease³˒⁴. Despite advances in diagnosis and treatment, ccRCC remains .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
circlize R package	Version 0.4.16	CRAN	Used for circos plots and chord diagrams.
clusterProfiler R package	Version 4.12.0	Bioconductor	Used for GO, KEGG and GSEA enrichment analyses.
ComplexHeatmap R package	Version 2.20.0	Bioconductor	Used for complex heatmap visualization and clinical annotation heatmaps.
e1071 R package	Version 1.7.16	CRAN	Used for support vector regression in CIBERSORT-related analysis.
enrichplot R package	Version 1.24.0	Bioconductor	Used for visualization of functional enrichment results.
estimate R package	Version 1.0.13	R package/source package	Used to calculate stromal, immune and ESTIMATE scores.
ggExtra R package	Version 0.10.1	CRAN	Used for scatter plots with marginal density distributions.
ggplot2 R package	Version 3.5.1	CRAN	Used for general data visualization.
ggpubr R package	Version 0.6.0	CRAN	Used for boxplots, violin plots and statistical comparisons.
ggrepel R package	Version 0.9.5	CRAN	Used for non-overlapping text labels in volcano plots.
limma R package	Version 3.60.4	Bioconductor	Used for expression data preprocessing and differential expression-related analysis.
oncoPredict R package	Version 1.2	CRAN	Used to predict drug sensitivity based on transcriptomic data.
org.Hs.eg.db R package	Version 3.19.1	Bioconductor	Used for gene annotation and conversion between gene symbols and Entrez IDs.
pheatmap R package	Version 1.0.12	CRAN	Used for heatmap visualization.
preprocessCore R package	Version 1.68.0	Bioconductor	Used for quantile normalization in CIBERSORT-related analysis.
R software	Version 4.4.0	R Foundation for Statistical Computing	Used for statistical analysis and visualization.
RColorBrewer R package	Version 1.1.3	CRAN	Used for color palette generation in visualization.
regplot R package	Version 1.1	CRAN	Used for nomogram visualization.
reshape2 R package	Version 1.4.4	CRAN	Used for data reshaping before visualization.
rms R package	Version 6.8.1	CRAN	Used for prognostic model construction, calibration analysis and nomogram-related analysis.
scales R package	Version 1.4.0	CRAN	Used for scale adjustment and color transparency settings.
survival R package	Version 3.5.8	CRAN	Used for Cox regression and Kaplan-Meier survival analysis.
survminer R package	Version 0.4.9	CRAN	Used for visualization of Kaplan-Meier survival curves.
timeROC R package	Version 0.4	CRAN	Used for time-dependent ROC curve analysis.