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Research Article

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

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DOI:

10.3791/72307

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September 3rd, 2026

 ,  ,  ,  ,  ,  , 
1Department of Gastroenterology, The First Affiliated Hospital of Jinzhou Medical University, 2Qilu Medical University, 3General Hospital of Fushun Mining Bureau of Liaoning Health Industry Group, 4Department of Urology, The First Affiliated Hospital of Jinzhou Medical University

* These authors contributed equally

In This Article

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 subset of patients presents with metastatic disease at diagnosis, and postoperative recurrence and progression remain common2˒5. At the molecular level, von Hippel–Lindau (VHL) inactivation and sustained activation of hypoxia-inducible factor (HIF) signaling are recognized as key events in ccRCC pathogenesis and are accompanied by widespread genomic and epigenetic alterations6˒7. Although targeted therapies and immunotherapies have improved outcomes in advanced disease, treatment responses remain highly heterogeneous, underscoring the need for reliable prognostic and immune-related biomarkers8,9,10.

Rho guanosine triphosphatases (Rho GTPases) are molecular switches that regulate cytoskeletal remodeling, cell polarity, adhesion, migration, and invasion and have multifaceted roles in tumorigenesis and cancer progression11˒12. In addition to regulating tumor-cell proliferation, apoptosis, and motility, Rho GTPase signaling contributes to angiogenesis, inflammatory responses, and remodeling of the tumor immune microenvironment13,14,15. The biological significance of Rho GTPase signaling in ccRCC has received increasing attention. Rac signaling has been reported to promote ccRCC growth and angiogenic switching, whereas VHL/HIF-driven ccRCC may depend on the Rho GTPase/Rho-associated coiled-coil-containing protein kinase (ROCK) pathway16˒17. Rho GTPase–related gene signatures have also been associated with poor prognosis, immunosuppressive status, and differential responses to immunotherapy in ccRCC, suggesting that this pathway may represent an important molecular link between tumor progression and tumor immunity18˒19.

ARHGAP22 belongs to the Rho GTPase-activating protein (RhoGAP) family and the FilGAP-related subfamily and primarily functions as a Rac-specific RhoGAP involved in antagonistic regulation of the RhoA–Rac1 axis20˒21. The protein contains a pleckstrin homology (PH) domain and a RhoGAP domain and can regulate Rac activity through endosomal localization and transport to the plasma membrane, thereby influencing lamellipodia formation, cell spreading, and migration20˒22˒23. Previous studies have associated ARHGAP22 with cytoskeletal dynamics, tumor-cell motility, the tumor immune microenvironment, and potential biomarker roles in several malignancies20,23,24,25. However, the expression pattern, prognostic significance, immune associations, and potential therapeutic relevance of ARHGAP22 in ccRCC remain insufficiently characterized. The present study therefore evaluated whether ARHGAP22 expression is associated with clinicopathological progression, patient prognosis, immune infiltration, computationally predicted drug sensitivity, and co-expression networks in ccRCC, with the aim of assessing its potential value as a prognostic and immune-related biomarker.

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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 obtained from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) project, and protein-expression data were retrieved from the Human Protein Atlas. This computational approach enabled efficient screening of large-scale transcriptomic and clinical datasets, supported the preliminary identification of candidate biomarkers, and provided a basis for subsequent experimental validation. All computational analyses were performed using statistical software.

2. Data acquisition and sample selection
The Genomic Data Commons Data Portal was accessed, and the TCGA-KIRC project was selected. Transcriptome-profiling data and corresponding clinical information for clear cell renal cell carcinoma (ccRCC) were downloaded. Transcripts per million (TPM)-normalized messenger RNA expression data were used for subsequent transcriptomic analyses. ARHGAP22 expression values were extracted from the TCGA-KIRC transcriptomic matrix and matched with the corresponding clinical records using TCGA sample barcodes. Primary tumor tissues and adjacent normal kidney tissues with available ARHGAP22 expression data and clinical annotations were included. Samples with missing ARHGAP22 expression values, incomplete key clinical or survival information, duplicated records, or survival times of less than 30 days were excluded. After filtering, 533 tumor samples and 72 adjacent normal tissue samples were retained for downstream analyses.

3. Pan-cancer expression analysis
Pan-cancer expression analysis of ARHGAP22 was performed using the Gene_DE module of TIMER2.0 (http://timer.cistrome.org/; accessed April 12, 2026). TCGA RNA-sequencing data were used to compare ARHGAP22 expression between tumor tissues and corresponding normal tissues across multiple cancer types. Expression values were presented as log2(TPM), and differential expression was evaluated using the Wilcoxon rank-sum test implemented in TIMER2.0. A two-sided P value of less than 0.05 was considered statistically significant.

4. External GEO validation
External validation was performed using the Gene Expression Omnibus dataset GSE167573 through the BEST online platform (https://rookieutopia.hiplot.com.cn/app_direct/BEST/; accessed July 9, 2026). ARHGAP22 expression in ccRCC and normal kidney tissues was compared using the normalized expression data provided by the platform and assessed using an unpaired Student’s t-test. For survival analysis, patients were divided into high- and low-expression groups using the platform’s optimal cutoff method, and overall survival was evaluated using Kaplan–Meier analysis with the log-rank test. A two-sided P value of less than 0.05 was considered statistically significant.

5. ARHGAP22 expression analysis
TPM-normalized ARHGAP22 messenger RNA expression data were extracted from the TCGA-KIRC cohort and transformed as log2(TPM + 1) before statistical analysis. Differential ARHGAP22 expression between primary tumor tissues and adjacent normal kidney tissues was evaluated. The Wilcoxon rank-sum test was used for unpaired comparisons between tumor and normal tissues. For paired analysis, matched tumor-normal pairs were identified using TCGA patient barcodes, and the Wilcoxon signed-rank test was applied to compare ARHGAP22 expression between adjacent normal tissues and the corresponding tumor tissues. A two-sided P value of less than 0.05 was considered statistically significant.

6. Survival and receiver operating characteristic analysis
Survival and time-dependent receiver operating characteristic (ROC) analyses were performed using TCGA-KIRC tumor samples with available clinical follow-up data. Overall survival time was converted from days to years. Patients were divided into high- and low-ARHGAP22 expression groups using the median ARHGAP22 expression value, and the same cutoff was applied consistently across all analyses involving expression-based grouping. Kaplan–Meier curves were generated using the survival package, and the survminer package. Differences between groups were assessed using the log-rank test. Cox proportional hazards regression was used to estimate hazard ratios and 95% confidence intervals. Time-dependent ROC curves were generated using the timeROC package. Areas under the curve at 1, 3, and 5 years were calculated using the Aalen weighting method. A two-sided P value of less than 0.05 was considered statistically significant.

7. Clinicopathological association analysis
The association between ARHGAP22 expression and clinicopathological characteristics was analyzed using TCGA-KIRC tumor samples. Normal samples were excluded. Age was categorized as 65 years or younger and older than 65 years. Samples with unknown or missing annotations were excluded from the corresponding analysis. The Wilcoxon rank-sum test was used for comparisons between two groups, and the Kruskal–Wallis test was used for comparisons involving three or more groups. Violin plots were generated using the ggpubr package ; ggplot2, and scales. For heatmap visualization, patients were classified into high- and low-ARHGAP22 expression groups using the median cutoff. Associations between expression groups and clinicopathological variables were assessed using chi-square tests. Heatmaps were generated using ComplexHeatmap, after preprocessing with limma. A two-sided P value of less than 0.05 was considered statistically significant.

8. Nomogram construction
A prognostic nomogram was constructed by integrating ARHGAP22 expression with available clinicopathological characteristics in a Cox proportional hazards regression model. The model was used to estimate 1-, 3-, and 5-year overall survival in the TCGA-KIRC cohort. Individual patient risk scores were calculated using the fitted Cox model. Calibration curves for 1-, 3-, and 5-year overall survival were generated using the Kaplan–Meier method with 1,000 bootstrap resampling iterations. Agreement between nomogram-predicted survival probabilities and observed survival outcomes was evaluated. Cox regression was performed using the survival package. Nomogram visualization and calibration analyses were conducted using regplot and rms. A two-sided P value of less than 0.05 was considered statistically significant.

9. Co-expression analysis
Transcriptomic data from TCGA-KIRC tumor samples were used to evaluate co-expression relationships between ARHGAP22 and all other genes by Pearson correlation analysis. Genes with an absolute Pearson correlation coefficient greater than 0.6 and a P value of less than 0.001 were defined as significantly co-expressed genes. Significantly co-expressed genes were ranked according to the absolute value of the correlation coefficient. The highest-ranking genes were selected to construct a correlation matrix, and a chord diagram was generated to visualize the ARHGAP22-associated co-expression network.

10. Differentially expressed gene and functional-enrichment analyses
Differential expression analysis between the high- and low-ARHGAP22 expression groups was performed using the Wilcoxon rank-sum test with false discovery rate (FDR) correction. Genes with an absolute log2 fold change greater than 1 and an FDR of less than 0.05 were defined as significantly differentially expressed genes. The results were visualized using a volcano plot and heatmap. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using clusterProfiler. Gene symbols were converted to Entrez identifiers using org.Hs.eg.db. Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways with both a nominal P value of less than 0.05 and an FDR-adjusted P value of less than 0.05 were considered significantly enriched. Gene Set Enrichment Analysis was performed using Kyoto Encyclopedia of Genes and Genomes gene sets from the Molecular Signatures Database file c2.cp.kegg.v7.4.symbols.gmt. Genes were ranked by log2 fold change, and gene sets with a nominal P-value < 0.05 were considered significantly enriched.

11. Immune infiltration and immune-checkpoint analysis
Immune-infiltration and immune-checkpoint analyses were performed using TCGA-KIRC tumor samples. StromalScore, ImmuneScore, and ESTIMATEScore values were calculated using the estimate package. Immune-cell fractions were estimated using the CIBERSORT R script, with 1,000 permutations and quantile normalization. Samples with a CIBERSORT deconvolution P value of less than 0.05 were retained. Patients were divided into ARHGAP22-high and ARHGAP22-low groups according to the median ARHGAP22 expression level. Differences in ESTIMATE scores and immune-cell fractions between the two groups were assessed using the Wilcoxon rank-sum test. Correlations between ARHGAP22 expression and immune-checkpoint genes were evaluated using Pearson correlation analysis. P values from multiple immune-cell comparisons and immune-checkpoint correlation tests were adjusted using the Benjamini–Hochberg FDR method, and an FDR of less than 0.05 was considered statistically significant. FDR-significant immune-checkpoint genes were visualized using correlation heatmaps generated with corrplot. Data preprocessing and visualization were performed primarily using limma; ggpubr; and corrplot.

12. Drug-sensitivity prediction
TCGA-KIRC tumor transcriptomic data were used after normal tissue samples had been excluded. Computationally predicted half-maximal inhibitory concentration values were calculated using the oncoPredict package and the Genomics of Drug Sensitivity in Cancer 2-based reference dataset. Patients were divided into ARHGAP22-high and ARHGAP22-low groups using the median ARHGAP22 expression value. Predicted half-maximal inhibitory concentration values were compared between groups using the Wilcoxon rank-sum test. P values from multiple drug comparisons were adjusted using the Benjamini–Hochberg method, and an FDR of less than 0.05 was considered statistically significant. The results were interpreted as computationally predicted drug-sensitivity estimates rather than experimentally validated or clinically observed drug responses.

13. Human protein atlas validation
The Human Protein Atlas database was accessed, and ARHGAP22 was searched. The Tissue section was reviewed to assess ARHGAP22 protein expression in normal kidney tissue. The Pathology section was opened, clear cell renal cell carcinoma was selected, and ARHGAP22 protein expression in tumor tissue was reviewed. Representative immunohistochemical images of normal kidney tissue and ccRCC tissue were retrieved and selected for inclusion in the manuscript to compare ARHGAP22 protein expression between normal and tumor tissues.

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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,...

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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 ...

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Disclosures

The authors declare no conflicts of interest.

Acknowledgements

Publicly available data from The Cancer Genome Atlas (TCGA) were used in this study. The TCGA Research Network is acknowledged for generating and providing these resources.

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Materials

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

References

  1. Winter TD et al. Using Mendelian randomization to investigate etiologic heterogeneity across renal cell carcinoma subtypes. Int J Epidemiol. 2025;54(6):dyaf177. https://doi.org/10.1093/ije/dyaf177
  2. Hsieh JJ et al. Renal cell carcinoma. Nat Rev Dis Primers. 2017;3:17009. https://doi.org/10.1038/nrdp.2017.9
  3. Capitanio U et al. Epidemiology of renal cell carcinoma. Eur Urol. 2019;75(1):74-84.
  4. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33.
  5. Wiechno P et al. Contemporary treatment of metastatic renal cell carcinoma. Med Oncol. 2018;35(12):156. https://doi.org/10.1007/s12032-018-1217-1
  6. Chen W et al. Targeting renal cell carcinoma with a HIF-2 antagonist. Nature. 2016;539(7627):112-117.
  7. Rydzanicz M, Wrzesiński T, Bluyssen HA, Wesoły J. Genomics and epigenomics of clear cell renal cell carcinoma: recent developments and potential applications. Cancer Lett. 2013;341(2):111-126.
  8. Barata PC, Rini BI. Treatment of renal cell carcinoma: current status and future directions. CA Cancer J Clin. 2017;67(6):507-524.
  9. Chowdhury N, Drake CG. Kidney cancer: an overview of current therapeutic approaches. Urol Clin North Am. 2020;47(4):419-431.
  10. Gulati S, Vaishampayan U. Current state of systemic therapies for advanced renal cell carcinoma. Curr Oncol Rep. 2020;22(3):26. https://doi.org/10.1007/s11912-020-0892-1
  11. Haga RB, Ridley AJ. Rho GTPases: regulation and roles in cancer cell biology. Small GTPases. 2016;7(4):207-221.
  12. Sahai E, Marshall CJ. RHO-GTPases and cancer. Nat Rev Cancer. 2002;2(2):133-142.
  13. Bustelo XR. RHO GTPases in cancer: known facts, open questions, and therapeutic challenges. Biochem Soc Trans. 2018;46(3):741-760.
  14. Crosas-Molist E et al. Rho GTPase signaling in cancer progression and dissemination. Physiol Rev. 2022;102(1):455-510.
  15. Li H, Peyrollier K, Kilic G, Brakebusch C. Rho GTPases and cancer. BioFactors. 2014;40(2):226-235.
  16. Goka ET, Chaturvedi P, Lopez DTM, Lippman ME. Rac signaling drives clear cell renal carcinoma tumor growth by priming the tumor microenvironment for an angiogenic switch. Mol Cancer Ther. 2020;19(7):1462-1473.
  17. Thompson JM, Landman J, Razorenova OV. Targeting the RhoGTPase/ROCK pathway for the treatment of VHL/HIF pathway-driven cancers. Small GTPases. 2020;11(1):32-38.
  18. Feng J et al. RhoJ promotes the progression of clear cell renal cell carcinoma via .the TNF-α/NF-κB axis. Transl Androl Urol. 2025;14(7):1849-1864.
  19. Guo K et al. Activation of RHO-GTPase gene pattern correlates with adverse clinical outcome and immune microenvironment in clear cell renal cell carcinoma. Clin Exp Med. 2025;25(1):67. https://doi.org/10.1007/s10238-025-01593-3
  20. Mori M, Saito K, Ohta Y. ARHGAP22 localizes at endosomes and regulates actin cytoskeleton. PLoS One. 2014;9(6):e100271. https://doi.org/10.1371/journal.pone.0100271
  21. Nakamura F. FilGAP and its close relatives: a mediator of Rho-Rac antagonism that regulates cell morphology and migration. Biochem J. 2013;453(1):17-25.
  22. Hu SH et al. The weak complex between RhoGAP protein ARHGAP22 and signal regulatory protein 14-3-3 has 1:2 stoichiometry and a single peptide binding mode. PLoS One. 2012;7(8):e41731. https://doi.org/10.1371/journal.pone.0041731
  23. Mori M et al. Endosomal localization of RacGAP protein ARHGAP22 regulates its GAP activity in human melanoma cells. Anticancer Res. 2022;42(12):5763-5771.
  24. El-Masry OS, Alamri AM, Alzahrani F, Alsamman K. ADAMTS14, ARHGAP22, and EPDR1 as potential novel targets in acute myeloid leukaemia. Heliyon. 2022;8(3):e09065. https://doi.org/10.1016/j.heliyon.2022.e09065
  25. Yang C et al. Transcriptomic analysis identified ARHGAP family as a novel biomarker associated with tumor-promoting immune infiltration and nanomechanical characteristics in bladder cancer. Front Cell Dev Biol. 2021;9:657219. https://doi.org/10.3389/fcell.2021.657219
  26. Schiavoni V et al. Recent advances in the management of clear cell renal cell carcinoma: novel biomarkers and targeted therapies. Cancers (Basel). 2023;15(12):3207. https://doi.org/10.3390/cancers15123207
  27. Sanz-Moreno V et al. Rac activation and inactivation control plasticity of tumor cell movement. Cell. 2008;135(3):510-523.
  28. Sakaguchi T et al. Bromodomain protein BRD4 inhibitor JQ1 regulates potential prognostic molecules in advanced renal cell carcinoma. Oncotarget. 2018;9(33):23003-23017.
  29. Stránská J et al. Copy number variation and clinical response to chemotherapy and bevacizumab in Czech patients with metastatic colorectal cancer. Klin Onkol. 2024;37(4):277-285.
  30. Parihar JS, Tunuguntla HS. Role of chemokines in renal cell carcinoma. Rev Urol. 2014;16(3):118-121.
  31. Chen Z, Wu R, Ma J, Zheng J. C chemokines are prognostic biomarkers correlated with diverse immune cell infiltrations in clear cell renal cell carcinoma. Transl Cancer Res. 2022;11(8):2501-2522.
  32. Şenbabaoğlu Y et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 2016;17(1):231. https://doi.org/10.1186/s13059-016-1092-z
  33. Song DM et al. LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning, and single-cell sequencing. Front Immunol. 2024;15:1419126. https://doi.org/10.3389/fimmu.2024.1419126
  34. Vuong L, Kotecha RR, Voss MH, Hakimi AA. Tumor microenvironment dynamics in clear-cell renal cell carcinoma. Cancer Discov. 2019;9(10):1349-1357.
  35. Burgers FH et al. Immunological features of clear-cell renal-cell carcinoma and resistance to immune checkpoint inhibitors. Nat Rev Nephrol. 2025;21(10):687-701.
  36. Davidsson S et al. Infiltration of M2 macrophages and regulatory T cells plays a role in recurrence of renal cell carcinoma. Eur Urol Open Sci. 2020;20:62-71.
  37. Zhang X et al. Tumor-associated M2 macrophages in the immune microenvironment influence the progression of renal clear cell carcinoma by regulating M2 macrophage-associated genes. Front Oncol. 2023;13:1157861. https://doi.org/10.3389/fonc.2023.1157861
  38. Tronik-Le Roux D et al. Comprehensive landscape of immune checkpoints uncovered in clear cell renal cell carcinoma reveals new and emerging therapeutic targets. Cancer Immunol Immunother. 2020;69(7):1237-1252.
  39. Hong J et al. A zinc metabolism-related gene signature for predicting prognosis and characteristics of breast cancer. Front Immunol. 2023;14:1276280. https://doi.org/10.3389/fimmu.2023.1276280
  40. Xu W et al. Prognostic immunophenotyping clusters of clear cell renal cell carcinoma defined by the unique tumor immune microenvironment. Front Cell Dev Biol. 2021;9:785410. https://doi.org/10.3389/fcell.2021.785410
  41. Gu J et al. A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma. Sci Rep. 2023;13(1):18922. https://doi.org/10.1038/s41598-023-45966-8

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