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Article

Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy

by
Xiangzhe Yin
,
Lu Wang
,
Yanwu Sun
,
Shiyi Li
,
Wentong Yu
,
Siyao Wang
,
Zhichao Geng
,
Hongying Zhao
* and
Li Wang
*
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2026, 27(8), 3467; https://doi.org/10.3390/ijms27083467
Submission received: 10 March 2026 / Revised: 10 April 2026 / Accepted: 10 April 2026 / Published: 13 April 2026
(This article belongs to the Section Molecular Immunology)
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Abstract

With the rapid development of cancer treatment, immunotherapy has revolutionized renal cell carcinoma (RCC) treatment, yet patient responses remain heterogeneous. Here, a computational pipeline was constructed by integrating single-cell and bulk RNA sequencing data to identify immune-related candidate driver genes and characterize their impact on RCC immunotherapy. Based on gene regulatory networks (GRN), 25 immune-related candidate driver genes were identified, leading to the stratification of patients into three clusters (C1–C3). Compared to the C2/C3 cluster, the C1 cluster exhibited elevated immune infiltration, tumor mutation burden and checkpoint expression, which may represent immunotherapy responders. Dynamic analysis of GRNs revealed the critical role of candidate driver genes in predicting the efficacy of immunotherapy. IRF1, IRF9 and STAT1 in lymphoid cells of C1 participated in anti-tumor immune response by impacting target genes CD8A, HLA-A/E, TAP1 and PD-1. JUN, FOS, STAT3, JUND and NR2F1 were up-regulated in clusters C2 and C3, leading to tumor progression and immune evasion by influencing target genes HSPA1A, CXCL9 and PDGFR. In conclusion, integration of the transcriptome with molecular networks provided a network-based framework to uncover immune-related candidate driver genes for stratifying RCC patients, thereby serving as potential therapeutic targets to improve the outcome of RCC immunotherapy.
Keywords: renal cell carcinoma; gene regulatory network; immune-related candidate driver genes; immunotherapy; tumor microenvironment; single-cell RNA sequencing renal cell carcinoma; gene regulatory network; immune-related candidate driver genes; immunotherapy; tumor microenvironment; single-cell RNA sequencing

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MDPI and ACS Style

Yin, X.; Wang, L.; Sun, Y.; Li, S.; Yu, W.; Wang, S.; Geng, Z.; Zhao, H.; Wang, L. Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy. Int. J. Mol. Sci. 2026, 27, 3467. https://doi.org/10.3390/ijms27083467

AMA Style

Yin X, Wang L, Sun Y, Li S, Yu W, Wang S, Geng Z, Zhao H, Wang L. Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy. International Journal of Molecular Sciences. 2026; 27(8):3467. https://doi.org/10.3390/ijms27083467

Chicago/Turabian Style

Yin, Xiangzhe, Lu Wang, Yanwu Sun, Shiyi Li, Wentong Yu, Siyao Wang, Zhichao Geng, Hongying Zhao, and Li Wang. 2026. "Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy" International Journal of Molecular Sciences 27, no. 8: 3467. https://doi.org/10.3390/ijms27083467

APA Style

Yin, X., Wang, L., Sun, Y., Li, S., Yu, W., Wang, S., Geng, Z., Zhao, H., & Wang, L. (2026). Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy. International Journal of Molecular Sciences, 27(8), 3467. https://doi.org/10.3390/ijms27083467

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