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.
Yin et al. (Mon,) studied this question.
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