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Published on: January 22, 2013
Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma
Xiangzhe Yin1, Lu Wang1, Yanwu Sun1
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
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.
Insights
This study identifies key immune genes in renal cell carcinoma (RCC) to predict immunotherapy response. Gene networks reveal distinct patient clusters, with one showing higher immune activity and potential for better treatment outcomes.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Immunotherapy has transformed renal cell carcinoma (RCC) treatment, but patient responses vary significantly.
- Identifying predictive biomarkers for immunotherapy efficacy in RCC is crucial.
Purpose of the Study:
- To develop a computational pipeline integrating single-cell and bulk RNA sequencing data.
- To identify immune-related candidate driver genes impacting RCC immunotherapy.
- To stratify RCC patients based on identified gene signatures.
Main Methods:
- Integration of single-cell and bulk RNA sequencing data.
- Construction of gene regulatory networks (GRNs) to identify candidate driver genes.
- Bioinformatic analysis for patient stratification and immune response characterization.
Main Results:
- Twenty-five immune-related candidate driver genes were identified, stratifying patients into three clusters (C1-C3).
- Cluster C1 showed increased immune infiltration, tumor mutation burden, and checkpoint expression, suggesting potential immunotherapy responders.
- Specific genes (e.g., IRF1, STAT1) in C1 were linked to anti-tumor immunity, while others (e.g., JUN, STAT3) in C2/C3 correlated with tumor progression and immune evasion.
Conclusions:
- The integration of transcriptomic data and molecular networks offers a framework for discovering immune-related driver genes in RCC.
- These identified genes can stratify patients and serve as potential therapeutic targets to enhance RCC immunotherapy outcomes.
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