Identification of cancer-associated fibroblast characteristics for predicting outcome and response to immunotherapy in renal cell carcinoma DOI
Jing Zhou, Chunxiang Li, Lei Qian

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: April 26, 2025

To elucidate the prognostic effect of cancer-associated fibroblasts (CAFs) on renal cell carcinoma. CAFs and stromal scores were calculated using various algorithms including Estimating Proportions Immune Cancer cells (EPIC), Microenvironment Cell Populations-counter (MCP counter), Tumor immune dysfunction exclusion (TIDE) xCell. Weighted gene co-expression network analysis (WGCNA) was conducted to determine CAF-associated modules key genes. The functional pathways genes in important CAF analyzed Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) analyses. signatures established through univariate Cox Least Absolute Shrinkage Selection Operator (LASSO) Kaplan-Meier receiver operating characteristic (ROC) analyses carried out assess predictive value signatures. WGCNA distinguished several for clear carcinoma (KIRC), chromophobe (KICH) papillary (KIRP) respectively. containing two four KIRC KIRP, In patients with high-risk had unfavorable outcome than those low-risk scores. Additionally, both ratio responding immunotherapy obviously higher group group. Finally, mutation frequency some differed significantly between groups. Our study provided valuable predicting KIRP patients. These also used predict response, providing strategies individualized therapy

Language: Английский

Identification of cancer-associated fibroblast characteristics for predicting outcome and response to immunotherapy in renal cell carcinoma DOI
Jing Zhou, Chunxiang Li, Lei Qian

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: April 26, 2025

To elucidate the prognostic effect of cancer-associated fibroblasts (CAFs) on renal cell carcinoma. CAFs and stromal scores were calculated using various algorithms including Estimating Proportions Immune Cancer cells (EPIC), Microenvironment Cell Populations-counter (MCP counter), Tumor immune dysfunction exclusion (TIDE) xCell. Weighted gene co-expression network analysis (WGCNA) was conducted to determine CAF-associated modules key genes. The functional pathways genes in important CAF analyzed Gene Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) analyses. signatures established through univariate Cox Least Absolute Shrinkage Selection Operator (LASSO) Kaplan-Meier receiver operating characteristic (ROC) analyses carried out assess predictive value signatures. WGCNA distinguished several for clear carcinoma (KIRC), chromophobe (KICH) papillary (KIRP) respectively. containing two four KIRC KIRP, In patients with high-risk had unfavorable outcome than those low-risk scores. Additionally, both ratio responding immunotherapy obviously higher group group. Finally, mutation frequency some differed significantly between groups. Our study provided valuable predicting KIRP patients. These also used predict response, providing strategies individualized therapy

Language: Английский

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