Literature DB >> 33634098

Identification of a Gene Signature for Renal Cell Carcinoma-Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis.

Bitian Liu1, Xiaonan Chen1, Yunhong Zhan1, Bin Wu1, Shen Pan2.   

Abstract

Background: Cancer-associated fibroblasts (CAFs) are mainly involved in cancer progression and treatment failure. However, the specific signature of CAFs and their related clinicopathological parameters in renal cell carcinoma (RCC) remain unclear. Here, methods to recognize gene signatures were employed to roughly assess the infiltration of CAFs in RCC, based on the data from The Cancer Genome Atlas (TCGA). Weighted Gene Coexpression Network Analysis (WGCNA) was used to cluster transcriptomes and correlate with CAFs to identify the gene signature. Single-cell and cell line sequencing data were used to verify the expression specificity of the gene signature in CAFs. The gene signature was used to evaluate the infiltration of CAFs in each sample, and the clinical significance of each key gene in the gene signature and CAFs was analyzed. We observed that the CAF infiltration was higher in kidney cancer and advanced tumor stage and grade than in normal tissues. The seven key genes of the CAF gene signature identified using WGCNA showed high expression of CAF-related characteristics in the cell clustering landscape and fibroblast cell lines; these genes were found to be associated with extracellular matrix function, collagen synthesis, cell surface interaction, and adhesion. The high CAF infiltration and the key genes were verified from the TCGA and Gene Expression Omnibus data related to the advanced grade, advanced stage, and poor prognosis of RCC. In summary, our findings indicate that the clinically significant gene signature may serve as a potential biomarker of CAFs in RCC, and the infiltration of CAFs is associated with the pathological grade, stage, and prognosis of RCC.
Copyright © 2021 Liu, Chen, Zhan, Wu and Pan.

Entities:  

Keywords:  cancer-associated fibroblast; gene signature; renal cell carcinoma; single sample gene set enrichment analysis; weighted gene co-expression network analysis

Year:  2021        PMID: 33634098      PMCID: PMC7901886          DOI: 10.3389/fcell.2020.604627

Source DB:  PubMed          Journal:  Front Cell Dev Biol        ISSN: 2296-634X


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