Integration of bulk RNA-seq and single-cell RNA-seq constructs: a cancer-associated fibroblasts-related signature to predict prognosis and therapeutic response in clear cell renal cell carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is a common and aggressive renal cancer with high mortality when metastasized. Cancer -associated fibroblasts (CAFs) are pivotal in ccRCC evolution; however, their significance in forecasting prognosis and guiding therapy is undetermined. Method: We used Weighted Correlation Network Analysis to identify modules correlated with CAFs in bulk RNA-seq data. We also screened fibroblast marker genes in single -cell RNA-seq data and upregulated genes in TCGA tumor samples and defined genes identified in all three analyses as CAFs-related genes (CRGs). We extracted a CRG signature using Least Absolute Shrinkage and Selection Operator analysis and investigated its biological mechanisms by combining Gene Set Enrichment Analysis and the AUCell algorithm. The Tumor Immune Dysfunction and Exclusion algorithm and the IMvigor 210 dataset were employed to assess the signature's capability to predict immunotherapeutic responses. Additionally, we analyzed the relationship between the signature and the IC50 of targeted agents. In vitro validation confirmed the relative mRNA expression of the CRGs and the function of CERCAM. Results: The CRG signature was anchored on six genes: CERCAM, TMEM132A, TIMP1, P4HA3, FKBP10, and CEBPB. Kaplan -Meier analysis indicated that patients with high expression of the signature experienced poorer survival than those with low expression. Furthermore, immunotherapy was more effective in patients with low signature expression. In vitro assays revealed CERCAM silencing led to a substantial reduction in the proliferative and migratory capacities of ccRCC cell lines. Conclusion: Our CRG signature holds promise in forecasting prognosis and guiding personalized treatment for patients with ccRCC.