Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer

Abstract

Background: Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC. Methods: We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions. Results: A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan–Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC. Conclusion: To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.