Machine learning integration of single-cell and bulk transcriptomics identifies fibroblast-driven prognostic markers in colorectal cancer

Abstract

Single-cell RNA sequencing (scRNA-seq) has significantly advanced our understanding of cellular heterogeneity and the complex interplay within the tumor microenvironment (TME) of colorectal cancer (CRC). However, translating these molecular insights into clinically actionable prognostic biomarkers and therapeutic strategies remains a considerable challenge. In this study, we conducted a comprehensive scRNA-seq analysis of 306 CRC samples comprising 448,255 cells to characterize the TME in depth. By constructing intercellular communication networks based on connection counts and communication probabilities, we identified fibroblasts as central regulatory hubs within the TME. Using Wilcoxon rank-sum tests and univariate survival analyses, we initially identified 23 prognostic fibroblast markers. These were refined to a seven-gene fibroblast-related prognostic signature via an integrated machine learning approach. The signature exhibited robust predictive performance in the The Cancer Genome Atlas - Colon Adenocarcinoma (TCGA-COAD) training cohort (n=351; C-index=0.65) and was successfully validated in the GSE17536 dataset (n=177; C-index=0.63). Functional enrichment analyses revealed that this signature is involved in immune regulation and multiple tumor-associated cellular pathways. Notably, high-risk patients displayed increased macrophage and NK cell infiltration, impaired immune function, and elevated immune rejection scores, while low-risk patients demonstrated heightened sensitivity to camptothecin and irinotecan. Together, our findings underscore the prognostic value of fibroblast-derived signatures in CRC and support their potential utility in risk stratification and the development of personalized therapeutic strategies, contributing to the advancement of precision oncology.