Establishing and validating a prognostic model for colorectal cancer patients by integrating data from single-cell RNA sequencing and bulk RNA sequencing

Abstract

Background Colorectal adenocarcinoma (COAD) is a major global cause of mortality. While conventional RNA sequencing (RNA-seq) has been used to study its prognostic indicators, it lacks precision in identifying cellular alterations. This study aimed to develop a predictive framework for COAD by integrating scRNA-seq with conventional RNA-seq. Methods This study acquired primary RNA sequencing data from The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing data on colorectal adenocarcinoma (COAD) from the Gene Expression Omnibus (GEO) database. The t-SNE method reduced dimensionality and identified clusters. Additionally, Weighted Gene Correlation Network Analysis (WGCNA) identified crucial modules and genes with differential expression (DEGs). Cox regression analysis was utilized to construct the prognostic model and explore mutation profiles and immune statuses across different risk groups. Results Integration of scRNA-seq data from four samples revealed 15 distinct clusters covering 8 cell types. Differential analysis identified important cell types, including B cells (Naïve and Plasma cells), Endothelial cells, Epithelial cells, Monocytes, Natural Killer (NK) cells, Smooth muscle cells, and T cells (CD8+). Subsequently, a prognostic model was built using 28 genes showing differential expression, with four DEGs displaying a significant correlation with higher risk scores, poorer survival outcomes, and increased APC mutation rates. Various prognostic and immune characteristics were observed within this context. Conclusion Integrating 10x scRNA-seq and bulk RNA-seq data, we established a prognostic framework for colorectal adenocarcinoma (COAD) in this study. Additionally, we identified two distinct groups, each displaying different prognoses and immune features.