Single-Cell RNA Sequencing Reveals the Role of Epithelial Cell Marker Genes in Predicting the Prognosis of Colorectal Cancer Patients

Abstract

Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.

Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis

“…By performing Cox regression analysis on these findings, the MECRG risk score was obtained, based on patients being divided into two groups low-risk and high-risk. They evaluated the performance of their model using signature geneset solely and in combination with clinical information, namely age and TNM stage [ 36 ] comparing to their result in using signature geneset alone, our model perform better on test datasets.…”

Therapeutic implementation of single-cell sequencing for dissecting colorectal cancer immune microenvironment

“…Ultimately, their study indicated that the overexpressed tyrosine kinases BMX or HCK could initiate adenoma formation and strongly facilitate the genesis of CRC, thereby adding to our understanding of the evolutionary progress of epithelial cells into CRC cells at a single-cell resolution [118]. Likewise, Shen et al utilized scRNAseq data from open databases to develop a predictive model for assessing the prognostic performance of CRC based on epithelial cell marker genes [119]. It could be concluded from these studies that SCS-based CRC-TIME analysis holds significant practical value in exploring CRC metastasis, intestinal metaplasia, and biomarker investigation.…”