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

Abstract

Colorectal cancer is the third most common cancer and second cancer with the highest mortality rate in the world. Progression, which leads to metastasis, is one of the biggest challenges in cancer treatment, and despite improvement in screening and treatment techniques, 5 years of survival of colorectal cancer patients drop from 91% in stage I to 12% in stage IV. Single-cell RNA sequencing is one of the most powerful tools to study complex diseases such as cancer, and despite its recent emergence, it’s rapidly growing. In contrast to bulk RNA sequencing, which averages out expression of thousands of cells, single-cell RNA sequencing can capture intra-tumor heterogeneity. Moreover, cellular dynamic events like progression can be studied by pseudotime trajectory analysis of single-cell RNA sequencing data. Herein we used Samsung Medical Center (SMC) colorectal cancer single-cell RNA sequencing dataset to find important tumor epithelial cells subtypes. Subsequently, we’ve found important genes with a dynamic pattern along cancer progression by using pseudo-time trajectory analysis.Also, we found TGFB1 and IL1B as effective ligands and several transcription factors which may regulate the expression of pseudo-time related genes. In the end, we’ve constructed a LASSO cox regression using 20 psudotime genes, which can predict 3-year survival of colorectal cancer patients with AUC >0.7.