Research Paper Volume 15, Issue 23 pp 13799—13821
Integrating scRNA-seq and bulk RNA-seq to characterize infiltrating cells in the colorectal cancer tumor microenvironment and construct molecular risk models
- 1 School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China
- 2 Shanxi Key Laboratory of Big Data for Clinical Decision Research, Taiyuan, China
- 3 Key Laboratory of Cellular Physiology at Shanxi Medical University, Ministry of Education, Taiyuan, China
- 4 The First clinical Medical College, Shanxi medical University, Taiyuan, China
- 5 School of Management, Shanxi Medical University, Taiyuan, China
- 6 Department of Anesthesiology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
- 7 The Fifth Clinical Medical School, Shanxi Medical University, Taiyuan, China
- 8 Department of Gastroenterology, The First Hospital of Shanxi Medical University, Taiyuan, China
Received: August 4, 2023 Accepted: October 19, 2023 Published: December 5, 2023
https://doi.org/10.18632/aging.205263How to Cite
Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Colorectal cancer (CRC) is a malignancy that is both highly lethal and heterogeneous. Although the correlation between intra-tumoral genetic and functional heterogeneity and cancer clinical prognosis is well-established, the underlying mechanism in CRC remains inadequately understood. Utilizing scRNA-seq data from GEO database, we re-isolated distinct subsets of cells, constructed a CRC tumor-related cell differentiation trajectory, and conducted cell-cell communication analysis to investigate potential interactions across cell clusters. A prognostic model was built by integrating scRNA-seq results with TCGA bulk RNA-seq data through univariate, LASSO, and multivariate Cox regression analyses. Eleven distinct cell types were identified, with Epithelial cells, Fibroblasts, and Mast cells exhibiting significant differences between CRC and healthy controls. T cells were observed to engage in extensive interactions with other cell types. Utilizing the 741 signature genes, prognostic risk score model was constructed. Patients with high-risk scores exhibited a significant correlation with unfavorable survival outcomes, high-stage tumors, metastasis, and low responsiveness to chemotherapy. The model demonstrated a strong predictive performance across five validation cohorts. Our investigation involved an analysis of the cellular composition and interactions of infiltrates within the microenvironment, and we developed a prognostic model. This model provides valuable insights into the prognosis and therapeutic evaluation of CRC.