A Prognostic Model of Colon Cancer Based on the Microenvironment Component Score via Single Cell Sequencing

Abstract

Background/aim: The development of colon cancer is influenced by the tumour immune microenvironment, in which specific immune cell subsets may be useful predictors for patient's clinical outcome and devising treatment strategies.

Materials and methods: The distribution of tumour-infiltrating immune cell subpopulations of three cohorts of The Cancer Genome Atlas (n=225), GSE39582 (n=493), and GSE17536 (n=137) datasets were analysed on the basis of single cell RNA sequencing data via the Cibersortx software. A prognostic model was constructed via a penalised Cox regression model with least absolute shrinkage and selection operator (LASSO) penalty according to the one standard error rule.

Results: Conventional type 2 dendritic cells were correlated with a good prognosis, whereas NLRP3-expressing macrophages, C1QC-expressing tumour-associated macrophages, and GALTB-expressing B cells were correlated with a poor prognosis. We constructed a prognostic model based on prognosis related cell subsets including nine specific immune cell subsets. By using the LASSO method, we found that the model had a superior prediction ability in all three cohorts of patients.

Conclusion: Multiple immune cell subpopulations in the tumour microenvironment are associated with the prognosis of colon cancer. The established prognostic model has important clinical value in predicting the clinical outcome of patients with colon cancer and in treatment decision.

Keywords: Cibersortx; Colon cancer; immune infiltrating cells; least absolute shrinkage and selection operator; microenvironment scores; single-cell RNA sequencing.