A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer.

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study wanted to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. Then, the authors used the prognostic genes to construct robust prognostic model through least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes. It may be an effective tool for prognostic prediction and CRC patient stratification, which helps in treatment decisions.