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. 2021 Feb 4;6:6. doi: 10.1038/s41525-021-00172-1

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a All genes were tested for their potential as prognostic genes with the use of nine independent multicenter data sets. Genes were filtered to identify differentially expressed genes between normal mucosa and CRC tissue with the use of the TCGA-COAD data set, and the identified genes were subjected to a meta-analysis with eight additional cohorts. A machine learning approach (lasso regression) then extracted 16 genes that are important for the prediction of relapse. The robustness of mPS_colon was demonstrated with various CRC subsets. Finally, we found that mPS_colon also estimates the prognosis of individuals with gastric cancer. b mPS_colon distribution in the TCGA-COAD training cohort. c Kaplan–Meier curves of relapse-free survival (RFS) for the GSE39582 CRC test cohort based on mPS_colon. The log-rank P value is shown.