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. 2021 Jan 28;8:577125. doi: 10.3389/fcell.2020.577125

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Copyright © 2021 Luo, Chen, Zhang, Huang, Zhao, Zhao, Li, Zhou, Liu, Cai and Bi.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Development and validation of immune prognostic model (IPM). (A) Immune-related genes in the immune biological process of gene set enrichment analysis (GSEA) results compared between metastasis evaluation model (MEM)-low and MEM-high risk clusters. (B,C) Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were employed to investigate the correlation between the patient progression-free survival (PFS) and immune-related differentially expressed genes (DEGs). (D) A heat map shows the expression of SLIT2, CDKN2A, and CLU in the IPM level. (E) The optimal cutoff value (0.4) of the IPM score found using X-tile 3.6.1 software (Yale University, New Haven, CT, USA). (F) Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive power of the prediction model. (G) The K–M survival curves for cases with a low or high metastasis risk in training cohort produced to show IPM's prediction ability (P < 0.0001). (H) The K–M survival curves for cases with a low- or high-recurrence risk in the Cancer Genome Atlas (TCGA) validation cohort approve IPM's prediction ability (P = 0.047).