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. 2022 Aug 12;12:978006. doi: 10.3389/fonc.2022.978006

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Copyright © 2022 Chen, Zhang, Zhou, Ye, Luo, Yang, Fang and Tang

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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Developing a prognosis prediction model using the LASSO COX regression. (A) LASSO coefficient profiles of 307 prognosis-related genes, which were selected from the HMOX1-mediated metastasis regulatory network and its neighbor genes. (B) Cross-validation for tuning parameter screening in the LASSO regression model. Optimal genes with the best discriminative capability (26 in number) were selected for developing the risk scores. (C) Forest plot of the risk score gene-expression profiles in univariate analysis. (D) Development of risk score in the TCGA training cohort and its predictive accuracy for survival in comparison to additional 5 signatures. (E) validation of risk score in the TCGA validating cohort. (F) A nomogram integrating pathological stage, risk score, and OS probability in the TCGA training cohort. (G) Calibration plots showing the correlation between actual and predicted OS rates in the TCGA training cohort. (H) ROC curve plot evaluating the nomogram model, risk score, and pathological stage in the TCGA training cohort ***P < 0.001.