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. 2024 Jul 17;13(7):3217–3241. doi: 10.21037/tcr-23-2332

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2024 Translational Cancer Research. All rights reserved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

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A consensus riskScore was developed and validated via the machine learning-based integrative procedure. (A) Venn plot showed the intersection of genes from differential and prognosis analysis. (B) A total of 101 predictive models were developed using the LOOCV framework, with the C-index of each model calculated across all validation datasets. (C) Kaplan-Meier curves of OS according to the riskScore in TCGA-LUAD. (D) Kaplan-Meier curves of DFI according to the riskScore in TCGA-LUAD. (E) Kaplan-Meier curves of DSS according to the riskScore in TCGA-LUAD. (F) Kaplan-Meier curves of PFI according to the riskScore in TCGA-LUAD. (G) Patients are divided into two clusters by ConsensusClusterPlus and Kaplan-Meier survival curves of OS in two clusters. TCGA, The Cancer Genome Atlas; LUAD, lung adenocarcinoma; RSF, random survival forest; StepCox, stepwise Cox; Enet, elastic network; Ridge, ridge regression; Lasso, least absolute shrinkage and selection operator; survivalSVM, survival support vector machine; plsRcox, partial least squares regression for Cox; SuperPC, supervised principal components; GBM, generalized boosted regression modeling; C-index, concordance index; LOOCV, leave-one-out cross-validation; OS, overall survival; DFI, disease-free interval; DSS, disease-specific survival; PFI, progression-free interval.