Research Paper Volume 16, Issue 10 pp 8772—8809
Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses
Figure 11. Identification of MPPS-related genes by WGCNA and machine learning. (A) Correlation analysis between module eigengenes and MPPS by WGCNA. (B) The intersection of WGCNA hub genes, TCGA-DEGs, and GEO genes. (C) The C-index of 117 machine learning algorithm combinations via LOOCV framework across all validation datasets. (D, E) Determination of the number of MPPS-related genes by the LASSO regression analysis. (F) The Kaplan-Meier analysis of the high- and low-gene risk scores groups stratified by LASSO and survival-SVM in the training and validation cohorts.