Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning

Abstract

BackgroundGlycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through bioinformatics and machine learning.MethodsThe authors screened the dataset GSE51024 from the GEO database for Gene set enrichment analysis (GSEA), and performed differentially expressed genes (DEGs) of glycolytic pathway gene sets. Then, Cox regression analysis was used to identify prognosis-associated glycolytic genes and establish a risk model. Further, the validity of the risk model was evaluated using the dataset GSE67487 in GEO database, and finally, a specimen classification model was constructed by support vector machine (SVM) and random forest (RF) to further screen prognostic genes.ResultsBy DEGs, five glycolysis-related pathway gene sets (17 glycolytic genes) were identified to be highly expressed in MPM tumor tissues. Also 11 genes associated with MPM prognosis were identified in TCGA-MPM patients, and 6 (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) of them were included by Multi-factor COX analysis to construct a prognostic risk model for MPM patients, with Area under the ROC curve (AUC) was 0.830. Further, dataset GSE67487 also confirmed the validity of the risk model, with a significant difference in overall survival (OS) between the low-risk and high-risk groups (P < 0.05). The final machine learning screened the five prognostic genes with the highest risk of MPM, in order of importance, were ALDH2, KIF20A, COL5A1, ADH1B and SDC1.ConclusionsA risk model based on six glycolytic genes (ALDH2, KIF20A, COL5A1, ADH1B, SDC1, VCAN) can effectively predict the prognosis of MPM patients.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

“…All these three machine learning methods have been universally used in gene selection with their advantages [ 69 – 74 ]. For instance, Xiao et al utilized random forest and SVM for screening prognostic gene in malignant pleural mesothelioma [ 71 ], Shen et al applied SVM for evaluating selective mutant genes and constructing a model for predicting HCC DFS [ 70 ], Xiao et al used SVM-RFE and LASSO for identify candidate hub genes related to colorectal cancer [ 72 ]. Our group had also developed a 25-lncRNA signature for predicting the early recurrence of HCC patients by LASSO, but 25 lncRNAs are too many for further validation and clinical application [ 75 ].…”

Comprehensive Analysis of WGCNA - Derived Cancer Associated Fibroblasts Model For Prognosis, Immune Features, and Candidate Drug Development in LUSC

“…COL4A1 as involved as type IV collagen to deposit in tumor environment and activate the integrin signaling pathway, which is responsible for high metastatic tendency and more lung modules due to low cell elasticity [54] . COL5A1 gene codi es for the α1-helix of collagen type V, is one of the prognostic markers (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) in malignant pleural mesothelioma [55] from glycolysis-related pathway gene sets. Moreover, COL5A1 is identi ed as hub gene in the process to explore the common pathogenesis of lung adenocarcinoma (LUAD) and LUSC [56] .…”