Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes - Zhao, Rongchang; Ding, Dan; Ding, Yan; Han, Rongbo; Wang, Xiujuan; Zhu, Chunrong (Frontiers in Genetics, 2022)
Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified. Methods: Based on tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score. Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981 - 3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib sensitivity between the low-risk and high-risk groups. Conclusions: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.