Zhang Wenting, Liu Yafeng, Hu Chunxiao, Wang Xueqin, Xie Jun, Zhang Xue, Hu Wanfa, Wu Jing, Xing Yingru, Hu Dong
School of Medicine, Anhui University of Science and Technology, Huainan 232001; Department of Laboratory, Tumor Hospital Affiliated to Anhui University of Science and Technology, Huainan 232001, China.
Department of Laboratory, Tumor Hospital Affiliated to Anhui University of Science and Technology, Huainan 232001, China.
Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2023 Jan;39(1):41-48.
Objective To construct and validate a prognostic model for lung adenocarcinoma based on bioinformatics of metabolic genes. Methods Lung adenocarcinoma-related data from The Cancer Genome Atlas (TCGA) database and gene expression omnibus (GEO) were acquired, and LASSO regression was used to construct multi-gene prognostic models and calculate risk-score (RS). Univariate and multivariate Cox independent prognostic analysis was performed. The area under receiver operating characteristic (ROC) curve (AUC) of the model was evaluated by ROC curve and survival analysis was performed. Nomogram were constructed to evaluate the feasibility of the model, and metabolic gene functional enrichment analysis was performed by GSEA. Tumor immune estimation resource (TIMER) database was used to analyze the correlation of patients RS with immune cell infiltration and with the expression of immune checkpoint molecules. Results The TCGA database was used to construct a prognostic model for lung adenocarcinoma based on 18 metabolism-related genes, and RS was used as an independent prognostic factor. The area under the ROC curve was 0.713. Survival analysis showed that overall survival was higher in the low-risk group compared to the high-risk group, and the prognostic model was associated with infiltration of immune cells and with the expression of immune checkpoint molecules. Conclusion RS is an independent prognostic factor in the prognostic model of lung adenocarcinoma with metabolic genes, suggesting a high prognostic value of this model.
