The establishment of a prognostic model based on bioinformatic analysis of DNA damage repair genes in lung adenocarcinoma

Objective: To analyze the expression of DNA damage repair related genes form TCGA and GEO databases and establish a clinical prognosis prediction model in lung adenocarcinoma. Methods The RNA sequencing and clinical information data of lung adenocarcinoma were downloaded from the TCGA database, and differential analysis of DNA damage repair genes was performed between cancer and normal tissues. Cox proportional risk model and Lasso regression were used to construct the prediction model of DNA repair related genes, and the external model was verified by GSE30219. Metascape and GSEA were used to analyze the relevant mechanisms. Results Total of 74 DNA damage repair related genes were screened out from RNA sequencing data of 515 cases of lung adenocarcinoma and 59 cases of normal lung tissues. Based on Cox and Lasso regression analysis, a risk prediction model composing of PLK1, NEIL3 and EXO1 was constructed, and the risk scoring formula was riskscore = PLK1*0.011259 + NEIL3*0.022537 + EXO1*0.015379. In the TCGA dataset and external validation set of GSE30219, the overall survival of the high-risk group was significantly lower than that of the low-risk group (P < 0.01). The results of mechanism analysis showed that the poor prognosis of high risk group patients was related to mTOR, Myc, G2M and E2F pathways. Conclusion The risk model composed of PLK1, NEIL3 and EXO1 is established in this study, which can accurately predict the prognosis of patients with lung adenocarcinoma.