Identification of Gene Markers for Survival Prediction of Lung Adenocarcinoma Patients Based on Integrated Multibody Data Analysis

Abstract

We constructed a prognostic-related risk prediction for patients with lung adenocarcinoma by integrating multiple omics information of lung adenocarcinoma clinical information group and genome and transcriptome. Blood samples and cancer and paracancerous lung tissue samples were collected from 480 patients with lung adenocarcinoma. DNA and RNA sequencing was performed on DNA samples and RNA samples. The first follow-up was carried out 3 months after discharge. Clinical information of patients including age, gender, smoking history, and TNM stage was collected. The Cox proportional hazard model evaluated more than 600 potential SNPs related to the prognosis of lung adenocarcinoma. After LASSO analysis, we obtained 4 SNPs related to the prognosis of lung adenocarcinoma (including rs1059292, rs995343, rs2013335, and rs8078328). Through the Cox proportional hazard model, 260 candidate genes related to the prognosis of lung adenocarcinoma were evaluated. After subsequent analysis, 3 genes related to the prognosis of lung adenocarcinoma (LDHA, SDHC, and TYMS) were obtained. All survived patients were spilt into a high-risk group ( $n=170$ ) and a low-risk group ( $n=170$ ) according to 4 SNPs and 3 genes related to the prognosis of lung adenocarcinoma. The overall survival rate of patients in the high-risk group was lower than that in the low-risk group. The prognostic risk prediction index constructed by combining clinical information group and genomic and transcriptome characteristics of multiomics information can effectively distinguish the prognosis of patients with lung adenocarcinoma, which will provide effective support for the precise treatment of patients with lung adenocarcinoma.