Identification of pivotal genes with prognostic evaluation value in lung adenocarcinoma by bioinformatics analysis.

Abstract

Lung cancer remains the leading cause of cancer morbidity and mortality worldwide, and over-diagnosis causes various unnecessary losses in patients' lives and health. How to more effectively screen lung cancer patients and their potential prognostic risk become the focus of our current study. By analyzing the LUAD expression profile in The Cancer Genome Atlas (TCGA), we constructed a weighted gene co-expression network using differentially expressed genes (DEGs) to find the key modules and pivotal genes. A COX proportional risk regression model based on the least absolute shrinkage and selection operator (LASSO) was used to assess the predictive value of the model for the prognosis of LUAD patients. A total of 4107 up-regulated DEGs and 2022 down-regulated DEGs were identified in this study, and enrichment analysis showed that these analyzes were associated with the extracellular matrix of cells and adhesion. Ten gene markers consisting of LDHA, TOP2A, UBE2C, TYMS, TRIP13, EXO1, TTK, TPX2, ZWINT, and UHRF1 were established by extracting the central genes in the key modules, and the upregulation of these genes was accompanied by an increased prognostic risk of patients. Among them, high expression of LDHA, TRIP13, and TTK in LUAD was associated with shorter overall survival and could be used as independent prognostic factors to participate in metabolic processes such as tumor NAD. The present study provides a powerful molecular target for the study of LUAD prognosis and provides a theoretical basis for the diagnosis and treatment of LUAD and the development of targeted inhibitors.