Abstract: Objective The objective of this study was to identify prognostic molecular biomarkers in lung adenocarcinoma(LUAD)through bioinformatics methods. Methods The gene expression profile data of LUAD were obtained from the Cancer Genome Atlas(TCGA)to analyze the distribution of tumor-infiltrating immune cells using the CIBERSORT algorithm.The Log-rank method was used to screen immune infiltrating cells associated related to overall survival(OS)in patients.The “limma” package was employed to identify differentially expressed genes(DEGs)in OS-related immune cells at high and low levels.The STRING method was used to construct a protein-protein interaction(PPI)network of DEGs.Based on the node scores of the MCC algorithm in the cytoHubba plugin,the top 30 genes were selected.The Kaplan-Meier survival curve and Cox proportional regression analysis were used to screen for genes related to LUAD patient OS,and gene expression was validated using the GEPIA2 database. Results The OS of LUAD patients with high levels of monocytes and eosinophils was significantly higher than those of the low level patients(P＜0.05).Based on the high and low level groups of immune infiltrating cells mentioned above,a total of 365 DEGs were screened,and 30 hub genes were identified through the constructed PPI network.Among them,NEK2 and HJURP were regarded as key genes affecting the OS of LUAD patients(P＜0.05),which their levels in cancer tissues were higher than those in normal tissues(P＜0.05). Conclusion NEK2 and HJURP are key immune infiltration related genes that affect the prognosis of LUAD patients and can serve as potential biomarkers for prognostic prediction of LUAD patients.

Key words: Lung adenocarcinoma, Key genes, Bioinformatics, Centrosome associated kinase 2, Holliday junction recognition protein