Objective   This study aimed to determine the key genes that are associated with lung cancer risk for further study.

  Methods   Techniques in bioinformatics were applied in lung cancer risk genes that were obtained from clinical trials.

  Results   We obtained 312 lung cancer risk genes that are mainly related to cellular physiological processes, metabolism, cell proliferation, DNA repair and cell cycle, apoptosis, and MAPK signaling pathway. A total of 762 functional classifications by the Gene Ontology (GO) and 81 pathways by the Kyoto _Encyclopedia of Genes and Genomes (KEGG) were involved. Among these risk genes, only 70 GO classifications and 8 KEGG pathways were significant ln(Bayes factor)≥0 and P-value≤0.001. Only 307 genes can be translated into proteins; thus, the complex pro tein-protein network that was built using the Online String software also revealed 307 nodes. This network was visualized and quantified using the Cytoscape software. We determined 24 key genes from the protein-protein network.

  Conclusion   Selecting the key genes from a large number of lung cancer risk genes and performing a large sample interracial clinical study are important in the analysis of lung cancer key risk genes through bioinformatics.