Nikita Singh

and 4 more

Introduction: Lung cancer is the most important cause of cancer related deaths across the world. The aim of the study is to find key genes and enriched pathways associated with lung cancer using bioinformatics and statistical techniques, hence providing potential targets for the identification and treatment of the cancer. Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of the lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p<0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer patients. Gene ontology (GO) and KEGG pathway enrichment analysis is performed and 49 significant pathways were obtained, out of which 10 pathways were found that were exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Additionally, module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had close association with lung cancer. Conclusion: it is seen that these identified DEGs do not directly participate in growth of lung cancer but regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.