Identification of Novel Biomarkers and enriched pathways involved in
lung cancer using Statistical and Bioinformatics techniques
Abstract
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.