Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Identification of Biomarkers and Enriched Pathways Involved in Lung Cancer

Version 1 : Received: 3 May 2020 / Approved: 5 May 2020 / Online: 5 May 2020 (12:28:25 CEST)

How to cite: Singh, N.; Kumar, M.; Bhattacharjee, A.; Kumar Sonker, P.; Saroj, A. Identification of Biomarkers and Enriched Pathways Involved in Lung Cancer. Preprints 2020, 2020050081. https://doi.org/10.20944/preprints202005.0081.v1 Singh, N.; Kumar, M.; Bhattacharjee, A.; Kumar Sonker, P.; Saroj, A. Identification of Biomarkers and Enriched Pathways Involved in Lung Cancer. Preprints 2020, 2020050081. https://doi.org/10.20944/preprints202005.0081.v1

Abstract

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of 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. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be 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. Module analysis of PPI found that ‘Viral carcinogenesis’, ‘pathways in cancer’, ‘notch signaling pathway’, ‘AMPK signaling pathways’ had a close association with lung cancer. Conclusion: These identified DEGs 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.

Keywords

Lung cancer; biomarker; gene ontology; protein-protein interaction networks; survival analysis

Subject

Biology and Life Sciences, Biochemistry and Molecular Biology

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