Comparison and Identification of Pathways and Genes Associated with Small Cell Lung Cancer and Chronic Obstructive Pulmonary Disease using Transcriptomic Data Analysis

Abstract

Chronic obstructive pulmonary disease (COPD) is a lethal lung disease that causes shortness of breath. It is a progressive disease that is caused due to exposure to cigarette smoke and air pollution. It is not curable. In small cell lung cancer(SCLC), malignant cells form in the lungs and it is caused due to smoking. Major symptoms include coughing, shortness of breath and chest pain. SCLC is an extremely bel ligerent disease with poor prognosis and is still not completely curable. COPD and SCLC share some common symptoms and causes. Previously discovered biomark ers for these diseases have a low significance due to less number of samples. The results from the previous studies can not be generalized for the whole world if the analysis is carried out in a particular geographical location. This research is car ried out to find the gene signature for SCLC and COPD through the integration of microarray and RNA-seq datasets results and by performing the comparative analy sis of more than three datasets for each condition. For SCLC, six publicly available datasets were analyzed using R. For comparative analysis, four out of six datasets were used that led to the identification of fifteen signature genes including ANXA3,C3, TGM2, SCGB1A1, TACSTD2,FAM107A, GPX3, CAV2, CTSH, EPAS1, TGFBR2, CFD, CYP4B1, MSLN and CX3CL1. In case of COPD, seven publicly accessible datasets including six from microarray and one from RNA-seq were analyzed. In most datasets for COPD, normal group was further categorized into former, current and Non-smokers. No common DEGs were identified in COPD and Nonsmokers where five microarray datasets and one RNA-seq dataset were considered. Five mi croarray datasets were used for comparative analysis of COPD and smokers. No common DEGs are found out in this case. For former smokers and COPD subjects three microarray datasets were considered, which resulted in the identification of four common DEGs including PID1, AHRR, SLC7A11 and CYP1B1.