Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective
Affiliations: Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Yangpu, Shanghai 200433, P.R. China
- Published online on: May 15, 2019 https://doi.org/10.3892/ol.2019.10362
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et al. This is an open access article distributed under the
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There is an inherent need to identify differentially expressed genes (DEGs), characterize these genes and provide functional enrichment analysis to the publicly available lung cancer datasets, primarily coming from next-generation sequencing data or microarray gene expression studies. The risk of lung cancer in patients with smokers is manifold, and with chronic obstructive pulmonary disease (COPD) it is 2- to 5-fold greater, compared with smokers without COPD. In the present study, differential expression analysis and gene functional enrichment analysis of lung cancer gene expression datasets obtained from NCBI-GEO were performed. The result identifies a significant number of DEGs which have at least a 2-fold change in their expression. Among them, six genes were found to have a 4-fold change in the expression level, and 47 genes exhibited a 3-fold change in the expression. It was also observed that most of the genes were upregulated and few genes were downregulated.