Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective

Bao,Minwei; Jiang,Gening

doi:10.3892/ol.2019.10362

Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective

Authors:
- Minwei Bao
- Gening Jiang
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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
Pages: 776-782
Copyright: © Bao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

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