Open Access

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

  • Authors:
    • Minwei Bao
    • Gening Jiang
  • View Affiliations

  • Published online on: May 15, 2019     https://doi.org/10.3892/ol.2019.10362
  • 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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APA
Bao, M., & Bao, M. (1899). Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective. Oncology Letters, 0, 0-0. https://doi.org/10.3892/ol.2019.10362
MLA
Bao, M., Jiang, G."Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective". Oncology Letters 0.0 (1899): 0-0.
Chicago
Bao, M., Jiang, G."Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective". Oncology Letters 0, no. 0 (1899): 0-0. https://doi.org/10.3892/ol.2019.10362