Data-driven Protein Networks of Early Lung Adenocarcinomas Harboring EGFR Mutations
Received Date: Aug 28, 2020 / Accepted Date: Sep 14, 2020 / Published Date: Sep 21, 2020
Abstract
It remains unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways, which would influence treatment outcomes of NSCLC patients. Our studies were conducted to unveil the influences of these mutations by assessing 36 tissue specimens of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted correlation network analysis, together with analysis of the variance-based or over-representative test, identified core co-expressed modules and their hub proteins. Data-driven molecular networks obtained by weighted correlation analysis will put an important foundation on biological and medical research. Network proteogenomic approaches will change the treatment system and will help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.
Keywords: Lung adenocarcinoma; EGFR mutation; Data-driven network; Weighted correlation network analysis; Network proteogenomics; Mass spectrometry
Citation: Nishimura T, Marko-Varga G. (2020) Data-driven protein networks of early lung adenocarcinomas harboring EGFR mutations. J Mucosal Immunol Res 4: 122.
Copyright: © 2020 Nishimura T, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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