| Literature DB >> 33444474 |
Sumaria Malik1, Rehan Zafar Paracha1, Maryam Khalid1, Maryum Nisar1, Amnah Siddiqa1, Zamir Hussain1, Raheel Nawaz2, Amjad Ali3, Jamil Ahmad1.
Abstract
Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.Entities:
Keywords: RNA; cancer; disease specific biomarkers; genetic abnormalities; genetics; integrative analysis; lung; lung adenocarcinoma; mRNAs expression profiles; medical diagnostic computing; miRNAs expression profiles; microRNAs; molecular biophysics; molecular configurations; noncoding regulators; nonsmokers; patient diagnosis; proteins; smokers; tumours; weighted gene coexpression network analysis
Year: 2019 PMID: 33444474 PMCID: PMC8687273 DOI: 10.1049/iet-syb.2018.5040
Source DB: PubMed Journal: IET Syst Biol ISSN: 1751-8849 Impact factor: 1.615