bioRxivBioinformaticsPreprint
Integrated Bioinformatics Analysis Deciphering the microRNA Regulation in Protein-Protein Interaction Network in Lung Adenocarcinoma
Pratyay SenguptaiD, Sayoni Saha, Moumita MajiiD, Monidipa GhoshiD
Abstract
BackgroundThe architecture of the protein-protein interaction (PPI) network in any organism relies on their gene expression signature. microRNAs (miRNAs) have recently emerged as major post transcriptional regulators that control PPI by targeting mainly untranslated regions of the gene encoding proteins. Here, we aimed to unveil the role of miRNAs in the PPI network for identifying potential molecular targets for lung adenocarcinoma (LUAD). Materials and methodsThe expression profiles of miRNAs and mRNAs were collected from the NCBI Gene Expression Omnibus (GEO) database (GSE74190 and GSE116959). Abnormally expressed mRNAs from the data were appointed to construct a PPI network and hence incorporated with the miRNA-mRNA regulatory network. The miRNAs and mRNAs in this network were subjected to functional enrichment. Through the network analysis, hubs were identified and their mutation rate and probability of cooccurrence were calculated. ResultsWe identified 17 miRNAs and 429 mRNAs signature for differentially altered transcriptome in LUAD. The combined miRNA-mRNA regulatory network exhibited scale-free characteristics. Network analysis showed 5 miRNA (including hsa-miR-486-5p, hsa-miR-200b-5p, and hsa-miR-130b-5p) and 10 mRNA (including ASPM, CCNB1, TTN, TPX2, and BIRC5) which expressively contribute in the LUAD. We further investigated the hub genes and noticed that ASPM and TTN had the maximum rate of mutation and possessed a high tendency of cooccurrence in LUAD. ConclusionThis study provides a unique network approach to the exploration of the underlying molecular mechanism in LUAD. Identified mRNAs and miRNAs may therefore, serve as significant prognostic predictors and therapeutic targets.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes in prostate cancer
Su-Liang Li, Yun Ye, Shengyu Wang·PLoS ONEPeer-reviewed·Aug 23, 2018
- IDENTIFICATION OF MIRNA-MRNA-HUB GENE NETWORK PREDICTION IN SQUAMOUS CELL LUNG CARCINOMA VIA STARBASE SERVER AND INSILICO ANALYSIS OF HUB GENES
Ashwini Kalaiselven, Venkataramanan Swaminathan·bioRxiv·Aug 22, 2021
- lncRNA-mRNA network analysis reveals novel biomarkers in prostate adenocarcinoma
S. H. Razavi, A. Katanforoush·bioRxiv·Aug 16, 2023
- Comprehensive analysis of hsa-miR-654-5p's tumor-suppressing functions
- Comprehensive Network Analysis of miRNA Biogenesis Proteins and Their Ligand Interaction Sites to Identify Novel Targets for Cancer Therapeutics
Hazem Almhanna, Hassan Hachim Naser, Arun H. S. Kumar·bioRxiv·Dec 4, 2024
- Integrated microRNA and proteome analysis of cancer datasets with MoPC
Marta Lovino, Elisa Ficarra, Loredana Martignetti·PLoS ONEPeer-reviewed·Mar 21, 2024