Integrated Bioinformatics Analysis Deciphering the microRNA Regulation in Protein-Protein Interaction Network in Lung Adenocarcinoma
Sengupta, P.; Saha, S.; Maji, M.; Ghosh, M.
Show 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.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Small RNA Sequencing Reveals a Distinct MicroRNA Signature between Glucocorticoid Responder and Glucocorticoid Non-responder Primary Human Trabecular Meshwork Cells after Dexamethasone Treatment 96%
- Molecular pathways associated with Kallikrein 6 overexpression in colorectal cancer 94%
- TCGA Pan-Cancer genomic analysis of Alternative Lengthening of Telomeres (ALT) related genes 94%
Similar papers in this journal
- Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method 95%
- Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model 94%
- Candidate genes associated with neurological manifestations of COVID-19: Meta-analysis using multiple computational approaches 94%
Similar papers in this journal
- Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data 96%
- Epigenetic regulator miRNA pattern differences among SARS-CoV, SARS-CoV-2 and SARS-CoV-2 world-wide isolates delineated the mystery behind the epic pathogenicity and distinct clinical characteristics of pandemic COVID-19 95%
- Analysis and Identification of Necroptosis Landscape on Therapy and Prognosis in Bladder Cancer 95%
Similar papers in this journal
- Construction of competing endogenous RNA interaction networks as prognostic markers in metastatic melanoma 96%
- Network based multifactorial modelling of miRNA-target interactions 95%
- DNMT family induced down-regulation of NDRG1 via DNA methylation and clinicopathological significance in gastric cancer 94%
Similar papers in this journal
- FishExp: a comprehensive database and analysis platform for gene expression and alternative splicing of fish species 93%
- Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning:esophageal cancer as an example 93%
- iMDA-BN: Identification of miRNA-Disease Associations based on the Biological Network and Graph Embedding Algorithm 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.