Dataset Information

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

ABSTRACT: Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

SUBMITTER: Tabrizi-Nezhadi P

PROVIDER: S-EPMC10513823 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

Tabrizi-Nezhadi Parinaz P MotieGhader Habib H Maleki Masoud M Sahin Soner S Nematzadeh Sajjad S Torkamanian-Afshar Mahsa M

TheScientificWorldJournal 20230914

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, ...[more]

PMID: 37746664

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Project description:Background: Prostate cancer is one of the most common malignancies among men worldwide currently. However, specific mechanisms of prostate cancer were still not fully understood due to lack of integrated molecular analyses. We performed this study to establish an mRNA-single nucleotide polymorphism (SNP)-microRNA (miRNA) interaction network by comprehensive bioinformatics analysis, and search for novel biomarkers for prostate cancer. Materials and methods: mRNA, miRNA, and SNP data were acquired from Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify differentially expressed genes (DEGs) and miRNAs (DEMs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) analysis and expression quantitative trait loci (eQTL) analysis of DEGs were conducted. SNPs related to DEMs (miRSNPs) were downloaded from the open-source website MirSNP and PolymiRTS 3.0. TargetScan and miRDB databases were used for the target mRNA prediction of miRNA. The mRNA-SNP-miRNA interaction network was then constructed and visualized by Cytoscape 3.9.0. Selected key biomarkers were further validated using the Cancer Genome Atlas (TCGA) database. A nomogram model was constructed to predict the risk of prostate cancer. Results: In our study, 266 DEGs and 11 DEMs were identified. KEGG pathway analysis showed that DEGs were strikingly enriched in focal adhesion and PI3K-Akt signaling pathway. A total of 60 mRNA-SNP-miRNAs trios were identified to establish the mRNA-SNP-miRNA interaction network. Seven mRNAs in mRNA-SNP-miRNA network were consistent with the predicted target mRNAs of miRNA. These results were largely validated by the TCGA database analysis. A nomogram was constructed that contained four variables (ITGB8, hsa-miR-21, hsa-miR-30b and prostate-specific antigen (PSA) value) for predicting the risk of prostate cancer. Conclusion: Our study established the mRNA-SNP-miRNA interaction network in prostate cancer. The interaction network showed that hsa-miR-21, hsa-miR-30b, and ITGB8 may be utilized as new biomarkers for prostate cancer.

Project description:BackgroundMicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.MethodsThe human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.ResultsUsing this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.ConclusionsOur analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Project description:Hepatoblastoma (HB) is a rare disease but nevertheless the most common hepatic tumor in the pediatric population. For patients with advanced HB, the prognosis is dismal and there are limited therapeutic options. Multiple microRNAs (miRNAs) were reported to be involved in HB development, but the miRNA-mRNA interaction network in HB remains elusive. Through a comparison between HB and normal liver samples in the GSE131329 dataset, we detected 580 upregulated differentially expressed mRNAs (DE-mRNAs) and 790 downregulated DE-mRNAs. As for the GSE153089 dataset, the first cluster of differentially expressed miRNAs (DE-miRNAs) were detected between fetal-type tumor and normal liver groups, while the second cluster of DE-miRNAs were detected between embryonal-type tumor and normal liver groups. Through the intersection of these two clusters of DE-miRNAs, 33 upregulated hub miRNAs, and 12 downregulated hub miRNAs were obtained. Based on the respective hub miRNAs, the upstream transcription factors (TFs) were detected via TransmiR v2.0, while the downstream target genes were predicted via miRNet database. The intersection of target genes of respective hub miRNAs and corresponding DE-mRNAs contributed to 250 downregulated candidate genes and 202 upregulated candidate genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses demonstrated the upregulated candidate genes mainly enriched in the terms and pathways relating to the cell cycle. We constructed protein-protein interaction (PPI) network, and obtained 211 node pairs for the downregulated candidate genes and 157 node pairs for the upregulated candidate genes. Cytoscape software was applied for visualizing the PPI network and respective top 10 hub genes were identified using CytoHubba. The expression values of hub genes in the PPI network were subsequently validated through Oncopression database followed by quantitative real-time polymerase chain reaction (qRT-PCR) in HB and matched normal liver tissues, resulting in six significant downregulated genes and seven significant upregulated genes. The miRNA-mRNA interaction network was finally constructed. In conclusion, we uncover various miRNAs, TFs, and hub genes as potential regulators in HB pathogenesis. Additionally, the miRNA-mRNA interaction network, PPI modules, and pathways may provide potential biomarkers for future HB theranostics.

Dataset Information

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

Publications

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers.

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