ArticleFrontiers in genetics2020
Identification of MicroRNA-Target Gene-Transcription Factor Regulatory Networks in Colorectal Adenoma Using Microarray Expression Data.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.BMC medical genomics · 2023Article
- Identification of the critical genes and miRNAs in hepatocellular carcinoma by integrated bioinformatics analysis.Medical oncology (Northwood, London, England) · 2022Article
- ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning.Frontiers in physiology · 2022Article
- Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis.BMC infectious diseases · 2021Article
- Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease.Frontiers in cardiovascular medicine · 2021Article
- Bioinformatics Analysis of Choriocarcinoma-Related MicroRNA-Transcription Factor-Target Gene Regulatory Networks and Validation of Key miRNAs.OncoTargets and therapy · 2021Article
- Signaling pathways of genetic variants and miRNAs in the pathogenesis of myasthenia gravis.Gland surgery · 2020Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of the study was to find the key genes, microRNAs (miRNAs) and transcription factors (TFs) and construct miRNA-target gene-TF regulatory networks to investigate the underlying molecular mechanism in colorectal adenoma (CRA).
methodsFour mRNA expression datasets and one miRNA expression dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were identified between CRA and normal samples. Moreover, functional enrichment analysis for DEGs was carried out utilizing the Cytoscape-plugin, known as ClueGO. These DEGs were mapped to STRING database to construct a protein-protein interaction (PPI) network. Then, a miRNA-target gene regulatory network was established to screen key DEMs. In addition, similar workflow of the analyses were also performed comparing the CRC samples with CRA ones to screen key DEMs. Finally, miRNA-target gene-TF regulatory networks were constructed for these key DEMs using iRegulon plug-in in Cytoscape.
resultsWe identified 514 DEGs and 167 DEMs in CRA samples compared to healthy samples. Functional enrichment analysis revealed that these DEGs were significantly enriched in several terms and pathways, such as regulation of cell migration and bile secretion pathway. A PPI network was constructed including 325 nodes as well as 890 edges. A total of 59 DEGs and 65 DEMs were identified in CRC samples compared to CRA ones. In addition, Two key DEMs in CRA samples compared to healthy samples were identified, such as hsa-miR-34a and hsa-miR-96. One key DEM, hsa-miR-29c, which was identified when we compared the differentially expressed molecules found in the comparison CRA versus normal samples to the ones obtained in the comparison CRC versus CRA, was also identified in CRC samples compared to CRA ones. The miRNA-target gene-TF regulatory networks for these key miRNAs included two TFs, one TF and five TFs, respectively.
conclusionThese identified key genes, miRNA, TFs and miRNA-target gene-TF regulatory networks associated with CRA, to a certain degree, may provide some hints to enable us to better understand the underlying pathogenesis of CRA.
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