Dataset Information

An integrated bioinformatical analysis of miR-19a target genes in multiple myeloma.

ABSTRACT: MicroRNA (miR)-19a, as an oncomiR, has been studied in several types of cancer; however, its role in the development and progression of multiple myeloma (MM) remains unclear. The present study used a bioinformatics approach to investigate the involvement of miR-19a in MM. miR-19a targets were predicted using target prediction programs, followed by screening for differentially expressed genes in MM. The function of these genes was then annotated using gene ontology term enrichment, signaling pathway enrichment and protein-protein interaction (PPI) analysis. In addition, natural language processing (NLP) was performed to identify genes associated with MM. A total of 715 putative targets of miR-19a were identified in the present study, of which 40 were experimentally validated. A total of 121 genes were identified to be differentially expressed in MM, including 80 upregulated genes and 41 downregulated genes. Among the differentially expressed genes, ras homolog family member B, clathrin heavy chain, prosaposin and protein phosphatase 6 regulatory subunit 2 were predicted target genes of miR-19a. The results of NLP revealed that 2 of the differentially expressed genes, Y-box binding protein 1 and TP53 regulated inhibitor of apoptosis 1, were reported to be associated with MM. In addition, 41 target genes of miR-19a were identified to be associated with the development and progression of MM. These results may aid in understanding the molecular mechanisms of miR-19a in the development and progression of MM. In addition, the results of the present study indicate that targets genes of miR-19a are potential candidate biomarkers for MM.

SUBMITTER: Lv H

PROVIDER: S-EPMC5704339 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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An integrated bioinformatical analysis of miR-19a target genes in multiple myeloma.

Lv Hongyan H Wu Xianda X Ma Guiru G Sun Lixia L Meng Jianbo J Song Xiaoning X Zhang Jinqiao J

Experimental and therapeutic medicine 20170921 5

MicroRNA (miR)-19a, as an oncomiR, has been studied in several types of cancer; however, its role in the development and progression of multiple myeloma (MM) remains unclear. The present study used a bioinformatics approach to investigate the involvement of miR-19a in MM. miR-19a targets were predicted using target prediction programs, followed by screening for differentially expressed genes in MM. The function of these genes was then annotated using gene ontology term enrichment, signaling path ...[more]

PMID: 29201171

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Project description:: Preeclampsia is a pregnancy-specific disorder characterized by the presence of hypertension with the onset of either proteinuria, maternal organ or uteroplacental dysfunction. Preeclampsia is one of the leading causes of maternal and fetal mortality and morbidity worldwide. However, the etiopathologies of preeclampsia are not fully understood. Many studies have indicated that genes are differentially expressed between normal and in the disease state. Hence, this study systematically searched the literature on human gene expression that was differentially expressed in preeclampsia. An electronic search was performed through 2019 through PubMed, Scopus, Ovid-Medline, and Gene Expression Omnibus where the following MeSH (Medical Subject Heading) terms were used and they had been specified as the primary focus of the articles: Gene, placenta, preeclampsia, and pregnancy in the title or abstract. We also found additional MeSH terms through Cochrane Library: Transcript, sequencing, and profiling. From 687 studies retrieved from the search, only original publications that had performed high throughput sequencing of human placental tissues that reported on differentially expressed genes in pregnancies with preeclampsia were included. Two reviewers independently scrutinized the titles and abstracts before examining the eligibility of studies that met the inclusion criteria. For each study, study design, sample size, sampling type, and method for gene analysis and gene were identified. The genes listed were further analyzed with the DAVID, STRING and Cytoscape MCODE. Three original research articles involving preeclampsia comprising the datasets in gene expression were included. By combining three studies together, 250 differentially expressed genes were produced at a significance setting of p < 0.05. We identified candidate genes: LEP, NRIP1, SASH1, and ZADHHC8P1. Through GO analysis, we found extracellular matrix organization as the highly significant enriched ontology in a group of upregulated genes and immune process in downregulated genes. Studies on a genetic level have the potential to provide new insights into the regulation and to widen the basis for identification of changes in the mechanism of preeclampsia. Integrated bioinformatics could identify differentially expressed genes which could be candidate genes and potential pathways in preeclampsia that may improve our understanding of the cause and underlying molecular mechanisms that could be used as potential biomarkers for risk stratification and treatment.

Project description:PurposesCervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.ResultsThe mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.ConclusionWe have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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An integrated bioinformatical analysis of miR-19a target genes in multiple myeloma.

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An integrated bioinformatical analysis of miR-19a target genes in multiple myeloma.

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