Dataset Information

Identification and analysis of dysregulated lncRNA and associated ceRNA in the pathogenesis of keloid.

ABSTRACT: Background:Keloid is an excessive fibrosis disease caused by the abnormal proliferation of collagen fibers following trauma. Previous studies have shown that genetic factors have been considered to play important roles in keloid formation. This study is aimed to investigate the regulatory network of messenger RNAs (mRNAs) microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in keloid, and identifying its key biomarkers. Methods:We performed RNA-seq and miRNA-seq on keloid and normal skin samples. Sequencing datasets were analyzed by bioinformatics. Gene ontology (GO) and pathway analysis presented the characteristics of associated protein-coding genes. Differentially expressed ceRNAs were validated by quantitative reverse transcriptase-PCR (qRT-PCR). Results:We identified a total of 319 lncRNAs, 1,533 mRNAs and 40 miRNAs as keloid-specific RNAs. Both the GO biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed for 1,219 specific genes with differentially expressed mRNAs. Then, with 509 key lncRNAs, 25 miRNAs, and 94 mRNAs, we constructed a ceRNA network and explored any potential underlying mechanisms. In the regulation of the actin cytokeleton pathway, we validated 2 pairs of ceRNAs EGFR/miR-370-3p/lnc-GLB1L-1 and ITGB5/ miR-204/ lnc-CASP9-3 in another sample size in keloid. Conclusions:Through RNA-seq and miRNA-seq, we identified keloid-associated lncRNAs, mRNAs and miRNAs, which can be used as potential therapeutic targets and biomarkers for keloid. Our study may lay a foundation for future pathogenesis studies.

SUBMITTER: Duan X

PROVIDER: S-EPMC7154391 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Identification and analysis of dysregulated lncRNA and associated ceRNA in the pathogenesis of keloid.

Duan Xilei X Wu Yuemeng Y Zhang Zheng Z Lu Zhong Z

Annals of translational medicine 20200301 5

<h4>Background</h4>Keloid is an excessive fibrosis disease caused by the abnormal proliferation of collagen fibers following trauma. Previous studies have shown that genetic factors have been considered to play important roles in keloid formation. This study is aimed to investigate the regulatory network of messenger RNAs (mRNAs) microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in keloid, and identifying its key biomarkers.<h4>Methods</h4>We performed RNA-seq and miRNA-seq on keloid and nor ...[more]

PMID: 32309369

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Project description:Background:Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database. Methods:We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients. Results:Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis. Conclusions:We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

Dataset Information

Identification and analysis of dysregulated lncRNA and associated ceRNA in the pathogenesis of keloid.

Publications

Identification and analysis of dysregulated lncRNA and associated ceRNA in the pathogenesis of keloid.

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