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Bioinformatics Analysis Identifies MicroRNAs and Target Genes Associated with Prognosis in Patients with Melanoma.

ABSTRACT: BACKGROUND Melanoma of the skin can be associated with early metastases and poor prognosis. This study aimed to identify microRNAs (miRNAs) and target genes associated with prognosis in melanoma using bioinformatics analysis. MATERIAL AND METHODS The Gene Expression Omnibus (GEO) database identified the microarray dataset GSE20994. Differentially expressed miRNAs (DE-miRNAs) were first identified using R language software and validated by GEO2R. Potential target genes of DE-miRNAs were screened, and their targets and prognostic role were evaluated in the miRTarBase database. Pathway enrichment and functional annotation analysis for target genes were established using the DAVID database. miRNA-hub gene networks and protein-protein interaction (PPI) networks were constructed and visualized using the STRING database and Cytoscape. Kaplan-Meier survival curves were constructed using transcriptome and survival data from the UALCAN web tool. RESULTS There were 132 upregulated and 134 down-regulated DE-miRNAs identified from human melanoma samples. From the top three upregulated miRNAs, there were 580 potential predicted target genes, and from the top three down-regulated miRNAs, there 543 potential predicted target genes. miR-300 was upregulated, and miR-629 was down-regulated in melanoma. Two pivotal bub genes, TP53 and GAPDH, were identified in the PPI network. Five out of ten hub genes were modulated by upregulated miR-580, and five by miR-629. Increased mRNA expression of DAPK2 was associated with increased OS, and increased mRNA expression of SKCM, TECPR2, and ZNF781 were associated with reduced OS. CONCLUSIONS Bioinformatics analysis identified miRNAs and target genes associated with melanoma that may represent potential prognostic and therapeutic biomarkers.

SUBMITTER: Li Q

PROVIDER: S-EPMC6820336 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Bioinformatics Analysis Identifies MicroRNAs and Target Genes Associated with Prognosis in Patients with Melanoma.

Li Qiao Q Zhang Li-Yu LY Wu Shuang S Huang Chen C Liu Juan J Wang Ping P Cao Yuan Y

Medical science monitor : international medical journal of experimental and clinical research 20191017

BACKGROUND Melanoma of the skin can be associated with early metastases and poor prognosis. This study aimed to identify microRNAs (miRNAs) and target genes associated with prognosis in melanoma using bioinformatics analysis. MATERIAL AND METHODS The Gene Expression Omnibus (GEO) database identified the microarray dataset GSE20994. Differentially expressed miRNAs (DE-miRNAs) were first identified using R language software and validated by GEO2R. Potential target genes of DE-miRNAs were screened, ...[more]

PMID: 31621692

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Project description:Melanoma, which is usually induced by ultraviolet light exposure and the following DNA damage, is the most dangerous skin cancer. The purpose of the present study was to screen key molecules involved in melanoma.Microarray data of E-MTAB-1862 were downloaded from the ArrayExpress database, which included 21 primary melanoma samples and 11 benign nevus samples. In addition, the RNASeq version 2 and microRNA (miRNA) sequencing data of cutaneous melanoma were downloaded from The Cancer Genome Atlas database. After identifying the differentially expressed genes (DEGs) using Limma package, enrichment analysis and protein-protein interaction (PPI) network analysis were performed separately for them using DAVID software and Cytoscape software. In addition, survival analysis and regulatory network analysis were further performed by log-rank test and Cytoscape software, respectively. Moreover, real-time reverse transcription polymerase chain reaction (RT-PCR) was performed to further verify the expression patterns of several selected DEGs.A total of 382 DEGs were identified in primary melanoma samples, including 206 upregulated genes and 176 downregulated genes. Functional enrichment analysis showed that COL17A1 was enriched in epidermis development. In the PPI network, CXCL8 (degree?=?29) and STAT1 (degree?=?28) had higher degrees and could interact with each other. Survival analysis showed that 21 DEGs, 55 long noncoding RNAs (lncRNAs) and 32 miRNAs were found to be associated with prognosis. Furthermore, several regulatory relationships were found in the lncRNA-gene regulatory network (such as RP11-361L15.4 targeting COL17A1) and the miRNA-gene regulatory network (such as hsa-miR-375 targeting CCL27 and hsa-miR-375 targeting insulin-like growth factor 1 receptor [IGF1R]). Real-time RT-PCR results showed that the overall direction of differential expression was consistent except COL17A1.CXCL8 interacted with STAT1, CCL27, and IGF1R targeted by hsa-miR-375, and COL17A1 targeted by RP11-361L15.4 might function in the development and progression of melanoma, which should be verified by more detailed experiments.

Project description:Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation. Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA-target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA-target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR). Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA-target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR (p < 0.05). Conclusion: We constructed miRNA-target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.

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Bioinformatics Analysis Identifies MicroRNAs and Target Genes Associated with Prognosis in Patients with Melanoma.

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Bioinformatics Analysis Identifies MicroRNAs and Target Genes Associated with Prognosis in Patients with Melanoma.

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