Integrative analysis of gene and microRNA expression profiles reveals candidate biomarkers and regulatory networks in psoriasis.
Psoriasis (PS) is a chronic inflammatory skin disease with a long course and tendency to recur, the pathogenesis of which is not fully understood. This article aims to identify the key differentially expressed genes (DEGs) and microRNA (miRNAs) of PS, construct the core miRNA-mRNA regulatory network, and investigate the underlying molecular mechanism through integrated bioinformatics approaches. Two gene expression profile datasets and 2 miRNA expression profile datasets were downloaded from the gene expression omnibus (GEO) database and analyzed by GEO2R. Intersection DEGs and intersection differentially expressed miRNAs (DEMs) were each screened. The Metascape database and R software were used to perform enrichment analysis of intersecting DEGs and study their functions. Target genes of DEMs were predicted from the online database miRNet. The protein-protein interaction files of the overlapping target genes were obtained from string and the miRNA-mRNA network was constructed by Cytoscape software. In addition, the online web tool CIBERSORT was used to analyze the immune infiltration of dataset GSE166388, and the relative abundance of 22 immune cells in the diseased and normal control tissues was calculated and assessed. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the relative expression of the screened miRNAs and mRNAs to assess the applicability of DEMs and DEGs as biomarkers in PS. A total of 205 mating DEGs and 6 mating DEMs were screened. 103 dysregulated crossover genes from 205 crossover DEGs and 7878 miRNA target genes were identified. The miRNA-mRNA regulatory network was constructed and the top 10 elements were obtained from CytoHubba, including hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2. QRT-PCR revealed significant differences in miRNA and gene expression between inflammatory and normal states. In this study, the miRNA-mRNA core regulator pairs hsa-miR-146a-5p, hsa-miR-17-5p, hsa-miR-106a-5p, hsa-miR-18a-5p, CDK1, CCNA2, CCNB1, MAD2L1, RRM2, and CCNB2 may be involved in the course of PS. This study provides new insights to discover new potential targets and biomarkers to further investigate the molecular mechanism of PS.
Chen L
，Wang X
，Liu C
，Chen X
，Li P
，Qiu W
，Guo K
... -
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Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis.
As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA.
Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007).
Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis.
We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.
Jiang Y
，Shen Y
，Ding L
，Xia S
，Jiang L
... -
《BMC MUSCULOSKELETAL DISORDERS》
Construction of miRNA-mRNA-TF Regulatory Network for Diagnosis of Gastric Cancer.
Gastric cancer (GC), as an epidemic cancer worldwide, has more than 1 million new cases and an estimated 769,000 deaths worldwide in 2020, ranking fifth and fourth in global morbidity and mortality. In mammals, both miRNAs and transcription factors (TFs) play a partial role in gene expression regulation. The mRNA expression profile and miRNA expression profile of GEO database were screened by GEO2R for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Then, DAVID annotated the functions of DEGs to understand the functions played in biological processes. The prediction of potential target genes of miRNA and key TFs of mRNA was performed by mipathDB V2.0 and CHEA3, respectively, and the gene list comparison was performed to look for overlapping genes coregulated by key TFs and DEMs. Finally, the obtained miRNAs, TF, and overlapping genes were used to construct the miRNA-mRNA-TF regulatory network, which was verified by RT-qPCR. 76 upregulated DEGs, 199 downregulated DEGs, and 3 upregulated miRNAs (miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p) were identified from the expression profiles of mRNA (GSE26899, GSE29998, GSE51575, and GSE13911) and miRNA (GSE93415), respectively. Through database prediction and gene list comparison, it was found that among the 199 downregulated DEGs, 61, 71, and 69 genes were the potential targets of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p, respectively. 199 downregulated DEGs were used as the gene list for the prediction of key TFs, and the results showed that RFX6 ranked the highest. The potential target overlap genes of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p were 4 genes (SH3GL2, ATP4B, CTSE, and SORBS2), 7 genes (SLC7A8, RNASE4, ESRRG, PGC, MUC6, Fam3B, and FMO5), and 6 genes (CHGA, PDK4, TMPRSS2, CLIC6, GPX3, and PSCA), respectively. Finally, we constructed a miRNA-mRNA-TF regulatory network based on the above 17 mRNAs, 3 miRNAs, and 1 TF and verified by RT-qPCR and western blot results that the expression of RFX6 was downregulated in GC tissues. These identified miRNAs, mRNAs, and TF have a certain reference value for further exploration of the regulatory mechanism of GC.
Fu Z
，Xu Y
，Chen Y
，Lv H
，Chen G
，Chen Y
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ResearchGate
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钛学术
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