Identification of biomarkers and construction of a microRNA-mRNA regulatory network for ependymoma using integrated bioinformatics analysis.

Ependymomas (EPNs) are one of the most common types of malignant neuroepithelial tumors. In an effort to identify potential biomarkers involved in the pathogenesis of EPN, the mRNA expression profiles of the GSE25604, GSE50161, GSE66354, GSE74195 and GSE86574 datasets, in addition to the microRNA (miRNA/miR) expression profiles of GSE42657 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between EPN and normal brain tissue samples were identified using the Limma package in R and GEO2R, respectively. Functional and pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed using the Search Tool for Retrieval of Interacting Genes database, which was visualized using Cytoscape. The targeted genes of DEMs were predicted using miRWalk2.0 and a miRNA-mRNA regulatory network was constructed. Following analysis, a total of 948 DEGs and 129 DEMs were identified. Functional enrichment analysis revealed that 609 upregulated DEGs were significantly enriched in 'PI3K-Akt signaling pathway', while 339 downregulated DEGs were primarily involved in 'cell junction' and 'retrograde endocannabinoid signaling'. In addition, 6 hub genes [cyclin dependent kinase 1, CD44 molecule (Indian blood group) (CD44), proliferating cell nuclear antigen (PCNA), MYC, synaptotagmin 1 (SYT1) and kinesin family member 4A] and 6 crucial miRNAs [homo sapiens (hsa)-miR-34a-5p, hsa-miR-449a, hsa-miR-106a-5p, hsa-miR-124-3p, hsa-miR-128-3p and hsa-miR-330-3p] were identified as biomarkers and potential therapeutic targets for EPN. Furthermore, a microRNA-mRNA regulatory network was constructed to highlight the interactions between DEMs and their target DEGs; this included the hsa-miR-449a-SYT1, hsa-miR-34a-5p-SYT1, hsa-miR-330-3p-CD44 and hsa-miR-124-3p-PCNA pairs, whose expression levels were confirmed using reverse transcription-quantitative polymerase chain reaction. In conclusion, the present study may provide important data for the investigation of the molecular mechanisms of EPN pathogenesis.

Yang B ，Dai JX ，Pan YB ，Ma YB ，Chu SH ... -  《-》

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 ... -  《-》

Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer.

Lung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients. The Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients. Four upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people. The results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC.

Zhang W ，Zhang Q ，Che L ，Xie Z ，Cai X ，Gong L ，Li Z ，Liu D ，Liu S ... -  《BMC CANCER》

Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis.

Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.

Tai Y ，Zhao C ，Gao J ，Lan T ，Tong H ... -  《PeerJ》

A Next-Generation Sequencing of Plasma Exosome-Derived microRNAs and Target Gene Analysis with a Microarray Database of Thermally Injured Skins: Identification of Blood-to-Tissue Interactions at Early Burn Stage.

Plasma exosome-derived microRNA (miRNA) profiles following thermal injury and their relationship with gene expression derangements in burned skin remain unexplored. This study focused on the identification of key miRNA-mRNA axes in potential blood-to-tissue interactions at early burn stage. Plasma exosomes were obtained from 6 severe burn patients 4-7 days post injury and 6 healthy volunteers. Next-generation sequencing (NGS) of exosomal small RNAs presented the differentially expressed miRNAs (DEMs). Target genes of the DEMs were predicted in the mirDIP database. Dataset GSE8056 was enrolled to acquire differentially expressed genes (DEGs) in burned skin compared to normal skin. Overlap between the DEGs and target genes of the DEMs were focus genes. The protein-protein interaction (PPI) network and enrichment analyses of the focus genes demonstrated hub genes and suggested underlying mechanisms and pathways. The hub genes and upstream DEMs were selected to construct key miRNA-mRNA axes. The NGS of plasma exosome-derived small RNAs identified 85 DEMs (14 downregulated miRNAs and 71 upregulated miRNAs) with 12,901 predicted target genes. Dataset GSE8056 exhibited 1861 DEGs in partial-thickness burned skins 4-7 days postburn. The overlap between DEGs and target genes of DEMs displayed 1058 focus genes. The top 9 hub genes (CDK1, CCNB1, CCNA2, BUB1B, PLK1, KIF11, AURKA, NUSAP1 and CDCA8) in the PPI network of the focus genes pointed to 16 upstream miRNAs in DEMs, including 4 downregulated miRNAs (hsa-miR-6848-3p, has-miR-4684-3p, has-miR-4786-5p and has-miR-365a-5p) and 12 upregulated miRNAs (hsa-miR-6751-3p, hsa-miR-718, hsa-miR-4754, hsa-miR-6754-3p, hsa-miR-4739, hsa-miR-6739-5p, hsa-miR-6884-3p, hsa-miR-1224-3p, hsa-miR-6878-3p, hsa-miR-6795-3p, hsa-miR-550a-3p, and hsa-miR-550b-3p). A key miRNA-mRNA network in potential blood-to-tissue interactions at early burn stage was therefore constructed. An NGS and bioinformatic analysis in the study identified key miRNA-mRNA axes in potential blood-to-tissue interactions at early burn stage, suggesting plasma exosome-derived miRNAs may impact on the alteration patterns of gene expressions in a burn wound.

Li SJ ，Cai ZW ，Yang HF ，Tang XD ，Fang X ，Qiu L ，Wang F ，Chen XL ... -  《Journal of Inflammation Research》