Identification of Functional Genes in Pterygium Based on Bioinformatics Analysis.

The competing endogenous RNA (ceRNA) network regulatory has been investigated in the occurrence and development of many diseases. This research aimed at identifying the key RNAs of ceRNA network in pterygium and exploring the underlying molecular mechanism. Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs were obtained from the Gene Expression Omnibus (GEO) database and analyzed with the R programming language. LncRNA and miRNA expressions were extracted and pooled by the GEO database and compared with those in published literature. The lncRNA-miRNA-mRNA network was constructed of selected lncRNAs, miRNAs, and mRNAs. Metascape was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on mRNAs of the ceRNA network and to perform Protein-Protein Interaction (PPI) Network analysis on the String website to find candidate hub genes. The Comparative Toxicogenomic Database (CTD) was used to find hub genes closely related to pterygium. The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR). There were 8 lncRNAs, 12 miRNAs, and 94 mRNAs filtered to construct the primary ceRNA network. A key lncRNA LIN00472 ranking the top 1 node degree was selected to reconstruct the LIN00472 network. The GO and KEGG pathway enrichment showed the mRNAs in ceRNA networks mainly involved in homophilic cell adhesion via plasma membrane adhesion molecules, developmental growth, regulation of neuron projection development, cell maturation, synapse assembly, central nervous system neuron differentiation, and PID FOXM1 PATHWAY. According to the Protein-Protein Interaction Network (PPI) analysis on mRNAs in LINC00472 network, 10 candidate hub genes were identified according to node degree ranking. Using the CTD database, we identified 8 hub genes closely related to pterygium; RT-qPCR verified 6 of them were highly expressed in pterygium. Our research found LINC00472 might regulate 8 hub miRNAs (miR-29b-3p, miR-183-5p, miR-138-5p, miR-211-5p, miR-221-3p, miR-218-5p, miR-642a-5p, miR-5000-3p) and 6 hub genes (CDH2, MYC, CCNB1, RELN, ERBB4, RB1) in the ceRNA network through mainly PID FOXM1 PATHWAY and play an important role in the development of pterygium.

Xu Y ，Qiao C ，He S ，Lu C ，Dong S ，Wu X ，Yan M ，Zheng F ... -  《-》

Exploring the Molecular Mechanisms of Pterygium by Constructing lncRNA-miRNA-mRNA Regulatory Network.

Xu N ，Cui Y ，Dong J ，Huang L ... -  《-》

Integrated analysis of long noncoding RNA-associated competing endogenous RNA network in periodontitis.

Long noncoding RNAs (lncRNAs) play critical and complex roles in regulating various biological processes of periodontitis. This bioinformatic study aims to construct a putative competing endogenous RNA (ceRNA) network by integrating lncRNA, miRNA and mRNA expression, based on high-throughput RNA sequencing and microarray data about periodontitis. Data from 1 miRNA and 3 mRNA expression profiles were obtained to construct the lncRNA-associated ceRNA network. Gene Ontology enrichment analysis and pathway analysis were performed using the Gene Ontology website and Kyoto Encyclopedia of Genes and Genomes. A protein-protein interaction network was constructed based on the Search Tool for the retrieval of Interacting Genes/Proteins. Transcription factors (TFs) of differentially expressed genes were identified based on TRANSFAC database and then a regulatory network was constructed. Through constructing the dysregulated ceRNA network, 6 genes (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL) and 3 miRNAs (miR-125a-3p, miR-200a, miR-142-3p) were detected. Three lncRNAs (MALAT1, TUG1, FGD5-AS1) were found to target both miR-125a-3p and miR-142-3p in this ceRNA network. Protein-protein interaction network analysis identified several hub genes, including VCAM1, ITGA4, UBC, LYN and SSX2IP. Three pathways (cytokine-cytokine receptor, cell adhesion molecules, chemokine signaling pathway) were identified to be overlapping results with the previous bioinformatics studies in periodontitis. Moreover, 2 TFs including FOS and EGR were identified to be involved in the regulatory network of the differentially expressed genes-TFs in periodontitis. These findings suggest that 6 mRNAs (HSPA4L, PANK3, YOD1, CTNNBIP1, EVI2B, ITGAL), 3 miRNAs (hsa-miR-125a-3p, hsa-miR-200a, hsa-miR-142-3p) and 3 lncRNAs (MALAT1, TUG1, FGD5-AS1) might be involved in the lncRNA-associated ceRNA network of periodontitis. This study sought to illuminate further the genetic and epigenetic mechanisms of periodontitis through constructing an lncRNA-associated ceRNA network.

Li S ，Liu X ，Li H ，Pan H ，Acharya A ，Deng Y ，Yu Y ，Haak R ，Schmidt J ，Schmalz G ，Ziebolz D ... -  《-》

Identification of the circRNA-miRNA-mRNA Regulatory Network in Pterygium-Associated Conjunctival Epithelium.

To investigate the regulatory mechanism of pterygium formation, we detected differentially expressed messenger RNAs (DE-mRNAs) and differentially expressed circular RNAs (DE-circRNAs) in pterygium-associated conjunctival epithelium (PCE) and normal conjunctival epithelium (NCE). Genome-wide mRNA and circRNA expression profiles of PCE and NCE were determined using high-throughput sequencing. Bioinformatics analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) analysis, were conducted. The microRNAs (miRNAs) interacting with the hub DE-mRNAs and DE-circRNAs were predicted and verified using real-time quantitative PCR (RT-qPCR). The data showed that there were 536 DE-mRNAs (280 upregulated and 256 downregulated mRNAs) and 78 DE-circRNAs (20 upregulated and 58 downregulated circRNAs) in PCE. KEGG enrichment analysis indicated that the DE-mRNAs were mainly involved in the following biological processes: IL-17 signalling pathway, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, ECM-receptor interaction, and focal adhesion. The GSEA results revealed that the epithelial mesenchymal transition (EMT) process was significantly enriched in upregulated mRNAs. The pterygium-associated circRNA-miRNA-mRNA network was established based on the top 10 DE-circRNAs, 4 validated miRNAs (upregulated miR-376a-5p and miR-208a-5p,downregulated miR-203a-3p and miR-200b-3p), and 31 DE-mRNAs. We found that miR-200b-3p, as a regulator of FN1, SDC2, and MEX3D, could be regulated by 5 upregulated circRNAs. In addition, we screened out EMT-related DE-mRNAs, including 6 upregulated DE-mRNAs and 6 downregulated DE-mRNAs. The EMT-related circRNA-miRNA-mRNA network was established with the top 10 circRNAs, 8 validated miRNAs (upregulated miR-17-5p, miR-181a-5p, and miR-106a-5p, downregulated miR-124-3p, miR-9-5p, miR-130b-5p, miR-1-3p, and miR-26b-5P), and 12 EMT-related DE-mRNAs. We found that hsa_circ_0002406 might upregulate FN1 and ADAM12 by sponging miR-26b-5p and miR-1-3p, respectively, thus promoting EMT in pterygium. Briefly, the study provides a novel viewpoint on the molecular pathological mechanisms in pterygium formation. CircRNA-miRNA-mRNA regulatory networks participate in the pathogenesis of pterygium and might become promising targets for pterygium prevention and treatment.

Yu J ，Luo J ，Li P ，Chen X ，Zhang G ，Guan H ... -  《-》

Construction of Endometrial Carcinoma ceRNA Network and Screening of Key Genes Based on TCGA Database.

Long noncoding RNA (lncRNA) has received more and more attention in human tumor research. This study is aimed at clarifying the regulatory network of lncRNAs-microRNAs- (miRNAs-) mRNAs and at determining the relevant targets in the development of endometrial cancers. Download the miRNA, mRNA, and lncRNA expression profile data of endometrial cancer patients from TCGA; use the "DESeq2" package of R software to identify the differential expression of miRNAs, mRNAs, and lncRNAs; construct a network of ceRNA; and perform gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment assessment on mRNAs in the network of ceRNA; string and Cytoscape 3.7.2 perform PPI assessment on target genes and TOP 10 hub gene screening; Cytoscape 3.7.2 computer program was employed for constructing the lncRNA-miRNA-TOP10 hub mRNA network diagram to determine the signal axis; StarBase database to verify the Top10 hub mRNA expression; the "survival" package in R computer program was implemented to analyze the survival rate of all genes on the lncRNA-miRNA-Top10 hub mRNA network diagram; RT-qPCR to verify the expression level of genes on the signal axis. 1119 differential mRNAs, 14 differential lncRNAs, and 65 differential miRNAs were screened in TCGA; we constructed a ceRNA regulatory network composed of 5 DELs, 7 DEMs, and 90 DEGs; String combined with Cytoscape to screen out Top10 hub genes, namely: LEFTY1, LIN28A, LHX3, ST8SIA3, CEP55, FBXO32, DCN, ANGPTL1, ADRA1A, and KCNMA1; the StarBase database verification results show that ADRA1A, ANGPTL1, FBXO32, KCNMA1, and DCN are downregulated in endometrial cancer tissues; LEFTY1, LIN28A, LHX3, ST8SIA3, and CEP55 are upregulated in endometrial cancer; the constructed lncRNA-miRNA-hub Top10 mRNA network map identified CTD-2314B22, RP11-89 K21/hsa-miR-143, hsa-miR-424/LEFTY1, LIN28A, LHX3, ST8SIA3, and CEP55 signal axis; survival analysis results show that CTD-2314B22, RP11-89 K21, hsa-miR-96, hsa-miR-211, LHX3, ST8SIA3, and DCN are all related to survival; RT-qPCR results indicate CTD-2314B22, RP11-89 K21, LEFTY1, LIN28A, LHX3, ST8SIA3, and CEP55 are upregulated in endometrial cancer cells, and hsa-miR-143 and hsa-miR-424 are downregulated in endometrial cancer cells. From the perspective of the lncRNA-miRNA-mRNA network, our study identified CTD-2314B22, RP11-89 K21/hsa-miR-143, hsa-miR-424/LEFTY1, LIN28A, LHX3, ST8SIA3, and CEP55 signal axis, which can present considerably potent biomarkers and therapeutic targets for treating endometrial cancer.

Song Y ，Chu P ，Li P ，Li F ... -  《-》