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Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA–target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA–miRNA–gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

Background Nonalcoholic fatty liver disease and its advanced stage, nonalcoholic steatohepatitis (NASH), are the major cause of hepatocellular carcinoma (HCC) and other end-stage liver disease. However, the potential mechanism and therapeutic strategies have not been clarified. This study aimed to identify potential roles of miRNA/mRNA axis in the pathogenesis and drug combinations in the treatment of NASH. Methods Microarray GSE59045 and GSE48452 were downloaded from the Gene Expression Omnibus and analyzed using R. Then we obtained differentially expressed genes (DE-genes). DAVID database was used for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis. Protein-protein interaction (PPI) networks were used for the identification of hub genes. We found upstream regulators of hub genes using miRTarBase. The expression and correlation of key miRNA and its targets were detected by qPCR. Drug Pair Seeker was employed to predict drug combinations against NASH. The expression of miRNA and hub genes in HCC was identified in the Cancer Genome Atlas database and Human Protein Atlas database. Results Ninety-four DE-genes were accessed. GO and KEGG analysis showed that these predicted genes were linked to lipid metabolism. Eleven genes were identified as hub genes in PPI networks, and they were highly expressed in cells with vigorous lipid metabolism. hsa-miR-335-5p was the upstream regulator of 9 genes in the 11 hub genes, and it was identified as a key miRNA. The hub genes were highly expressed in NASH models, while hsa-miR-335-5p was lowly expressed. The correlation of miRNA-mRNA was established by qPCR. Functional verification indicated that hsa-miR-335-5p had inhibitory effect on the development of NASH. Finally, drug combinations were predicted and the expression of miRNA and hub genes in HCC was identified. Conclusions In the study, potential miRNA-mRNA pathways related to NASH were identified. Targeting these pathways may be novel strategies against NASH.

Purpose This study aims to identify differentially expressed genes (DEGs) in macrophages exposed to ultra-high-molecular-weight polyethylene (UHMWPE) or vitamin E-blended UHMWPE (VE-UHMWPE) particles, thereby providing potential targets for the treatment of inflammatory osteolysis. Methods The GSE104589 dataset of genome expression in macrophages exposed to UHMWPE and VE-UHMWPE was downloaded from the Gene Expression Omnibus database to identify DEGs. Functional enrichment analysis was performed using DAVID, and the corresponding protein–protein interaction (PPI) network was constructed from the STRING database. Important modules were selected using the molecular complex detection algorithm, and hub genes were identified in cytoHubba. MicroRNAs targeting these DEGs were obtained from the TarBase, miRTarBase, and miRecords databases, while transcription factors (TFs) targeting DEGs were predicted from the ENCODE database. Finally, the top five DEGs were validated by quantitative real‐time polymerase chain reaction (qRT-PCR). Results A total of 112 DEGs (44 upregulated and 68 downregulated DEGs) were screened. Immune and inflammatory responses were significantly related in gene ontology analysis, and 18 signaling pathways were enriched according to Kyoto Encyclopedia of Genes and Genomes pathway analysis. The PPI network involving 85 nodes and 266 protein pairs indicated that IL1β, CXCL1, ICAM1, CCL5 and CCL4 showed higher degrees. qRT-PCR analysis of the top five DEGs revealed a decreasing trend in the VE-UHMWPE group compared with the UHMWPE group. Key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) were predicted to be correlated with the pathogenesis of inflammatory osteolysis through microRNA-TF regulatory network analysis. Conclusion The present study helps shed light on the molecular mechanisms underlying the changes in the wear-induced inflammatory process after blending vitamin E with UHMWPE. Hub genes including IL1β, CXCL1, ICAM1, CCL5, and CCL4, key microRNAs (hsa-miR-144, hsa-miR-21, and hsa-miR-221) and TFs (RELA and NFKB1) may serve as prognostic and therapeutic targets of inflammatory osteolysis., Video abstract Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://youtu.be/IkgdRqf1aSU

Background Clear‐cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts in understanding the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) was involved in the development of varied tumors. However, a comprehensive analysis of the prognostic model based on lncRNA‐miRNA‐mRNA ceRNA regulatory network of ccRCC with large‐scale sample size and RNA‐sequencing expression data is still limited. Methods RNA‐sequencing expression data were taken out from GTEx database and TCGA database, a total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC‐specific genes were obtained by WGCNA and differential expression analysis. Following, the communication of mRNAs and lncRNAs with targeted miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was further constructed by univariate Cox regression, Lasso methods, and multivariate Cox regression analysis. Results A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dysregulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature including eight genes based on this ceRNA was determined followed by the development of a nomogram predicting 1‐, 3‐, and 5‐year survival probability for ccRCC. Conclusion It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision., We constructed a nomogram predicting 1‐, 3‐, and 5‐OS of ccRCC patients with a gene signature based on the ccRCC‐specific dysregulated ceRNA network and some other clinical factors. It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

Aim. This study is aimed at constructing the competing endogenous RNA (ceRNA) network in chromophobe renal cell carcinoma (ChRCC). Methods. Clinical and RNA sequence profiles of patients with ChRCC, including messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), were obtained from The Cancer Genome Atlas (TCGA) database. “edgeR” and “clusterProfiler” packages were utilized to obtain the expression matrices of differential RNAs (DERNAs) and to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Weighted gene coexpression network analysis (WGCNA) was performed to screen the highly related RNAs, and miRcode, StarBase, miRTarBase, miRDB, and TargetScan datasets were used to predict the connections between them. Univariate and multivariate Cox proportional hazards regressions were performed in turn to elucidate prognosis-related mRNAs in order to construct the ceRNA regulatory network. Results. A total of 1628 DElncRNAs, 104 DEmiRNAs, and 2619 DEmRNAs were identified. WGCNA showed significant correlation in 1534 DElncRNAs, 98 DEmiRNAs, and 2543 DEmRNAs, which were related to ChRCC. Fourteen DEmiRNAs, 113 DElncRNAs, and 43 DEmRNAs were screened. Nine mRNAs (ALPL, ARHGAP29, CADM2, KIT, KLRD1, MYBL1, PSD3, SFRP1, and SLC7A11) significantly contributed to the overall survival (OS) of patients with ChRCC ( P < 0.05 ). Furthermore, two mRNAs (CADM2 and SFRP1) appeared to be independent risk factors for ChRCC. Conclusion. The findings revealed the molecular mechanism of ChRCC and potential therapeutic targets for the disease.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder in women of childbearing age. Recent studies suggest important roles for lncRNAs in PCOS development. Based on the hypothesis that lncRNAs are able to regulate mRNA functions by competitive binding to shared miRNAs, the present work sought to construct a PCOS-related lncRNA-mRNA network (PCLMN) to identify key lncRNAs with dysregulated expression and potential prognostic and therapeutic relevance. A global background network was constructed after retrieving lncRNA-miRNA and miRNA-mRNA pairs from the lncRNASNP2, miRTarBase and StarBase databases. Based on gene expression profiles from ovarian granulosa cells from PCOS patients and controls in the GEO’s GSE95728 dataset, the PCLMN was then constructed by applying hypergeometric testing. Using topological analysis, we identified 3 lncRNAs (LINC00667, AC073172.1 and H19) ranking within the top-ten gene lists for all three centrality measures. We then explored their subcellular localization, performed functional module analyses, and identified 4 sex hormone-related transcription factors as potential regulators of their expression. Significant associations with inflammation, oxidative stress, and apoptosis-related processes and pathways were revealed for the key lncRNAs in our PCMLN. Further studies verifying the mRNA/lncRNA relationships identified herein are needed to clarify their clinical significance.

Long non-coding RNAs (lncRNAs) were considered to be involved in vascular complications in diabetes mellitus, but still only limited knowledge in this regard has been obtained. Herein, we further explored the roles of lncRNAs and mRNAs in diabetic vasculopathy (DV) through conducting bioinformatics analysis using data set downloaded from GEO database. The differentially expressed lncRNAs and mRNAs were identified by edge package. GO enrichment analysis and KEGG pathway analysis were performed based on clusterprofiler package. The relationship between lncRNA and miRNA was predicted using starBase database, and the potential mRNAs targeted by miRNAs were predicted by TargetScan, miRTarbase and miRDB database. The string database was used to analyze the protein-protein interaction (PPI). As a result, a total of 12 lncRNAs and 711 mRNAs were found to be differentially expressed in the diabetic subdermal endothelial cells compared with normal controls. A ceRNA network was established, which was composed of seven lncRNA nodes, 49 miRNA nodes, 58 mRNA nodes and 183 edges, and MSC-AS1 and LINC01550 may serve as key nodes. GO function enrichment analysis showed enrichments of epithelial cell proliferation, intercellular junction, and cell adhesion molecule binding. KEGG pathway analysis revealed 33 enriched pathways. PPI protein interaction analysis identified 57 potential ceRNA-related proteins. Overall, this study suggests that multiple lncRNAs, specifically MSC-AS1 and LINC01550, may play an important role in DV development and they are like to be developed as the therapeutic targets for DV. However, further experiments in vitro and in vivo should be conducted to validate our results., Graphical Abstract

The aim of this study was to establish a novel competing endogenous RNA (ceRNA) network able to predict prognosis in patients with triple-negative breast cancer (TNBC). Differential gene expression analysis was performed using the GEO2R tool. Enrichr and STRING were used to conduct protein-protein interaction and pathway enrichment analyses, respectively. Upstream lncRNAs and miRNAs were identified using miRNet and mirTarBase, respectively. Prognostic values, expression, and correlational relationships of mRNAs, lncRNAs, and miRNAs were examined using GEPIA, starBase, and Kaplan-Meier plotter. It total, 860 upregulated and 622 downregulated differentially expressed mRNAs were identified in TNBC. Ten overexpressed and two underexpressed hub genes were screened. Next, 10 key miRNAs upstream of these key hub genes were predicted, of which six upregulated miRNAs were significantly associated with poor prognosis and four downregulated miRNAs were associated with good prognosis in TNBC. NEAT1 and MAL2 were selected as key lncRNAs. An mRNA-miRNA-lncRNA network in TNBC was constructed. Thus, we successfully established a novel mRNA-miRNA-lncRNA regulatory network, each component of which is prognostic for TNBC.

Background: Sepsis is a life-threatening disease caused by the dysregulated host response to the infection and the major cause of death of patients in the intensive care unit (ICU). Objective: Early diagnosis of sepsis could significantly reduce in-hospital mortality. Though generated from infection, the development of sepsis follows its own psychological process and disciplines, alters with gender, health status and other factors. Hence, the analysis of mass data by bioinformatics tools and machine learning is a promising method for exploring early diagnosis. Methods: We collected miRNA and mRNA expression data of sepsis blood samples from Gene Expression Omnibus (GEO) and ArrayExpress databases, screened out differentially expressed genes (DEGs) by R software, predicted miRNA targets on TargetScanHuman and miRTarBase websites, conducted Gene Ontology (GO) term and KEGG pathway enrichment analysis based on overlapping DEGs. The STRING database and Cytoscape were used to build protein-protein interaction (PPI) network and predict hub genes. Then we constructed a Random Forest model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 46 overlapping DEGs in sepsis. The PPI network analysis identified five hub genes, SOCS3, KBTBD6, FBXL5, FEM1C and WSB1. Random Forest model based on these five hub genes was used to assess GSE95233 and GSE95233 datasets, and the area under the curve (AUC) of ROC was 0.900 and 0.7988, respectively, which confirmed the efficacy of this model. Conclusion: The integrated analysis of gene expression in sepsis and the effective Random Forest model built in this study may provide promising diagnostic methods for sepsis.

Purpose Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, studies to acquire a more comprehensive understanding of the mechanisms are urgent. However, the pivotal molecules are still unexplored. Materials and methods We downloaded GSE121367 from the GEO database. The "limma" R language package was applied to identify differentially expressed genes (DEGs). We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). Protein-protein interaction was constructed by STRING and visualized in Cytoscape. The tumor immune microenvironment was explored by the TISIDB database. Methylation validation and overall survival analysis were conducted by the TCGA database. In addition, the upstream modulators of hub genes were predicted by miRTarBase and Network Analyst databases. The expression levels of candidate genes were validated by quantitative real-time PCR (qRT-PCR), Western blot, and immunohistochemical assay (IHC). Cell growth, clone formation, migration, invasion, and wound healing assays were studied to explore the role of MSX1 in progestin resistance in vitro. Results A total of 3,282 DEGs were identified and they were mostly enriched in the cell adhesion pathway. We screened out ten hub genes whose genomic alteration rates were low based on the current endometrial carcinoma sample sets. Has-miR-335-5p, has-miR-124-3p, MAZ, and TFDP1 were the most prominent upstream regulators. The methylation status of CDH1, JAG1, EPCAM, and MSX1 was decreased, corresponding to their high protein expression, which also predicted better overall survival. The homeobox protein of MSX1 showed significant tissue specificity and better prognostic value and its knockdown inhibited epithelial-mesenchymal transitions (EMT) and enhanced progesterone efficacy. Conclusion Our study identified that the gene of MSX1 promised to be the specific indicator and therapeutic target for progestin resistance. This would shed new light on the underlying biological mechanism to overcome progestin resistance of endometrial cancer.

This study aims to explore the molecular mechanism by which HAS2-AS1 acts as a ceRNA to promote the invasion and migration of glioma cells, which will provide a novel potential target for the targeted therapy of glioma. Gene expression profiles and corresponding clinical data were accessed from the TCGA_LGG and TCGA_GBM databases and then differential analysis was conducted using the "edgeR" package. miRDB, miRTarBase and TargetScan databases were employed to predict target genes and sequentially a ceRNA network was constructed. Quantitative real-time PCR was performed to detect gene expression in glioma cells. Transwell assay was operated to assess cell migratory and invasive abilities. Western blot was conducted to evaluate the protein expression. Dual-luciferase reporter assay and RNA immunoprecipitation experiment were performed to validate the targeting relationship between genes. HAS2-AS1 was markedly upregulated in glioma, and the overall survival time of patients with high HAS2-AS1 expression was significantly shorter than that of patients with low one. Silencing HAS2-AS1 inhibited the migration and invasion of glioma cells, while overexpressing HAS2-AS1 produced opposite results. miR-137 was validated as a direct target of and negatively regulated by HAS2-AS1. Further exploration of the downstream target gene indicated that EZH2 competed with HAS2-AS1 to interact with miR-137. Suppressing miR-137 or up-regulating EZH2 reversed the impact of HAS2-AS1 knockdown on glioma cell invasion and migration. HAS2-AS1 regulates EZH2 by sponging miR-137 for the migratory and invasive abilities of glioma cells, which provides a new idea for exploring metastasis mechanism of glioma.

Hepatoblastoma (HB) is one of the most common liver malignancies in children, while the molecular basis of the disease is largely unknown. Therefore, this study aims to explore the key genes and molecular mechanisms of the pathogenesis of HB using a bioinformatics approach. The gene expression dataset GSE131329 was used to find differentially expressed genes (DEGs). Functional and enrichment analyses of the DEGs were performed by the EnrichR. Then, the protein-protein interaction (PPI) network of the up-regulated genes was constructed and visualized using STRING database and Cytoscape software, respectively. MCODE was used to detect the significant modules of the PPI network, and cytoHubba was utilized to rank the important nodes (genes) of the PPI modules. Overall, six ranking methods were employed and the results were validated by the Oncopression database. Moreover, the upstream regulatory network and the miRNA-target interactions of the up-regulated DEGs were analyzed by the X2K web and the miRTarBase respectively. A total of 594 DEGs, including 221 up- and 373 down-regulated genes, were obtained, which were enriched in different cellular and metabolic processes, human diseases, and cancer. Furthermore, 15 hub genes were screened, out of which, 11 were validated. Top 10 transcription factors, kinases, and miRNAs were also determined. To the best of our knowledge, the association of RACGAP1, MKI67, FOXM1, SIN3A, miR-193b, and miR-760 with HB was reported for the first time. Our findings may be used to shed light on the underlying mechanisms of HB and provide new insights for better prognosis and therapeutic strategies.

Background Pancreatic carcinoma is one of the most malignant cancers globally. However, a systematic mRNA-miRNA-lncRNA/pseudogene network associated with the molecular mechanism of pancreatic cancer progression has not been described. Results The significant DEGs identified comprised 159 up-regulated and 92 down-regulated genes. According to the expression and survival analysis, three genes (COL12A1, APOL1, and MMP14) were significantly higher in tumor samples when compared with normal controls and their upregulation indicated a poor prognosis. Subsequently, 28, 17, and 11 miRNAs were predicted to target COL12A1, APOL1, and MMP14, respectively. The hsa-miR-26b-5p-COL12A1 axis showed a potential in suppressing the progression of pancreatic cancer. Moreover, 12 lncRNAs and 92 pseudogenes were predicted to potentially bind to the hsa-miR-26b-5p. Based on the results from expression and correlation analysis, NAMPTP1/HCG11-hsa-miR-26b-5p-COL12A1 competing endogenous RNA (ceRNA) sub-network was associated with the prognosis of pancreatic cancer. Conclusions In a word, we elucidate a new NAMPTP1/ HCG11-hsa-miR-26b-5p-COL12A sub-network in the progression of pancreatic cancer, which may serve as a promising diagnostic biomarker or effective therapeutic target for pancreatic cancer. Materials and methods Differentially expressed genes (DEGs) were first identified by mining GSE28735, GSE62452 and GSE41368 datasets. Functional enrichment analysis was conducted using the DAVID database. Protein-protein interaction (PPI) network was performed using the STRING database, and hub genes were identified by Cytoscape. Upstream miRNAs and pseudogenes /lncRNAs of mRNAs were forecast using miRTarBase, miRNet, and starBase. Expression, survival, and correlation analysis of genes, miRNAs, and pseudogenes /lncRNAs were validated using GEPIA, Kaplan-Meier, and starBase.

Lots of studies indicated that many microRNAs (miRNAs) are associated with the prognosis of patients with laryngeal cancer (LC). The objective of our study is to identify potential core miRNAs associated with the pathogenesis and prognosis of LC. Using the Cancer Genome Atlast database, we identified 70 differentially expressed miRNAs between LC tumor specimens and non-tumor specimens. Then Cox regression analyses and the least absolute shrinkage and selection operator regression signature were performed to detect miRNA prognostic markers. A nomogram integrating miRNA prognostic markers was constructed to predict overall survival (OS) for LC patients. The potential target genes of the key miRNA were predicted by miRTarBase and miRDB databases. Subsequently, their potential functions were revealed by gene ontology annotation and kyoto encyclopedia of genes and genomes pathway enrichment analysis. Related biological pathways of the key target gene involved in LC were detected through gene set enrichment analysis (GSEA). A prognostic miRNA signature was constructed. The up-regulated miR‐105–1 was related to a worse OS (p = 0.043), which suggested that miR‐105–1 may likely be the key miRNA prognostic marker. Survival analyses and paired expression analyses of target genes indicated that ENDOU may be the key target gene. Finally, we conducted GSEA to elucidate the pathways enriched between low- and high-ENDOU expression datasets. Our findings might bring some new light on the pathogenesis of LC. Then, it might facilitate doctors to predict the prognosis and improve treatment outcomes for LC patients. However, the behaviors of LC are relatively heterogeneous, and the TCGA database cannot provide detailed information about the subsites and treatment modalities of LC. Further molecular biological experiments and clinical investigations would be required to confirm this conclusion.

Objective. MicroRNAs play vital roles in the development of endometriosis. It is reported that miR-200b-3p is downregulated in endometriosis, although its mechanisms in this disease remain still unclear. Therefore, the purpose of this study was to explore the function and potential regulatory network of miR-200b-3p in endometriosis through database analysis.Methods. The endometriosis gene expression profiles were downloaded from the GEO database to screen differentially expressed genes (DEGs). The predicted and validated target genes of miR-200b-3p were obtained from miRWalk and miRTarBase database. Then, a comparison was performed between miR-200b-3p target genes and DEGs. GO enrichment and KEGG pathway analysis of the target genes was performed using clusterProfiler package. STRING was used to predict the protein-protein interaction among the proteins encoded by the target genes. Then, TransmiR, LncBase, StarBase, PROMO, and AnimalTFDB were employed to identify interactive transcription factors and lncRNAs of miR-200b-3p.Results. miR-200b-3p was associated with the transcription factors DNMT1, EZH2, HNF1B, JUN, MYB, ZEB1, and ZEB2 during the pathogenesis of endometriosis. The downstream 110 target genes were involved in the biological processes of positive regulation of MAPK cascade, muscle cell proliferation, organ growth, vasculogenesis, and axon development. KEGG analysis revealed that the main pathways related to miR-200b-3p were microRNAs in cancer, PI3K-Akt signaling pathway, colorectal cancer, and tight junction. In addition, four lncRNAs such as MALAT1, NEAT1, SNHG22, and XIST interacted with miR-200b-3p and were associated with transcription factors FOXP3 and YY1.Conclusion. The predicted target genes and molecular regulatory network of miR-200b-3p in endometriosis not only revealed its biological function but also provided a valuable guideline for further research.

Background Emerging evidence suggests that competing endogenous RNAs plays a crucial role in the development and progress of pancreatic adenocarcinoma (PAAD). The objective was to identify a new lncRNA-miRNA-mRNA network as prognostic markers, and develop and validate a multi-mRNAs-based classifier for predicting overall survival (OS) in PAAD. Methods Data on pancreatic RNA expression and clinical information of 445 PAAD patients and 328 normal subjects were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype-Tissue Expression (GTEx). The weighted correlation network analysis (WGCNA) was used to analyze long non-coding RNA (lncRNA) and mRNA, clustering genes with similar expression patterns. MiRcode was used to predict the sponge microRNAs (miRNAs) corresponding to lncRNAs. The downstream targeted mRNAs of miRNAs were identified by starBase, miRDB, miRTarBase and Targetscan. A multi-mRNAs-based classifier was develop using least absolute shrinkage and selection operator method (LASSO) COX regression model, which was tested in an independent validation cohort. Results A lncRNA-miRNA-mRNA co-expression network which consisted of 60 lncRNAs, 3 miRNAs and 3 mRNAs associated with the prognosis of patients with PAAD was established. In addition, we constructed a 14-mRNAs-based classifier based on a training cohort composed of 178 PAAD patients, of which the area under receiver operating characteristic (AUC) in predicting 1-year, 3-year, and 5-year OS was 0.719, 0.806 and 0.794, respectively. The classifier also shown good prediction function in independent verification cohorts, with the AUC of 0.604, 0.639 and 0.607, respectively. Conclusions A novel competitive endogenous RNA (ceRNA) network associated with progression of PAAD could be used as a reference for future molecular biology research.

Wilms tumor (WT) is the most common malignant renal neoplasm in children; however, the underlying molecular mechanisms are not well understood. According to the competing endogenous RNA (ceRNA) theory, long non-coding RNAs (lncRNAs) can regulate the expression of target genes by adsorbing microRNAs (miRNAs/miRs). However, the role of lncRNAs in WT has not been fully elucidated. The aim of the present study was to construct a ceRNA network to identify the potential lncRNAs involved in WT. The expression profiles of lncRNAs, miRNAs and mRNAs in 120 WT and six normal tissues were obtained from the Therapeutically Applicable Research to Generate Effective Treatments database. A total of 442 lncRNAs, 214 miRNAs and 4,912 mRNAs were identified as differentially expressed in WT and were enriched in 472 Gene Ontology terms (355 biological processes, 89 cellular components and 29 molecular functions) and 18 Kyoto Encyclopedia of Genes and Genomes pathways. A lncRNA-miRNA-mRNA ceRNA network of WT consisting of with 32 lncRNAs, 14 miRNAs and 158 mRNAs was constructed, based on the bioinformatics analysis of the miR target prediction database and the miRNAcode, miRTarBase and TargetScan databases. Subsequently, three lncRNAs, three miRNAs and 17 mRNAs, which had a significant effect on the overall survival rate of patients with WT, were identified based on the survival analysis. The three lncRNAs were also differentially expressed in the late and early stages of WT and were validated using the GSE66405 dataset obtained from the Gene Expression Omnibus database. In conclusion, the present study generated a specific lncRNA-related ceRNA network of WT, which may provide a novel perspective on the molecular mechanisms underlying the progression and prognosis of the disease.

Background Circular RNAs (circRNAs) were elucidated to act as competing endogenous RNAs (ceRNAs) and to play significant roles in cancer initiation and progression. We aimed to identify the important circRNAs in lung adenocarcinoma and intended to predict the functions of them via the bioinformatics analysis. Methods We extracted data from three Gene Expression Omnibus (GEO) datasets, GSE101586, GSE101684, and GSE104854, and identified the common differentially expressed circRNAs. Agarose gel electrophoresis and Sanger sequencing were used to verify these significant circRNAs. Then, qRT-PCR was performed to validate the expression through matched tissues and cell lines. Afterwards, a ceRNA network was constructed and functional analysis was performed to predict the potential mechanisms of circRNAs. Results Five circRNAs (hsa_circ_0072088, hsa_circ_0082564, hsa_circ_0008274, hsa_circ_0000519 and hsa_circ_0003528) were identified differentially expressed in the three datasets. Following the Agarose gel electrophoresis and qRT-PCR validation, hsa_circ_0072088 and hsa_circ_0008274 were chosen for further analysis. A ceRNA network of hsa_circ_0072088 and hsa_circ_0008274 was built based on the CSCD/TargetScan/MiRTarbase/StarBase 3.0. Then, the functional analysis showed that several meaningful terms were identified, such as "epithelial to mesenchymal transition (EMT)", "Cellular response to TGF-β stimulus", "MAPK signaling pathway", and "PI3K-AKT signaling pathway". Finally, several significant circRNA/miRNA/mRNA regulatory axes, which were predicted relating to cancer progress, were noted from the network. Conclusion We identified significant circRNAs and meaningful circRNA-miRNA-mRNA networks to provide novel insight into pathogenesis and therapy of lung adenocarcinoma.

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.

Sialic acids and MMPs play critical roles in inflammatory diseases. Furthermore, Interaction between Sialic acid and receptors such as siglecs leads phosphorylation of ITIM domains and promote downstream inhibitory signaling through SHP-1 phosphatases. SHP1 could positively regulate TNF-α and by control, the production of TNF-α could play a crucial role in inflammation. Besides, TNF-α could mediate the signaling pathway leading to MMP-9 gene expression. MMP-9 also is recognized as therapeutic targets in a variety of diseases including vascular pathologies, cancers, and auto-immuned diseases. The present in-silico study aims to identify the most potent micro-RNAs could control the signaling pathway from siglec to MMP-9. To this end, with review some articles and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis 21 genes involved in this pathway have been selected. Then TARGET SCAN, DIANA-TarBase8, and miRDB database were utilized to predict the miRNAs which have the most effective to target genes. Finally, using bio-studying Software Cytoscape, three microRNA-mRNA networks were constructed for existing banks. We found shared micro-RNAs that in the three networks. Eventually, using miRTarBase database microRNAs that were linked to more genes in this path were assigned a higher privilege. The twenty-one selected micro-RNAs could be the proper options for experimental studies from sialic acid receptors (siglecs) to MMP-9. Among them miR-34a-5p could be the most interesting target.

The incidence and mortality rate of hepatocellular carcinoma (HCC) nowadays is still at high levels. The regulatory roles of pseudogene in cancers have been gradually recognized in recent years. However, comprehensive investigation of abnormally expressed pseudogene and related mechanisms in HCC remains lacking. GSE124535 dataset was used to identify differentially expressed pseudogenes in HCC tissues compared with normal tissues. Prognostic value of these differentially expressed pseudogenes was analyzed at GEPIA. StarBase used to analyze microRNAs (miRNAs) can bind with pseudogene, while the targets for these miRNAs were analyzed at miRTarBase. Protein-protein interaction (PPI) network was then established for miRNA targets, after that hub genes were selected. Expression correlation of pseudogene and hub genes was analyzed at StarBase. In total, 16 upregulated and 17 downregulated pseudogenes were identified. Pseudogene HSPB1P1 was identified abnormally expressed in 20 types of human cancers and could be used as an indicator for poorer overall survival of patients with HCC. Functional analyses showed that HSPB1P1 was strongly correlated with signaling pathways related to cancer progression. Further studied revealed that HSPB1P1 could direct regulate the EZH2 expression in HCC. In summary, our study indicated that HSPB1P1 was a predictor for poorer overall survival of patients with HCC and may be potential therapeutic target against HCC.

BACKGROUND Gastric adenocarcinoma (GAC) mortality rates have remained relatively changed over the past 30 years, and it continues to be one of the leading causes of cancer-related death. AIM To search for novel miRNAs related to GAC prognosis and further investigate the effect of miR-96-5p on MGC-803 cells. METHODS The miRNA expression profile data of GAC based on The Cancer Genome Atlas were obtained and used to screen differently expressed miRNAs (DEMs) and DEMs related to GAC prognosis. Then, the expression of DEMs related to GAC prognosis was identified in GAC tumor samples and adjacent normal samples by qRT-PCR. The target gene, ZDHHC5, of miR-96-5p was predicted using TargetScan, miRTarBase, and miRDB databases and confirmed by luciferase reporter assay. Furthermore, MGC-803 cells were transfected with inhibitor NC, miR-96-5p inhibitor, si-ZDHHC5, or miR-96-5p inhibitor + si-ZDHHC5, and then cell apoptosis was detected by flow cytometry. The expression of ZDHHC5, Bcl-2, and COX-2 was detected using western blotting. RESULTS A total of 299 DEMs and 35 DEMs related to GAC prognosis were screened based on The Cancer Genome Atlas. Then compared with adjacent normal samples, the levels of miR-96-5p, miR-222-5p, and miR-652-5p were remarkably increased, while miR-125-5p, miR-145-3p, and miR-379-3p levels were reduced in GAC tumor samples (P < 0.01), which were consistent with bioinformatics analysis. Furthermore, ZDHHC5 was defined as a direct target gene of miR-96-5p. miR-96-5p inhibition increased the number of apoptotic cells as well as promoted the expression of ZDHHC5, Bcl-2, and COX-2 in MGC-803 cells (P < 0.01). After ZDHHC5 inhibition, the number of apoptotic cells and the expression of ZDHHC5, Bcl-2, and COX-2 were reduced. The addition of an miR-96-5p inhibitor partly reversed these effects (P < 0.01). CONCLUSION Our findings identified six miRNAs related to GAC prognosis and suggested that downregulated miR-96-5p might induce cell apoptosis via upregulating ZDHHC5 expression in MGC-803 cells.

Ovarian cancer (OC) is a leading cause of death among women worldwide. Various evidences suggest that oncomiRs and Toll-like receptor 4 (TLR-4) signaling pathways appear to be key players in the initiation and progression of OC. It seems there exists a continuous intercommunication between cancer cells and normal microbiota of the vagina. The biological impacts of vaginal isolated lactococcus lactis on CAOV-4 cells were investigated using several molecular biology experiments, including flow cytometry, DAPI staining, DNA ladder, and scratch assay. The expression of microRNAs (miRNAs/miRs) 21, 200b, and TLR-4 in the CAOV-4 cells was also evaluated by the real-time RT-PCR assay. Furthermore, an integrative in silico analysis was conducted using normalized web-available microarray data (GSE14407) to revalidate the experimental findings and identify potential biomarkers in ovarian cancer. Protein-protein interactions (PPIs) network was studied by means of the STRING database using Cytoscape v3.6.1. The miRNA target genes were identified using the dbDEMC v2.0, miRTarBase, and miRDB databases. Our data demonstrated that L. lactis probiotic candidate downregulates TLR-4, miR-21, and miR-200b expression levels, which correlates with induction of apoptosis as confirmed by DAPI staining, DNA ladder assay, annexin V/PI staining, and inhibition of migration validated by scratch assay. By in silico analysis, several targets (miR-17-5p-BCL2, miR-21-5p-MKNK2, miR-129-5p-CDK6) were identified, while BCL2, CCNB1, and VEGFA were found as the hub proteins in the miRNA-target and PPI networks. Further, downregulation of the TLR-4, miR-21, and miR-200b was partially validated by the in silico analysis. Based on our findings, the vaginal isolated probiotic strain presents great potential to control the ovarian cancer which may provide beneficial impact on the clinical management of ovarian cancer.

The 2020 Nucleic Acids Research Database Issue contains 148 papers spanning molecular biology. They include 59 papers reporting on new databases and 79 covering recent changes to resources previously published in the issue. A further ten papers are updates on databases most recently published elsewhere. This issue contains three breakthrough articles: AntiBodies Chemically Defined (ABCD) curates antibody sequences and their cognate antigens; SCOP returns with a new schema and breaks away from a purely hierarchical structure; while the new Alliance of Genome Resources brings together a number of Model Organism databases to pool knowledge and tools. Major returning nucleic acid databases include miRDB and miRTarBase. Databases for protein sequence analysis include CDD, DisProt and ELM, alongside no fewer than four newcomers covering proteins involved in liquid–liquid phase separation. In metabolism and signaling, Pathway Commons, Reactome and Metabolights all contribute papers. PATRIC and MicroScope update in microbial genomes while human and model organism genomics resources include Ensembl, Ensembl genomes and UCSC Genome Browser. Immune-related proteins are covered by updates from IPD-IMGT/HLA and AFND, as well as newcomers VDJbase and OGRDB. Drug design is catered for by updates from the IUPHAR/BPS Guide to Pharmacology and the Therapeutic Target Database. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been revised, updating 305 entries, adding 65 new resources and eliminating 125 discontinued URLs; so bringing the current total to 1637 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

BACKGROUND: MicroRNA (miRNA), short noncoding RNA, plays role in various physiological process such as aging through epigenetic regulation of gene expression. miRNA present intracellular as well as extracellular in body fluids. miRNA that present in blood circulatory system is often referred as circulatory miRNA (c-miRNA). A number of studies trying to identify c-miRNAs as biomarker for ageing have been reported, but majority did not yield results that corroborate one with another. This study reports the identification of a differentially expressed c-miRNAs between elderly and youth groups of individuals, the first step in tracking specific miRNAs that play role in physiologic ageing. METHODS: The miRNA expression profiles of grandfathers and grandsons from 2 Javanese families were compared to select 5 miRNA candidates with widest expression difference. The 5 candidates were subjected to validation using quantitative polymerase chain reaction (qPCR) in 11 elderly men and 9 young men of the same ethnicity to identify differentially expressed miRNA between elder and younger male groups in the represented population. RESULTS: Amongst 5 selected c-miRNA candidates, the hsa-miR-376c-3p was validated to be upregulated in the elderly group when compared to the young individuals. Bioinformatic analysis using miRTarBase 7.0, miRTargetLink Human and GeneCards® Human Gene Database suggest the involvement of hsa-miR-376c-3p in pathways relevant with cellular ageing. CONCLUSION: This study showed that hsa-miR-376c-3p in the circulating plasma to be significantly upregulated in a group of elderly Javanese males compared to their younger counterparts. The results of this study warrant further study to elucidate the specific role of hsa-miR-376c-3p in physiologic ageing mechanism. KEYWORDS: circulating, microRNA, miR-376c-3p, elderly, Javanese, male

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia; however, early diagnosis of the disease is challenging. Research suggests that biomarkers found in blood, such as microRNAs (miRNA), may be promising for AD diagnostics. Experimental data on miRNA–target interactions (MTI) associated with AD are scattered across databases and publications, thus making the identification of promising miRNA biomarkers for AD difficult. In response to this, a list of experimentally validated AD-associated MTIs was obtained from miRTarBase. Cytoscape was used to create a visual MTI network. STRING software was used for protein–protein interaction analysis and mirPath was used for pathway enrichment analysis. Several targets regulated by multiple miRNAs were identified, including: BACE1, APP, NCSTN, SP1, SIRT1, and PTEN. The miRNA with the highest numbers of interactions in the network were: miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR-146, and miR-181c. The analysis revealed seven subnetworks, representing disease modules which have a potential for further biomarker development. The obtained MTI network is not yet complete, and additional studies are needed for the comprehensive understanding of the AD-associated miRNA targetome.

Endothelial PAS domain-containing protein 1 (EPAS1), also HIF2α, is an alpha subunit of hypoxia-inducible transcription factor (HIF), which mediates cellular and systemic response to hypoxia. EPAS1 has an important role in the transcription of many hypoxia-responsive genes, however, it has been less researched than HIF1α. The aim of this study was to integrate an increasing number of data on EPAS1 into a map of diverse OMICs elements. Publications, databases, and bioinformatics tools were examined, including Ensembl, MethPrimer, STRING, miRTarBase, COSMIC, and LOVD. The EPAS1 expression, stability, and activity are tightly regulated on several OMICs levels to maintain complex oxygen homeostasis. In the integrative EPAS1 map we included: 31 promoter-binding proteins, 13 interacting miRNAs and one lncRNA, and 16 post-translational modifications regulating EPAS1 protein abundance. EPAS1 has been associated with various cancer types and other diseases. The development of neuroendocrine tumors and erythrocytosis was shown to be associated with 11 somatic and 20 germline variants. The integrative map also includes 12 EPAS1 target genes and 27 interacting proteins. The study introduced the first integrative map of diverse genomics, transcriptomics, proteomics, regulomics, and interactomics data associated with EPAS1, to enable a better understanding of EPAS1 activity and regulation and support future research.

Coronary heart disease (CHD) is a global health concern with high morbidity and mortality rates. This study aimed to identify the possible long non-coding RNA (lncRNA) biomarkers of CHD. The lncRNA- and mRNA-related data of patients with CHD were downloaded from the Gene Expression Omnibus database (GSE113079). The limma package was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs, respectively). Then, miRcode, TargetScan, miRDB, and miRTarBase databases were used to form the competing endogenous RNA (ceRNA) network. Furthermore, SPSS Modeler 18.0 was used to construct a logistic stepwise regression prediction model for CHD diagnosis based on DElncRNAs. Of the microarray data, 70% was used as a training set and 30% as a test set. Moreover, a validation cohort including 30 patients with CHD and 30 healthy controls was used to verify the hub lncRNA expression through real-time reverse transcription-quantitative PCR (RT-qPCR). A total of 185 DElncRNAs (114 upregulated and 71 downregulated) and 382 DEmRNAs (162 upregulated and 220 downregulated) between CHD and healthy controls were identified from the microarray data. Furthermore, through bioinformatics prediction, a 38 lncRNA-21miRNA-40 mRNA ceRNA network was constructed. Next, by constructing a logistic stepwise regression prediction model for 38 DElncRNAs, we screened two hub lncRNAs AC010082.1 and AC011443.1 (p < 0.05). The sensitivity, specificity, and area under the curve were 98.41%, 100%, and 0.995, respectively, for the training set and 93.33%, 91.67%, and 0.983, respectively, for the test set. We further verified the significant upregulation of AC010082.1 (p < 0.01) and AC011443.1 (p < 0.05) in patients with CHD using RT-qPCR in the validation cohort. Our results suggest that lncRNA AC010082.1 and AC011443.1 are potential biomarkers of CHD. Their pathological mechanism in CHD requires further validation.

Co-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF.

Background. MicroRNAs (miRNAs) are confirmed to participate in occurrence, development, and prevention of membranous nephropathy (MN), but their mechanism of action is unclear. Objective. With the GEO database and the use of bioinformatics, miRNA-mRNA regulatory network genes relevant to MN were explored and their potential mechanism of action was explained. Methods. The MN-related miRNA chip data set (GSE51674) and mRNA chip data set (GSE108109) were downloaded from the GEO database. Differential analysis was performed using the GEO2R online tool. TargetScan, miRTarBase, and StarBase databases were used to predict potential downstream target genes regulated by differentially expressed miRNAs, and the intersection with differential genes were taken to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair was clarified and Cytoscape was used to construct a miRNA-mRNA regulatory network. WebGestalt was used to conduct enrichment analysis of the biological process of differential mRNAs in the regulatory network; FunRich analyzes the differential mRNA pathways in the miRNA-mRNA regulatory network. And the STRING database was used to construct a PPI network for candidate target genes, and Cytoscape visually analyzes the PPI network. Results. Experiments were conducted to screen differentially expressed miRNAs and mRNAs. There were 30 differentially expressed miRNAs, including 22 upregulated and 8 downregulated; and 1267 differentially expressed mRNAs, including 536 upregulated and 731 downregulated. Using TargetScan, miRTarBase, and StarBase databases to predict the downstream targets of differentially expressed miRNAs, 2957 downstream target genes coexisting in the 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 175 candidate target genes. Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed. Further analysis revealed that the miRNA regulatory network genes may be involved in the development of membranous nephropathy by mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways. Conclusion. The miRNA regulatory network genes may participate in the regulation of podocyte autophagy, lipid metabolism, and renal fibrosis through mTOR, PDGFR-β, LKB1, and VEGF/VEGFR signaling pathways, thereby affecting the occurrence and development of membranous nephropathy.

Background: Previous studies have identified differentially expressed microRNAs in autism spectrum disorder (ASD), however, results are discrepant. We aimed to systematically review this topic and perform bioinformatic analysis to identify genes and pathways associated with ASD miRNAs.Methods: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses, we searched the Web of Science, PubMed, Embase, Scopus, and OVID databases to identify all studies comparing microRNA expressions between ASD persons and non-ASD controls on May 11, 2020. We obtained ASD miRNA targets validated by experimental assays from miRTarBase and performed pathway enrichment analysis using Metascape and DIANA-miRPath v3. 0.Results: Thirty-four studies were included in the systematic review. Among 285 altered miRNAs reported in these studies, 15 were consistently upregulated, 14 were consistently downregulated, and 39 were inconsistently dysregulated. The most frequently altered miRNAs including miR-23a-3p, miR-106b-5p, miR-146a-5p, miR-7-5p, miR-27a-3p, miR-181b-5p, miR-486-3p, and miR-451a. Subgroup analysis of tissues showed that miR-146a-5p, miR-155-5p, miR-1277-3p, miR-21-3p, miR-106b-5p, and miR-451a were consistently upregulated in brain tissues, while miR-4742-3p was consistently downregulated; miR-23b-3p, miR-483-5p, and miR-23a-3p were consistently upregulated in blood samples, while miR-15a-5p, miR-193a-5p, miR-20a-5p, miR-574-3p, miR-92a-3p, miR-3135a, and miR-103a-3p were consistently downregulated; miR-7-5p was consistently upregulated in saliva, miR-23a-3p and miR-32-5p were consistently downregulated. The altered ASD miRNAs identified in at least two independent studies were validated to target many autism risk genes. TNRC6B, PTEN, AGO1, SKI, and SMAD4 were the most frequent targets, and miR-92a-3p had the most target autism risk genes. Pathway enrichment analysis showed that ASD miRNAs are significantly involved in pathways associated with cancer, metabolism (notably Steroid biosynthesis, Fatty acid metabolism, Fatty acid biosynthesis, Lysine degradation, Biotin metabolism), cell cycle, cell signaling (especially Hippo, FoxO, TGF-beta, p53, Thyroid hormone, and Estrogen signaling pathway), adherens junction, extracellular matrix-receptor interaction, and Prion diseases.Conclusions: Altered miRNAs in ASD target autism risk genes and are involved in various ASD-related pathways, some of which are understudied and require further investigation.

Hypoxia-inducible factor (HIF) family of transcription factors (HIF1A, EPAS1, and HIF3A) are regulators of the cellular response to hypoxia. They have been shown to be involved in development of various diseases such as cancer, diabetes, and erythrocytosis. A complete map of connections between HIF family of genes with various omics types has not yet been developed. The main aim of the present analysis was to construct the integrative map of genomic elements associated with HIF1A gene and prioritize potentially deleterious variants. Various genomic databases and bioinformatics tools were used, including Ensembl, MirTarBase, STRING, Cytoscape, MethPrimer, CADD, SIFT, and UALCAN. Integrative HIF1A gene map was visualized and includes transcriptional and post-transcriptional regulators, downstream targets, and genetic variants. One CpG island overlaps transcription start site of the HIF1A gene. Out of over 450 missense variants, four have predicted deleterious effect on protein function by at least five bioinformatics tools. Currently there are 85 miRNAs reported to target HIF1A. HIF1A downstream targets include protein-coding genes, long noncoding RNAs, and microRNAs (hypoxamiRs). The study presents the first integration of heterogeneous molecular interactions associated with HIF1A gene enabling a holistic view of the gene and lays the groundwork for supplementing the data in the future.

Background: Coronary artery disease (CAD) is the leading cause of cardiovascular death. The competitive endogenous RNAs (ceRNAs) hypothesis is a new theory that explains the relationship between lncRNAs and miRNAs. The mechanism of ceRNAs in the pathological process of CAD has not been fully elucidated. The objective of this study was to explore the ceRNA mechanism in CAD using the integrative bioinformatics analysis and provide new research ideas for the occurrence and development of CAD.Methods: The GSE113079 dataset was downloaded, and differentially expressed lncRNAs (DElncRNAs) and genes (DEGs) were identified using the limma package in the R language. Weighted gene correlation network analysis (WGCNA) was performed on DElncRNAs and DEGs to explore lncRNAs and genes associated with CAD. Functional enrichment analysis was performed on hub genes in the significant module identified via WGCNA. Four online databases, including TargetScan, miRDB, miRTarBase, and Starbase, combined with an online tool, miRWalk, were used to construct ceRNA regulatory networks.Results: DEGs were clustered into ten co-expression modules with different colors using WGCNA. The brown module was identified as the key module with the highest correlation coefficient. 188 hub genes were identified in the brown module for functional enrichment analysis. DElncRNAs were clustered into sixteen modules, including seven modules related to CAD with the correlation coefficient more than 0.5. Three ceRNA networks were identified, including OIP5-AS1-miR-204-5p/miR-211-5p-SMOC1, OIP5-AS1-miR-92b-3p-DKK3, and OIP5-AS1-miR-25-3p-TMEM184B.Conclusion: Three ceRNA regulatory networks identified in this study may play crucial roles in the occurrence and development of CAD, which provide novel insights into the ceRNA mechanism in CAD.

Background: The complicated molecular mechanisms underlying the therapeutic effect of electroacupuncture (EA) on ischemic stroke are still unclear. Recently, more evidence has revealed the essential role of the microRNA (miRNA)–mRNA networks in ischemic stroke. However, a systematic analysis of novel key genes, miRNAs, and miRNA–mRNA networks regulated by EA in ischemic stroke is still absent.Methods: We established a middle cerebral artery occlusion (MCAO) mouse model and performed EA therapy on ischemic stroke mice. Behavior tests and measurement of infarction area were applied to measure the effect of EA treatment. Then, we performed RNA sequencing to analyze differentially expressed genes (DEGs) and functional enrichment between the EA and control groups. In addition, a protein–protein interaction (PPI) network was built, and hub genes were screened by Cytoscape. Upstream miRNAs were predicted by miRTarBase. Then hub genes and predicted miRNAs were verified as key biomarkers by RT-qPCR. Finally, miRNA–mRNA networks were constructed to explore the potential mechanisms of EA in ischemic stroke.Results: Our analysis revealed that EA treatment could significantly alleviate neurological deficits in the affected limbs and reduce infarct area of the MCAO model mice. A total of 174 significant DEGs, including 53 upregulated genes and 121 downregulated genes, were identified between the EA and control groups. Functional enrichment analysis showed that these DEGs were associated with the FOXO signaling pathway, NF-kappa B signaling pathway, T-cell receptor signaling pathway, and other vital pathways. The top 10 genes with the highest degree scores were identified as hub genes based on the degree method, but only seven genes were verified as key genes according to RT-qPCR. Twelve upstream miRNAs were predicted to target the seven key genes. However, only four miRNAs were significantly upregulated and indicated favorable effects of EA treatment. Finally, comprehensive analysis of the results identified the miR-425-5p-Cdk1, mmu-miR-1186b-Prc1, mmu-miR-434-3p-Prc1, and mmu-miR-453-Prc1 miRNA–mRNA networks as key networks that are regulated by EA and linked to ischemic stroke. These networks might mainly take place in neuronal cells regulated by EA in ischemic stroke.Conclusion: In summary, our study identified key DEGs, miRNAs, and miRNA–mRNA regulatory networks that may help to facilitate the understanding of the molecular mechanism underlying the effect of EA treatment on ischemic stroke.

Made available in DSpace on 2022-04-29T08:45:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-09-22 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) MicroRNAs (miRNAs) play an important role in the pathophysiology of preeclampsia (PE). However, the expression of circulating miRNAs was not analyzed in the second trimester of pregnancy, a period of major relevance to identify predictive biomarkers for PE. Therefore, we examined the expression profiles of 84 circulating miRNAs using a PCR array in plasma collected between 20 and 25 weeks of gestation from pregnant women, who subsequently developed PE and those who remained healthy during pregnancy, randomly selected from a prospective cohort. Overall, 23 miRNAs had a fold change > 2.0 and were considered to be upregulated in plasma from pregnant women who subsequently developed PE, even before the onset of clinical symptoms of PE. However, only miR-204-5p was statistically significant (P = 0.0082). Experimentally validated interactions for the target genes of miR-204-5p extracted from miRTarBase were used in the gene set functional enrichment analysis to identify Reactome pathways. The network connecting the 37 target genes for miR-204-5p revealed pathways of known pathophysiological relevance during the early development of PE and included key genes related to PE, such as BDNF, MMP-9, MALAT1, TGFBR2, and SIRT1. We further depicted downstream targets of SIRT1 that are related to the vascular endothelial function or implicated in the pathophysiology of PE, namely, FOXO1, NFκB, HIF-1α, NOS3, and PPAR-γ. Our novel findings provide for circulating miRNAs upregulated in the second trimester on plasma from pregnant women who subsequently developed PE that is potentially related to the early development of PE, which may guide further studies focused on the validation of potential predictive biomarkers in PE. Department of Genetics Ecology and Evolution Institute of Biological Sciences Federal University of Minas Gerais Department of Biophysics and Pharmacology Institute of Biosciences Universidade Estadual Paulista (UNESP) Department of Gynecology and Obstetrics Ribeirao Preto Medical School University of São Paulo Ribeirao Preto Department of Biophysics and Pharmacology Institute of Biosciences Universidade Estadual Paulista (UNESP) FAPESP: #2008/53593-0 CNPq: #2014-5/305587 FAPESP: #2015/20461-8 CAPES: [Finance Code 001]

Gallbladder cancer (GBC), the most common malignancy in the biliary tract, is highly lethal malignant due to seldomly specific symptoms in the early stage of GBC. This study aimed to identify exosome-derived miRNAs mediated competing endogenous RNAs (ceRNA) participant in GBC tumorigenesis. A total of 159 differentially expressed miRNAs (DEMs) was identified as exosome-derived miRNAs, contains 34 upregulated exo-DEMs and 125 downregulated exo-DEMs based on the expression profiles in GBC clinical samples downloaded from the Gene Expression Omnibus database with the R package. Among them, 2 up-regulated exo-DEMs, hsa-miR-125a-3p and hsa-miR-4647, and 5 down-regulated exo-DEMs, including hsa-miR-29c-5p, hsa-miR-145a-5p, hsa-miR-192-5p, hsa-miR-194-5p, and hsa-miR-338-3p, were associated with the survival of GBC patients. Results of the gene set enrichment analysis showed that the cell cycle-related pathways were activated in GBC tumor tissues, mainly including cell cycle, M phase, and cell cycle checkpoints. Furthermore, the dysregulated ceRNA network was constructed based on the lncRNA-miRNA-mRNA interactions using miRDB, TargetScan, miRTarBase, miRcode, and starBase v2.0., consisting of 27 lncRNAs, 6 prognostic exo-DEMs, and 176 mRNAs. Together with prognostic exo-DEMs, the STEAP3-AS1/hsa-miR-192-5p/MAD2L1 axis was identified, suggesting lncRNA STEAP3-AS1, might as a sponge of exosome-derived hsa-miR-192-5p, modulates cell cycle progression via affecting MAD2L1 expression in GBC tumorigenesis. In addition, the biological functions of genes in the ceRNA network were also annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Our study promotes exploration of the molecular mechanisms associated with tumorigenesis and provide potential targets for GBC diagnosis and treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s12032-021-01594-8.

Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to regulate mRNA expression through sponging microRNA in tumorigenesis and progression. However, following the discovery of new RNA interaction, the differentially expressed RNAs and ceRNA regulatory network are required to update. Our study comprehensively analyzed the differentially expressed RNA and corresponding ceRNA network and thus constructed a potentially predictive tool for prognosis. “DESeq2” was used to perform differential expression analysis. Two hundred and six differentially expressed (DE) lncRNAs, 222 DE miRNAs, and 2,463 DE mRNAs were found in this study. The lncRNA-mRNA interactions in the miRcode database and the miRNA-mRNA interactions in the starBase, miRcode, and mirTarBase databases were searched, and a competing endogenous RNA (ceRNA) network with 186 nodes and 836 interactions was subsequently constructed. Aberrant expression patterns of lncRNA NR2F1-AS1 and lncRNA AC010168.2 were evaluated in two datasets (GSE89006, GSE31684), and real-time polymerase chain reaction was also performed to validate the expression pattern. Furthermore, we found that these two lncRNAs were independent prognostic biomarkers to generate a prognostic lncRNA signature by univariate and multivariate Cox analyses. According to the lncRNA signature, patients in the high-risk group were associated with a poor prognosis and validated by an external dataset. A novel genomic-clinicopathologic nomogram to improve prognosis prediction of bladder cancer was further plotted and calibrated. Our study deepens the understanding of the regulatory ceRNA network and provides an easy-to-do genomic-clinicopathological nomogram to predict the prognosis in patients with bladder cancer.

Objective. This study is aimed at identifying key genes involved in neurological damage in preterm infants and at determining their potential circRNA-miRNA-mRNA regulatory mechanisms. Methods. Differentially expressed miRNAs, mRNAs, and circRNAs were downloaded from the GEO database. GO and KEGG enrichment analyses were used to determine possible relevant functions of differentially expressed mRNAs. The TTRUST database was used to predict differential TF-mRNA regulatory relationships. Then, CircMIR, miRDB, TargetScan and miRTarBase were then used to map circRNA/miRNA-TF/mRNA interaction networks. Finally, GSEA enrichment analysis was performed on the core transcription factors. Results. A total of 640 mRNAs, 139 circRNAs, and 206 differentially expressed miRNAs associated with neurological injury in preterm infants were obtained. Based on the findings of Cytoscape and PPI network analysis, the hsa_circ_0008439-hsa-mir-3665-STAT3-MMP3 regulatory axis was established. GSEA analysis revealed that suppressed expression levels of STAT3 were associated with upregulated oxidative phosphorylation pathways in the neurological injury group of preterm infants. Conclusions. The circRNA-miRNA-TF-mRNA regulatory network of neurological injury in preterm infants can be used to elucidate on the pathogenesis of brain injury and help us with the early detection of brain injury in preterm infants.

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent., Author summary Dengue fever is a mosquito borne viral disease caused by a single stranded RNA virus with four serotypes. DENV infection can cause various diseases, such as breakbone fever, haemorrhagic fever, and shock syndrome. As one of the most viral diseases leading to incidence rate and mortality in animal arthropods, Dengue fever has become an increasingly serious global health threat. However, the pathogenesis of dengue fever has not been fully elucidated. In this study, we used bioinformatics analysis to identify potential biomarkers associated with dengue fever and elucidate their underlying mechanisms. Finally, we predicted that IFI44L and IFI6 might be potential biomarkers of DENV infection. This finding provides a promising target for the treatment of dengue fever to a certain extent. In addition, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) network were implemented to analyze the key differentially expressed genes after DENV infection, and the related mechanisms were illuminated by this study.

Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan–Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies in CRC.

Parkinson's disease (PD) and Multiple System Atrophy (MSA) are progressive neurodegenerative diseases with overlap of symptoms in early stages of disease. No reliable biomarker exists and the diagnosis is mainly based on clinical features. Several studies suggest that miRNAs are involved in PD and MSA pathogenesis. Our goal was to study two serum circulating microRNAs (miR-96-5p and miR-339-5p) as novel biomarkers for the differential diagnosis between PD and MSA. Serum samples were obtained from 51 PD patients, 52 MSA patients and 56 healthy controls (HC). We measured levels of miRNAs using quantitative PCR and compared the levels of miR-96-5p and miR-339-5p among PD, MSA and HC groups using a one-way analysis of variance. Correlations between miRNA expression and clinical data were calculated using Pearson's rho test. We used the miRTarBase to detect miRNA targets and STRING to evaluate co-expression relationship among target genes. MiR-96-5p was significantly increased in MSA patients compared with HC (Fold change (fc): 3.6; p = 0.0001) while it was decreased in PD patients compared with HC (Fold change: 4; p = 0.0002). Higher miR-96-5P levels were directly related to longer disease duration in MSA patients. We observed a significant increase of miR-339-5p in MSA patients compared with PD patients (fc: 2.5; p = 0.00013). miR-339-5p was increased in MSA patients compared with HC (fc: 2.4; p = 0.002). We identified 32 target genes of miR-96-5p and miR-339-5p, some of which are involved in neurodegenerative diseases. The study of those miRNAs could be useful to identify non-invasive biomarkers for early differential diagnosis between PD and MSA.

BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease that can affect multiple systems. Pulmonary arterial hypertension (PAH) has a close linkage with SLE. However, the inter-relational mechanisms between them are still unclear. This article aimed to explore the shared gene signatures and potential molecular mechanisms in SLE and PAH.MethodsThe microarray data of SLE and PAH in the Gene Expression Omnibus (GEO) database were downloaded. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify the co-expression modules related to SLE and PAH. The shared genes existing in the SLE and PAH were performed an enrichment analysis by ClueGO software, and their unique genes were also performed with biological processes analyses using the DAVID website. The results were validated in another cohort by differential gene analysis. Moreover, the common microRNAs (miRNAs) in SLE and PAH were obtained from the Human microRNA Disease Database (HMDD) and the target genes of whom were predicted through the miRTarbase. Finally, we constructed the common miRNAs–mRNAs network with the overlapped genes in target and shared genes. ResultsUsing WGCNA, four modules and one module were identified as the significant modules with SLE and PAH, respectively. A ClueGO enrichment analysis of shared genes reported that highly activated type I IFN response was a common feature in the pathophysiology of SLE and PAH. The results of differential analysis in another cohort were extremely similar to them. We also proposed a disease road model for the possible mechanism of PAH secondary to SLE according to the shared and unique gene signatures in SLE and PAH. The miRNA–mRNA network showed that hsa-miR-146a might regulate the shared IFN-induced genes, which might play an important role in PAH secondary to SLE.ConclusionOur work firstly revealed the high IFN response in SLE patients might be a crucial susceptible factor for PAH and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.

Background The mechanism of recurrent implantation failure (RIF) is unclear at present and poor endometrial receptivity may be one of the leading reasons. This study aims to construct a competing endogenous RNA (ceRNA) network and identify potential hub genes underlying the development of RIF. Methods Weighted gene co-expression network analysis was performed based on differentially expressed mRNAs (DEMs) and lncRNAs (DELs) from the GSE111974 dataset. Functional enrichment analyses of gene modules were conducted using Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway. A lncRNA-miRNA-mRNA ceRNA regulatory network was constructed according to predictive interaction derived from the LncRNADisease, miRTarBase, miRDB and TargetScan databases. Topological analysis determined the key genes with the highest centroid and their expressions were further verified using public datasets and quantitative real-time polymerase chain reaction. Results A total of 1500 DEMs and 3 DELs were significantly up-regulated, whereas 1022 DEMs and 4 DELs were significantly down-regulated in the RIF group compared with the control group. Six functional co-expression modules were enriched in various biological processes, such as cell adhesion, regulation of cell motility and cellular response to vascular endothelial growth factor stimulus. Five hub genes were identified in the ceRNA network, of which GJA1 was down-regulated whereas TET2, MAP2K6, LRRC1 and TRPM6 were up-regulated in RIF endometrium. Conclusions We constructed a lncRNA-associated ceRNA network and identified five novel hub genes in RIF. This finding could be helpful to understand the molecular mechanism for RIF pathogenesis, and may provide novel insights for its early diagnosis and treatment.

Buze Chen,1,2,&ast; Xin Jin,3,&ast; Haihong Wang,1 Qingmei Zhou,1 Guilin Li,3 Xiaoyuan Lu1 1Department of Gynecology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China; 2Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China; 3Department of Gynecology, Maternal and Child Health Care Hospital Affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, 221000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaoyuan LuDepartment of Gynecology, The Affiliated Hospital of Xuzhou Medical University, No. 99 West Huaihai Road, Quanshan District, Xuzhou, 221000, Jiangsu, People’s Republic of ChinaTel +86-180 52268119Email 18052268119@189.cnGuilin LiDepartment of Gynecology, Maternal and Child Health Care Hospital Affiliated to Xuzhou Medical University, No. 46 Heping Road, Yunlong District, Xuzhou, Jiangsu, 221000, People’s Republic of ChinaTel +86-18936372957Email liguilin8@126.comBackground: The present study aims to explore the expression, clinical significance, and prospective pathway signaling of miR-501-3p in ovarian cancer (OC) based on database and informatics analysis.Methods: Kruskal–Wallis test, Wilcoxon sign-rank test, and logistic regression were used to evaluate the relationship between clinical features and miR-501-3p expression. Kaplan–Meier survival curve analysis was used to explore the relationship between miR-501-3p expression and the prognosis of OC patients. The miRNA targets were obtained from databases TargetScan, miRanda, TarBase, miRTarBase, miR2Disease, miRecords, and miRWalk. GO and KEGG analyses were used to analyze the significant involvement of miR-501-3p target genes in function.Results: The low miR-501-3p expression in OC was significantly associated with histologic grade (P=0.015). Low miR-501-3p expression predicted a poorer overall survival (HR: 0.77; 95% CI: 0.61– 0.96; P=0.02) and disease-specific survival (HR: 0.77; 95% CI: 0.61– 0.99; P=0.038). GO and KEGG analyses demonstrated that miR-501-3p might participate in the development of OC by pathways including one carbon pool by folate, protein digestion and absorption, cell cycle, kaposi sarcoma-associated herpesvirus infection, and viral carcinogenesis.Conclusions: Low miR-501-3p expression is significantly associated with poor survival in OC patients. It may be a promising prognostic biomarker for OC patients.Keywords: microRNA-501-3p, ovarian cancer, prognosis, the cancer genome atlas, gene expression

Systemic sclerosis (SSc) is an immune-mediated connective tissue disease characterized by fibrosis of multi-organs, and SSc-related interstitial lung disease (SSc-ILD) is a leading cause of morbidity and mortality. To explore molecular biological mechanisms of SSc-ILD, we constructed a competing endogenous RNA (ceRNA) network for prediction. Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database, and differential expressed mRNAs and miRNAs analysis was further conducted between normal lung tissue and SSc lung tissue. Also, the interactions of miRNA–lncRNA, miRNA–mRNA, and lncRNA–mRNA were predicted by online databases including starBase, LncBase, miRTarBase, and LncACTdb. The ceRNA network containing 11 lncRNAs, 7 miRNAs, and 20 mRNAs were constructed. Based on hub genes and miRNAs identified by weighted correlation network analysis (WGCNA) method, three core sub-networks—SNHG16, LIN01128, RP11-834C11.4(LINC02381)/hsa-let-7f-5p/IL6, LINC01128/has-miR-21-5p/PTX3, and LINC00665/hsa-miR-155-5p/PLS1—were obtained. Combined with previous studies and enrichment analyses, the lncRNA-mediated network affected LPS-induced inflammatory and immune processes, fibrosis development, and tumor microenvironment variations. The ceRNA network, especially three core sub-networks, may be served as early biomarkers and potential targets for SSc, which also provides further insights into the occurrence, progression, and accurate treatment of SSc at the molecular level.

BackgroundA detailed means of prognostic stratification in patients with non-small cell lung cancer (NSCLC) is urgently needed to support individualized treatment plans. Recently, microRNAs (miRNAs) have been used as biomarkers due to their previously reported prognostic roles in cancer. This study aimed to construct an immune-related miRNA signature that effectively predicts NSCLC patient prognosis.MethodsThe miRNAs and mRNA expression and mutation data of NSCLC was obtained from The Cancer Genome Atlas (TCGA). Immune-associated miRNAs were identified using immune scores calculated by the ESTIMATE algorithm. LASSO-penalized multivariate survival models were using for development of a tumor immune-related miRNA signature (TIM-Sig), which was evaluated in several public cohorts from the Gene Expression Omnibus (GEO) and the CellMiner database. The miRTarBase was used for constructing the miRNA-target interactions.ResultsThe TIM-Sig, including 10 immune-related miRNAs, was constructed and successfully predicted overall survival (OS) in the validation cohorts. TIM-Sig score negatively correlated with CD8+ T cell infiltration, IFN-γ expression, CYT activity, and tumor mutation burden. The correlation between TIM-Sig score and genomic mutation and cancer chemotherapeutics was also evaluated. A miRNA-target network of 10 miRNAs in TIM-Sig was constructed. Further analysis revealed that these target genes showed prognostic value in both lung squamous cell carcinoma and adenocarcinoma.ConclusionsWe concluded that the immune-related miRNAs demonstrated a potential value in clinical prognosis.

BACKGROUND: The aim of the study was to suggest a high specific and sensitive blood biomarker for early GC diagnosis. METHODS: the expression data of miRNAs and mRNAs were collected from the blood samples of the GC patients based on literature mining. Bioinformatics tools and databases (PANTHER, TargetScan, miRTarBase, miRDB, STRING, and Cytoscape) were used to predict the regulatory relationship. Subsequently, expression level of the selected miRNA was evaluated in the blood samples of gastritis patients to recognize the common miRNA between the GC and gastritis patients. RESULTS: Analysis of 40 target genes by MCODE (installed in Cytoscape software) indicated 4 hub genes (WWP1, SKP2, KLHL42, and FBXO11) as a significant cluster in the PPI network related to miR-21, with Node Score Cutoff: 0.2, Degree Cutoff: 2 and K-Core: 2. In addition, the miRNA RT-qPCR results showed that, the expression level of miR-21 was significantly higher in gastritis group compared to the healthy group (p< 0.05). CONCLUSION: the present study clearly demonstrated the increasing level of blood miR-21 among the gastritis patients infected by H. pylori. Therefore, the altered miRNAs, especially overexpression of onco-miRs, may identify a potential link between miRNAs and pathogenesis of the H. pylori–related complications.

BackgroundFunctions of miRNAs involved in tumorigenesis are well reported, yet, their roles in normal cell lineage commitment remain ambiguous. Here, we investigated a specific “transcription factor (TF)-miRNA-Target” regulatory network during the lineage maturation of biliary tree stem cells (BTSCs) into adult hepatocytes (hAHeps).MethodBioinformatic analysis was conducted based on our RNA-seq and microRNA-seq datasets with four human hepatic-lineage cell lines, including hBTSCs, hepatic stem cells (hHpSCs), hepatoblasts (hHBs), and hAHeps. Short time-series expression miner (STEM) analysis was performed to reveal the time-dependent dynamically changed miRNAs and mRNAs. GO and KEGG analyses were applied to reveal the potential function of key miRNAs and mRNAs. Then, the miRDB, miRTarBase, TargetScan, miRWalk, and DIANA-microT-CDS databases were adopted to predict the potential targets of miRNAs while the TransmiR v2.0 database was used to obtain the experimentally supported TFs that regulate miRNAs. The TCGA, Kaplan–Meier Plotter, and human protein atlas (HPA) databases and more than 10 sequencing data, including bulk RNA-seq, microRNA-seq, and scRNA-seq data related to hepatic development or lineage reprogramming, were obtained from both our or other published studies for validation.ResultsSTEM analysis showed that during the maturation from hBTSCs to hAHeps, 52 miRNAs were downwardly expressed and 928 mRNA were upwardly expressed. Enrichment analyses revealed that those 52 miRNAs acted as pluripotency regulators for stem cells and participated in various novel signaling pathways, including PI3K/AKT, MAPK, and etc., while 928 mRNAs played important roles in liver-functional metabolism. With an extensive sorting of those key miRNAs and mRNAs based on the target prediction results, 23 genes were obtained which not only functioned as the targets of 17 miRNAs but were considered critical for the hepatic lineage commitment. A “TF-miRNA-Target” regulatory network for hepatic lineage commitment was therefore established and had been well validated by various datasets. The network revealed that the PI3K/AKT pathway was gradually suppressed during the hepatic commitment.ConclusionA total of 17 miRNAs act as suppressors during hepatic maturation mainly by regulating 23 targets and modulating the PI3K/AKT signaling pathway. The regulatory network uncovers possible signatures and guidelines enabling us to identify or obtain the functional hepatocytes for future study.

PURPOSE Endometrial carcinoma (EC), a common gynecological malignancy with high incidence, affects the mental and physical health of women. Mounting evidence shows that long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) have instrumental roles in various biological processes associated with the pathogenesis of EC. In this research, we intend to further study the mechanism of EC and the potential predictive markers of EC. METHODS First, we obtained original data of EC RNA transcripts from The Cancer Genome Atlas database and performed differential analysis. Subsequently, according to the miRcode online software, relationship pairs of lncRNA-miRNA were constructed, and miRNA-mRNA pairs were established based on miRDB, TargetScan, and miRTarBase. Then, we constructed the competing endogenous RNA (ceRNA) network based on lncRNA-miRNA and miRNA-mRNA pairs. To further explain the function of the ceRNA network and explore the potential prognostic markers, functional enrichment analysis, and survival analysis were carried out. RESULTS The research showed that there were 744 differential expression lncRNAs (DElncRNAs), 164 differential expression miRNAs (DEmiRNAs), and 2447 differential expression mRNAs (DEmRNAs) between EC tissues and normal tissues. Subsequently, we built 103 DEmiRNA-DEmRNA interaction pairs and 369 DElncRNA-DEmiRNA pairs. Then, we established the ceRNA network of EC, including 62 DElncRNAs, 26 DEmiRNAs, and 70 DEmRNAs. Moreover, 10 of 62 lncRNAs, 19 of 70 mRNAs, and 4 of 26 miRNAs that closely related to the survival of EC with P

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.