Role of miRNA polymorphism in recurrent pregnancy loss: a systematic review and meta-analysis

There are a plethora of publications on the role of miRNA gene polymorphism and its association with recurrent pregnancy loss (RPL), but a lack of uniformity in the studies available due to the variable subject population, heterogeneity and contrary results of significance. Rigorous data mining was done through PubMed, SCOPUS, Cochrane library, Elsevier and Google Scholar to extract the studies of interest published until June 2021. A total of eight SNPs of miRNAs have been included, where ≥2 studies per SNPs were available. Analysis was done on the basis of pooled odds ratios and 95% CI. This is the first meta-analysis on miRNA SNPs in RPL that suggests that rs11614913, rs3746444 and rs2292832 biomarkers may decrease the risk of RPL under different genetic models.

Bioinformatics Analysis of the Potentially Functional circRNA-miRNA-mRNA Network in Breast Cancer

Objective: Breast cancer (BC) is a heterogeneous type of cancer that occurs as a result of distinct molecular alterations in breast tissue. Although there are many new developments in treatment and targeted therapy for BC in recent years, this cancer type is still the most common one among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. Methods: GSE101124 and GSE182471 datasets were obtained from Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray (PAM50) classification. The circRNA-miRNA-gene relationship was investigated using the Cancer Specific CircRNA (v2.0) (CSCD), miRDB, miRWalk and miRTarBase databases. The circRNA-miRNA-mRNA regulatory network was constructed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation. The protein-protein interaction network was constructed by the STRING 2021 database and visualized by the Cytoscape tool (v3.9.0). Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The overall survival (OS) and disease-free survival (DFS) analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. Results: We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. Conclusions: We conclude that hsa_circ_0000515/ miR-486-5p/ SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC group, especially according to the PAM50 classification of BC.

Establishment of a miRNA profile in paediatric HIV-1 patients and its potential as a biomarker for effectiveness of the combined antiretroviral therapy

AbstractmiRNAs have been extensively studied in pathological conditions, including viral infections, such as those provoked by HIV-1. Several cellular and circulating miRNAs are altered during HIV-1 infection, with either beneficial effects on host defenses or enhanced virus infectivity. Blood samples were collected in sterile EDTA tubes and plasma was separated and stored, as were PBMCs. RNA was isolated and reverse-transcribed. Finally, the miRNA gene expression profile was assessed using TaqMan Array Human microRNA Card A v2.0. A comprehensive statistical analysis was performed on the results obtained. This is the first study on miRNAs in HIV-1 paediatric patients, and a miRNA profile differentiating patients starting combination antiretroviral therapy (cART) at different times after HIV-1 diagnosis was established. Thirty-four miRNAs were observed to have different expression levels between the control group and the cART group. The data indicates the need to start cART as soon as possible after the establishment of HIV-1 infection to assure the best outcome possible. Finally, the selected 34 miRNAs may be used as biomarkers for prognosis and assessing therapy effectiveness. However, more research must be conducted to establish adequate quantitative correlations.

Network-based cancer genomic data integration for pattern discovery

Background Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted. Results In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered. Conclusions All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

Identification of Potential Long Non-Coding RNA Candidates That Contribute to Triple-Negative Breast Cancer in Humans through Computational Approach

Breast cancer (BC) is the most frequent malignancy identified in adult females, resulting in enormous financial losses worldwide. Owing to the heterogeneity as well as various molecular subtypes, the molecular pathways underlying carcinogenesis in various forms of BC are distinct. Therefore, the advancement of alternative therapy is required to combat the ailment. Recent analyses propose that long non-coding RNAs (lncRNAs) perform an essential function in controlling immune response, and therefore, may provide essential information about the disorder. However, their function in patients with triple-negative BC (TNBC) has not been explored in detail. Here, we analyzed the changes in the genomic expression of messenger RNA (mRNA) and lncRNA in standard control in response to cancer metastasis using publicly available single-cell RNA-Seq data. We identified a total of 197 potentially novel lncRNAs in TNBC patients of which 86 were differentially upregulated and 111 were differentially downregulated. In addition, among the 909 candidate lncRNA transcripts, 19 were significantly differentially expressed (DE) of which three were upregulated and 16 were downregulated. On the other hand, 1901 mRNA transcripts were significantly DE of which 1110 were upregulated and 791 were downregulated by TNBCs subtypes. The Gene Ontology (GO) analyses showed that some of the host genes were enriched in various biological, molecular, and cellular functions. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that some of the genes were involved in only one pathway of prostate cancer. The lncRNA-miRNA-gene network analysis showed that the lncRNAs TCONS_00076394 and TCONS_00051377 interacted with breast cancer-related micro RNAs (miRNAs) and the host genes of these lncRNAs were also functionally related to breast cancer. Thus, this study provides novel lncRNAs as potential biomarkers for the therapeutic intervention of this cancer subtype.

Hub microRNAs and genes in the development of atrial fibrillation identified by weighted gene co-expression network analysis

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.

Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.

PSVI-23 Depot- specific patterns of mRNA and miRNA gene expression in adipose tissue from Texel-cross and Suffolk lambs

Excess fat deposition is costly to the producer in terms of input and final product; it also usually does not occur equally across all adipose depots. Further examination is necessary to determine a correlation between varying gene expression and fatty acid composition in different tissue depots, and further, across different breeds. Texel-Suffolk (n = 5) and Suffolk-Suffolk (n = 4) lambs were finished to 203 d of age and used to compare both mRNA and microRNA (miR) gene expression changes between breed and among tissue depots. Seven different depots were harvested and snap-frozen from all nine lambs. The liver, longissimus muscle of the rib, kidney fat, mesenteric fat, omental fat, subcutaneous fat, and intermuscular fat were all harvested. Texel-sired lambs had greater (P < 0.05) flank streaking, quality grade, and weight of fat depots compared to Suffolk. Texel-cross lambs had higher (P < 0.05) oleic-to-stearic fatty acid ratio than Suffolk lambs in this study, displaying a breed difference concerning this desaturation ratio. Tissue and breed interactions were observed for oleic-to-stearic and palmitoleic-to-palmitic ratio differences (P < 0.05) depending on tissue type. Tissue and breed interactions were trending in various tissues concerning the expression of the gene, stearoyl-CoA desaturase-1(SCD-1). SCD-1 seemed to be upregulated (P < 0.10) in a multitude of tissues while others do not appear to be differentially expressed, dependent upon breed. Data showed an association between SCD-1 and mi-199a-3p among different tissue variations. This may suggest that adipose tissue is more complex than what is currently known. Lipogenic gene expression differed between tissue and adipose depots, and could potentially broaden targets that could aid in maximizing animal efficiency.

Evaluation of Diagnostic Potential of Epigenetically Deregulated MiRNAs in Epithelial Ovarian Cancer

BackgroundEpithelial ovarian cancer (EOC) is one of the most lethal gynecological malignancies among women worldwide. Early diagnosis of EOC could help in ovarian cancer management. MicroRNAs, a class of small non-coding RNA molecules, are known to be involved in post-transcriptional regulation of ~60% of human genes. Aberrantly expressed miRNAs associated with disease progression are confined in lipid or lipoprotein and secreted as extracellular miRNA in body fluid such as plasma, serum, and urine. MiRNAs are stably present in the circulation and recently have gained an importance to serve as a minimally invasive biomarker for early detection of epithelial ovarian cancer.MethodsGenome-wide methylation pattern of six EOC and two normal ovarian tissue samples revealed differential methylation regions of miRNA gene promoter through MeDIP-NGS sequencing. Based on log2FC and p-value, three hypomethylated miRNAs (miR-205, miR-200c, and miR-141) known to have a potential role in ovarian cancer progression were selected for expression analysis through qRT-PCR. The expression of selected miRNAs was analyzed in 115 tissue (85 EOC, 30 normal) and 65 matched serum (51 EOC and 14 normal) samples.ResultsAll three miRNAs (miR-205, miR-200c, and miR-141) showed significantly higher expression in both tissue and serum cohorts when compared with normal controls (p < 0.0001). The receiver operating characteristic curve analysis of miR-205, miR-200c, and miR-141 has area under the curve (AUC) values of 87.6 (p < 0.0001), 78.2 (p < 0.0001), and 86.0 (p < 0.0001), respectively; in advance-stage serum samples, however, ROC has AUC values of 88.1 (p < 0.0001), 78.9 (p < 0.0001), and 86.7 (p < 0.0001), respectively, in early-stage serum samples. The combined diagnostic potential of the three miRNAs in advance-stage serum samples and early-stage serum samples has AUC values of 95.9 (95% CI: 0.925–1.012; sensitivity = 96.6% and specificity = 80.0%) and 98.1 (95% CI: 0.941–1.021; sensitivity = 90.5% and specificity = 100%), respectively.ConclusionOur data correlate the epigenetic deregulation of the miRNA genes with their expression. In addition, the miRNA panel (miR-205 + miR-200c + miR-141) has a much higher AUC, sensitivity, and specificity to predict EOC at an early stage in both tissue and serum samples.