Datasets exploring putative lncRNA-miRNA-mRNA axes in breast cancer cell lines.
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ABSTRACT:
SUBMITTER: Wasson MD
PROVIDER: S-EPMC8250161 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Breast cancer is the most prevalent type of malignancy in women worldwide. Taxanes (paclitaxel and docetaxel) are widely applied as first-line chemotherapeutic agents, while the therapeutic effect is seriously limited by the development of drug resistance. In the present study, we screened out several miRNAs dysregulated in taxanes-resistant breast cancer samples and confirmed that two miRNAs (miR-335-5p and let-7c-5p) played a major role in cell proliferation, apoptosis, and chemo-resistance. In addition, the weighted gene co-expression network analysis (WGCNA) for potential target genes of miR-335-5p and let-7c-5p identified three hub genes (CXCL9, CCR7, and SOCS1) with a positive relationship to taxanes-sensitivity. Further, target relationships between miR-335-5p and CXCL9, let-7c-5p and CCR7/SOCS1 were confirmed by dual-luciferase reporter assays. Importantly, the regulatory functions of CXCL9, CCR7, and SOCS1 on proliferation and chemoresistance were validated. In conclusion, our study shed light on clinical theragnostic relationships between miR-335-5p/CXCL9, let-7c-5p/CCR7/SOCS1 axes, and taxanes-resistance in breast cancer.
| S-EPMC7473300 | biostudies-literature
Project description:Recently, rapid advances in bioinformatics analysis have expanded our understanding of the transcriptome to a genome-wide level. miRNA-mRNA-lncRNA interactions have been shown to play critical regulatory role in cancer biology. In this study, we discussed the use of an integrated systematic approach to explore new facets of the oestrogen receptor (ER)-regulated transcriptome. The identification of RNAs that are related to the expression status of the ER may be useful in clinical therapy and prognosis. We used a network modelling strategy. First, microarray expression profiling of mRNA, lncRNA and miRNA was performed in MCF-7 (ER-positive) and MDA-MB-231 cells (ER- negative). A co-expression network was then built using co-expression relationships of the differentially expressed mRNAs and lncRNAs. Finally, the selected miRNA-mRNA network was added to the network. The key miRNA-mRNA-lncRNA interaction can be inferred from the network. The mRNA and non-coding RNA expression profiles of the cells with different ER phenotypes were distinct. Among the aberrantly expressed miRNAs, the expression levels of miR-19a-3p, miR-19b-3p and miR-130a-3p were much lower in the MCF-7 cells, whereas that of miR-148b-3p was much higher. In a cluster of miR-17-92, the expression levels of six of seven miRNAs were lower in the MCF-7 cells, in addition to miR-20b in the miR-106a-363 cluster. However, the levels of all the miRNAs in the miR-106a-25 cluster were higher in the MCF-7 cells. In the co-expression networking, CD74 and FMNL2 gene which is involved in the immune response and metastasis, respectively, had a stronger correlation with ER. Among the aberrantly expressed lncRNAs, lncRNA-DLEU1 was highly expressed in the MCF-7 cells. A statistical analysis revealed that there was a co-expression relationship between ESR1 and lncRNA-DLEU1. In addition, miR-19a and lncRNA-DLEU1 are both located on the human chromosome 13q. We speculate that miR-19a might be co-expressed with lncRNA-DLEU1 to co-regulate the expression of ESR1, which influences the occurrence and development of breast cancer cells with different levels of ER expression. Our findings reveal that the status of ER is mainly due to the differences in the mRNA and ncRNA profile between the breast cancer cell lines, and highlight the importance of studying the miRNA-mRNA-lncRNA interactions to completely illustrate the intricate transcriptome.
| S-EPMC4687702 | biostudies-literature
Project description:PurposeThis research explores the aberrant expression of the long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) in pterygium. A competitive endogenous RNA (ceRNA) network was constructed to elucidate the molecular mechanisms in pterygium.MethodsWe obtained the differentially expressed mRNAs based on three datasets (GSE2513, GSE51995, and GSE83627), and summarized the differentially expressed miRNAs (DEmiRs) and differentially expressed lncRNAs (DELs) data by published literature. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI), and gene set enrichment analysis (GSEA) analysis were performed. DEmiRs were verified in GSE21346, and the regulatory network of hub mRNAs, DELs, and DEmiRs were constructed.ResultsOverall, 40 upregulated and 40 downregulated differentially expressed genes (DEGs) were obtained. The KEGG enrichment showed the DEGs mainly involved in extracellular matrix (ECM)-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway. The GSEA results showed that cornification, keratinization, and cornified envelope were significantly enriched. The validation outcome confirmed six upregulated DEmiRs (miR-766-3p, miR-184, miR-143-3p, miR-138-5p, miR-518b, and miR-1236-3p) and two downregulated DEmiRs (miR-200b-3p and miR-200a-3p). Then, a ceRNA regulatory network was constructed with 22 upregulated and 15 downregulated DEmiRs, 4 downregulated DELs, and 26 upregulated and 33 downregulated DEGs. The network showed that lncRNA SNHG1/miR-766-3p/FOS and some miRNA-mRNA axes were dysregulated in pterygium.ConclusionsOur study provides a novel perspective on the regulatory mechanism of pterygium, and lncRNA SNHG1/miR-766-3p/FOS may contribute to pterygium development.
| S-EPMC7425729 | biostudies-literature
Project description:Gastric cancer (GC), the fifth most common malignancy worldwide, has an extremely poor prognosis at the advanced stage or the early stage if inadequately treated. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs all function as competing endogenous RNAs (ceRNAs) that target and regulate each other. Changes in their expression and their regulatory bioprocesses play important roles in GC. However, the roles of key RNAs and their regulatory networks remain unclear. In this study, RNA profiles were extracted from The Cancer Genome Atlas database, and R language was used to discover the differentially expressed (DE) lncRNAs, miRNAs and mRNAs in GC. Then, the DERNAs were paired by miRcode, miRDB, TargetScan and DIANA, and the ceRNA network was further constructed and visualized using Cytoscape. Moreover, a functional enrichment analysis was performed using Metascape. Afterward, the "survival" package was employed to identify candidate prognostic targets (DERNA-os) in the ceRNA network. Ultimately, the ceRNA network was analyzed to identify crucial lncRNA/miRNA/mRNA axes. Based on 374 gastric adenocarcinoma and gastric adenoma samples, 283 DEceRNAs (69 lncRNAs, 10 miRNAs, and 204 mRNAs) were identified. The 204 mRNAs were significantly enriched in some interesting functional clusters, such as the trans-synaptic signaling cluster and the protein digestion and absorption cluster. The ceRNA network consisted of 43 ceRNAs (13 lncRNAs, 2 miRNAs, and 28 mRNAs) that were related to prognosis. Among them, 2 lncRNAs (LNC00469 and AC010145.1) and 1 mRNA (PRRT4) were potential new biomarkers. In addition, according to the lncRNA/miRNA/mRNA regulatory relationships among the 43 ceRNAs, we identified four axes that might play important roles in the progression of GC and investigated the potential mechanism of the most promising axis (POU6F2-AS2/hsa-mir-137/OPCML) in promoting the proliferation and invasiveness of GC.
| S-EPMC9185605 | biostudies-literature
Project description:BackgroundExisting computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.ResultsIn this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk.ConclusionsTo the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.
| S-EPMC8641245 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA-seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA-miRNA-mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool-based ceRNA network, an HCC-specific ceRNA network was obtained, and lncRNA-miRNA-mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer-related pathways. Four HCC-specific lncRNA-miRNA-mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK-AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK-AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1-year, 3-year, and 5-year survival was 0.777 (0.657-0.865), 0.722 (0.640-0.848), and 0.630 (0.528-0.823), and 0.751 (0.664-0.870), 0.773 (0.707-0.849), and 0.734 (0.638-0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC.
| S-EPMC7774738 | biostudies-literature
Project description:The lack of a reliable and easy-to-operate screening pipeline for disease-related noncoding RNA regulatory axis is a problem that needs to be solved urgently. To address this, we designed a hybrid pipeline, disease-related lncRNA-miRNA-mRNA regulatory axis prediction from multiomics (DLRAPom), to identify risk biomarkers and disease-related lncRNA-miRNA-mRNA regulatory axes by adding a novel machine learning model on the basis of conventional analysis and combining experimental validation. The pipeline consists of four parts, including selecting hub biomarkers by conventional bioinformatics analysis, discovering the most essential protein-coding biomarkers by a novel machine learning model, extracting the key lncRNA-miRNA-mRNA axis and validating experimentally. Our study is the first one to propose a new pipeline predicting the interactions between lncRNA and miRNA and mRNA by combining WGCNA and XGBoost. Compared with the methods reported previously, we developed an Optimized XGBoost model to reduce the degree of overfitting in multiomics data, thereby improving the generalization ability of the overall model for the integrated analysis of multiomics data. With applications to gestational diabetes mellitus (GDM), we predicted nine risk protein-coding biomarkers and some potential lncRNA-miRNA-mRNA regulatory axes, which all correlated with GDM. In those regulatory axes, the MALAT1/hsa-miR-144-3p/IRS1 axis was predicted to be the key axis and was identified as being associated with GDM for the first time. In short, as a flexible pipeline, DLRAPom can contribute to molecular pathogenesis research of diseases, effectively predicting potential disease-related noncoding RNA regulatory networks and providing promising candidates for functional research on disease pathogenesis.
| S-EPMC8921741 | biostudies-literature
Project description: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.
| S-EPMC7835059 | biostudies-literature
Project description:Breast cancer is one of the most prevalent and recurring cancer types that leads to deaths in women. Triple-negative breast cancer (TNBC) is difficult to treat due to the lack of therapeutic targets. Many studies have focused on identifying drugs for use as alternative treatments for breast cancer. Thioguanine (6-TG) exerts antitumor effects in cancer. Increasing evidence has demonstrated that competitive endogenous ribonucleic acids (ceRNAs) are involved in cancer processes. However, the mechanism by which 6-TG regulates lncRNA-miRNA-mRNAs has not been elucidated. We evaluated the antitumor effect of 6-TG in MDA-MB-231 cells and comprehensively analyzed the RNA-Seq data of MDA-MB-231 cells treated with 6-TG. Our results showed that most tumor pathways were blocked by 6-TG. The hub genes were FN1, FLNA, FLNB, VCL, GSN, MYH10, ACTN4, KDR and EREG, and they were all down-regulated after 6-TG treatment. The coexpression network consisted of 18 microRNAs (miRNAs), 9 long noncoding RNAs (lncRNAs) and 20 mRNAs. Hsa-mir-16-5p and Hsa-mir-335-5p targeted the greatest number of mRNAs in the network. These molecules could bind to PAX8-AS1 and eliminate the inhibition of target mRNA expression. We showed that PAX8-AS1 is the main lncRNA affected by 6-TG and that PAX8-AS1 regulates the hub genes in tumor pathways by competitively binding with miR-16-5p and miR-335-5p.
| S-EPMC7859320 | biostudies-literature
Project description:Non?coding RNAs serve important roles in regulating the expression of certain genes and are involved in the principal biological processes of breast cancer. The majority of studies have focused on defining the regulatory functions of long non?coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), and few studies have investigated how lncRNAs and miRNAs are transcriptionally regulated. In the present study, based on the breast invasive carcinoma dataset from The Cancer Genome Atlas at cBioPortal, and using a bioinformatics computational approach, an lncRNA?miRNA?mRNA network was constructed. The network consisted of 601 nodes and 706 edges, which represented the complex web of regulatory effects between lncRNAs, miRNAs and target genes. The results of the present study demonstrated that miR?510 was the most potent miRNA controller and regulator of numerous target genes. In addition, it was observed that the lncRNAs PVT1, CCAT1 and linc00861 exhibited possible interactions with clinical biomarkers, including receptor tyrosine?protein kinase erbB?2, estrogen receptor and progesterone receptor, demonstrated using RNA?protein interaction prediction software. The network of lncRNA?miRNA?mRNA interactions will facilitate further experimental studies and may be used to refine biomarker predictions for developing novel therapeutic approaches in breast cancer.
| S-EPMC5647044 | biostudies-literature