Dataset Information

In silico model for miRNA-mediated regulatory network in cancer.

ABSTRACT: Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet.

SUBMITTER: Ahmed KT

PROVIDER: S-EPMC8575005 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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In silico model for miRNA-mediated regulatory network in cancer.

Ahmed Khandakar Tanvir KT Sun Jiao J Chen William W Martinez Irene I Cheng Sze S Zhang Wencai W Yong Jeongsik J Zhang Wei W

Briefings in bioinformatics 20211101 6

Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introd ...[more]

PMID: 34279571

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Project description:BackgroundCircular RNAs (circRNAs), a novel class of non-coding RNAs, have been found to act as microRNA (miRNA) sponges and thus play key roles in biological processes and pathogenesis. However, studies regarding circRNAs in colorectal cancer (CRC) remain inadequate.ResultsBy differential expression analysis, 10 candidate circRNAs (6 upregulated and 4 downregulated circRNAs) were chosen. 9 of 10 circRNAs were available on CSCD and their structure showed the binding potential of miRNA. Intersection analysis revealed that miR-145-5p, miR-3127-5p, miR-761, miR-4766-3p, miR-135a-5p, miR-135b-5p, miR-374a-3p and miR-330-3p were 8 miRNAs with the most potential in binding circRNAs. Further expression validation and correlation analysis demonstrated hsa_circ_0001955/miR-145-5p and hsa_circ_0000977/miR-135b-5p axes as key pathways in CRC. Subsequently, target gene prediction, differential expression analysis, intersection analysis and correlation analysis showed that CDK6, MMP12 and RAB3IP were the three potential downstream targets of hsa_circ_0001955/miR-145-5p axis and FOXO1, MBNL1, MEF2C, RECK, PPM1E, TTLL7 and PCP4L1 were the seven potential downstream targets of hsa_circ_0000977/miR-135b-5p axis in CRC. Finally, we also confirmed that expression of hsa_circ_0001955 or hsa_circ_0000977 was significantly positively correlated with their individual targets in CRC.ConclusionsIn the present work, we constructed a potential hsa_circ_0001955/hsa_circ_0000977-mediated circRNA-miRNA-mRNA regulatory network in CRC by a series of in silico analysis and experimental validation.MethodsWhole-transcriptome microarrays from CRC and matched normal samples were obtained from GEO. The structure of circRNA was identified by CSCD. starBase and miRNet were successively used to predict miRNA of circRNA and target gene of miRNA. Expression correlation between RNA-RNA interactions was assessed using GEO and TCGA data. Finally, a potential circRNA-miRNA-mRNA network was established based on competing endogenous RNA (ceRNA) hypothesis.

Project description:BackgroundMicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.MethodsThe human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.ResultsUsing this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.ConclusionsOur analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Project description:The pathogenesis of gastric cancer (GC) is still not fully understood. We aimed to find the potential regulatory network for ceRNA (circRNA-miRNA-immune-related mRNA) to uncover the pathological molecular mechanisms of GC. The expression profiles of circRNA, miRNA, and mRNA in gastric tissue from GC patients were downloaded from the Gene Expression Omnibus (GEO) datasets. Differentially expressed circRNAs, miRNAs, and immune-related mRNAs were filtered, followed by the construction of the ceRNA (circRNA-miRNA-immune-related mRNA) network. Functional annotation and protein-protein interaction (PPI) analysis of immune-related mRNAs in the network were performed. Expression validation of circRNAs and immune-related mRNAs was performed in the new GEO and TCGA datasets and in-vitro experiment. A total of 144 differentially expressed circRNAs, 216 differentially expressed miRNAs, and 2,392 differentially expressed mRNAs were identified in GC. Some regulatory pairs of circRNA-miRNA-immune-related mRNA were obtained, including hsa_circ_0050102-hsa-miR-4537-NRAS-Tgd cells, hsa_circ_0001013-hsa-miR-485-3p-MAP2K1-Tgd cells, hsa_circ_0003763-hsa-miR-145-5p-FGF10-StromaScore, hsa_circ_0001789-hsa-miR-1269b-MET-adipocytes, hsa_circ_0040573-hsa-miR-3686-RAC1-Tgd cells, and hsa_circ_0006089-hsa-miR-5584-3p-LYN-neurons. Interestingly, FGF10, MET, NRAS, RAC1, MAP2K1, and LYN had potential diagnostic value for GC patients. In the KEGG analysis, some signaling pathways were identified, such as Rap1 and Ras signaling pathways (involved NRAS and FGF10), Fc gamma R-mediated phagocytosis and cAMP signaling pathway (involved RAC1), proteoglycans in cancer (involved MET), T-cell receptor signaling pathway (involved MAP2K1), and chemokine signaling pathway (involved LYN). The expression validation of hsa_circ_0003763, hsa_circ_0004928, hsa_circ_0040573, FGF10, MET, NRAS, RAC1, MAP2K1, and LYN was consistent with the integrated analysis. In conclusion, the identified ceRNA (circRNA-miRNA-immune-related mRNA) regulatory network may be associated with the development of GC.

Project description:BackgroundLong non-coding RNAs (lncRNAs) are essential regulators for various human cancers. However, these lncRNAs need to be further classified for cancer. In the present study, we identified novel competing endogenous RNA (ceRNA) network for bladder cancer (BC) and explored the gene functions of the ceRNA regulatory network.MethodsDifferential gene expression analysis were performed on The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) datasets to identify differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs). Based on the competing endogenous RNA (ceRNA) hypothesis, a lncRNA-miRNA-mRNA network was constructed using the StarBase database and visualization by Cytoscape software. Functional enrichment analyses of Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed via R package ClusterProfiler. The protein-protein interaction network was constructed by STRING database and visualization by Cytoscape. Finally, we used CIBERSORT and the TIMER database to analyze the immune infiltrations for BC.ResultsThe regulatory network was constructed via TCGA BLCA cohort. The differential expressions of lncRNA, miRNA, and mRNA were 186, 200, and 2,661, respectively. There were 106 lncRNA, miRNA, and mRNA included in the ceRNA network. In this network, Calcium Voltage-gated Channel Auxiliary Subunit Alpha2delta1 (CACNA2D1, P<0.001), domain containing engulfment adaptor1 (GULP1, P=0.001), latent transforming growth factor beta binding protein 1 (LTBP1, P=0.006), myosin light chain kinase (MYLK, P=0.001), serpin family E member 2 (SERPINE2, P=0.002), spectrin beta non-erythrocytic 2 (SPTBN2, P=0.047), and hsa-miR-590-3p (P<0.001) significantly affected the prognosis of BC patients. Functional enrichment analyses showed that the biological functions included negative regulation of protein phosphorylation, cell morphogenesis, and sensory organ morphogenesis. Important cancer pathways of KEGG included parathyroid hormone synthesis secretion action, the notch signaling pathway, MAPK signaling pathway, the Rap1 signaling pathway, signaling pathways regulating the pluripotency of stem cells, and the transforming growth factor-β signaling pathway. Our findings demonstrated that the ceRNA network has important biological functions and a significant influence on the prognosis of BC.ConclusionsThe lncRNA-miRNA-mRNA network constructed in the present study could provide useful insight into the underlying tumorigenesis of BC, and can determine new molecular biomarkers for the diagnosis and therapeutical treatment of BC.

Project description:Gastric cancer (GC), as an epidemic cancer worldwide, has more than 1 million new cases and an estimated 769,000 deaths worldwide in 2020, ranking fifth and fourth in global morbidity and mortality. In mammals, both miRNAs and transcription factors (TFs) play a partial role in gene expression regulation. The mRNA expression profile and miRNA expression profile of GEO database were screened by GEO2R for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Then, DAVID annotated the functions of DEGs to understand the functions played in biological processes. The prediction of potential target genes of miRNA and key TFs of mRNA was performed by mipathDB V2.0 and CHEA3, respectively, and the gene list comparison was performed to look for overlapping genes coregulated by key TFs and DEMs. Finally, the obtained miRNAs, TF, and overlapping genes were used to construct the miRNA-mRNA-TF regulatory network, which was verified by RT-qPCR. 76 upregulated DEGs, 199 downregulated DEGs, and 3 upregulated miRNAs (miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p) were identified from the expression profiles of mRNA (GSE26899, GSE29998, GSE51575, and GSE13911) and miRNA (GSE93415), respectively. Through database prediction and gene list comparison, it was found that among the 199 downregulated DEGs, 61, 71, and 69 genes were the potential targets of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p, respectively. 199 downregulated DEGs were used as the gene list for the prediction of key TFs, and the results showed that RFX6 ranked the highest. The potential target overlap genes of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p were 4 genes (SH3GL2, ATP4B, CTSE, and SORBS2), 7 genes (SLC7A8, RNASE4, ESRRG, PGC, MUC6, Fam3B, and FMO5), and 6 genes (CHGA, PDK4, TMPRSS2, CLIC6, GPX3, and PSCA), respectively. Finally, we constructed a miRNA-mRNA-TF regulatory network based on the above 17 mRNAs, 3 miRNAs, and 1 TF and verified by RT-qPCR and western blot results that the expression of RFX6 was downregulated in GC tissues. These identified miRNAs, mRNAs, and TF have a certain reference value for further exploration of the regulatory mechanism of GC.

Dataset Information

In silico model for miRNA-mediated regulatory network in cancer.

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In silico model for miRNA-mediated regulatory network in cancer.

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