miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Chou, Chih‐Hung; Shrestha, Sirjana; Yang, Chi; Chang, Nai Wen; Lin, Yuling; Liao, Kuang‐Wen; Wei, Huang; Sun, Ting; Tu, Siang Jyun; Lee, Wei Hsiang; Chiew, Men Yee; Tai, Chun San; Wei, Ting; Tsai, Tzi Ren; Huang, Hsin Tzu; Wang, Chung Yu; Wu, Hung Tsung; Ho, Shu Yi; Chen, Pin Rong; Chuang, Ching-Te; Hsieh, Pei Jung; Wu, Yi; Chen, Wenliang; Li, Meng Ju; Wu, Yu Chun; Huang, Xin; Ng, Fung Ling; Buddhakosai, Waradee; Huang, Pei Hsin; Lan, Kuan Chun; Huang, Chia-Yen; Weng, Shun Long; Cheng, Yeong-Nan; Liang, Chao; Hsu, Wen Lian

doi:10.1093/nar/gkx1067

Cited by 1,593 publications

(1,371 citation statements)

References 47 publications

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“…Four robust consensus molecular subtypes (CMSs) were identified, including a hypermutated, microsatellite unstable CMS1 subtype, a CMS2 marked by WNT and MYC signaling activation, a CMS3 showing evident metabolic dysregulation, and a CMS4 with marked activation of transforming growth factor-β signaling. Through characterizing the subtypes of CRC with transcriptomics approaches, this study revealed convergent pathway dependencies in thousands of cases, and proposed a classification system with clear biological interpretability, which could prove useful for clinical stratification and targeted interventions [26,27].…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index

Hou

Wang

et al. 2018

Cell Physiol Biochem

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Background/Aims: Colorectal cancer (CRC) is one of leading cancers in both incidence and mortality rate. The 5-year survival rate varies considerably depending on the pathological stage of the tumor. Although prominent progress has been made through screening for survival-associated factors from a certain type of genetic or epigenetic modifications, few attempts have been made to apply a network-based approach in prognostic factor identification, which could prove valuable for a complex, multi-faceted disease such as CRC. Methods: In this study, a TCGA dataset of 379 CRC patients was subjected to a network-based analysis strategy consisting of multivariate regression, co-expression network and gene regulatory network analyses, and survival analyses. Both genetic and epigenetic aberrations, including those in gene expression and DNA methylation at specific sites, were screened for significant association with patient survival. A prognostic index (PI) integrating all potential prognostic factors was subsequently validated for its prognostic value. Results: A collection of six miRNAs, eleven mRNAs, and nine DNA methylation sites were identified as potential prognostic factors. The low- and high-risk patient groups assigned based on PI level showed significant difference in overall survival (hazard ratio = 1.32, 95% confidence interval 1.29-1.36, p < 0.0001). Patients in the low- and high-risk groups can be further divided into a total of four subgroups, based on pathological staging. In the two high-risk subgroups (PI > 0), there was significant different (Cox p < 0.0001) in OS between the earlier (stages I/II) and later stages (stages III/IV). However, in the two low-risk subgroups (PI < 0), earlier (stages I/II) and later stages (stages III/IV) showed no significant difference in OS (Cox p = 0.185). On the other hand, there were significant differences in OS between the low- and high-risk subgroups when both subgroups were of earlier stages (Cox p < 0.001) or of later stages (Cox p < 0.0001). Conclusion: The novel network-based, integrative analysis adopted in this study was efficient in screening for prognostic predictors. Along with PI, the set of 6 miRNAs, 11 mRNAs, and 9 DNA methylation sites could serve as the basis for improved prognosis estimation for CRC patients in future clinical practice.

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Section: Discussionmentioning

confidence: 99%

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index

Hou

Wang

et al. 2018

Cell Physiol Biochem

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“…As a database, miRTarBase has accumulated more than 360, 000 miRNA-target interactions (MTIs), which were collected by manually surveying pertinent literature after systematic, natural language processing of the text to filter research articles related to the functional study of miRNAs [22]. TargetScan predicts the biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA, as an option, predictions with only poorly conserved sites are also provided [23].…”

Section: Target Mrnas Of Demirnasmentioning

confidence: 99%

How Long Non-Coding RNAs and MicroRNAs Mediate the Endogenous RNA Network of Head and Neck Squamous Cell Carcinoma: a Comprehensive Analysis

Zhao

et al. 2018

Cell Physiol Biochem

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Background/Aims: Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to compete for microRNAs (miRNAs) in cancer metastasis. Head and neck squamous cell carcinoma (HNSCC) is one of the most common human cancers and rare biomarkers could predict the clinical prognosis of this disease and its therapeutic effect. Methods: Weighted gene co-expression network analysis (WGCNA) was performed to identify differentially expressed mRNAs (DEmRNAs) that might be key genes. GO enrichment and protein–protein interaction (PPI) analyses were performed to identify the principal functions of the DEmRNAs. An lncRNA-miRNA-mRNA network was constructed to understand the regulatory mechanisms in HNSCC. The prognostic signatures of mRNAs, miRNAs, and lncRNAs were determined by Gene Expression Profiling Interactive Analysis (GEPIA) and using Kaplan–Meier survival curves for patients with lung squamous cell carcinoma. Results: We identified 2,023 DEmRNAs, 1,048 differentially expressed lncRNAs (DElncRNAs), and 82 differentially expressed miRNAs (DEmiRNAs). We found that eight DEmRNAs, 53 DElncRNAs, and 16 DEmiRNAs interacted in the ceRNA network. Three ceRNAs (HCG22, LINC00460 and STC2) were significantly correlated with survival. STC2 transcript levels were significantly higher in tumour tissues than in normal tissues, and the STC2 expression was slightly upregulated at different stages of HNSCC. Conclusion: LINC00460, HCG22 and STC2 exhibited aberrant levels of expression and may participate in the pathogenesis of HNSCC.

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“…For the significantly up-regulated genes, the ARCHS4 (Lachmann et al, 2018), miRTarBase 2017 (Chou et al, 2018) and BioCarta 2016 (Rouillard et al, 2016) databases were used to determine the Transcription Factors, MiRNAs and Pathways associated with them respectively.…”

Section: Gene Set Enrichment Analysis (Gsea) For the Up-regulated Genesmentioning

confidence: 99%

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Ahammad

2018

Preprint

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Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and downregulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

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miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Cited by 1,593 publications

References 47 publications

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index

Genome-Wide Network-Based Analysis of Colorectal Cancer Identifies Novel Prognostic Factors and an Integrative Prognostic Index

How Long Non-Coding RNAs and MicroRNAs Mediate the Endogenous RNA Network of Head and Neck Squamous Cell Carcinoma: a Comprehensive Analysis

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

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