ArticleScientific reports2016
Prioritizing cancer-related microRNAs by integrating microRNA and mRNA datasets.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.Medical & biological engineering & computing · 2021Article
- Targeting Non-coding RNA for Glioblastoma Therapy: The Challenge of Overcomes the Blood-Brain Barrier.Frontiers in medical technology · 2021Review
- Expression Analyses of MicroRNAs in Hamster Lung Tissues Infected by SARS-CoV-2.Molecules and cells · 2020Article
- miRNA-216 knockdown has effects to suppress osteosarcoma via stimulating PTEN.Food science & nutrition · 2020Article
- Bioinformatics Analysis of Evolution and Human Disease Related Transposable Element-Derived microRNAs.Life (Basel, Switzerland) · 2020Review
- MicroRNA-106b promotes the proliferation, migration and invasion of retinoblastoma cells by inhibiting the expression of ZBTB4 protein.Experimental and therapeutic medicine · 2018Article
- Identification and Characterization of MicroRNAs Associated with Somatic Copy Number Alterations in Cancer.Cancers · 2018Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs regulating the expression of target genes, and they are involved in cancer initiation and progression. Even though many cancer-related miRNAs were identified, their functional impact may vary, depending on their effects on the regulation of other miRNAs and genes. In this study, we propose a novel method for the prioritization of candidate cancer-related miRNAs that may affect the expression of other miRNAs and genes across the entire biological network. For this, we propose three important features: the average expression of a miRNA in multiple cancer samples, the average of the absolute correlation values between the expression of a miRNA and expression of all genes, and the number of predicted miRNA target genes. These three features were integrated using order statistics. By applying the proposed approach to four cancer types, glioblastoma, ovarian cancer, prostate cancer, and breast cancer, we prioritized candidate cancer-related miRNAs and determined their functional roles in cancer-related pathways. The proposed approach can be used to identify miRNAs that play crucial roles in driving cancer development, and the elucidation of novel potential therapeutic targets for cancer treatment.
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