ArticleIET systems biology2018
Identifying cancer-related microRNAs based on subpathways.
Article in IET systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- MicroRNA‑125a‑5p regulates liver cancer cell growth, migration and invasion and EMT by targeting HAX1.International journal of molecular medicine · 2020Article
- MiR-30e-5p inhibits the migration and invasion of nasopharyngeal carcinoma via regulating the expression of MTA1.Bioscience reports · 2020Article
- A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers.Frontiers in genetics · 2020Article
- miRNAmotif-A Tool for the Prediction of Pre-miRNA⁻Protein Interactions.International journal of molecular sciences · 2018Article
- A Deep Differential Analysis in Four Subtypes of Breast Cancer Based on Regulations of miRNA-mRNA.IET systems biologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are a class of small endogenous non-coding genes that play important roles in post-transcriptional regulation as well as other important biological processes. Accumulating evidence indicated that miRNAs were extensively involved in the pathology of cancer. However, determining which miRNAs are related to a specific cancer is problematic because one miRNA may target multiple genes and one gene may be targeted by multiple miRNAs. The authors proposed a new approach, named miR_SubPath, to identify cancer-associated miRNAs by three steps. The targeted genes were determined based on differentially expressed genes in significant dysfunctional subpathways. Then the candidate miRNAs were determined according to miRNA-genes associations. Finally, these candidate miRNAs were ranked based on their relations with some seed miRNAs in a functional similarity network. Results on real-world datasets showed that the proposed miR_SubPath method was more robust and could identify more cancer-related miRNAs than a prior approach, miR_Path, miR_Clust and Zhang's method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.