ArticleOncotarget2016
Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Long non‑coding RNA HOTTIP promotes hepatocellular carcinoma tumorigenesis and development: A comprehensive investigation based on bioinformatics, qRT‑PCR and meta‑analysis of 393 cases.International journal of oncology · 2017Pooled it
- Research on RNA secondary structure predicting via bidirectional recurrent neural network.BMC bioinformatics · 2021Article
- Prognosis of clear cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an investigation based on RNA-sequencing data.Journal of translational medicine · 2019Article
- Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.BMC bioinformatics · 2018Article
- Article
- GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.RNA biology · 2018Article
- Using computer simulation models to investigate the most promising microRNAs to improve muscle regeneration during ageing.Scientific reports · 2017Article
- Clinical roles of the aberrantly expressed lncRNAs in lung squamous cell carcinoma: a study based on RNA-sequencing and microarray data mining.Oncotarget · 2017Article
- Integrated analysis of dosage effect lncRNAs in lung adenocarcinoma based on comprehensive network.Oncotarget · 2017Article
- BRWLDA: bi-random walks for predicting lncRNA-disease associations.Oncotarget · 2017Article
- The landscape of DNA methylation-mediated regulation of long non-coding RNAs in breast cancer.Oncotarget · 2017Article
- Article
- Selection of reference genes for microRNA analysis associated to early stress response to handling and confinement in Salmo salar.Scientific reports · 2017Article
- Systems-level understanding of ethanol-induced stresses and adaptation in E. coli.Scientific reports · 2017Article
- Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data.PloS one · 2017Article
- Relevance popularity: A term event model based feature selection scheme for text classification.PloS one · 2017Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers' generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages. However, the results can be notably different. In this study, we compared miRNAs obtained from 6 common significance tests/R packages (t-test, Limma, DESeq, edgeR, LRT and MARS) with the miRNAs archived in two databases; HMDD 2.0 database, which collects experimentally validated differentially expressed miRNAs, and Infer microRNA-disease association database, which contains the potential disease-associated miRNAs by network forecasting. Finally, we sought the MARS method in DEGseq package more effectively searched out differentially expressed miRNAs than other common methods.
Indexed as
Identifiers
What OpenQuestion holds
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.