ArticleOncology letters2018
Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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.
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Who cites it
3 citing papers in PubMed.
- Recent advancement in inflammatory biomarkers for cervical cancer diagnosis and therapy.Frontiers in oncology · 2026Review
- Identification of MicroRNAs as potential biomarkers for detecting ischemic stroke.Genes & genomics · 2022Article
- Increased miR-20b Level in High Grade Cervical Intraepithelial Neoplasia.Pathology oncology research : POR · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer (CC) is a leading cause of cancer-associated mortality in women; thus, the present study aimed to investigated potential target genes and pathways in patients with CC by utilizing an ensemble method and pathway enrichment analysis. The ensemble method integrated a correlation method [Pearson's correlation coefficient (PCC)], a causal inference method (IDA) and a regression method [least absolute shrinkage and selection operator (Lasso)] using the Borda count election algorithm, forming the PCC, IDA and Lasso (PIL) method. Subsequently, the PIL method was validated to be a feasible approach to predict microRNA (miRNA) targets by comparing predicted miRNA targets against those from a confirmed database. Finally, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was conducted for target genes in the 1,000 most frequently predicted miRNA-mRNA interactions to determine target pathways. A total of 10 target genes were obtained that were predicted >5 times, including secreted frizzled-related protein 4, maternally expressed 3 and NIPA like domain containing 4. Additionally, a total of 17 target pathways were identified, of which cytokine-cytokine receptor interaction (P=8.91×10
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