ArticleCancer informatics2023
In Silico Analysis of MicroRNA Expression Data in Liver Cancer.
Article in Cancer informatics, 2023. 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.
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
5 citing papers in PubMed, 7 citations in OpenAlex.
- Let-7a and miR-34a Interplay Potent Suppressive Roles in Hepatocellular Carcinoma via Co-TargetingInternational journal of molecular sciences · 2026Article
- Microbial signatures in head and neck squamous cell carcinoma: an in silico study.Journal of applied oral science : revista FOB · 2025Article
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- Review
- In-silico identification of deleterious non-synonymous SNPs of TBX1 gene: Functional and structural impact towards 22q11.2DS.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (
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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.