Observational studyMedicine2020
MicroRNA expression profiling and target gene analysis in gastric cancer.
Observational study in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- miR-3921 functions as a tumor suppressor and negatively regulates RIPK1 in gastric cancer.PeerJ · 2026Article
- Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Biomarkers of gastric cancer: current advancement.Heliyon · 2022Review
- CircRNA protein tyrosine phosphatase receptor type a suppresses proliferation and induces apoptosis of lung adenocarcinoma cells via regulation of microRNA-582-3p.Bioengineered · 2022Article
- Long noncoding RNA RP11-70C1.3 confers chemoresistance of breast cancer cells through miR-6736-3p/NRP-1 axis.Bosnian journal of basic medical sciences · 2022Article
- miR-31-3p functions as a tumor suppressor by directly targeting GABBR2 in prostate cancer.Frontiers in oncology · 2022Article
- Predicting Associations of miRNAs and Candidate Gastric Cancer Genes for Nanomedicine.Nanomaterials (Basel, Switzerland) · 2021Article
- PHLPP1 Overexpression was Associated With a Good Prognosis With Decreased AKT Activity in Gastric Cancer.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to identify differentially expressed microRNAs (miRNAs) in gastric cancer by comparing gastric cancerous tissues with normal tissues, explore the potential roles.The miRNA expression microarray was employed on gastric cancer tissues, and apparently normal para-cancerous tissues from 3 patients undergoing radical surgery were matched. Quantitative RT-PCR was performed on the other 7 patients to validate the findings of the microarray. Furthermore, Gene Ontology (GO) analysis and enrichment analysis of KEGG Pathway were performed for 5 dysregulated candidate miRNAs, including 3 upregulated (miR-31-3p, miR-6736-3p, and miR-147b) and 2 downregulated (miR-3065-5p and miR-3921) miRNAs, in order to determine the role of miRNAs in tumorigenesis and development.Among these miRNAs, 17 miRNAs were found to be upregulated, and 19 miRNAs were found to be downregulated. The dysregulated expression of 5 candidate miRNAs, including miR-31-3p, miR-147b, miR-6736-3p, miR-3065-5p, and miR-3921, were verified by quantitative RT-PCR in the validation set. Among these miRNAs, miR-31-3p, miR-6736-3p, miR-3065-5p, and miR-3921 had 551 target gene intersections. The GO and KEGG Pathway analyses Revealed that miR-31-3p, miR-6736-3p, miR-3065-5p, and miR-3921 may participate in multiple pathophysiological processes, such as foreign substance metabolism and chemical carcinogenesis.The profile of differentially expressed miRNAs was successfully screened, and 4 miRNAs (i.e., miR-31-3p, miR-6736-3p, miR-3065-5p, and miR-3921) appeared to be involved in gastric carcinogenesis. These might serve as promising biomarkers for gastric cancer.
Indexed as
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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.