ArticleGenes & genomics2021
Bioinformatic and integrated analysis identifies an lncRNA-miRNA-mRNA interaction mechanism in gastric adenocarcinoma.
Article in Genes & genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Exploring the Roles of Wnt Signaling Pathway and Non-Coding RNAs in COPD.International journal of chronic obstructive pulmonary disease · 2026Review
- LncRNA VCAN-AS1 Sponges miR-374c-3p to Promote Proliferation, Invasion, Migration, and EMT in Thyroid Cancer.International journal of endocrinology · 2026Article
- Competing Endogenous RNA Networks Reveal Long Non-coding RNAs as Potential Prognostic Biomarkers in Gastric Cancer: A Systematic Review and Meta-Analysis.Advanced biomedical research · 2025Review
- Long non-coding RNA VCAN-AS1 promotes gastric cancer progression via the HuR/F11R pathway.American journal of translational research · 2024Article
- Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer.Scientific reports · 2023Article
- Role of non‑coding RNA intertwined with the Wnt/β‑catenin signaling pathway in endometrial cancer (Review).Molecular medicine reports · 2023Review
- Bioinformatics analysis of potential Key lncRNA-miRNA-mRNA molecules as prognostic markers and important ceRNA axes in gastric cancer.American journal of cancer research · 2022Article
- Long non-coding RNA CRNDE exacerbates NPC advancement mediated by the miR-545-5p/CCND2 axis.Cancer cell international · 2021Article
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Authors and funding
14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundlncRNAs-miRNAs-mRNAs networks play an important role in Gastric adenocarcinoma (GA). Identification of these networks provide new insight into the role of these RNAs in gastric cancer.
objectivesBiological information databases were screened to characterize and examine the regulatory networks and to further investigate the potential prognostic relationship this regulation has in GA.
methodsBy mining The Cancer Genome Atlas (TCGA) database, we gathered information on GA-related lncRNAs, miRNAs, and mRNAs. We identified differentially expressed (DE) lncRNAs, miRNAs, and mRNAs using R software. The lncRNA-miRNA-mRNA interaction network was constructed and subsequent survival examination was performed. Representative genes were selected out using The Biological Networks Gene Ontology plug-in tool on Cytoscape. Additional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were used to screen representative genes for functional enrichment. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to identify the expression of five candidate differential expressed RNAs.
resultsInformation of samples from 375 cases of gastric cancer and 32 healthy cases (normal tissues) were downloaded from the TCGA database. A total of 1632 DE-mRNAs, 1008 DE-lncRNAs and 104 DE-miRNAs were identified and screened. Among them, 65 DE-lncRNAs, 10 DE-miRNAs, and 10 DE-mRNAs form lncRNAs-miRNAs-mRNAs regulatory network. Additionally, 10 lncRNAs and 2 mRNAs were associated with the prognosis of GA. Multivariable COX analysis revealed that AC018781.1 and VCAN-AS1 were independent risk factors for GA. GO functional enrichment analysis found DE-mRNA was significantly enriched TERM (P < 0.05). The KEGG signal regulatory network analysis found 11 significantly enrichment networks, the most prevailing was for the AGE-RAGE signaling pathway associated with Diabetic complications. Results of RT-qPCR was consistent with the in silico results.
conclusionsThe results of the present study represent a view of GA from a analysis of lncRNA, miRNA and mRNA. The network of lncRNA-miRNA-mRNA interactions revealed here may potentially further experimental studies and may help biomarker development for GA.
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