ArticleMolecular genetics & genomic medicine2020
Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.
Article in Molecular genetics & genomic medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 13 citations in OpenAlex.
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Research progress on the development and progression of long non-coding RNA in upper gastrointestinal adenocarcinoma.Discover oncology · 2025Review
- Non-Coding RNA as Biomarkers and Their Role in the Pathogenesis of Gastric Cancer-A Narrative Review.International journal of molecular sciences · 2024Review
- SEC24D gene as a biomarker in human cancers and its association with CD8+ T cell immune cell infiltration.American journal of translational research · 2023Article
- Identification of the three subtypes and the prognostic characteristics of stomach adenocarcinoma: analysis of the hypoxia-related long non-coding RNAs.Functional & integrative genomics · 2022Article
- Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas.Cancers · 2021Review
- Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma.Aging · 2021Article
- Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA-lncRNA co-expression network analysis.Molecular genetics & genomic medicine · 2020Article
- Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundStomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD.
methodsBase on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between STAD and normal tissue. The machine learning and survival analysis were performed to evaluate the potential diagnostic and prognostic value of lncRNAs for STAD. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real-time polymerase chain reaction (qRT-PCR) and GSE27342 dataset. GSE27342 dataset were also to perform gene set enrichment analysis.
resultsA total of 814 DEmRNAs and 106 DElncRNAs between STAD and normal tissue were obtained. FOXD2-AS1, LINC01235, and RP11-598F7.5 were defined as optimal diagnostic lncRNA biomarkers for STAD. The area under curve (AUC) of the decision tree model, random forests model, and support vector machine (SVM) model were 0.797, 0.981, and 0.983, and the specificity and sensitivity of the three model were 75.0% and 97.1%, 96.9% and 96%, and 96.9% and 97.1%, respectively. Among them, LINC01235 was not only an optimal diagnostic lncRNA biomarkers, but also related to survival time. The expression of three DEmRNAs (ESM1, WNT2, and COL10A1) and three optimal diagnostic lncRNAs biomarkers (FOXD2-AS1, RP11-598F7.5, and LINC01235) in qRT-PCR validation was were consistent with our integrated analysis. Except for FOXD2-AS1, ESM1, WNT2, COL10A1, and LINC01235 were upregulated in STAD, which was consistent with our integration results. Gene set enrichment analysis results indicated that DNA replication, Cell cycle, ECM-receptor interaction, and P53 signaling pathway were four significantly enriched pathways in STAD.
conclusionOur study identified three DElncRNAs as potential diagnostic biomarkers of STAD. Among them, LINC01235 also was a prognostic lncRNA biomarkers.
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