ArticleGenes2022
Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma.Journal of cellular and molecular medicine · 2023Pooled it
- Exploring the TRIM15 protein interaction network as a drug target using machine learning in pancreatic cancer.Discover oncology · 2025Article
- Dissecting lncRNA-mRNA regulatory network in type 2 diabetes as the risk factor of pancreatic cancer.Scientific reports · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Advances in Expression Regulation, Molecular Targeting Mechanisms, and Therapeutic Applications of the Let-7 MicroRNA Family in Gastric Cancer.Oncology research · 2025Review
- Identification of Anoikis-related potential biomarkers and therapeutic drugs in chronic thromboembolic pulmonary hypertension via bioinformatics analysis and in vitro experiment.Scientific reports · 2024Article
- Article
- Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer.Biomarker insights · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.
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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 Socratic graph.