ArticleMolecular therapy. Nucleic acids2023
A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model.
Article in Molecular therapy. Nucleic acids, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 47 citations in OpenAlex.
- A stromal-derived five-gene signature predicts gastric cancer recurrence through integrated bioinformatics and single-cell analysis.Translational cancer research · 2026Article
- ADGRF4 and ADGRL4 as novel prognostic biomarkers and potential therapeutic implications in stomach adenocarcinoma.BMC gastroenterology · 2026Article
- Refined immune-based molecular subtypes of gastric cancer: Integrating mismatch repair status and tumor microenvironment for enhanced immunotherapy prediction.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Tumor Budding in Gastric Carcinoma: Beyond Counting Cells at the Invasive Front-A Review of Current Evidence and Biological Perspectives.International journal of molecular sciences · 2026Review
- The Dysfunctional Self-Focus Attributes Scale-7 (DSAS-7): A Machine Learning-based Development of a Shortened Version of the DSAS.Journal of medical systems · 2026Article
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Review
- Evolving Paradigms in Gastric Cancer Staging: From Conventional Imaging to Advanced MRI and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation.Frontiers in immunology · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Letter to the editor: prediction of hematogenous metastasis risk from molecular classification in gastric cancer.International journal of surgery (London, England) · 2026Article
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Prognostic Significance and Molecular Classification of Triple Negative Breast Cancer: A Systematic Review.European journal of breast health · 2025Article
- DOMSCNet: a deep learning model for the classification of stomach cancer using multi-layer omics data.Briefings in bioinformatics · 2025Article
- Identification of a stromal immunosuppressive barrier orchestrated by SPP1Frontiers in immunology · 2025Article
- Cell-in-cell associated lncRNA signature predicts prognosis and immunotherapy response in gastric cancer.Frontiers in oncology · 2025Article
- Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.Ecancermedicalscience · 2025Review
- Unveiling Cancer-Related Metaplastic Cells in Both Helicobacter pylori Infection and Autoimmune Gastritis.Gastroenterology · 2025Article
- Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives.Current medicinal chemistry · 2025Review
- Integrated triple signal amplification strategy for ultrasensitive electrochemical detection of gastric cancer-related microRNA utilizing MoSJournal of nanobiotechnology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.
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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.