ArticleScientific reports2024
Multiomics integration and machine learning reveal prognostic programmed cell death signatures in gastric cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Gastric Cancer Organoids: Mechanistic Insights, Drug Discovery, and Translational Advances in Precision Medicine.Journal of gastric cancer · 2026Review
- Precision oncology in gastric cancer: navigating molecular subtypes, therapeutic targets, and future horizons.Cellular oncology (Dordrecht, Netherlands) · 2026Review
- A pan-cancer multi-omicFrontiers in artificial intelligence · 2026Article
- Therapeutic vulnerability shaped by the microenvironment: multi-omics and AI biomarkers for precision surgical planning in gastrointestinal tumors.Frontiers in cell and developmental biology · 2026Article
- Urinary metabolomics and proteomics for early detection of gastric cancer: insights from a two-center multicenter study.Frontiers in oncology · 2026Article
- Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate.Frontiers in oncology · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Innovative insights and future research directions in gastric cancer through single-cell RNA sequencing.World journal of gastrointestinal oncology · 2025Article
- CER1 as a manganese ion metabolism gene drives gastric cancer progression and therapeutic potential via oxidative stress and tumor microenvironment regulation.Discover oncology · 2025Article
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Multiomics Signature Reveals Network Regulatory Mechanisms in a CRC Continuum.International journal of molecular sciences · 2025Article
- Integrative multi-omics analysis of gastric cancer evolution from precancerous lesions to metastasis identifies a deep learning-based prognostic model.Frontiers in immunology · 2025Article
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Authors and funding
6 authors.
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Abstract
Gastric cancer (GC) is characterized by notable heterogeneity and the impact of molecular subtypes on treatment and prognosis. The role of programmed cell death (PCD) in cellular processes is critical, yet its specific function in GC is underexplored. This study applied multiomics approaches, integrating transcriptomic, epigenetic, and somatic mutation data, with consensus clustering algorithms to classify GC molecular subtypes and assess their biological and immunological features. A machine learning model was developed to create the Gastric Cancer Multi-Omics Programmed Cell Death Signature (GMPS), targeting PCD-related genes. We verified the expression of the GMPS hub genes using the RT-qPCR method. The prognostic influence of GMPS on GC was then evaluated. Single-cell analysis was performed to examine the heterogeneity of PCD characteristics in GC. Findings indicate that GMPS notably correlates with patient survival rates, tumor mutational burden (TMB), and copy number variations (CNV), demonstrating substantial prognostic predictive power. Moreover, GMPS is closely associated with the tumor microenvironment (TME) and immune therapy response. This research elucidates the molecular subtypes of GC, highlighting PCD's critical role in prognosis assessment. The relationship between GMPS and immune therapy response, alongside gastric cancer's microenvironmental features, provides insights for personalized treatment.
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