ArticleCancer cell international2024
Integration of mult-omics and nucleotide metabolism reprogramming signature analysis reveals gastric cancer immunological and prognostic features.
Article in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Metabolic remodeling by circular RNAs in gastric tumorigenesis: From mechanisms to biomarker discovery (Review).International journal of oncology · 2026Review
- Identification of a novel ion channel-related gene signature to predict prognosis and immune response of gastric cancer.Translational cancer research · 2026Article
- Transcriptome and experimental validation identify prognostic genes associated with lipotoxicity in thyroid carcinoma.BMC cancer · 2026Article
- Emerging insights into plant disease management: multi-omics approaches elucidate the molecular mechanisms underlying pathogen virulence differentiation in natural populations.Applied and environmental microbiology · 2026Article
- Diagnostic performance of serum origin recognition complex subunit 1 protein for hepatitis B virus-related hepatocellular carcinoma.World journal of gastroenterology · 2025Article
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- A novel machine learning-based predictive model for gastric cancer.Translational cancer research · 2025Article
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Abstract
backgroundGastric cancer is a frequent and lethal solid tumor that has a poor prognosis and treatment result. Reprogramming of nucleotide metabolism is a characteristic of cancer development and progression.
methodsWe used a variety of machine learning techniques to create a novel nucleotide metabolism-related index (NMRI) using gastric cancer sample data obtained from the TCGA and GEO databases. This index is based on genes associated to nucleotide metabolism. Gastric cancer patients were categorized into high and low NMRI groups based on NMRI results. The clinical features, tumor immune microenvironment, response to chemotherapy, and response to immunotherapy were then thoroughly examined. In vitro experiments were then used to confirm the biological role of SERPINE1 in gastric cancer.
resultsThe four nucleotide metabolism-related genes that make up NMRI (GAMT, ORC1, CNGB3, and SERPINE1) were verified in an external dataset and are a valid predictor of prognosis for patients with gastric cancer. The high NMRI group was more responsive to immunotherapy and had greater levels of immune cell infiltration than the low NMRI group. The proliferation and migration of stomach cancer was shown to be decreased by SERPINE1 knockdown in vitro.
conclusionsThis study's NMRI can reliably predict a patient's prognosis for stomach cancer and pinpoint the patient group that will benefit from immunotherapy, offering important new information on the clinical treatment of stomach cancer.
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