ArticleFrontiers in oncology2022
Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Huangqi fuling decoction inhibits the invasion and metastasis of gastric cancer via the TNF signaling pathway.Scientific reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer.International journal of molecular sciences · 2024Review
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Prognostic Value and Potential Regulatory Mechanism of H19 in Stomach Adenocarcinoma.Journal of oncology · 2022Article
- Propensity score matching comparisons of postoperative complications and morbidity between digestive tract reconstruction methods after gastrectomy in gastric cancer patients with visceral obesity.Frontiers in oncology · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.
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