A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38608069", "kind": "science-record", "revision": 2, "content_hash": "sha256:1428c693bc32c46a380ca46977646a874454bf8061e4c98c17f966e902501438", "created_at": "2026-11-01T19:20:34Z", "updated_at": "2026-11-01T21:32:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure.", "doi": "10.1097/MD.0000000000037728", "pmid": "38608069", "authors": ["Ziqing Deng", "Qian Feng", "Dan Zhao", "Zhihao Huang"], "journal": "Medicine", "year": 2024, "abstract": "Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8+ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.", "claims": [{"id": "claim-method", "statement": "Algorithm combinations were performed on the prognostic signature to fit prediction models based on the leave-one-out cross-validation (LOOCV) framework in the TCGA dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38608069", "locator": "sections[4] \"2.2. Integrative machine learning algorithms constructed an optimal DPS\" paragraph 2", "quote": "Algorithm combinations were performed on the prognostic signature to fit prediction models based on the leave-one-out cross-validation (LOOCV) framework in the TCGA dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article [and its supplementary information files].Supplemental Digital Content is available for this article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38608069", "locator": "sections[19] \"(untitled)\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article [and its supplementary information files].Supplemental Digital Content is available for this article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic signature constructed by Enet[alpha = 0.3] method (highest average C-index = 0.62) was referred as the optimal DPS (Fig. 2A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38608069", "locator": "sections[10] \"3.2. Integrative machine learning algorithms developed a prognostic DPS\" paragraph 2", "quote": "The prognostic signature constructed by Enet[alpha = 0.3] method (highest average C-index = 0.62) was referred as the optimal DPS (Fig. 2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic signature constructed by Enet[alpha = 0.3] method (highest average C-index = 0.62) was referred as the optimal DPS (Fig. 2A).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38608069"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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