A ubiquitination-related risk model for predicting the prognosis and immunotherapy response of gastric adenocarcinoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38313020", "kind": "science-record", "revision": 3, "content_hash": "sha256:85e784924d7915d0e85aae2a978a3d7a05438a7dfb30815a9058892c9054bf81", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:11:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A ubiquitination-related risk model for predicting the prognosis and immunotherapy response of gastric adenocarcinoma patients.", "doi": "10.7717/peerj.16868", "pmid": "38313020", "authors": ["Shuai Shao", "Yang Sun", "Dongmei Zhao", "Yu Tian", "Yifan Yang", "Nan Luo"], "journal": "PeerJ", "year": 2024, "abstract": "Ubiquitination is crucial for the growth of cancer. However, the role of ubiquitination-related genes (URGs) in stomach adenocarcinoma (STAD) remains unclear. Differentially expressed URGs (DE-URGs) were examined in the whole TCGA-STAD dataset, and the prognosis-related genes were discovered from the The Cancer Genome Atlas (TCGA) training set. Prognostic genes were discovered using selection operator regression analysis and absolute least shrinkage (LASSO). A multivariate Cox analysis was further employed, and a polygene-based risk assessment system was established. Signatures were verified using the Gene Expression Omnibus (GEO) database record GSE84433 and the TCGA test set. Using the MEXPRESS dataset, a detailed analysis of gene expression and methylation was carried out. Using the DAVID database, DE-URG function and pathway enrichment was examined. The identified 163 DE-URGs were significantly associated with pathways related to protein ubiquitination, cell cycle, and cancer. A prognostic signature based on 13 DE-URGs was constructed, classifying patients into two risk groups. Compared to low-risk patients, people at high risk had considerably shorter survival times. Cox regression analyses considered prognostic parameters independent of age and risk score and were used to generate nomograms. Calibration curves show good agreement between nomogram predictions and observations. Furthermore, the results of the MEXPRESS analysis indicated that 13 prognostic DE-URGs had an intricate methylation profile. The enhanced Random Forest-based model showed greater efficacy in predicting prognosis, mutation, and immune infiltration. The in vitro validation, including CCK8, EdU, Transwell, and co-culture Transwell, proved that RNF144A was a potent oncogene in STAD and could facilitate the migration of M2 macrophages. In this research, we have created a genetic model based on URGs that can appropriately gauge a patient's prognosis and immunotherapy response, providing clinicians with a reliable tool for prognostic assessment and supporting clinical treatment decisions.", "claims": [{"id": "claim-method", "statement": "LASSO regression analysis also included genes connected to the above prognosis to predict STAD prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38313020", "locator": "sections[6] \"Selection of prognosis-related genes\" paragraph 2", "quote": "LASSO regression analysis also included genes connected to the above prognosis to predict STAD prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The raw measurements, R codes, matrices and the raw data of the in vitro validation are available in the Supplementary Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38313020", "locator": "sections[35] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:The raw measurements, R codes, matrices and the raw data of the in vitro validation are available in the Supplementary Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk scoring system’s AUCs for projecting STAD patients’ 1-, 3-, and 5-year OS in the TCGA training group were, respectively, 0.665, 0.618, and 0.722, according to the ROC curve (Fig. 1F).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38313020", "locator": "sections[17] \"Establishment and evaluation of the 13 DE-URGs-based prognostic signatures in the TCGA-training set\" paragraph 2", "quote": "The risk scoring system’s AUCs for projecting STAD patients’ 1-, 3-, and 5-year OS in the TCGA training group were, respectively, 0.665, 0.618, and 0.722, according to the ROC curve (Fig. 1F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk scoring system’s AUCs for projecting STAD patients’ 1-, 3-, and 5-year OS in the TCGA training group were, respectively, 0.665, 0.618, and 0.722, according to the ROC curve (Fig. 1F).", "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:38313020"], "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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