Identification of metabolism-related genes for predicting peritoneal metastasis in patients with gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36503378", "kind": "science-record", "revision": 3, "content_hash": "sha256:9fb32ff2648200b0a97584913081640ee1c553d1e8fa545e9ea4050ecf66f439", "created_at": "2026-11-01T19:00:59Z", "updated_at": "2026-11-01T22:08:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of metabolism-related genes for predicting peritoneal metastasis in patients with gastric cancer.", "doi": "10.1186/s12863-022-01096-0", "pmid": "36503378", "authors": ["Chenyu Tian", "Junjie Zhao", "Dan Liu", "Jie Sun", "Chengbo Ji", "Quan Jiang", "Haojie Li", "Xuefei Wang", "Yihong Sun"], "journal": "BMC genomic data", "year": 2022, "abstract": "OBJECTIVE: The reprogramming of metabolism is an important factor in the metastatic process of cancer. In our study, we intended to investigate the predictive value of metabolism-related genes (MRGs) in recurrent gastric cancer (GC) patients with peritoneal metastasis.\nMETHODS: The sequencing data of mRNA of GC patients were obtained from Asian Cancer Research Group (ACRG) and the GEO databases (GSE53276). The differentially expressed MRGs (DE-MRGs) between a cell line without peritoneal metastasis (HSC60) and one with peritoneal metastasis (60As6) were analyzed with the Limma package. According to the LASSO regression, eight MRGs were identified as crucially related to peritoneal seeding recurrence in patients. Then, disease free survival related genes were screened using Cox regression, and a promising prognostic model was constructed based on 8 MRGs. We trained and verified it in two independent cohort.\nRESULTS: We confirmed 713 DE-MRGs and the enriched pathways. Pathway analysis found that the MRG-related pathways were related to tumor metabolism development. With the help of Kaplan-Meier analysis, we found that the group with higher risk scores had worse rates of peritoneal seeding recurrence than the group with lower scores in the cohorts.\nCONCLUSIONS: This study developed an eight-gene signature correlated with metabolism that could predict peritoneal seeding recurrence for GC patients. This signature could be a promising prognostic model, providing better strategy in treatment.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression analysis was implemented on 87 significant genes, and 8 genes (CNPY3, TF, ANGPT2, GSR, VKORC1, LRP12, FGF19 and SLC30A7) that could be independent prognostic predictors in PMGC were screened (Fig. 4A, and 4B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36503378", "locator": "sections[11] \"Construction of the MRG-based prognostic model\" paragraph 2", "quote": "LASSO Cox regression analysis was implemented on 87 significant genes, and 8 genes (CNPY3, TF, ANGPT2, GSR, VKORC1, LRP12, FGF19 and SLC30A7) that could be independent prognostic predictors in PMGC were screened (Fig. 4A, and 4B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used or analyzed during the current study are available from GEO repository: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36503378", "locator": "sections[19] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used or analyzed during the current study are available from GEO repository: https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC was 0.78, which indicated that this model had an effective value for predicting the recurrence of gastric cancer patients with peritoneal metastasis.", "kind": "quantitative", "target": {"value": 0.78, "unit": "auc", "tolerance": null, "source": "The AUC was 0.78, which indicated that this model had an effective value for predicting the recurrence of gastric cancer patients with peritoneal metastasis."}, "evidence": [{"asset": "source:pmid:36503378", "locator": "sections[11] \"Construction of the MRG-based prognostic model\" paragraph 4", "quote": "The AUC was 0.78, which indicated that this model had an effective value for predicting the recurrence of gastric cancer patients with peritoneal metastasis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC was 0.78, which indicated that this model had an effective value for predicting the recurrence of gastric cancer patients with peritoneal metastasis.", "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:36503378"], "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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