Construction of a novel signature and prediction of the immune landscape in gastric cancer based on necroptosis-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35918354", "kind": "science-record", "revision": 3, "content_hash": "sha256:54e33d1b675de156a73e5b9c0a8436052af5029263dd2366ba3c6d088259d7e6", "created_at": "2026-11-01T18:15:00Z", "updated_at": "2026-11-01T21:28:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a novel signature and prediction of the immune landscape in gastric cancer based on necroptosis-related genes.", "doi": "10.1038/s41598-022-15854-8", "pmid": "35918354", "authors": ["Zhengtian Li", "Wenkang Yang", "Dejun Liu", "Weizheng Ye", "Gang Du", "Xi Li"], "journal": "Scientific reports", "year": 2022, "abstract": "Necroptosis, a type of programmed cell death, has become a potential therapeutic target for solid tumors. Nevertheless, the potential roles of necroptosis-related genes (NRGs) in gastric cancer (GC) remain unknown. The objective of the present study was to create a necroptosis-related prognostic signature that can provide more accurate assessment of prognosis in GC. Using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, we identified differentially expressed NRGs. Univariate analysis and Lasso regression were performed to determine the prognostic signature. Risk scores were calculated and all GC patients were divided into high- and low-risk score group according to the median risk score value. The robustness of this signature was externally validated with data from GSE84437 cohort (n = 431). Survival analysis revealed high-risk score patients had a worse prognosis. Results evidenced that the signature was an independent prognosis factor for survival. Single-sample sequence set enrichment analysis (ssGSEA) exhibited different enrichment of immune cells and immune-related pathways in the two risk groups. Furthermore, a predictive nomogram was generated and showed excellent predictive performance based on discrimination and calibration. In addition, the risk score positively correlated with tumor mutational burden and was associated with sensitivity to multiple anti-cancer drugs. Overall, our work demonstrates a close relationship between necroptosis and the prognosis of GC. The signature we constructed with potential clinical application value, can be used for prognosis prediction and being a potential therapeutic responses indicator in GC patients.", "claims": [{"id": "claim-method", "statement": "Prognostic associated genes were screened from DEGs by univariate Cox regression analysis and were next subjected to the lasso Cox regression in TCGA-GC cohort and yielded optimal genes22–24.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35918354", "locator": "sections[7] \"Construction and validation of necroptosis-related prognostic signature\" paragraph 2", "quote": "Prognostic associated genes were screened from DEGs by univariate Cox regression analysis and were next subjected to the lasso Cox regression in TCGA-GC cohort and yielded optimal genes22–24."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data of this study are derived from the TCGA (https:// portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35918354", "locator": "sections[33] \"notes\" paragraph 1", "quote": "The raw data of this study are derived from the TCGA (https:// portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/), which are publicly available databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The NRGsig achieved a 5-year survival AUC of 0.743 in the TCGA-GC cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35918354", "locator": "sections[16] \"Construction and validation of NRGsig based on necroptosis-related subtypes\" paragraph 3", "quote": "The time-dependent ROC curves were performed to evaluate the prediction performance of the NRGsig and the areas under the curve for 5-year was 0.743 in the TCGA-GC cohort (Fig. 6D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The NRGsig achieved a 5-year survival AUC of 0.743 in the TCGA-GC cohort.", "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:35918354"], "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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