Necroptosis-Related Prognostic Signature and Nomogram Model for Predicting the Overall Survival of Patients with Lung Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36101745", "kind": "science-record", "revision": 4, "content_hash": "sha256:b928d250e4a592dd10bd5256e432b446a7fc55f121352819f1b83f815534bbb0", "created_at": "2026-11-01T18:15:20Z", "updated_at": "2026-11-01T22:07:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Necroptosis-Related Prognostic Signature and Nomogram Model for Predicting the Overall Survival of Patients with Lung Cancer.", "doi": "10.1155/2022/4908608", "pmid": "36101745", "authors": ["Yunpeng Xuan", "Xiangfeng Jin", "Mingzhao Wang", "Zizong Wang"], "journal": "Genetics research", "year": 2022, "abstract": "Background: Necroptosis is a type of programmed cell death mode and it serves an important role in the tumorigenesis and tumor metastasis. The purpose of this study is to develop a prognostic model based on necroptosis-related genes and nomogram for predicting the overall survival of patients with lung cancer.\nMethod: Differentially expressed necroptosis-related genes (NRDs) between lung cancer and normal samples were identified. Univariate and LASSO regression analyses were performed to establish a risk score (RS) model, followed by validation within TCGA and GSE37745. The correlation between RS model and tumor microenvironment, mutation status, or drug susceptibility was analyzed. By combining clinical factors, nomogram was developed to predict 1-, 3-, and 5-year survival probability of an individual. The biological function involved by different risk groups was conducted by GSEA.\nResults: A RS model containing six NRDs (FLNC, PLK1, ID1, MYO1C, SERTAD1, and LEF1) was constructed, and patients were divieded into low-risk (LR) and high-risk (HR) groups. Patients in HR group were associated with shorter survival time than those in the LR group; this model had better prognostic performance. Nomogram based on necroptosis score, T stage, and stage had been confirmed to predict survival of patients. The number of resting NK cells and M0 macrophages was higher in HR group. In addition, higher tumor mutational burden and drug sensitivity were observed in the HR group. Patients in HR group were involved in p53 signaling pathway and cell cycle.\nConclusion: This study constructed a robust six-NRDs signature and established a prognostic nomogram for survival prediction of lung cancer.", "claims": [{"id": "claim-method", "statement": "To assess the prognostic value of NRDs, univariate Cox regression analysis in survival package (version 3.2–13) was performed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36101745", "locator": "sections[5] \"2.3. Identification of Prognosis-Related NRDs\" paragraph 2", "quote": "To assess the prognostic value of NRDs, univariate Cox regression analysis in survival package (version 3.2–13) was performed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data could be downloaded at https://xenabrowser.net/, https://www.ncbi.nlm.nih.gov/, and https://www.genecards.org/, and the codes used during the present study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36101745", "locator": "sections[27] \"Data Availability\" paragraph 2", "quote": "The data could be downloaded at https://xenabrowser.net/, https://www.ncbi.nlm.nih.gov/, and https://www.genecards.org/, and the codes used during the present study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A RS model containing six NRDs (FLNC, PLK1, ID1, MYO1C, SERTAD1, and LEF1) was constructed, and patients were divieded into low-risk (LR) and high-risk (HR) groups.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36101745", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A RS model containing six NRDs (FLNC, PLK1, ID1, MYO1C, SERTAD1, and LEF1) was constructed, and patients were divieded into low-risk (LR) and high-risk (HR) groups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A RS model containing six NRDs (FLNC, PLK1, ID1, MYO1C, SERTAD1, and LEF1) was constructed, and patients were divieded into low-risk (LR) and high-risk (HR) groups.", "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:36101745"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE37745"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE37745"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36101745", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/4908608", "sha256": 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