Construction and validation of a novel prognostic model for lung squamous cell cancer based on N6-methyladenosine-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35220962", "kind": "science-record", "revision": 4, "content_hash": "sha256:c70bcb66d2d848ef0946ce9706c6ab7a7b289ed7b971a850fb9b946dc5a3568a", "created_at": "2026-11-01T17:36:58Z", "updated_at": "2026-11-01T21:27:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a novel prognostic model for lung squamous cell cancer based on N6-methyladenosine-related genes.", "doi": "10.1186/s12957-022-02509-1", "pmid": "35220962", "authors": ["Erna Jia", "Na Ren", "Bo Guo", "Zhi Cui", "Boyin Zhang", "Jinru Xue"], "journal": "World journal of surgical oncology", "year": 2022, "abstract": "BACKGROUND: N6-methyladenosine (m6A) is the most prevalent modification in mRNA in biological processes and associated with various malignant tumor initiation and progression. The present study aimed to construct a prognostic risk model based on m6A-related genes (the downstream genes influenced by m6A modulators) for LUSC.\nMETHODS: Based on TCGA, we stratified LUSC patients with and without genetic alteration of m6A modulators into altered and unaltered groups. Using univariate Cox and Lasso regression analyses, we identified prognostic m6A-related genes to construct a prognostic risk model. We then applied a multivariate Cox proportional regression model and the survival analysis to evaluate the risk model. Moreover, we performed the Receiver operating characteristic curve to assess the efficiency of the prognostic model based on TCGA and GSE43131. We analyzed the characteristics of tumor-associated immune cell infiltration in LUSC through the CIBERSORT method.\nRESULTS: Three m6A-related genes (FAM71F1, MT1E, and MYEOV) were identified as prognostic genes for LUSC. A novel prognostic risk model based on the three m6A-related genes was constructed. The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029). Patients with a high-risk group had worse overall survival both in TCGA (p = 0.018) and GSE43131 (p = 0.00017). The 1, 2, and 3-year AUC value in TCGA was 0.662, 0.662, and 0.655, respectively; The 1, 2, and 3-year AUC value in GSE43131 was 0.724, 0.724, and 0.722, respectively. The proportion of infiltrated neutrophils in the high-risk group was higher than that in the low-risk group (p = 0.028), whereas that of resting NK cells (p = 0.002) was lower.\nCONCLUSION: A novel prognostic risk model based on three m6A-related genes for LUSC was generated in this study.", "claims": [{"id": "claim-method", "statement": "To further address the prognosis association of the risk model, we conducted univariate and multivariate Cox proportional hazard analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35220962", "locator": "sections[5] \"Construction of a prognostic risk score model and the ROC curves for LUSC prognosis\" paragraph 5", "quote": "To further address the prognosis association of the risk model, we conducted univariate and multivariate Cox proportional hazard analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated and/or analyzed during this study are available from TCGA (https://www.cancer.gov, LUSC project) and a GEO external validation dataset (https://www.ncbi.nlm.nih.gov/, GSE43131).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35220962", "locator": "sections[24] \"notes\" paragraph 1", "quote": "All data generated and/or analyzed during this study are available from TCGA (https://www.cancer.gov, LUSC project) and a GEO external validation dataset (https://www.ncbi.nlm.nih.gov/, GSE43131)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029)."}, "evidence": [{"asset": "source:pmid:35220962", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The multivariate Cox analysis showed that the prognostic risk model was an independent risk factor (HR = 2.44, 95% CI = 1.21~3.56, p = 0.029).", "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:35220962"], "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:GSE43131"], "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:GSE43131"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35220962", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12957-022-02509-1", "sha256": "sha256:f8d1ad4607b72451e0470c18eaf88ec8687d9ffeb42f5f8b0517fafac4d4e3d6", "bytes": 32591, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12957-022-02509-1", "version": null, "note": "Full article fetched from PMC; 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