Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36341390", "kind": "science-record", "revision": 3, "content_hash": "sha256:68f4bc2260c65d02b5de86af3169e82bb928d1ca26dbeb0719842c045a3492e1", "created_at": "2026-11-01T18:15:47Z", "updated_at": "2026-11-01T21:29:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes.", "doi": "10.3389/fimmu.2022.1018942", "pmid": "36341390", "authors": ["Junsheng Li", "Jia Wang", "Dongjing Liu", "Chuming Tao", "Jizong Zhao", "Wen Wang"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Objective: Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.\nMethods: The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.\nResults: The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P&lt;0.001). The high SnG-risk score indicated a worse outcome in LGG (P&lt;0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P&lt;0.001 for all).\nConclusion: The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.", "claims": [{"id": "claim-method", "statement": "Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36341390", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36341390", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The result of Cox regression showed that the SnG-risk score was an independent indicator for LGG survival (HR=2.763, 95%CI=1.660-4.599, P<0.001, Table 2 ).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The result of Cox regression showed that the SnG-risk score was an independent indicator for LGG survival (HR=2.763, 95%CI=1.660-4.599, P<0.001, Table 2 )."}, "evidence": [{"asset": "source:pmid:36341390", "locator": "sections[13] \"Establishment of the SnG-related signature and evaluation of its prognostic role\" paragraph 6", "quote": "The result of Cox regression showed that the SnG-risk score was an independent indicator for LGG survival (HR=2.763, 95%CI=1.660-4.599, P<0.001, Table 2 )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The result of Cox regression showed that the SnG-risk score was an independent indicator for LGG survival (HR=2.763, 95%CI=1.660-4.599, P<0.001, Table 2 ).", "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:36341390"], "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:GSE16011"], "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:GSE16011"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36341390", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.1018942", "sha256": "sha256:93c12a7270b238ecd43b77a7a0c357e3f69ff46feb048f553b58f3b3bd4b7757", "bytes": 41848, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.1018942", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9633681", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE16011", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE16011", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. 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