Tertiary lymphoid structure stratifies glioma into three distinct tumor subtypes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34954691", "kind": "science-record", "revision": 3, "content_hash": "sha256:e82e3ab5da742456ddf68cc383f54f0b4228d612268049983c38896ff28aea35", "created_at": "2026-11-01T17:36:40Z", "updated_at": "2026-11-01T21:26:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Tertiary lymphoid structure stratifies glioma into three distinct tumor subtypes.", "doi": "10.18632/aging.203798", "pmid": "34954691", "authors": ["Xingwang Zhou", "Wenyan Li", "Jie Yang", "Xiaolan Qi", "Yimin Chen", "Hua Yang", "Liangzhao Chu"], "journal": "Aging", "year": 2021, "abstract": "OBJECTIVE: Tertiary lymphoid structure (TLS), also known as ectopic lymphoid organs, are found in cancer, chronic inflammation, and autoimmune diseases. However, the heterogeneity of TLS in gliomas is unclear. Therefore, it is necessary to identify TLS differences and define TLS subtypes.\nMETHODS: The TLS gene profile of 697 gliomas from The Cancer Genome Atlas (TCGA) was used for consensus clustering to identify robust clusters, and the reproducibility of the stratification method was assessed in Chinese Glioma Genome Atlas (CGGA) cohort1, CGGA_cohort2, and GSE16011. Analyses of clinical characteristics, immune infiltration, and potential biological functions were performed for each subtype.\nRESULTS: Three resulting clusters (A, B, and C) were identified based on consensus clustering on the gene expression profile of TLS genes. There was a significant prognostic difference among the clusters, with a shorter survival for C than B and A. In comparison with the A and B subtypes, the C subtype was significantly enriched in primary immunodeficiency, intestinal immune network for lgG production, antigen processing and presentation, natural killer cell-mediated cytotoxicity, complement and coagulation cascades, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and some immune-related diseases. The levels of 23 immune cell types were higher in the C subtype than in the A and B subtypes. Finally, we developed and validated a riskscore based on TLS subtypes with better performance of prognosis prediction.\nCONCLUSIONS: This study presents a new stratification method according to the TLS gene profile and highlights TLS heterogeneity in gliomas.", "claims": [{"id": "claim-method", "statement": "To ensure that all TLS signatures can be detected, consensus clustering was performed based on the 12 chemokine signatures, TFH cell signatures, TH1 cell and B cell signatures, plasma cell signature, and CXCL13 signature to identify robust clusters of glioma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34954691", "locator": "sections[14] \"Construction and validation of TLS subtypes\" paragraph 2", "quote": "To ensure that all TLS signatures can be detected, consensus clustering was performed based on the 12 chemokine signatures, TFH cell signatures, TH1 cell and B cell signatures, plasma cell signature, and CXCL13 signature to identify robust clusters of glioma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "For the TCGA dataset (http://cancergenome.nih.gov/), we downloaded the RNA-seq data, copy number alterations (CNAs), somatic mutations, and the corresponding clinical information [29].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34954691", "locator": "sections[13] \"Patients and datasets\" paragraph 2", "quote": "For the TCGA dataset (http://cancergenome.nih.gov/), we downloaded the RNA-seq data, copy number alterations (CNAs), somatic mutations, and the corresponding clinical information [29]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three resulting clusters (A, B, and C) were identified based on consensus clustering on the gene expression profile of TLS genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34954691", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three resulting clusters (A, B, and C) were identified based on consensus clustering on the gene expression profile of TLS genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Three resulting clusters (A, B, and C) were identified based on consensus clustering on the gene expression profile of TLS genes.", "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:34954691"], "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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The CGGA database included two RNA-seq datasets and one microarray dataset with the", "al information (http://www.cgga.org.cn). The RNA-seq, microarray datasets, and the corresponding", "ion were extracted [29]. We combined the two RNA-seq datasets and named it CGGA_cohort1, and the"], "order_basis": "paper_text_order:2001", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tabase included two RNA-seq datasets and one microarray dataset with the corresponding clinical info", "ation (http://www.cgga.org.cn). The RNA-seq, microarray datasets, and the corresponding clinical inf", "datasets and named it CGGA_cohort1, and the microarray dataset was named CGGA_cohort2. For the GSE1", "hort2. 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