Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35036121", "kind": "science-record", "revision": 3, "content_hash": "sha256:69dcb895fd6959c8b61ae305bdaa963af1152a006b7f15572daa20495ee2cbd8", "created_at": "2026-11-01T17:36:45Z", "updated_at": "2026-11-01T21:26:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma.", "doi": "10.7717/peerj.12547", "pmid": "35036121", "authors": ["Zihao Yan", "Siwen Chu", "Chen Zhu", "Yunhe Han", "Qingyu Liang", "Shuai Shen", "Wen Cheng", "Anhua Wu"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM.\nMATERIALS AND METHODS: This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting.\nRESULTS: The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. \"Hot tumors\" were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy.\nCONCLUSIONS: As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.", "claims": [{"id": "claim-method", "statement": "We used the R survival package and GraphPad Prism 8 to perform univariate and multivariate Cox regression analyses of patients, as well as to analyze the differences in survival between the high- and low-risk groups and the high- and low-immunotherapy score groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35036121", "locator": "sections[6] \"Survival analysis\" paragraph 2", "quote": "We used the R survival package and GraphPad Prism 8 to perform univariate and multivariate Cox regression analyses of patients, as well as to analyze the differences in survival between the high- and low-risk groups and the high- and low-immunotherapy score groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data AvailabilityThe following information was supplied regarding data availability:The Glioblastoma (GBM) mRNA-seq dataset and TCGA Glioblastoma (GBM) phenotypes dataset were retrieved from TCGA (https://tcga-data.nci.nih.gov).The microarray is available at GSE16011.The CGGA325 RNA-seq cohort (Part C, DataSet ID: mRNAseq_325) and CGGA693 RNA-seq cohort (Part B, DataSet ID: mRNAseq_693) are available at the Chinese Glioma Genome Atlas:http://www.cgga.org.cn/download/20191128/download.jsp.The patients’ scores of the five cohorts involved in the article are available in the Supplementary File.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35036121", "locator": "sections[23] \"Additional Information and Declarations\" paragraph 4", "quote": "Data AvailabilityThe following information was supplied regarding data availability:The Glioblastoma (GBM) mRNA-seq dataset and TCGA Glioblastoma (GBM) phenotypes dataset were retrieved from TCGA (https://tcga-data.nci.nih.gov).The microarray is available at GSE16011.The CGGA325 RNA-seq cohort (Part C, DataSet ID: mRNAseq_325) and CGGA693 RNA-seq cohort (Part B, DataSet ID: mRNAseq_693) are available at the Chinese Glioma Genome Atlas:http://www.cgga.org.cn/download/20191128/download.jsp.The patients’ scores of the five cohorts involved in the article are available in the Supplementary File."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The T-cell activation-related risk score was an independent risk factor for poor prognosis in glioblastoma.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35036121", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score was an independent risk factor for poor prognosis."}, {"asset": "source:pmid:35036121", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The T-cell activation-related risk score was an independent risk factor for poor prognosis in glioblastoma.", "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:35036121"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35036121", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.12547", "sha256": "sha256:1e3e27257719f3d9a79eda23f6648c02401173b513e132e3d74c465b20c57e3d", "bytes": 50684, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.12547", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8710057", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We used the R survival package and GraphPad Prism 8 to perform univariate and multivariate Cox regression analyses of patients, as well as to analyze the differences in survival between the high- and low-risk groups and the high- and low-immunotherapy score groups."}}, {"id": "method-2", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-3", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "Chi-square"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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