Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37292257", "kind": "science-record", "revision": 3, "content_hash": "sha256:618504c0ada35ffe544e1eca18baf55a9d38b4ac8f9f20b56e74ad72d6062d5d", "created_at": "2026-11-01T19:02:01Z", "updated_at": "2026-11-01T22:09:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma.", "doi": "10.1155/2023/3648946", "pmid": "37292257", "authors": ["Jiayu Wen", "Qiaoyi Huang", "Jiuxiu Yao", "Wei Wei", "Zehui Li", "Huiqin Zhang", "Surui Chang", "Hui Pei", "Yu Cao", "Hao Li"], "journal": "Mediators of inflammation", "year": 2023, "abstract": "Background: The clinical outcomes of low-grade glioma (LGG) are associated with T cell infiltration, but the specific contribution of heterogeneous T cell types remains unclear.\nMethod: To study the different functions of T cells in LGG, we mapped the single-cell RNA sequencing results of 10 LGG samples to obtain T cell marker genes. In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. Subsequently, three immunotherapy cohorts, PRJEB23709, GSE78820, and IMvigor210, were used to explore the efficacy of immunotherapy.\nResults: The Human Primary Cell Atlas was used as a reference dataset to identify each cell cluster; a total of 15 cell clusters were defined and cells in cluster 12 were defined as T cells. According to the distribution of T cell subsets (CD4+ T cell, CD8+ T cell, Naïve T cell, and Treg cell), we selected the differentially expressed genes. Among the CD4+ T cell subsets, we screened 3 T cell-related genes, and the rest were 28, 4, and 13, respectively. Subsequently, according to the T cell marker genes, we screened six genes for constructing the model, namely, RTN1, HERPUD1, MX1, SEC61G, HOPX, and CHI3L1. The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. In addition, we found that risk scores were positively correlated with immune infiltration and immune checkpoints. To this end, we obtained three immunotherapy cohorts to verify their predictive ability of immunotherapy effects and found that high-risk patients had better clinical effects of immunotherapy.\nConclusion: This single-cell RNA sequencing combined with bulk RNA sequencing may elucidate the composition of the tumor microenvironment and pave the way for the treatment of low-grade gliomas.", "claims": [{"id": "claim-method", "statement": "LASSO algorithm was used to reduce the correlation between T cell marker genes and play a role in defitting. Subsequently, the multivariate Cox regression analysis algorithm was used to assign the coefficient of each gene to construct the prognosis model of T cell marker genes, in which the TCGA cohort was used as the training group and the CGGA cohort was used as the validation group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37292257", "locator": "sections[5] \"2.3. Construction of the Prognostic Model of T Cell Marker Genes\" paragraph 2", "quote": "LASSO algorithm was used to reduce the correlation between T cell marker genes and play a role in defitting. Subsequently, the multivariate Cox regression analysis algorithm was used to assign the coefficient of each gene to construct the prognosis model of T cell marker genes, in which the TCGA cohort was used as the training group and the CGGA cohort was used as the validation group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data were obtained from CGGA, TCGA, and GEO, as detailed in the methodology section.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37292257", "locator": "sections[16] \"Data Availability\" paragraph 2", "quote": "Data were obtained from CGGA, TCGA, and GEO, as detailed in the methodology section."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ROC curve shows that the prediction ability of the prognostic model at 1, 3, and 5 years in the TCGA cohort is 0.881, 0.817, and 0.749, respectively, and the prediction ability at 1, 3, and 5 years in the CGGA cohort is 0.749, 0.751, and 0.734, respectively (Figures 3(g) and 3(h)).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37292257", "locator": "sections[11] \"3.2. Prognostic Model\" paragraph 4", "quote": "The ROC curve shows that the prediction ability of the prognostic model at 1, 3, and 5 years in the TCGA cohort is 0.881, 0.817, and 0.749, respectively, and the prediction ability at 1, 3, and 5 years in the CGGA cohort is 0.749, 0.751, and 0.734, respectively (Figures 3(g) and 3(h))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ROC curve shows that the prediction ability of the prognostic model at 1, 3, and 5 years in the TCGA cohort is 0.881, 0.817, and 0.749, respectively, and the prediction ability at 1, 3, and 5 years in the CGGA cohort is 0.749, 0.751, and 0.734, respectively (Figures 3(g) and 3(h)).", "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:37292257"], "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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