T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38619693", "kind": "science-record", "revision": 2, "content_hash": "sha256:b9bb098bf0d7e2074fca31e5950ecb5c7f98c44b0407ce712977742224d31f22", "created_at": "2026-11-01T19:20:35Z", "updated_at": "2026-11-01T21:32:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "T cell-mediated tumor killing sensitivity gene signature-based prognostic score for acute myeloid leukemia.", "doi": "10.1007/s12672-024-00962-w", "pmid": "38619693", "authors": ["Yiyun Pan", "FangFang Xie", "Wen Zeng", "Hailong Chen", "Zhengcong Chen", "Dechang Xu", "Yijian Chen"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND AND OBJECTIVE: Acute myeloid leukemia (AML) is an aggressive, heterogenous hematopoetic malignancies with poor long-term prognosis. T-cell mediated tumor killing plays a key role in tumor immunity. Here, we explored the prognostic performance and functional significance of a T-cell mediated tumor killing sensitivity gene (GSTTK)-based prognostic score (TTKPI).\nMETHODS: Publicly available transcriptomic data for AML were obtained from TCGA and NCBI-GEO. GSTTK were identified from the TISIDB database. Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed. Prognostic performance of the TTKPI was examined using Kaplan-Meier survival analysis, Receiver operating curves, and nomogram analysis. Association of TTKPI with clinical phenotypes, tumor immune cell infiltration patterns, checkpoint expression patterns were analysed. Drug docking was used to identify important candidate drugs based on the TTKPI-component genes.\nRESULTS: From 401 differentially expressed GSTTK in AML, 24 genes were identified as signature genes and used to construct the TTKPI score. High-TTKPI risk score predicted worse survival and good prognostic accuracy with AUC values ranging from 75 to 96%. Higher TTKPI scores were associated with older age and cancer stage, which showed improved prognostic performance when combined with TTKPI. High TTKPI was associated with lower naïve CD4 T cell and follicular helper T cell infiltrates and higher M2 macrophages/monocyte infiltration. Distinct patterns of immune checkpoint expression corresponded with TTKPI score groups. Three agents; DB11791 (Capmatinib), DB12886 (GSK-1521498) and DB14773 (Lifirafenib) were identified as candidates for AML.\nCONCLUSION: A T-cell mediated killing sensitivity gene-based prognostic score TTKPI showed good accuracy in predicting survival in AML. TTKPI corresponded to functional and immunological features of the tumor microenvironment including checkpoint expression patterns and should be investigated for precision medicine approaches.", "claims": [{"id": "claim-method", "statement": "Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38619693", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Signature GSTTK for AML were identified by differential expression analysis, COX proportional hazards and LASSO regression analysis and a comprehensive TTKPI score was constructed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analysed during this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38619693", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The two datasets were de-batched and combined into the training set data. GSE71014 and GSE37642 datasets were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/). 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