Prediction of prognosis and immunotherapy response of tryptophan metabolism genes in acute myeloid leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41573739", "kind": "science-record", "revision": 3, "content_hash": "sha256:c302fc8f79b62b19ffe49cf0c481dbe5b4e7710313ea91339d59e8326ac7998b", "created_at": "2026-11-01T20:31:32Z", "updated_at": "2026-11-01T22:15:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prediction of prognosis and immunotherapy response of tryptophan metabolism genes in acute myeloid leukemia.", "doi": "10.3389/fphar.2025.1714246", "pmid": "41573739", "authors": ["Tantan Zhong", "Meirui Zhang", "Xinran Qi", "Zhihui Qi", "Peipei Li", "Na Wang", "Xianghua Wang", "Panpan Feng", "Xiaosheng Fang"], "journal": "Frontiers in pharmacology", "year": 2025, "abstract": "Background: Acute myeloid leukemia (AML) is an aggressive and heterogeneous disease, associated with significant morbidity and mortality rates. Tryptophan metabolism has been implicated in the development of several tumors. The immune landscape within the tumor microenvironment plays a pivotal role in both leukemogenesis and the determination of patient prognosis. Nonetheless, the influence of tryptophan metabolic patterns and corresponding immune signatures in AML remains largely unclear.\nMethods: Transcriptomic, genomic, and clinical data from TCGA were analyzed, and GSE71014 was used for external validation. Molecular subtypes were identified via consensus clustering of tryptophan metabolism-related genes (TRPRGs). Immune infiltration was quantified using ESTIMATE. A tryptophan-related prognostic risk score (TRPRS) was constructed using LASSO-Cox regression and evaluated for prognostic performance.\nResults: We characterized alterations in 39 TRPRGs across AML cohorts and delineated the clinical and tumor microenvironmental features of two molecular subtypes. First, a TRPRG-based scoring system was established, identifying seven candidate genes significantly associated with patient outcomes. After LASSO-Cox regression selection, six genes were incorporated into the final prognostic model, stratify overall survival risk. The TRPRS effectively stratified overall survival in both the TCGA and GEO cohorts and remained an independent prognostic factor after multivariate adjustment. High-TRPRS patients exhibited distinct immune characteristics and differential drug sensitivity patterns. Functional experiments demonstrated that HADH and ECHS1 promote AML cell proliferation and survival.\nDiscussion: Our integrative analysis identified key tryptophan-metabolism-related genes in AML and developed a six-gene TRPRS capable of accurately distinguishing survival risk. This model not only provides mechanistic insights into AML progression but also offers a framework for individualized risk stratification and therapeutic guidance.", "claims": [{"id": "claim-method", "statement": "A tryptophan-related prognostic risk score (TRPRS) was constructed using LASSO–Cox regression and evaluated for prognostic performance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41573739", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A tryptophan-related prognostic risk score (TRPRS) was constructed using LASSO–Cox regression and evaluated for prognostic performance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41573739", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A six-gene tryptophan-related prognostic risk score was incorporated into the final prognostic model to stratify overall survival risk in AML.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41573739", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After LASSO-Cox regression selection, six genes were incorporated into the final prognostic model, stratify overall survival risk."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A six-gene tryptophan-related prognostic risk score was incorporated into the final prognostic model to stratify overall survival risk in AML.", "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:41573739"], "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:GSE37642"], "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:GSE37642"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41573739", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2025.1714246", "sha256": "sha256:0df8dba89eaec24338b9d9b5fee0539b894df8e915e2cdce9bbe9534b66c7db9", "bytes": 59443, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2025.1714246", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12819808", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE37642", "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=GSE37642", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE37642", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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