{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:02:30Z","timestamp":1767769350039,"version":"3.48.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2013related genes (TRPRGs). Immune infiltration was quantified using ESTIMATE. A tryptophan-related prognostic risk score (TRPRS) was constructed using LASSO\u2013Cox regression and evaluated for prognostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Our integrative analysis identified key tryptophan-metabolism\u2013related 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2025.1714246","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T06:59:17Z","timestamp":1767769157000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of prognosis and immunotherapy response of tryptophan metabolism genes in acute myeloid leukemia"],"prefix":"10.3389","volume":"16","author":[{"given":"Tantan","family":"Zhong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meirui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinran","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peipei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panpan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaosheng","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1111\/bjh.16310","article-title":"Acute myeloid leukaemia and the immune system: implications for immunotherapy","volume":"188","author":"Barrett","year":"2020","journal-title":"Br. 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