{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T18:15:13Z","timestamp":1783361713797,"version":"3.54.6"},"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Key Clinical Specialty Program","award":["shslczdzk04301"],"award-info":[{"award-number":["shslczdzk04301"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Colorectal cancer (CRC) is characterized by genetic variation, epigenetic alterations, microenvironmental imbalance, and metabolic reprogramming. Currently, abnormalities amino acid metabolism has been shown to play an important role in the occurrence and progression of CRC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>AUCell, UCell, singscore, ssGSEA and AddModuleScore algorithms were used to determine the pattern of tryptophan metabolism in CRC at the cellular level. Differential expression and correlation analyses were performed to identify core candidate genes associated with upregulation of metabolic activity. Four machine learning algorithms\u2014\u2014random forest, Boruta, LASSO, and gradient boosting machine\u2014were further integrated for feature selection. Finally, to enhance robustness and reduce algorithm\u2011specific bias, the results of these algorithms were combined to identify the key feature genes related to tryptophan metabolism in CRC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The findings demonstrated significant differences in tryptophan metabolic activity among different cell types in CRC, with macrophages and Paneth cells exhibiting higher activity. Among the tryptophan metabolism-related genes,\n                      <jats:italic>CYP1A1<\/jats:italic>\n                      and\u00a0aryl hydrocarbon receptor (\n                      <jats:italic>AHR<\/jats:italic>\n                      ) were significantly upregulated in CRC, suggesting their involvement in regulating of immune response and inflammatory responses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This study reveals, for the first time, the cellular pattern of tryptophan metabolism in CRC, with macrophages and Paneth cells playing a major role in tumor development.\n                      <jats:italic>CYP1A1<\/jats:italic>\n                      and\n                      <jats:italic>AHR<\/jats:italic>\n                      were identified as consensus feature\u2011selected genes involved in tryptophan metabolism in CRC, highlighting their potential as biomarkers and therapeutic targets.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0352871","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:26:53Z","timestamp":1783358813000},"page":"e0352871","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Investigating tryptophan metabolism in colorectal cancer using Single-cell RNA sequencing based on machine learning 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