{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:07Z","timestamp":1788419767052,"version":"build-2803163510"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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. Cell Dev. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Succinylation-linked metabolic rewiring and neutrophil-driven inflammation are key drivers of colorectal carcinoma (COAD) progression; however, they have rarely been translated to an end-to-end artificial intelligence (AI) drug-design pipeline connecting target nomination, resistance-relevant tumor microenvironment states, and candidate evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We integrated bulk transcriptomes from The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) and Gene Expression Omnibus databases to derive succinylation\u2013neutrophil (SN)-associated genes using the limma package, single-sample gene-set enrichment analysis, weighted gene coexpression network analysis, and protein\u2013protein interaction network analysis; we then constructed a cross-cohort prognostic system by screening and selecting the machine-learning combination of random survival forest and ridge regression as the optimal predictor. To enable precision stratification, we applied deep-learning self-organizing maps (SOMs) to define the SN-driven molecular subtypes and characterized their immune and pathway heterogeneities at both bulk and single-cell resolutions through AI-powered virtual perturbation analysis to interrogate the cell-state shifts. Importantly, we extended AI approaches in drug design beyond risk modeling to a target-to-candidate workflow by nominating a druggable SN hub gene. We also performed co-culture\n                      <jats:italic>in vitro<\/jats:italic>\n                      assays for identifying NOX4 molecular insights into neutrophil patterns and their associations with COAD progression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our approach identifies SN biology as a clinically actionable axis in COAD, highlights NOX4 as a central SN-associated target with immune relevance, and prioritizes talazoparib as a repurposing candidate supported by integrated\n                      <jats:italic>in silico<\/jats:italic>\n                      evidence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings of this study provide a precision-oncology blueprint that couples AI stratification with therapeutic nomination and evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcell.2026.1808514","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:43:55Z","timestamp":1788417835000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial-intelligence- and multi-omics-guided predictive and drug repurposing framework construction for colorectal cancer: evidence from succinylation\u2013neutrophil signatures"],"prefix":"10.3389","volume":"14","author":[{"given":"Huimin","family":"Jie","sequence":"first","affiliation":[{"name":"Shanxi Provincial People\u2019s Hospital, Fifth Clinical Medical College, Shanxi Medical University","place":["Taiyuan, Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaying","family":"Huo","sequence":"additional","affiliation":[{"name":"Shanxi Provincial People\u2019s Hospital, Fifth Clinical Medical College, Shanxi Medical University","place":["Taiyuan, Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiamei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanxi Provincial People\u2019s Hospital, Fifth Clinical Medical College, Shanxi Medical University","place":["Taiyuan, Shanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1016\/j.cmet.2020.11.007","article-title":"SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution","volume":"32","author":"Arg\u00fcello","year":"2020","journal-title":"Cell Metab."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1093\/rheumatology\/ker123","article-title":"Relative \u03b11-anti-trypsin deficiency in systemic sclerosis","volume":"50","author":"Barnes","year":"2011","journal-title":"Rheumatol. 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