{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:04:22Z","timestamp":1789362262576,"version":"build-2803163510"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Anticancer treatment-induced cardiovascular toxicity (CTox) represents a critical and emerging challenge in cardio-oncology. Cancer patients with concurrent chronic kidney disease (CKD) or end-stage renal disease (ESRD) receiving maintenance hemodialysis (HD) are a uniquely vulnerable population, facing additive cardiovascular (CV) risk from uremic milieu, polypharmacy, and cytotoxic therapy. No transcriptomics-based machine learning (ML) model mechanistically validated in the uremic vascular milieu has been reported for identifying candidate cardiovascular risk genes at the CKD\u2013cancer intersection. We aimed to construct and mechanistically validate a precision multi-omics cardiovascular risk gene signature to support future individualized risk stratification in cancer patients with CKD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Blood transcriptomic data (GEO; accession GSE40447;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200924 breast cancer samples) were processed through differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and four machine learning algorithms (LASSO, random forest, SVM, XGBoost) to derive a four-gene cardiovascular risk signature (THBS1, S100A8, MMP9, ITGB3). An ensemble model integrating molecular variables was constructed. Immune cell infiltration was estimated by ssGSEA.\n                      <jats:italic>In vitro<\/jats:italic>\n                      mechanistic validation was performed by RT-qPCR in human umbilical vein endothelial cells (HUVECs; ATCC CRL\u22121730) stimulated with the uremic toxin indoxyl sulfate (IS) at three concentrations (0.1, 0.25, and 0.5\u2005mM, 24\u2005h;\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20096 biological replicates per group), with dual-reference normalization to GAPDH and\n                      <jats:italic>\u03b2<\/jats:italic>\n                      -actin.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 487 DEGs were identified; the turquoise WGCNA module (r\u2009=\u20090.72 with CVD,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) contained all four signature genes as hub nodes. The ensemble ML model achieved AUC=0.923 (training) and AUC=0.881 (validation). RT-qPCR demonstrated concentration-dependent upregulation of all four signature genes in IS-stimulated HUVECs, with statistically significant induction at \u22650.25\u2005mM IS (THBS1 FC\u2009=\u20092.6\u2009\u00b1\u20090.3; S100A8 FC\u2009=\u20093.1\u2009\u00b1\u20090.4; MMP9 FC\u2009=\u20092.9\u2009\u00b1\u20090.4; ITGB3 FC\u2009=\u20091.9\u2009\u00b1\u20090.3 at 0.5\u2005mM, all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.01), confirming transcriptional responsiveness of the signature to uremic endothelial injury.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      We identified and mechanistically validated\u2014at bioinformatic and\n                      <jats:italic>in vitro<\/jats:italic>\n                      levels\u2014a transcriptomics-machine learning four-gene cardiovascular risk signature (THBS1, S100A8, MMP9, ITGB3) in the context of uremic endothelial injury. These genes capture thrombo-inflammatory, endothelial, and matrix remodeling pathways shared between CKD-associated vascular toxicity and anticancer treatment-induced CTox, providing a mechanistically grounded candidate panel for future prospective clinical validation in cancer patients with CKD.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1915820","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T04:33:54Z","timestamp":1789360434000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Transcriptomics- and machine learning-based identification and mechanistic in vitro validation of a cardiovascular risk gene signature at the CKD\u2013cancer intersection: a multi-omics approach"],"prefix":"10.3389","volume":"13","author":[{"given":"Chongze","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Nephrology, Wenzhou TCM Hospital of Zhejiang Chinese Medical University","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisi","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Wenzhou TCM Hospital of Zhejiang Chinese Medical University","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meizi","family":"Xia","sequence":"additional","affiliation":[{"name":"Hemodialysis Department, Wenzhou Geriatric Hospital","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shourong","family":"Lu","sequence":"additional","affiliation":[{"name":"Hemodialysis Department, Wenzhou Geriatric Hospital","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2768","DOI":"10.1093\/eurheartj\/ehw211","article-title":"2016 ESC position paper on cancer treatments and cardiovascular toxicity","volume":"37","author":"Zamorano","year":"2016","journal-title":"Eur Heart J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"4229","DOI":"10.1093\/eurheartj\/ehac244","article-title":"2022 ESC guidelines on cardio-oncology","volume":"43","author":"Lyon","year":"2022","journal-title":"Eur Heart J"},{"key":"B3","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.annonc.2019.10.023","article-title":"Management of cardiac disease in cancer patients throughout oncological treatment: eSMO consensus recommendations","volume":"31","author":"Curigliano","year":"2020","journal-title":"Ann Oncol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1056\/NEJMra1100265","article-title":"Cardiovascular toxic effects of targeted cancer therapies","volume":"375","author":"Moslehi","year":"2016","journal-title":"N Engl J Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"S112","DOI":"10.1053\/ajkd.1998.v32.pm9820470","article-title":"Clinical epidemiology of cardiovascular disease in chronic renal disease","volume":"32","author":"Foley","year":"1998","journal-title":"Am J Kidney Dis"},{"key":"B6","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1038\/ki.2011.223","article-title":"Cardiovascular disease in chronic kidney disease. 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