ArticleFrontiers in cardiovascular medicine2026
Transcriptomics- and machine learning-based identification and mechanistic
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: 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-cancer 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. Methods: Blood transcriptomic data (GEO; accession GSE40447; Results: A total of 487 DEGs were identified; the turquoise WGCNA module (r = 0.72 with CVD, Conclusions: We identified and mechanistically validated-at bioinformatic and
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