Transcriptomics- and machine learning-based identification and mechanistic in vitro validation of a cardiovascular
Chongze Lin1, Sisi Shao1, Meizi Xia2
1Department of Nephrology, Wenzhou TCM Hospital of Zhejiang Chinese Medical University, Wenzhou, Zhejiang, China.
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; n = 24 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. In vitro mechanistic validation was performed by RT-qPCR in human umbilical vein endothelial cells (HUVECs; ATCC CRL-1730) stimulated with the uremic toxin indoxyl sulfate (IS) at three concentrations (0.1, 0.25, and 0.5 mM, 24 h; n = 6 biological replicates per group), with dual-reference normalization to GAPDH and β-actin.
Results:
A total of 487 DEGs were identified; the turquoise WGCNA module (r = 0.72 with CVD, p < 0.001) 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 ≥0.25 mM IS (THBS1 FC = 2.6 ± 0.3; S100A8 FC = 3.1 ± 0.4; MMP9 FC = 2.9 ± 0.4; ITGB3 FC = 1.9 ± 0.3 at 0.5 mM, all p < 0.01), confirming transcriptional responsiveness of the signature to uremic endothelial injury.
Conclusions:
We identified and mechanistically validated-at bioinformatic and in vitro levels-a 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.
