Supplementary file 1_Transcriptomics- and machine learning-based identification and mechanistic in vitro validation of a cardiovascular risk gene signature at the CKD–cancer intersection: a multi-omics approach.docx

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.</p>Results<p>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.</p>Conclusions<p>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.</p>