A combined microRNA and target protein-based panel for predicting the probability and severity of uraemic vascular calcification: a translational study

Vascular calcification (VC) increases the future risk of cardiovascular events in uraemic patients, but effective therapies are still unavailable. Accurate identification of those at risk of developing VC using pathogenesis-based biomarkers is of particular interest and may facilitate individualized risk stratification. We aimed to uncover microRNA (miRNA)-target protein-based biomarker panels for evaluating uraemic VC probability and severity.

We created a three-tiered in vitro VC model and an in vivo uraemic rat model receiving high phosphate diet to mimic uraemic VC. RNAs from the three-tiered in vitro and in vivo uraemic VC models underwent miRNA and mRNA microarray, with results screened for differentially expressed miRNAs and their target genes as biomarkers. Findings were validated in original models and additionally in an ex vivo VC model and human cells, followed by functional assays of identified miRNAs and target proteins, and tests of sera from end-stage renal disease (ESRD) and non-dialysis-dependent chronic kidney disease (CKD) patients without and with VC. Totally 122 down-regulated and 119 up-regulated miRNAs during calcification progression were identified initially; further list narrowing based on miRNA–mRNA pairing, anti-correlation, and functional enrichment left 16 and 14 differentially expressed miRNAs and mRNAs. Levels of four miRNAs (miR-10b-5p, miR-195, miR-125b-2-3p, and miR-378a-3p) were shown to decrease throughout all models tested, while one mRNA (SULF1, a potential target of miR-378a-3p) exhibited the opposite trend concurrently. Among 96 ESRD (70.8% with VC) and 59 CKD patients (61% with VC), serum miR-125b2-3p and miR-378a-3p decreased with greater VC severity, while serum SULF1 levels increased. Adding serum miR-125b-2-3p, miR-378a-3p, and SULF1 into regression models for VC substantially improved performance compared to using clinical variables alone.

Using a translational approach, we discovered a novel panel of biomarkers for gauging the probability/severity of uraemic VC based on miRNAs/target proteins, which improved the diagnostic accuracy.