Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease.

aimsCoronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD. METHODS AND

resultsTwelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD-HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme-linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL-23, and FGF-21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme-linked immunosorbent assay.

conclusionsThe combination of VEGFR2, FLRG, IL-23, and FGF-21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD.