TRAF3IP2 as a Novel Inflammatory Biomarker for Coronary Artery Disease: Development and Validation of a Multimodal Prediction Model

Peibing GeTIAN YI NILiu MengjieDu QiuyaoXu ChangjiangHu TingtingYang GuHai-lang Liu^*Jin Geng^*

• The Affiliated Huaian NO.1 People’s Hospital of Nanjing Medical University, Huaian, China

Background: Coronary artery disease (CAD) is currently among the leading cardiovascular diseases with considerable morbidity/mortality worldwide. While inflammation drives atherosclerosis, clinically actionable biomarkers remain elusive. The role of TRAF3IP2, a proinflammatory adaptor molecule, in the pathogenesis and prediction of coronary artery disease warrants systematic investigation. The purpose of this study was to explore the role of TRAF3IP2 in coronary artery disease and to develop and validate a nomogram for predicting the risk of coronary artery disease. Methods: GSE12288 gene expression profiles were downloaded from the Gene Expression Omnibus database, and key genes and pathways involved in CAD (n=222) were identified. LASSO and multivariate logistic regression analyses were applied to investigate the risk factors for severe coronary artery stenosis in a clinical cohort (n=280). A nomogram model was developed to predict CAD, and the clinical utility of the nomogram model was evaluated using calibration curves and decision curve analysis (DCA). Results: Multiple bioinformatics tools revealed that TRAF3IP2 expression was higher in patients with CAD than in controls. Moreover, TRAF3IP2 is involved in the cellular response to inflammation, which is a basic process of atherosclerosis. Clinical data from a total of 280 patients were retrospectively reviewed for our study. Sex (OR 0.446 [0.230–0.863], p=0.017), diabetes history (OR 2.099 [1.131–3.896], p=0.019), phosphoremia (OR 0.252 [0.065–0.972], p=0.045) and TRAF3IP2 (OR 1.040 [1.004–1.076], p=0.027) were independent risk factors for atherosclerosis. The nomogram was composed of these factors, and the calibration curves and DCA curve showed that the model has great potential for clinical utility. Conclusions: In summary, this study demonstrated that TRAF3IP2 could be a potential biomarker for CAD. A nomogram composed of sex, diabetes history, phosphoremia, and TRAF3IP2 expression may predict the risk of CAD.