Iron metabolism-related genes reveal predictive value of acute coronary syndrome

• Main Authors: Cong Xu (Author), Wanyang Li (Author), Tangzhiming Li (Author), Jie Yuan (Author), Xinli Pang (Author), Tao Liu (Author), Benhui Liang (Author), Lixin Cheng (Author), Xin Sun (Author), Shaohong Dong (Author)
• Format: Book
• Published: Frontiers Media S.A., 2022-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1663-9812; 10.3389/fphar.2022.1040845
• Keywords: acute coronary syndrome; iron metabolism; transcriptome; prediction model; diagnosis; Therapeutics. Pharmacology; RM1-950
• Source: Frontiers in Pharmacology, Vol 13 (2022)

Iron deficiency has detrimental effects in patients with acute coronary syndrome (ACS), which is a common nutritional disorder and inflammation-related disease affects up to one-third people worldwide. However, the specific role of iron metabolism in ACS progression is opaque. In this study, we construct an iron metabolism-related genes (IMRGs) based molecular signature of ACS and to identify novel iron metabolism gene markers for early stage of ACS. The IMRGs were mainly collected from Molecular Signatures Database (mSigDB) and two relevant studies. Two blood transcriptome datasets GSE61144 and GSE60993 were used for constructing the prediction model of ACS. After differential analysis, 22 IMRGs were differentially expressed and defined as DEIGs in the training set. Then, the 22 DEIGs were trained by the Elastic Net to build the prediction model. Five genes, PADI4, HLA-DQA1, LCN2, CD7, and VNN1, were determined using multiple Elastic Net calculations and retained to obtain the optimal performance. Finally, the generated model iron metabolism-related gene signature (imSig) was assessed by the validation set GSE60993 using a series of evaluation measurements. Compared with other machine learning methods, the performance of imSig using Elastic Net was superior in the validation set. Elastic Net consistently scores the higher than Lasso and Logistic regression in the validation set in terms of ROC, PRC, Sensitivity, and Specificity. The prediction model based on iron metabolism-related genes may assist in ACS early diagnosis.