Zhu F, Zuo L, Hu R, et al. - This study provides a predictive model based on 10 potential atherosclerosis-related genes (STAT3, IL1RN, C5AR1, CXCL16, IL17RA, SLC11A1, TLR2, IL1B, LYN and CKAP4), which should throw light on the diagnostic research of atherosclerosis.

• This study utilized the gene chip data of 135 peripheral blood samples, including 57 samples with atherosclerosis and 78 healthy persons from the GEO database.

• For predicting normal and atherosclerosis samples, the logistic regression diagnostic model was developed.

• Screening of a gene module, which comprised 532 genes associated with the occurrence of atherosclerosis, was performed.

• A total of 235 significantly enriched GO (Gene ontology) terms and 44 significantly enriched KEGG (Kyoto encyclopedia of genes and genomes) pathways were revealed in functional enrichment analysis based on the 532 genes.

• Experts identified the top 50 hub genes of the protein–protein interaction network.

• The final logistic regression diagnostic model was constructed based on the optimal 10 key genes (abovementioned), which could differentiate atherosclerosis samples from normal samples.