Qichong Yang ^1,2 Juncheng Liu ^2,3 Tingting Zhang ⁴ Tingting Zhu ⁵ Siyu Yao ^1,2 Rongzi Wang ^1,2 Wenjuan Wang ^1,2 Haliminai Dilimulati ^1,2 Junbo Ge ^6* Songtao An ^1,2*

• ¹ Central China Fuwai Hospital of Zhengzhou University, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
• ² Key Laboratory of Cardiac Regenerative Medicine, National Health Commission, Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center,, Zhengzhou, China
• ³ People's Hospital of Zhengzhou University, Henan Province People's Hospital, Zhengzhou, China
• ⁴ Center for Clinical Single-Cell Biomedicine, Henan Province People’s Hospital, People's Hospital of Zhengzhou University, Zhengzhou, China
• ⁵ Department of Cardiopulmonary Functions Test, Henan Province People’s Hospital, People's Hospital of Henan University, Zhengzhou, China
• ⁶ Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China

Background Insomnia (ISM) is one of the non-traditional drivers of atherosclerosis (AS) and an important risk factor for AS-related cardiovascular disease. Our study aimed to explore the shared pathways and diagnostic biomarkers of ISM-related AS using integrated bioinformatics analysis.We download the datasets from the Gene Expression Omnibus database and the GeneCards database. Weighted gene co-expression network analysis and gene differential expression analysis were applied to screen the AS-related gene set. The shared genes of ISM and AS were obtained by intersecting with ISM-related genes. Subsequently, candidate diagnostic biomarkers were identified by constructing protein-protein interaction networks and machine learning algorithms, and a nomogram was constructed. Moreover, to explore potential mechanisms, a comprehensive analysis of shared genes was carried out, including enrichment analysis, protein interactions, immune cell infiltration, and single-cell sequencing analysis.We successfully screened 61 genes shared by ISM and AS, of which 3 genes (IL10RA, CCR1, and SPI1) were identified as diagnostic biomarkers. A nomogram with excellent predictive value was constructed (the area under curve of the model constructed by the biomarkers was 0.931, and the validation set was 0.745). In addition, the shared genes were mainly enriched in immune and inflammatory response regulation pathways. The biomarkers were associated with a variety of immune cells, especially myeloid immune cells.We constructed a diagnostic nomogram based on IL10RA, CCR1, and SPI1 and explored the inflammatory-immune mechanisms, which indicated new insights for early diagnosis and treatment of ISM-related AS.