Cross-Disease Identification of FBP1 in Atherosclerosis and Calcific Aortic Valve Disease via Integrated Bioinformatics and Validation: Functional Analysis and Therapeutic Target Exploration

Haowen Xu,1,* Yifan Xu,2,* Xueyi Wang,3 Zhisheng Yan,4 Ting Geng,5 Jinpeng Wu,2 Yongxin Li,1 Mingjin Guo1 1Department of Vascular Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, People’s Republic of China; 2Department of Neurosurgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, People’s Republic of China; 3Department of Vascular Surgery, Rongcheng City People’s Hospital, Rongcheng, 264300, People’s Republic of China; 4Department of Interventional Medicine, The Eighth People’s Hospital of Qingdao, Qingdao, 266000, People’s Republic of China; 5Interventional Operating Room, The Eighth People’s Hospital of Qingdao, Qingdao, 266000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mingjin Guo, Department of Vascular Surgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao, 266000, People’s Republic of China, Email [email protected] Yongxin Li, Department of Vascular Surgery, The Affiliated Hospital of Qingdao University, 16 Jiang Su Road, Qingdao, 266000, People’s Republic of China, Email [email protected]: Atherosclerosis (AS) and calcific aortic valve disease (CAVD) are common in aging populations and share metabolic dysregulation, chronic inflammation, and cellular aging. Shared immunometabolic biomarkers and therapeutic targets remain insufficiently defined. This study aimed to identify Cross-disease biomarkers linking AS and CAVD and to explore their translational potential.Methods: Four Gene Expression Omnibus (GEO) microarray datasets related to AS and CAVD were integrated. Differentially expressed genes (DEGs) were identified within each disease, and Cross-disease genes (CGs) were obtained by intersecting DEGs across the two diseases. Functional enrichment and protein–protein interaction analyses were performed. Machine learning (LASSO and Random Forest) refined candidate biomarkers. Immune infiltration was estimated with CIBERSORT, and a microRNA–transcription factor regulatory network was constructed. Molecular docking screened small molecules targeting the hub gene. Diagnostic performance was evaluated in independent datasets, and expression was validated in human tissues by qPCR and Western blot.Results: We identified 147 CGs enriched in immune and metabolic pathways. Fructose-1,6-bisphosphatase 1 (FBP1) emerged as a hub gene with strong diagnostic value across datasets. FBP1 expression correlated with alterations in multiple immune cell populations and was embedded within a regulatory network of predicted microRNAs and transcription factors. Docking analysis highlighted apigenin and kaempferol as candidate FBP1-targeting compounds. Experimental validation confirmed FBP1 upregulation in AS and CAVD tissues.Discussion: FBP1 represents a shared immunometabolic biomarker and potential therapeutic target that links metabolic reprogramming to immune dysregulation in AS and CAVD. These findings provide a rationale for further translational studies evaluating FBP1-centered interventions.Keywords: atherosclerosis, calcific aortic valve disease, machine learning, immunology, molecular docking