Haihong Zhao,1,2,* Bo Li,3,* Xia Jing,1,2 Jixiu Fan,1,2 Xiaofang Zhao,1,2 Jing Li,1,2 Shulan Wu,1,2 Jifang Liang,4,5 Suojuan Zhang1,2 

1Department of Geriatric Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, People’s Republic of China; 2Department of Geriatric Medicine,Tongji Hospital,Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, People’s Republic of China; 4Department of Critical Care Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China; 5Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Suojuan Zhang, Department of Geriatric Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences,Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China, Tel +86 18234972205, Email zhangsuojuan198310@163.com Jifang Liang, Department of Critical Care Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China, Tel +86 15235132923, Email liangjifang@sxmu.edu.cn

Purpose: Sepsis, which is triggered by infection and characterized by a systemic inflammatory response, has been associated with the diagnosis of sepsis, although the detailed molecular basis remains unclear. This study explores parthanatos-related genes (PRGs) as potential biomarkers for sepsis diagnosis and treatment.
Patients and Methods: Data from GSE65682, GSE167363, and GSE95233 were analyzed. PRGs were identified, and candidate genes were selected by intersecting differentially expressed genes (DEGs) with key PRG-associated module genes. Biomarkers were determined through Mendelian randomization (MR), machine learning, Receiver Operating Characteristic (ROC) analysis, and validation. A nomogram was constructed. Additional analyses included immune cell infiltration, Gene Set Enrichment Analysis (GSEA), molecular networks, and drug predictions. Single-cell analysis of biomarkers was performed in GSE167363. Biomarker expression was validated using Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR).
Results: BRD1 and FOXJ3 were identified as biomarkers for sepsis. The nomogram based on these biomarkers showed strong predictive power. Immune infiltration analysis revealed that Macrophages M0 negatively correlated with both BRD1 and FOXJ3. A lncRNA-miRNA-mRNA network involving 26 miRNAs and 299 lncRNAs was predicted. GSEA showed associations with extracellular matrix organization, keratinization, and mRNA splicing. Drug prediction indicated digoxin, doxorubicin, and daunorubicin could target BRD1 and FOXJ3. The single-cell analysis results showed a significant differential expression of the FOXJ3 gene between spermatogenic cells. RT-qPCR confirmed that BRD1 was significantly decreased in sepsis, while FOXJ3 showed no significant difference.
Conclusion: BRD1 and FOXJ3 were identified as sepsis biomarkers, offering new insights into sepsis pathogenesis and potential clinical applications for diagnosis and treatment.

Keywords: immune cell infiltration, molecular mechanisms, therapeutic targets, diagnostic markers