Li Song ¹ Shuo Huang ² Qing Ma ³ Qihan Luo ² Qiu Jiang ⁴ Honghao Yan ⁵ Zongyuan Li ^2* He Jiang ^6* Yufan Chen ^6* Fangming Chen ^7* Yu Du ^5* Haozhe Fu ^2* Lisha Zhao ^1* Kanglu Zhao ^8,9* Ping Qiu ^5*

• ¹ Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, China
• ² School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
• ³ The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
• ⁴ School of Medicine, Hangzhou Normal University, Hangzhou, China
• ⁵ School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, Jiangsu Province, China
• ⁶ The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
• ⁷ Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
• ⁸ Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
• ⁹ Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, China

Abstract Background Alcoholic hepatitis is a severe inflammatory liver disease. In recent years, the incidence of AH has been on the rise, leading to an increasingly severe disease burden. Currently, there is a lack of specific biomarkers for the diagnosis and prognosis of AH in clinical practice. Therefore, the main objective of this study is to identify biomarkers closely associated with the progression of AH, to address the shortcomings in pathological diagnosis, and to identify potential therapeutic targets. Methods Bioinformatics and machine learning methods were used to comparatively study the differentially expressed genes (DEGs) between AH patients and healthy individuals by analyzing four mRNA microarray data sets obtained from the GEO database. Subsequently, the role of potential biomarkers in AH and their mechanism of action were further confirmed by AH patients and in vitro and in vivo experiments. Results Using differential analysis and WGCNA of the data set, a total of 167 key genes that may be related to AH were obtained. Among 167 genes, the LASSO logistic regression algorithm identified 4 potential biomarkers (KCNJ10, RPL21P23, ADRB2, and AC025279.1). Notably, ADRB2 showed biomarker potential in GSE28619, GSE94397, and E-MTAB-2664 datasets, and clinical liver samples. Furthermore， AH patients and in vivo experiments demonstrated ADRB2 inhibition and suppression of SIRT1/PPARα/PGC-1α signaling pathways, accompanied by elevated inflammatory factors and lipid deposition. In vitro experiments showed that ADRB2 overexpression mitigated the inhibition of the SIRT1/PPARα/PGC-1α signaling pathway, reversing the decrease in mitochondrial membrane potential, cell apoptosis, oxidative stress, and lipid deposition induced by alcohol exposure. Besides, the results also showed that ADRB2 expression in AH was negatively correlated with the levels of inflammatory factors (e.g., CCL2, CXCL8, and CXCL10). Conclusion This study points to ADRB2 as a promising biomarker with potential diagnostic and prognostic value in clinical cohort data. In addition, in AH patients, in vivo and in vitro experiments confirmed the key role of ADRB2 in the progression of AH. These findings suggest that ADRB2 may alleviate AH by activating the SIRT1/PPARα/PGC-1α pathway. This finding provides a new perspective for the diagnosis and treatment of AH.