Weihua Yu,* Jionghuang Chen,* Shengxi Jin, Xiaoxiao Fan, Xiujun Cai

Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Xiujun Cai, Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, People’s Republic of China, Tel/Fax +86-571-86006617, Email srrsh_cxj@zju.edu.cn

Background: Obesity is reaching epidemic proportions in the developed world. The biosynthesis and degradation of human glycoproteins take place at the highest level in the liver. However, the association between glycosylation and the factors affecting obesity and metabolism-associated steatohepatitis (MASH) is still unclear.
Materials and Methods: Gene expression data of liver samples from obese patients were retrieved from GSE83452 and GSE89632 databases. Difference analysis and machine learning were used to identify hub genes involved in glycosylation and associated with the response of weight loss treatment. A total of 7 glycosylation-related hub genes were identified and then subjected to correlation analysis, immune cells infiltration analysis and ROC (Receiver Operating Characteristic) analysis. We also evaluated the potential function of 7 hub genes in obesity patients. MASH mice were used to validate the glycosylation-related hub genes.
Results: A total of 25 overlapped glycosylation-related genes were identified by DEGs analysis. ACER2, STX17, ARF5, GPC4, ENTPD5, NANP, and DPY19L2 were identified as hub genes. Among these hub genes, ACER2, STX17, ARF5, and ENTPD5 were also differential expressed in MASH patients. ENTPD5 showed increased transcription in obese MASH mice.
Conclusion: The current study identified seven glycosylation-related genes, ACER2, STX17, ARF5, GPC4, ENTPD5, NANP, and DPY19L2, that might play key roles in the development of obesity. ENTPD5 might play a key role in the development of MASH. These findings provide fresh perspectives for expanding the investigation of obesity and MASH.

Keywords: obesity, MASH, glycosylation, machine learning