Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

Zhaotian Ma; Fan Yang; Jiajia Fan; Xin Li; Yuanyuan Liu; Wei Chen; Honghao Sun; Tengfei Ma; Qiongying Wang; Yueriguli Maihaiti; Xiaoqiao Ren

doi:10.3389/fnagi.2022.968190

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

Front Aging Neurosci. 2022 Nov 2:14:968190. doi: 10.3389/fnagi.2022.968190. eCollection 2022.

Authors

Zhaotian Ma^{1

2}, Fan Yang^{1

3}, Jiajia Fan^{1

2}, Xin Li^{1

2}, Yuanyuan Liu^{1

2}, Wei Chen^{1

2}, Honghao Sun^{1

2}, Tengfei Ma^{1

2}, Qiongying Wang^{1

2}, Yueriguli Maihaiti^{1

2}, Xiaoqiao Ren^{1

2}

Affiliations

¹ School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
² Institute of Ethnic Medicine, Beijing University of Chinese Medicine, Beijing, China.
³ National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.

Abstract

Background: Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD.

Materials and methods: Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers.

Results: Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs.

Conclusion: Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

Keywords: Alzheimer’s disease; diagnostic model; immune cells; molecular subtypes; protein glycosylation-related genes.