Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning

Frontiers in Immunology

Immunology

Hao Chi¹  Baoqiang Song²  Zhiwei Cui²  Botao Gao²  Yichen Du²  Zhou Yu²  Binyu Song²  Guo Chen²  Lin Chen²  Yuhan Zhu²  Yu Zheng³ 

[1] Clinical Medical College, Southwest Medical University, Luzhou, China;Department of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, China;Hospital for Skin Disease (Institute of Dermatology), Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, Jiangsu, China;

关键词: glycosphingolipid;    keloid;    single cell;    machine learning;    immune;   

DOI  :  10.3389/fimmu.2023.1139775

 received in 2023-01-07, accepted in 2023-04-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

Keloid is a pathological scar formed by abnormal wound healing, characterized by the persistence of local inflammation and excessive collagen deposition, where the intensity of inflammation is positively correlated with the size of the scar formation. The pathophysiological mechanisms underlying keloid formation are unclear, and keloid remains a therapeutic challenge in clinical practice. This study is the first to investigate the role of glycosphingolipid (GSL) metabolism pathway in the development of keloid. Single cell sequencing and microarray data were applied to systematically analyze and screen the glycosphingolipid metabolism related genes using differential gene analysis and machine learning algorithms (random forest and support vector machine), and a set of genes, including ARSA,GBA2,SUMF2,GLTP,GALC and HEXB, were finally identified, for which keloid diagnostic model was constructed and immune infiltration profiles were analyzed, demonstrating that this set of genes could serve as a new therapeutic target for keloid. Further unsupervised clustering was performed by using expression profiles of glycosphingolipid metabolism genes to discover keloid subgroups, immune cells, inflammatory factor differences and the main pathways of enrichment between different subgroups were calculated. The single-cell resolution transcriptome landscape concentrated on fibroblasts. By calculating the activity of the GSL metabolism pathway for each fibroblast, we investigated the activity changes of GSL metabolism pathway in fibroblasts using pseudotime trajectory analysis and found that the increased activity of the GSL metabolism pathway was associated with fibroblast differentiation. Subsequent analysis of the cellular communication network revealed the existence of a fibroblast-centered communication regulatory network in keloids and that the activity of the GSL metabolism pathway in fibroblasts has an impact on cellular communication. This contributes to the further understanding of the pathogenesis of keloids. Overall, we provide new insights into the pathophysiological mechanisms of keloids, and our results may provide new ideas for the diagnosis and treatment of keloids.

【 授权许可】

Unknown   
Copyright © 2023 Song, Zheng, Chi, Zhu, Cui, Chen, Chen, Gao, Du, Yu and Song