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

被引:9
|
作者
Song, Binyu [1 ]
Zheng, Yu [2 ]
Chi, Hao [3 ]
Zhu, Yuhan [1 ]
Cui, Zhiwei [1 ]
Chen, Lin [1 ]
Chen, Guo [1 ]
Gao, Botao [1 ]
Du, Yichen [1 ]
Yu, Zhou [1 ]
Song, Baoqiang [1 ]
机构
[1] Fourth Mil Med Univ, Xijing Hosp, Dept Plast Surg, Xian, Shaanxi, Peoples R China
[2] Chinese Acad Med Sci & Peking Union Med Coll, Hosp Skin Dis, Inst Dermatol, Nanjing, Jiangsu, Peoples R China
[3] Southwest Med Univ, Clin Med Coll, Luzhou, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2023年 / 14卷
基金
中国国家自然科学基金;
关键词
glycosphingolipid; keloid; single cell; machine learning; immune; GENE;
D O I
10.3389/fimmu.2023.1139775
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
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.
引用
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页数:16
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