Table_2_Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning.xlsx

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.

瘢痕疙瘩（Keloid）是一类由伤口愈合异常所形成的病理性瘢痕，以局部炎症持续存在与胶原过度沉积为核心特征，且炎症强度与瘢痕形成面积呈正相关。目前瘢痕疙瘩形成的病理生理机制尚未阐明，仍是临床实践中亟待解决的治疗难题。

本研究首次探索了糖鞘脂（glycosphingolipid, GSL）代谢通路在瘢痕疙瘩发生发展中的作用。本研究借助单细胞测序与微阵列数据，通过差异基因分析及机器学习算法（随机森林与支持向量机）系统筛选糖鞘脂代谢相关基因，最终确定ARSA、GBA2、SUMF2、GLTP、GALC及HEXB等一组核心基因；基于该基因集构建了瘢痕疙瘩诊断模型并分析免疫浸润特征，证实其可作为瘢痕疙瘩的新型治疗靶点。

本研究进一步通过糖鞘脂代谢基因的表达谱开展无监督聚类，以挖掘瘢痕疙瘩亚型，并计算不同亚型间免疫细胞、炎症因子的表达差异及富集的主要通路。同时构建了聚焦于成纤维细胞的单细胞分辨率转录组图谱：通过计算每个成纤维细胞的GSL代谢通路活性，结合拟时间轨迹分析探究成纤维细胞中GSL代谢通路的活性变化，发现该通路活性升高与成纤维细胞分化密切相关。

后续对细胞通讯网络的分析显示，瘢痕疙瘩中存在以成纤维细胞为核心的通讯调控网络，且成纤维细胞的GSL代谢通路活性可影响细胞通讯过程。上述发现有助于进一步阐明瘢痕疙瘩的发病机制。

综上，本研究为瘢痕疙瘩的病理生理机制提供了全新视角，其研究结果可为瘢痕疙瘩的临床诊断与治疗提供新思路。