PDF(Pubmed)
Abstract：
BACKGROUND: Keloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids.
METHODS: Bulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids.
RESULTS: In the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion.
CONCLUSIONS: In summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.
METHODS: Bulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids.
RESULTS: In the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion.
CONCLUSIONS: In summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.
摘要：
背景：瘢痕疙瘩是一种以皮肤组织愈合后纤维组织增生为特征的疾病，严重影响患者的日常生活。然而,瘢痕疙瘩的临床治疗仍有局限性,也就是说,它不能有效控制瘢痕疙瘩，导致高复发率。因此，迫切需要识别新的特征以改善瘢痕疙瘩的诊断和治疗。
方法：从GEO数据库下载BulkRNAseq和scRNAseq数据。首先,我们使用WGCNA和MEGENA共同鉴定瘢痕疙瘩/免疫相关DEG。随后，我们使用了三种机器学习算法(Randomforest，SVM-RFE,和LASSO）以鉴定瘢痕疙瘩（KHIGs）的枢纽免疫相关基因，并使用scRNA-seq研究成纤维细胞亚群分化过程中KHIGs的异质表达。最后,我们用HE和Masson染色,定量逆转录-PCR,西方印迹,免疫组织化学，和免疫荧光法研究维甲酸在瘢痕疙瘩中的表达异常及其机制。
结果：在本研究中，我们确定了PTGFR，RBP5和LIF作为KHIGs，并验证了它们的诊断性能。随后，我们基于KHIGs的转录组模式构建了一种新的人工神经网络分子诊断模型，有望突破目前临床上瘢痕疙瘩分子诊断面临的困境。同时,构建的IG评分还可以有效预测瘢痕疙瘩风险，这为瘢痕疙瘩的预防提供了新的策略。此外,我们观察到KHIGs也在成纤维细胞亚型的分化轨迹中异质表达，这可能会影响成纤维细胞亚型的分化，从而导致瘢痕疙瘩免疫微环境的失调。最后,我们发现维甲酸可能通过抑制RBP5将促炎性成纤维细胞（PIF）分化为间充质成纤维细胞（MF）来治疗或减轻瘢痕疙瘩，这进一步减少了胶原蛋白的分泌。
结论：总之，本研究提供了新的免疫特征(PTGFR，RBP5和LIF）用于瘢痕疙瘩的诊断和治疗，并确定视黄酸是潜在的抗瘢痕疙瘩药物。更重要的是,我们为理解瘢痕疙瘩中不同成纤维细胞亚型之间的相互作用及其免疫微环境的重塑提供了新的视角。
方法：从GEO数据库下载BulkRNAseq和scRNAseq数据。首先,我们使用WGCNA和MEGENA共同鉴定瘢痕疙瘩/免疫相关DEG。随后，我们使用了三种机器学习算法(Randomforest，SVM-RFE,和LASSO）以鉴定瘢痕疙瘩（KHIGs）的枢纽免疫相关基因，并使用scRNA-seq研究成纤维细胞亚群分化过程中KHIGs的异质表达。最后,我们用HE和Masson染色,定量逆转录-PCR,西方印迹,免疫组织化学，和免疫荧光法研究维甲酸在瘢痕疙瘩中的表达异常及其机制。
结果：在本研究中，我们确定了PTGFR，RBP5和LIF作为KHIGs，并验证了它们的诊断性能。随后，我们基于KHIGs的转录组模式构建了一种新的人工神经网络分子诊断模型，有望突破目前临床上瘢痕疙瘩分子诊断面临的困境。同时,构建的IG评分还可以有效预测瘢痕疙瘩风险，这为瘢痕疙瘩的预防提供了新的策略。此外,我们观察到KHIGs也在成纤维细胞亚型的分化轨迹中异质表达，这可能会影响成纤维细胞亚型的分化，从而导致瘢痕疙瘩免疫微环境的失调。最后,我们发现维甲酸可能通过抑制RBP5将促炎性成纤维细胞（PIF）分化为间充质成纤维细胞（MF）来治疗或减轻瘢痕疙瘩，这进一步减少了胶原蛋白的分泌。
结论：总之，本研究提供了新的免疫特征(PTGFR，RBP5和LIF）用于瘢痕疙瘩的诊断和治疗，并确定视黄酸是潜在的抗瘢痕疙瘩药物。更重要的是,我们为理解瘢痕疙瘩中不同成纤维细胞亚型之间的相互作用及其免疫微环境的重塑提供了新的视角。
