Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis.

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY Human Genomics Pub Date : 2024-07-16 DOI:10.1186/s40246-024-00647-z
Kui Xiao, Sisi Wang, Wenxin Chen, Yiping Hu, Ziang Chen, Peng Liu, Jinli Zhang, Bin Chen, Zhi Zhang, Xiaojian Li
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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.

Method: 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.

Conclusion: 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.

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为瘢痕疙瘩的诊断和治疗鉴定新型免疫相关特征:综合大量 RNA-seq 和 scRNA-seq 分析的启示。
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存在局限性,即不能有效控制瘢痕疙瘩,导致复发率较高。因此,确定新的特征以改善瘢痕疙瘩的诊断和治疗迫在眉睫:方法:从 GEO 数据库下载大量 RNA 序列和 scRNA 序列数据。首先,我们使用 WGCNA 和 MEGENA 共同鉴定瘢痕疙瘩/免疫相关 DEGs。随后,我们使用三种机器学习算法(Randomforest、SVM-RFE和LASSO)识别了瘢痕疙瘩的枢纽免疫相关基因(KHIGs),并利用scRNA-seq研究了成纤维细胞亚群分化过程中KHIGs的异质性表达。最后,我们采用HE和Masson染色、定量逆转录-PCR、Western印迹、免疫组织化学和免疫荧光检测等方法研究了维甲酸在瘢痕疙瘩中的表达失调及其机制:结果:在本研究中,我们确定了 PTGFR、RBP5 和 LIF 为 KHIGs,并验证了它们的诊断性能。随后,我们根据 KHIGs 的转录组模式构建了一个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所面临的困境。同时,构建的IG评分还能有效预测瘢痕疙瘩的风险,为瘢痕疙瘩的预防提供了新策略。此外,我们还观察到,在构建的成纤维细胞亚型分化轨迹中,KHIGs也存在异质性表达,这可能会影响成纤维细胞亚型的分化,从而导致瘢痕疙瘩中免疫微环境的失调。最后,我们发现维甲酸可通过抑制 RBP5 使促炎症成纤维细胞(PIF)分化为间充质成纤维细胞(MF),从而进一步减少胶原蛋白的分泌,从而治疗或缓解瘢痕疙瘩:总之,本研究为瘢痕疙瘩的诊断和治疗提供了新的免疫特征(PTGFR、RBP5 和 LIF),并确定维甲酸为潜在的抗瘢痕疙瘩药物。更重要的是,我们为了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一个新的视角。
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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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