Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms.

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL Precision Clinical Medicine Pub Date : 2023-06-01 DOI:10.1093/pcmedi/pbad009
Jiachao Xiong, Guodong Chen, Zhixiao Liu, Xuemei Wu, Sha Xu, Jun Xiong, Shizhao Ji, Minjuan Wu
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引用次数: 1

Abstract

Objectives: Alopecia areata (AA) is an autoimmune-related non-cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe forms of AA. However, there are limitations in early identification of AA, and intervention of AA patients who may progress to severe AA will help to improve the incidence rate and prognosis of severe AA.

Methods: We obtained two AA-related datasets from the gene expression omnibus database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of a protein-protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed to clarify the underlying biological mechanisms of severe AA. Subsequently, pivotal immune monitoring genes (IMGs) were screened through multiple machine-learning algorithms, and the diagnostic effectiveness of the pivotal IMGs was validated by receiver operating characteristic.

Results: A total of 150 severe AA-related DEGs were identified; the upregulated DEGs were mainly enriched in immune response, while the downregulated DEGs were mainly enriched in pathways related to hair cycle and skin development. Four IMGs (LGR5, SHISA2, HOXC13, and S100A3) with good diagnostic efficiency were obtained. As an important gene of hair follicle stem cells stemness, we verified in vivo that LGR5 downregulation may be an important link leading to severe AA.

Conclusion: Our findings provide a comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification of four potential IMGs, which is helpful for the early diagnosis of severe AA.

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基于多机器学习算法的斑秃进展调控网络构建及免疫监测基因鉴定。
目的:斑秃(Alopecia areata, AA)是一种自身免疫性非瘢痕性脱发,完全性脱发(complete Alopecia, AT)或全身性脱发(generalized Alopecia, AU)是斑秃的严重形式。然而,早期识别AA存在局限性,对可能发展为重度AA的AA患者进行干预将有助于改善重度AA的发病率和预后。方法:从基因表达综合数据库中获取两组AA相关数据集,鉴定差异表达基因(differential expression genes, DEGs),并通过加权基因共表达网络分析,鉴定与重度AA最相关的模块基因。通过功能富集分析、蛋白质相互作用网络和竞争内源RNA网络的构建以及免疫细胞浸润分析,阐明严重AA的潜在生物学机制。随后,通过多种机器学习算法筛选关键免疫监测基因(IMGs),并通过受试者操作特征验证关键免疫监测基因的诊断有效性。结果:共鉴定出150例严重aa相关deg;上调的DEGs主要富集于免疫应答,下调的DEGs主要富集于与毛发周期和皮肤发育相关的通路。获得了诊断效能较好的4个img (LGR5、SHISA2、HOXC13、S100A3)。作为毛囊干细胞干性的重要基因,我们在体内证实LGR5下调可能是导致严重AA的重要环节。结论:我们的研究结果为AA患者的发病机制和潜在的生物学过程提供了全面的认识,并确定了4种潜在的IMGs,有助于早期诊断重度AA。
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来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
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