发现 KYNU 是化脓性汗腺炎的一个特征基因。

Discovering KYNU as a feature gene in hidradenitis suppurativa.

机构信息

Department of Dermatology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

Department of Dermatology, The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, China.

出版信息

Int J Immunopathol Pharmacol. 2023 Jan-Dec;37:3946320231216317. doi: 10.1177/03946320231216317.

DOI:10.1177/03946320231216317

PMID:37997679

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10668573/

Abstract

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic auto-inflammatory skin condition characterized by nodules, abscesses, and fistulae in skin folds. The underlying pathogenesis of HS remains unclear, and effective therapeutic drugs are limited.

METHODS

We acquired mRNA expression profiles from the Gene Expression Omnibus (GEO) database and conducted differential expression analysis between control and HS samples using R software. Four machine learning algorithms (SVM, RF, ANN, and lasso) and WCGNA were utilized to identify feature genes. GO, KEGG, Metascape, and GSVA were utilized for the enrichment analysis. CIBERSORT and ssGSEA were employed to analyze immune infiltration.

RESULTS

A total of 29 DEGs were identified, with the majority showing up-regulation in HS. Enrichment analysis revealed their involvement in immune responses and cytokine activities. KEGG analysis highlighted pathways such as IL-17 signaling, rheumatoid arthritis, and TNF signaling in HS. Immune infiltration analysis revealed the predominant presence of neutrophils, monocytes, and CD8 T cells. Machine learning algorithms and WCGNA identified KYNU as a feature gene associated with HS. We have also identified 59 potential drugs for HS based on the DEGs. Additionally, ceRNA network analysis identified the MUC19_hsa-miR-382-5p_KYNU pathway as a potential regulatory pathway.

CONCLUSIONS

KYNU emerged as a feature gene associated with HS, and the ceRNA network analysis identified the MUC19_hsa-miR-382-5p_KYNU pathway as a potential regulator.

摘要

背景

化脓性汗腺炎（HS）是一种慢性自身炎症性皮肤病，其特征为皮肤褶皱处出现结节、脓肿和瘘管。HS 的潜在发病机制尚不清楚，有效的治疗药物也有限。

方法

我们从基因表达综合数据库（GEO）中获取了 mRNA 表达谱，并使用 R 软件对对照和 HS 样本进行了差异表达分析。我们使用了四种机器学习算法（SVM、RF、ANN 和lasso）和 WGCNA 来识别特征基因。GO、KEGG、Metascape 和 GSVA 用于富集分析。CIBERSORT 和 ssGSEA 用于分析免疫浸润。

结果

共鉴定出 29 个差异表达基因，其中大多数在 HS 中呈上调表达。富集分析表明它们参与了免疫反应和细胞因子活性。KEGG 分析突出了 HS 中的 IL-17 信号、类风湿关节炎和 TNF 信号等途径。免疫浸润分析显示，中性粒细胞、单核细胞和 CD8 T 细胞占主导地位。机器学习算法和 WGCNA 确定 KYNU 是与 HS 相关的特征基因。我们还根据 DEGs 确定了 59 种潜在的 HS 药物。此外，ceRNA 网络分析确定了 MUC19_hsa-miR-382-5p_KYNU 途径作为潜在的调节途径。