Wang Chenguang, Liu Zhiyong, He Yan, Zhang Yashu, Chen Shiqi, Zhou Yuhao, Yang Wenqing, Fan Lijun
Centre for Endemic Disease Control, Chinese Centre for Disease Control and Prevention, Harbin Medical University, Harbin, China.
PLoS One. 2025 Jun 25;20(6):e0317666. doi: 10.1371/journal.pone.0317666. eCollection 2025.
Psoriasis is an inflammatory skin disease, and current treatments have their own limitations, including moderate treatment effectiveness, poor compliance, and potential safety risks, etc. Therefore, the primary focus of this study is to explore novel molecular targets and improve the diagnosis and treatment of psoriasis patients.
In this study, comprehensive bioinformatics analysis was performed on the expression profiles of tissue samples from patients with psoriasis in the clinical trial of TYK2/JAK1 inhibitor treatment (NCT02310750). Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were performed to identify characteristic genes and construct the diagnostic models. Gene set enrichment analysis (GSEA) was used to identify the biological processes of psoriasis characteristic gene sets. GO and KEGG pathway analysis were combined to elucidate the potential biological significance of differentially expressed genes (DEGs). The accuracy of biomarker identification was further validated using immune cell infiltration and receiver operating characteristic (ROC) curves based on external data (GSE6710\GSE30999\GSE14905).
A total of 5 genes (DEFB103A, OAS3, OASL, SAMD9, STAT1) were co-identified as characteristic genes in psoriasis progression and treatment. The feature of the immune cell infiltration was highly consistent with association of characteristic biomarkers with immune cells. A total of 14 up-regulated genes and 5 down-regulated genes were identified in respective modules (AUC NL/LS = 0.9783; AUC pre/post = 0.9395; AUC external = 0.9469). In addition, 8 genes (DEFB103A, OASL, HERC6, ISG15, MKI67, MX1, MXD1, SCO2) were considered to have statistically significant differences in sensitivity of short-term treatment for psoriasis.
The research findings provide an understanding of the role of novel biomarkers and offer a perspective for further in-depth investigation into the progression and treatment of psoriasis.
银屑病是一种炎症性皮肤病，目前的治疗方法都有其自身的局限性，包括治疗效果中等、依从性差以及潜在的安全风险等。因此，本研究的主要重点是探索新的分子靶点，改善银屑病患者的诊断和治疗。
在本研究中，对参与TYK2/JAK1抑制剂治疗临床试验（NCT02310750）的银屑病患者组织样本的表达谱进行了全面的生物信息学分析。进行加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）回归以鉴定特征基因并构建诊断模型。基因集富集分析（GSEA）用于鉴定银屑病特征基因集的生物学过程。结合GO和KEGG通路分析以阐明差异表达基因（DEG）的潜在生物学意义。基于外部数据（GSE6710\GSE30999\GSE14905），使用免疫细胞浸润和受试者工作特征（ROC）曲线进一步验证生物标志物鉴定的准确性。
共有5个基因（DEFB103A、OAS3、OASL、SAMD9、STAT1）被共同鉴定为银屑病进展和治疗中的特征基因。免疫细胞浸润特征与特征生物标志物与免疫细胞的关联高度一致。在各个模块中分别鉴定出14个上调基因和5个下调基因（AUC NL/LS = 0.9783；AUC pre/post = 0.9395；AUC external = 0.9469）。此外，8个基因（DEFB103A、OASL、HERC6、ISG15、MKI67、MX1、MXD1、SCO2）被认为在银屑病短期治疗敏感性方面具有统计学显著差异。
研究结果有助于了解新型生物标志物的作用，并为进一步深入研究银屑病的进展和治疗提供了一个视角。
