Liu Xuewei, Jia Huangchao, Wang Liyun, Wang Ziwen, Xu Mengyue, Li Yunfei, Wang Ronghui
（ 450046） Henan University of Chinese Medicine, Zhengzhou 450046, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2024 Mar 20;55(2):337-345. doi: 10.12182/20240360402.
To screen for the key characteristic genes of the psoriasis vulgaris (PV) patients with different Traditional Chinese Medicine (TCM) syndromes, including blood-heat syndrome (BHS), blood stasis syndrome (BSS), and blood-dryness syndrome (BDS), through bioinformatics and machine learning and to provide a scientific basis for the clinical diagnosis and treatment of PV of different TCM syndrome types.
The GSE192867 dataset was downloaded from Gene Expression Omnibus (GEO). The limma package was used to screen for the differentially expressed genes (DEGs) of PV, BHS, BSS, and BDS in PV patients and healthy populations. In addition, KEGG (Kyoto Encyclopedia of Genes and Genes) pathway enrichment analysis was performed. The DEGs associated with PV, BHS, BSS, and BDS were identified in the screening and were intersected separately to obtain differentially characterized genes. Out of two algorithms, the support vector machine (SVM) and random forest (RF), the one that produced the optimal performance was used to analyze the characteristic genes and the top 5 genes were identified as the key characteristic genes. The receiver operating characteristic (ROC) curves of the key characteristic genes were plotted by using the pROC package, the area under curve () was calculated, and the diagnostic performance was evaluated, accordingly.
The numbers of DEGs associated with PV, BHS, BSS, and BDS were 7699, 7291, 7654, and 6578, respectively. KEGG enrichment analysis was focused on Janus kinase (JAK)/signal transducer and activator of transcription (STAT), cyclic adenosine monophosphate (cAMP), mitogen-activated protein kinase (MAPK), apoptosis, and other pathways. A total of 13 key characteristic genes were identified in the screening by machine learning. Among the 13 key characteristic genes, malectin ), TUB like protein 3 (3), SET domain containing 9 (9), nuclear envelope integral membrane protein 2 (2), and BTG anti-proliferation factor 3 (3) were the key characteristic genes of BHS; phosphatase 15 (15), C1q and tumor necrosis factor related protein 7 (17), solute carrier family 12 member 5 (125), tripartite motif containing 63 (63), and ubiquitin associated protein 1 like (1) were the key characteristic genes of BSS; recombinant mouse protein (1), GTPase-activating protein ASAP3 Protein (3), and human myomesin 2 (2) were the key characteristic genes of BDS. Moreover, all of them showed high diagnostic efficacy.
There are significant differences in the characteristic genes of different PV syndromes and they may be potential biomarkers for diagnosing TCM syndromes of PV.
通过生物信息学和机器学习筛选寻常型银屑病（PV）不同中医证型，包括血热证（BHS）、血瘀证（BSS）和血燥证（BDS）的关键特征基因，为PV不同中医证型的临床诊断和治疗提供科学依据。
从基因表达综合数据库（GEO）下载GSE192867数据集。使用limma软件包筛选PV患者及健康人群中PV、BHS、BSS和BDS的差异表达基因（DEGs）。此外，进行京都基因与基因组百科全书（KEGG）通路富集分析。在筛选出的与PV、BHS、BSS和BDS相关的DEGs中分别进行交集运算，以获得差异特征基因。在支持向量机（SVM）和随机森林（RF）两种算法中，选择性能最优的算法分析特征基因，并将排名前5的基因确定为关键特征基因。使用pROC软件包绘制关键特征基因的受试者工作特征（ROC）曲线，计算曲线下面积（AUC），并据此评估诊断性能。
与PV、BHS、BSS和BDS相关的DEGs数量分别为7699、7291、7654和6578。KEGG富集分析聚焦于Janus激酶（JAK）/信号转导子和转录激活子（STAT）、环磷酸腺苷（cAMP）、丝裂原活化蛋白激酶（MAPK）、凋亡等通路。通过机器学习筛选共鉴定出13个关键特征基因。在这13个关键特征基因中，malectin、微管样蛋白3（TUB like protein 3）、含SET结构域蛋白9（SET domain containing 9）、核膜整合膜蛋白2（nuclear envelope integral membrane protein 2）和BTG抗增殖因子3（BTG anti-proliferation factor 3）是BHS的关键特征基因；蛋白磷酸酶15（phosphatase 15）、C1q和肿瘤坏死因子相关蛋白7（C1q and tumor necrosis factor related protein 7）、溶质载体家族12成员5（solute carrier family 12 member 5）、含三联基序蛋白63（tripartite motif containing 63）和泛素相关蛋白1样蛋白（ubiquitin associated protein 1 like）是BSS的关键特征基因；重组小鼠蛋白（recombinant mouse protein）、GTP酶激活蛋白ASAP3（GTPase-activating protein ASAP3 Protein）和人肌间蛋白2（human myomesin 2）是BDS的关键特征基因。而且，它们均显示出较高的诊断效能。
PV不同证型的特征基因存在显著差异，可能是PV中医证型诊断的潜在生物标志物。
