银屑病铁死亡相关基因的诊断标志物及潜在中药治疗预测

doi:10.5246/jcps.2025.02.012

摘要/Abstract

摘要：

本研究旨在通过生物信息学分析筛选并验证银屑病相关的铁死亡特征基因, 并进行中药预测, 为银屑病铁死亡的机制研究及中药干预提供依据。首先, 从基因表达合成数据库(GEO)获取了银屑病病患与健康个体对照的基因表达谱数据并标准化处理; 利用FerrDb数据库筛选并识别银屑病相关的铁死亡差异表达基因(Fer-DEGs); GO和KEGG分析Fer-DEGs的生物学内涵和信号通路; 采用机器学习获取核心Fer-DEGs, 结合外部验证集分析其表达, 以判断诊断能力, 并通过symMap数据库反向预测可以作用这些特征基因的中药。最终筛选出具有显著差异的265个Fer-DEGs, GO富集分析发现涉及多种生物学角度, KEGG分析揭示了它们在铁死亡、自噬、肿瘤、感染、代谢以及PI3K-Akt、FoxO、mTOR和HIF-1等多条信号通路上发挥作用。机器学习得到PRKAA2、ANO6、POR、PTEN、MAPK8、ZFAS1、ADAM23、TMBIM4和PARP14共9个核心银屑病Fer-DEGs特征基因, 诊断效能均优, 预测中药发现以清热解毒、活血化瘀和化痰类为主。研究表明, PRKAA2、ANO6、POR、PTEN、MAPK8、ZFAS1、ADAM23、TMBIM4和PARP14基因可能在银屑病铁死亡途径中发挥着一定的作用, 而清热解毒、活血化瘀和化痰类中药可以作为抗银屑病铁死亡途径的参考。

关键词: 银屑病, 铁死亡, Fer-DEGs, 中药预测

Abstract:

This research aimed to identify and validate ferroptosis-related signature genes associated with psoriasis through a comprehensive bioinformatics approach, while also predicting potential traditional Chinese medicines (TCMs) targeting these genes. The findings might offer a foundation for understanding ferroptosis mechanisms in psoriasis and exploring TCM-based therapeutic strategies. To begin, we retrieved gene expression profile data from psoriasis patients and healthy controls from the Gene Expression Omnibus (GEO) database, followed by data normalization. Ferroptosis-associated differentially expressed genes (Fer-DEGs) were identified using the FerrDb database. Subsequent GO and KEGG enrichment analyses provided insights into the biological functions and signaling pathways of these Fer-DEGs. Core Fer-DEGs were identified using machine learning algorithms, and their expression levels were further validated with an external dataset to evaluate diagnostic potential. Additionally, the symMap database facilitated the reverse prediction of TCMs targeting these key signature genes. The analysis identified 265 significant Fer-DEGs. GO enrichment indicated their involvement in diverse biological processes, while KEGG analysis highlighted their roles in various pathways, including ferroptosis, autophagy, cancer, infection, and metabolism, as well as PI3K-Akt, FoxO, mTOR, and HIF-1 signaling pathways. Machine learning pinpointed nine core psoriasis-related Fer-DEGs: PRKAA2, ANO6, POR, PTEN, MAPK8, ZFAS1, ADAM23, TMBIM4, and PARP14, all demonstrating strong diagnostic performance. Predicted TCMs primarily included those with heat-clearing, detoxifying, blood-activating, stasis-resolving, and phlegm-resolving properties. In conclusion, our study suggested that PRKAA2, ANO6, POR, PTEN, MAPK8, ZFAS1, ADAM23, TMBIM4, and PARP14 were key players in the ferroptosis pathway in psoriasis. TCMs with properties such as heat-clearing, blood activation, and phlegm resolution might hold promise for anti-ferroptosis interventions in psoriasis treatment.

Key words: Psoriasis, Ferroptosis, Fer-DEGs, Traditional Chinese medicine prediction

Supporting:

马晓燕, 伍迪. 银屑病铁死亡相关基因的诊断标志物及潜在中药治疗预测[J]. 中国药学(英文版), 2025, 34(2): 150-162.

Xiaoyan Ma, Di Wu. Ferroptosis-related gene biomarkers in psoriasis: diagnostic potential and insights for traditional chinese medicine treatments[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(2): 150-162.

图/表 7

Figure 1. Expression of DEGs. (A) Volcano plot of DEG expression, where blue represents down-regulated genes, and red represents up-regulated genes; (B) The top 50 up-regulated and down-regulated DEGs, where blue represents down-regulation, and red represents up-regulation.

Figure 2. Heatmap of Fer-DEG Expression. Note: The heatmap indicates the expression of Fer-DEGs in the training set. Blue represents low expression, and red represents high expression. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3. GO Enrichment analyse and KEGG pathway Enrichment. (A) GO enrichment bar chart. The x-axis of the BP, CC, and MF columns represents the number of enriched genes in each item, and the length of the bars represents the number of enriched genes; (B) GO enrichment circle diagram. In the circle diagram, the outer blue circle represents BP, the yellow circle represents CC, and the green circle represents MF; (C) KEGG enrichment bubble chart; (D) KEGG network chord diagram.

Figure 4. Screening of characteristic genes of ferroptosis in psoriasis. (A) LASSO logistic regression was employed, and the penalty parameter was adjusted through 10-fold cross-validation to select 24 characteristic genes of ferroptosis in psoriasis; (B) The SVM-RFE algorithm was utilized for biomarker screening. The vertical axis represents the magnitude of the error, and the horizontal axis represents the number of genes; (C) Venn diagram of the intersection of characteristic genes screened by the two machine learning approaches.

Figure 5. ROC curves of the characteristic genes of Fer-DEGs in the training set.

Figure 6. Expression of the nine Fer-DEGs characteristic genes. Note: Green indicates the control group, and yellow indicates the psoriasis group. *P < 0.05; **P < 0.01; ***P < 0.001.

Table 1. Top three function predicted by the symMap database (P < 0.001).

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