Anhui Provincial Key Laboratory of Molecular Enzymology and Mechanism of Major Diseases, Key Laboratory of Biomedicine in Gene Diseases, Health of Anhui Higher Education Institutes, College of Life Sciences, Anhui Normal University, Wuhu, China.
Hum Genomics. 2023 Aug 17;17(1):76. doi: 10.1186/s40246-023-00526-z.
As one of the most common intestinal inflammatory diseases, celiac disease (CD) is typically characterized by an autoimmune disorder resulting from ingesting gluten proteins. Although the incidence and prevalence of CD have increased over time, the diagnostic methods and treatment options are still limited. Therefore, it is urgent to investigate the potential biomarkers and targeted drugs for CD.
Gene expression data was downloaded from GEO datasets. Differential gene expression analysis was performed to identify the dysregulated immune-related genes. Multiple machine algorithms, including randomForest, SVM-RFE, and LASSO, were used to select the hub immune-related genes (HIGs). The immune-related genes score (IG score) and artificial neural network (ANN) were constructed based on HIGs. Potential drugs targeting HIGs were identified by using the Enrichr platform and molecular docking method.
We identified the dysregulated immune-related genes at a genome-wide level and demonstrated their roles in CD-related immune pathways. The hub genes (MR1, CCL25, and TNFSF13B) were further screened by integrating several machine algorithms. Meanwhile, the CD patients were divided into distinct subtypes with either high- or low-immunoactivity using single-sample gene set enrichment analysis (ssGSEA) and consensus clustering. By constructing IG score based on HIGs, we found that patients with high IG score were mainly attributed to high-immunoactivity subgroups, which suggested a strong link between HIGs and immunoactivity of CD patients. In addition, the novel constructed ANN model showed the sound diagnostic ability of HIGs. Mechanistically, we validated that the HIGs play pivotal roles in regulating CD's immune and inflammatory state. Through targeting the HIGs, we also found potential drugs for anti-CD treatment by using the Enrichr platform and molecular docking method.
This study unveils the HIGs and elucidates the networks regulated by these genes in the context of CD. It underscores the pivotal significance of HIGs in accurately predicting the presence or absence of CD in patients. Consequently, this research offers promising prospects for the development of diagnostic biomarkers and therapeutic targets for CD.
作为最常见的肠道炎症性疾病之一，乳糜泻（CD）通常表现为由于摄入麸质蛋白而导致的自身免疫紊乱。尽管 CD 的发病率和流行率随着时间的推移而增加，但诊断方法和治疗选择仍然有限。因此，迫切需要研究 CD 的潜在生物标志物和靶向药物。
从 GEO 数据集下载基因表达数据。进行差异基因表达分析以鉴定失调的免疫相关基因。使用多种机器学习算法，包括随机森林、SVM-RFE 和 LASSO，选择枢纽免疫相关基因（HIG）。基于 HIG 构建免疫相关基因评分（IG 评分）和人工神经网络（ANN）。使用 Enrichr 平台和分子对接方法鉴定针对 HIG 的潜在药物。
我们在全基因组水平上鉴定了失调的免疫相关基因，并证明了它们在 CD 相关免疫途径中的作用。通过整合几种机器学习算法，进一步筛选出枢纽基因（MR1、CCL25 和 TNFSF13B）。同时，通过单样本基因集富集分析（ssGSEA）和共识聚类，将 CD 患者分为具有高或低免疫活性的不同亚型。通过基于 HIG 构建 IG 评分，我们发现具有高 IG 评分的患者主要归因于高免疫活性亚组，这表明 HIG 与 CD 患者的免疫活性之间存在很强的联系。此外，新构建的 ANN 模型显示出 HIG 良好的诊断能力。从机制上讲，我们验证了 HIG 在调节 CD 的免疫和炎症状态中起着关键作用。通过针对 HIG，我们还使用 Enrichr 平台和分子对接方法发现了针对 CD 治疗的潜在药物。
本研究揭示了 HIG，并阐明了它们在 CD 背景下调节的网络。它强调了 HIG 在准确预测患者是否存在 CD 方面的重要性。因此，这项研究为 CD 的诊断生物标志物和治疗靶点的开发提供了有希望的前景。
