Aims: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms. Background: Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking. objective: The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes. Objective: The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes. method: A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database (GEO). The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Support Vector Machines model (SVM), and Least absolute shrinkage and selection operator regression model (LASSO). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on this we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples. Methods: A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method. result: 8 hub genes were determined, ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158, GSN. The diagnostic model composed showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma. Results: 8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma. Conclusion: Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

中文翻译：

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基于批量 RNA 测序数据和多机器学习模型识别青光眼的中心基因

目的：本研究的目的是通过多种机器学习算法确定青光眼的中枢基因。背景：青光眼多年来一直困扰着许多患者，眼压过高持续损害神经系统并导致严重失明。目前缺乏有效的分子诊断方法。目的：本研究试图揭示Hub基因在青光眼中的分子机制和基因调控网络，进而揭示Hub基因调控的药物-基因-疾病网络。目的：本研究试图揭示Hub基因在青光眼中的分子机制和基因调控网络，进而揭示Hub基因调控的药物-基因-疾病网络。方法：通过基因表达综合数据库（GEO）获得微阵列测序数据集（GSE9944）。鉴定了青光眼中的差异表达基因。基于这些基因，我们构建了三种用于特征训练的机器学习模型：随机森林模型（RF）、支持向量机模型（SVM）和最小绝对收缩和选择算子回归模型（LASSO）。同时，对 GSE9944 表达谱进行加权基因共表达网络分析 (WGCNA)，以鉴定青光眼相关基因。四组中重叠的基因被认为是青光眼的中心基因。在此基础上我们还构建了青光眼的分子诊断模型。在本研究中，我们还进行了分子对接分析，以探索针对枢纽基因的基因药物网络。此外，我们还评估了青光眼样本和正常样本中的免疫细胞浸润情况。方法：通过基因表达综合数据库获得微阵列测序数据集（GSE9944）。鉴定了青光眼中的差异表达基因。基于这些基因，我们构建了三种用于特征训练的机器学习模型：随机森林模型（RF）、最小绝对收缩和选择算子回归模型（LASSO）和支持向量机模型（SVM）。同时，对 GSE9944 表达谱进行加权基因共表达网络分析 (WGCNA)，以鉴定青光眼相关基因。四组中重叠的基因被认为是青光眼的中心基因。基于这些基因，我们还构建了青光眼的分子诊断模型。在本研究中，我们还进行了分子对接分析，以探索针对枢纽基因的基因药物网络。此外，我们应用 CIBERSORT 方法评估了青光眼样本和正常样本中的免疫细胞浸润情况。结果：确定了8个hub基因，ATP6V0D1、PLEC、SLC25A1、HRSP12、PKN1、RHOD、TMEM158、GSN。所组成的诊断模型表现出优异的诊断性能（曲线下面积=1）。GSN 可能正向调节 T 细胞 CD4 naïve，也可能负向调节 T 细胞调节 (Treg)。此外，我们构建了基因药物网络，试图探索青光眼的新型治疗药物。结果：确定了8个中心基因：ATP6V0D1、PLEC、SLC25A1、HRSP12、PKN1、RHOD、TMEM158和GSN。该诊断模型显示出优异的诊断性能（曲线下面积=1）。GSN 可能正向调节 T 细胞 CD4 naïve，也可能负向调节 T 细胞调节 (Treg)。此外，我们构建了基因药物网络，试图探索青光眼的新型治疗药物。结论：我们的结果系统地确定了 8 个中心基因，并建立了可以诊断青光眼的分子诊断模型。我们的研究为未来青光眼发病机制的系统研究提供了基础。