Bioinformatics-Based Identification of Key Prognostic Genes in Neuroblastoma with a Focus on Immune Cell Infiltration and Diagnostic Potential of VGF.

IF 1.8 4区 医学 Q3 PHARMACOLOGY & PHARMACY Pharmacogenomics & Personalized Medicine Pub Date : 2024-10-10 eCollection Date: 2024-01-01 DOI:10.2147/PGPM.S461072
Qiang Guo, Yang Xiao, Jing Chu, Yu Sun, Shaomei Li, Shihai Zhang
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Abstract

Objective: This study aims to identify differentially expressed genes (DEGs) in neuroblastoma (NB) through comprehensive bioinformatics analysis and machine learning techniques. We seek to elucidate these DEGs' biological functions and associated signaling pathways. Furthermore, our objective extends to predicting upstream microRNAs (miRNAs) and relevant transcription factors of pivotal genes, with the ultimate goal of guiding clinical diagnostics and informing future treatment strategies for Neuroblastoma.

Methods: In this study, we sourced datasets GSE49710 and TARGET from the GEO and UCSC-XENA databases, respectively. Differentially expressed genes (DEGs) were identified using the R language "limma" package. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs were conducted using the "clusterProfiler" package. We employed Weighted Gene Co-expression Network Analysis (WGCNA) to isolate the most significant modules associated with death and MYCN amplification, specifically MEpink and MEbrown modules. These modules were then cross-referenced with the DEGs for further GO and KEGG pathway analyses. LASSO regression analysis, facilitated by the "glmnet" package, was utilized to pinpoint three hub genes. We performed differential analysis on these genes and constructed Receiver Operating Characteristic (ROC) curves for disease diagnosis purposes. Immune infiltration analysis was conducted using the "GSVA" package's ssGSEA function. Additionally, single-gene Gene Set Enrichment Analysis (GSEA) on the hub gene was carried out based on Reactome and KEGG databases. Upstream miRNA and transcription factors associated with the hub gene were predicted using RegNetwork, with visual representations created in Cytoscape. Furthermore, to validate the three identified markers in neuroblastoma tissues, quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) analysis was conducted.

Results: We identified 483 differentially expressed genes (DEGs) in neuroblastoma. These genes predominantly function in protein translation, membrane composition, and RNA transcription regulation, and are implicated in multiple signaling pathways relevant to neurodegenerative diseases. Utilizing LASSO regression analysis, we pinpointed three hub genes: VGF, DGKD, and C19orf52. The Receiver Operating Characteristic (ROC) curve analysis yielded Area Under Curve (AUC) values of 0.751 and 0.722 for VGF, 0.79 and 0.656 for DGKD, and 0.8 and 0.753 for C19orf52, respectively. Our immune infiltration analysis revealed significant correlations among monocytes, follicular helper T cells, and CD4+ T cells. Notably, in the death group, we observed heightened infiltration levels of activated CD4+ T cells, macrophages, and Th2 cells. C19orf52 exhibited a close association with the infiltration of monocytes, CD4+ T cells, and Th2 cells, with P-values less than 0.05. Furthermore, qRT-PCR analysis corroborated the upregulation of VGF in neuroblastoma tissues, further validating our findings.

Conclusion: The hub genes (VGF, DGKD, and C19orf52) of neuroblastoma are screened. VGF, one of the hub genes, may have a high diagnostic value and is involved in the immune cell infiltration in neuroblastoma tissue, which may be used as a biomarker for the diagnosis of neuroblastoma and provides a new direction for clinical prognosis prediction and management improvement.

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基于生物信息学的神经母细胞瘤关键预后基因鉴定,重点关注免疫细胞浸润和 VGF 的诊断潜力。
研究目的本研究旨在通过全面的生物信息学分析和机器学习技术,识别神经母细胞瘤(NB)中的差异表达基因(DEGs)。我们试图阐明这些 DEGs 的生物学功能和相关信号通路。此外,我们的目标还扩展到预测关键基因的上游微RNA(miRNA)和相关转录因子,最终目的是指导神经母细胞瘤的临床诊断并为未来的治疗策略提供依据:在这项研究中,我们分别从 GEO 和 UCSC-XENA 数据库中获取了数据集 GSE49710 和 TARGET。使用 R 语言 "limma "软件包确定了差异表达基因(DEGs)。随后使用 "clusterProfiler "软件包对这些 DEGs 进行了基因本体(GO)和京都基因组百科全书(KEGG)富集分析。我们采用加权基因共表达网络分析(WGCNA)分离出与死亡和 MYCN 扩增相关的最重要模块,特别是 MEpink 和 MEbrown 模块。然后将这些模块与 DEGs 相互参照,进一步进行 GO 和 KEGG 通路分析。在 "glmnet "软件包的帮助下,我们利用 LASSO 回归分析确定了三个中心基因。我们对这些基因进行了差异分析,并构建了用于疾病诊断的接收者操作特征曲线(ROC)。我们使用 "GSVA "软件包的ssGSEA功能进行了免疫浸润分析。此外,还根据 Reactome 和 KEGG 数据库对枢纽基因进行了单基因基因组富集分析(Gene Set Enrichment Analysis,GSEA)。利用 RegNetwork 预测了与枢纽基因相关的上游 miRNA 和转录因子,并在 Cytoscape 中创建了可视化表示。此外,为了验证在神经母细胞瘤组织中发现的三个标记,还进行了定量实时聚合酶链反应(qRT-PCR)分析:结果:我们在神经母细胞瘤中发现了 483 个差异表达基因(DEGs)。这些基因主要参与蛋白质翻译、膜组成和 RNA 转录调控,并与神经退行性疾病相关的多种信号通路有关。利用 LASSO 回归分析,我们确定了三个枢纽基因:VGF、DGKD 和 C19orf52。通过接收者操作特征曲线(ROC)分析,VGF 的曲线下面积(AUC)分别为 0.751 和 0.722,DGKD 为 0.79 和 0.656,C19orf52 为 0.8 和 0.753。我们的免疫浸润分析显示,单核细胞、滤泡辅助 T 细胞和 CD4+ T 细胞之间存在显著相关性。值得注意的是,在死亡组中,我们观察到活化的 CD4+ T 细胞、巨噬细胞和 Th2 细胞的浸润水平升高。C19orf52 与单核细胞、CD4+ T 细胞和 Th2 细胞的浸润密切相关,P 值小于 0.05。此外,qRT-PCR 分析证实了神经母细胞瘤组织中 VGF 的上调,进一步验证了我们的发现:结论:对神经母细胞瘤的枢纽基因(VGF、DGKD 和 C19orf52)进行了筛选。结论:该研究筛选出了神经母细胞瘤的枢纽基因(VGF、DGKD和C19orf52),其中VGF可能具有较高的诊断价值,它参与了神经母细胞瘤组织中免疫细胞的浸润,可作为诊断神经母细胞瘤的生物标记物,为临床预后预测和管理改进提供了新的方向。
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来源期刊
Pharmacogenomics & Personalized Medicine
Pharmacogenomics & Personalized Medicine Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
3.30
自引率
5.30%
发文量
110
审稿时长
16 weeks
期刊介绍: Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability. In particular, emphasis will be given to: Genomic and proteomic profiling Genetics and drug metabolism Targeted drug identification and discovery Optimizing drug selection & dosage based on patient''s genetic profile Drug related morbidity & mortality intervention Advanced disease screening and targeted therapeutic intervention Genetic based vaccine development Patient satisfaction and preference Health economic evaluations Practical and organizational issues in the development and implementation of personalized medicine programs.
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