Ke Yuhan, Ge Wenliang
Department of Pediatric Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, China.
Department of Pediatric Surgery, Medical School of Nantong University, Nantong, 226001, China.
Discov Oncol. 2024 Sep 20;15(1):469. doi: 10.1007/s12672-024-01334-0.
Neuroblastoma (NB) is one of the most frequent parenchymal tumors among children, with a high degree of heterogeneity and wide variation in clinical presentation. Despite significant therapeutic advances in recent years, long-term survival in high-risk patients remains low, emphasizing the urgent need to find new biomarkers and construct reliable prognostic models.
In this study, data from neuroblastoma samples in the ArrayExpress database were utilized to identify key gene modules and pivotal genes associated with NB prognosis by weighted gene co-expression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis was performed using the DAVID database. Based on these hub genes, survival prognosis models were constructed and validated on an independent validation set in the Gene Expression Omnibus (GEO) database. Differences in biological functions and immune microenvironments and the sensitivity to pharmacological and immunotherapeutic treatments of patients in the high- and low-risk groups were examined by gene set enrichment analysis (GSEA) and immune infiltration analysis.
WGCNA identified 14 gene modules and screened the module with the highest relevance to the International Neuroblastoma Staging System (INSS), containing 60 pivotal genes. GO and KEGG analyses demonstrated that these pivotal genes were mainly implicated in biological processes and signaling pathways including DNA replication, cell division, mitotic cell cycle, and cell cycle. Based on Lasso regression and COX regression analysis, a prognostic model containing DHFR, GMPS and E2F3 was constructed, and the RiskScore was significantly correlated with the 1-, 3- and 5-year survival of the patients. GSEA and immune infiltration analyses revealed significant differences in the levels of cell cycle-related pathways and immune cell infiltration between the high and low RiskScore groups. In particular, patients in the high-risk group are less likely to benefit from immunotherapy and may be better suited for treatment with drugs such as Oxaliplatin and Alpelisib.
This research systematically identified biomarkers related to NB prognosis and developed a reliable prognostic model applying WGCNA and multiple bioinformatics methods. The model has important application value in predicting patients' prognosis, evaluating drug sensitivity and immunotherapy effect, and provides new ideas and directions for precise treatment of neuroblastoma.
神经母细胞瘤（NB）是儿童最常见的实质性肿瘤之一，具有高度异质性，临床表现差异很大。尽管近年来治疗取得了重大进展，但高危患者的长期生存率仍然很低，这凸显了寻找新生物标志物和构建可靠预后模型的迫切需求。
在本研究中，利用ArrayExpress数据库中神经母细胞瘤样本的数据，通过加权基因共表达网络分析（WGCNA）来识别与NB预后相关的关键基因模块和枢纽基因。使用DAVID数据库进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）功能富集分析。基于这些枢纽基因，构建生存预后模型，并在基因表达综合数据库（GEO）的独立验证集上进行验证。通过基因集富集分析（GSEA）和免疫浸润分析，研究高低风险组患者在生物学功能和免疫微环境方面的差异以及对药物和免疫治疗的敏感性。
WGCNA识别出14个基因模块，并筛选出与国际神经母细胞瘤分期系统（INSS）相关性最高的模块，其中包含60个枢纽基因。GO和KEGG分析表明，这些枢纽基因主要参与包括DNA复制、细胞分裂、有丝分裂细胞周期和细胞周期等生物学过程和信号通路。基于Lasso回归和COX回归分析，构建了一个包含二氢叶酸还原酶（DHFR）、鸟苷酸合成酶（GMPS）和E2F转录因子3（E2F3）的预后模型，风险评分与患者的1年、3年和5年生存率显著相关。GSEA和免疫浸润分析显示，高低风险评分组在细胞周期相关通路水平和免疫细胞浸润方面存在显著差异。特别是，高危组患者从免疫治疗中获益的可能性较小，可能更适合使用奥沙利铂和阿培利司等药物进行治疗。
本研究通过WGCNA和多种生物信息学方法系统地识别了与NB预后相关的生物标志物，并开发了一个可靠的预后模型。该模型在预测患者预后、评估药物敏感性和免疫治疗效果方面具有重要应用价值，为神经母细胞瘤的精准治疗提供了新的思路和方向。
