Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
J Cancer Res Clin Oncol. 2024 Mar 1;150(3):109. doi: 10.1007/s00432-024-05630-8.
Neuroblastoma (NB), a prevalent pediatric solid tumor, presents formidable challenges due to its high malignancy and intricate pathogenesis. The role of disulfidptosis, a novel form of programmed cell death, remains poorly understood in the context of NB.
Gaussian mixture model (GMM)-identified disulfidptosis-related molecular subtypes in NB, differential gene analysis, survival analysis, and gene set variation analysis were conducted subsequently. Weighted gene co-expression network analysis (WGCNA) selected modular genes most relevant to the disulfidptosis core pathways. Integration of machine learning approaches revealed the combination of the Least absolute shrinkage and selection operator (LASSO) and Random Survival Forest (RSF) provided optimal dimensionality reduction of the modular genes. The resulting model was validated, and a nomogram assessed disulfidptosis characteristics in NB. Core genes were filtered and subjected to tumor phenotype and disulfidptosis-related experiments.
GMM clustering revealed three distinct subtypes with diverse prognoses, showing significant variations in glucose metabolism, cytoskeletal structure, and tumor-related pathways. WGCNA highlighted the red module of genes highly correlated with disulfide isomerase activity, cytoskeleton formation, and glucose metabolism. The LASSO and RSF combination yielded the most accurate and stable prognostic model, with a significantly worse prognosis for high-scoring patients. Cytological experiments targeting core genes (CYFIP1, EMILIN1) revealed decreased cell proliferation, migration, invasion abilities, and evident cytoskeletal deformation upon core gene knockdown.
This study showcases the utility of disulfidptosis-related gene scores for predicting prognosis and molecular subtypes of NB. The identified core genes, CYFIP1 and EMILIN1, hold promise as potential therapeutic targets and diagnostic markers for NB.
神经母细胞瘤（NB）是一种常见的小儿实体瘤，由于其高度恶性和复杂的发病机制，带来了巨大的挑战。细胞程序性死亡的一种新形式——二硫键凋亡，在 NB 中的作用尚未被充分了解。
采用高斯混合模型（GMM）对 NB 中二硫键凋亡相关的分子亚型进行鉴定，然后进行差异基因分析、生存分析和基因集变异分析。加权基因共表达网络分析（WGCNA）选择与二硫键凋亡核心途径最相关的模块基因。通过机器学习方法的整合，揭示了最小绝对收缩和选择算子（LASSO）和随机生存森林（RSF）的组合为模块基因提供了最佳的降维效果。对该模型进行验证，并通过列线图评估 NB 中二硫键凋亡特征。筛选核心基因并进行肿瘤表型和二硫键凋亡相关实验。
GMM 聚类显示出三种具有不同预后的不同亚型，其在葡萄糖代谢、细胞骨架结构和肿瘤相关途径方面存在显著差异。WGCNA 突出显示了与二硫键异构酶活性、细胞骨架形成和葡萄糖代谢高度相关的红色模块基因。LASSO 和 RSF 的组合产生了最准确和稳定的预后模型，高分患者的预后明显较差。针对核心基因（CYFIP1、EMILIN1）的细胞学实验显示，核心基因敲低后，细胞增殖、迁移和侵袭能力下降，细胞骨架明显变形。
本研究展示了二硫键凋亡相关基因评分预测 NB 预后和分子亚型的应用价值。鉴定的核心基因 CYFIP1 和 EMILIN1 有望成为 NB 的潜在治疗靶点和诊断标志物。
