Scholarlink

<p>Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We applied weighted gene co-expression network analysis (WGCNA) to transcriptomic data from IDH-wildtype histological GBM in the CGGA-693 (n = 190) and CGGA-325 (n = 111) cohorts to identify recurrence-associated genes. These genes were validated using RT-qPCR and single-cell RNA sequencing (scRNA-seq) datasets (GSE174554, GSE131928). Their associations with immune cell composition were analyzed. Finally, we evaluated 113 machine learning algorithms to develop a multi-gene predictive model for GBM recurrence, with model performance assessed using receiver operating characteristic (ROC) curves and confusion matrix analysis. We identified eight recurrence-associated genes (<i>CERS2, EML2, FNBP1, ICOSLG, MFAP3L, NPC1, ROGDI, SLAIN1</i>) that were significantly differentially expressed between primary and recurrent GBM. The scRNA-seq analysis revealed cell-type-specific expression patterns, with eight genes predominantly enriched in oligodendrocytes, malignant GBM subtypes, and immune cells. Immune cell deconvolution showed significant alterations in macrophage polarization and NK cell activation in recurrent GBM. Machine learning analysis demonstrated that random forest (RF) was the most effective model, achieving AUC values of 0.998, 0.968, and 0.998 in the training, CGGA-693 validation, and CGGA-325 validation cohorts, respectively, suggesting high predictive accuracy. This study identifies novel recurrence-associated molecular signatures and establishes a machine learning-based predictive model in IDH-wildtype histological GBM.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of Recurrence-associated Gene Signatures and Machine Learning-based Prediction in IDH-Wildtype Histological Glioblastoma

  • 期刊论文

  • 发表日期：

    2025年4月14日

查看全文
  • Min Yuan,
  • Xueqin Hu,
  • Zeng Yang,
  • Jingsheng Cheng,
  • Haibin Leng,
  • Zhiwei Zhou

摘要

Glioblastoma (GBM) is a highly aggressive brain tumor with frequent recurrence, yet the molecular mechanisms driving recurrence remain poorly understood. Identifying recurrence-associated genes may improve prognosis and treatment strategies. We applied weighted gene co-expression network analysis (WGCNA) to transcriptomic data from IDH-wildtype histological GBM in the CGGA-693 (n = 190) and CGGA-325 (n = 111) cohorts to identify recurrence-associated genes. These genes were validated using RT-qPCR and single-cell RNA sequencing (scRNA-seq) datasets (GSE174554, GSE131928). Their associations with immune cell composition were analyzed. Finally, we evaluated 113 machine learning algorithms to develop a multi-gene predictive model for GBM recurrence, with model performance assessed using receiver operating characteristic (ROC) curves and confusion matrix analysis. We identified eight recurrence-associated genes (CERS2, EML2, FNBP1, ICOSLG, MFAP3L, NPC1, ROGDI, SLAIN1) that were significantly differentially expressed between primary and recurrent GBM. The scRNA-seq analysis revealed cell-type-specific expression patterns, with eight genes predominantly enriched in oligodendrocytes, malignant GBM subtypes, and immune cells. Immune cell deconvolution showed significant alterations in macrophage polarization and NK cell activation in recurrent GBM. Machine learning analysis demonstrated that random forest (RF) was the most effective model, achieving AUC values of 0.998, 0.968, and 0.998 in the training, CGGA-693 validation, and CGGA-325 validation cohorts, respectively, suggesting high predictive accuracy. This study identifies novel recurrence-associated molecular signatures and establishes a machine learning-based predictive model in IDH-wildtype histological GBM.