Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with 3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.

多形性胶质母细胞瘤（Glioblastoma multiforme, GBM）是成人最常见且侵袭性最强的原发性脑恶性肿瘤。本研究旨在识别与长期生存相关的生物标志物，并评估低级别胶质瘤（Low-grade Glioma, LGG）与长期生存GBM（Long-term Survivor GBM, LTS GBM）之间的分子特征相似性。研究从癌症基因组图谱（Cancer Genome Atlas, TCGA）中获取GBM患者队列数据，采用逻辑回归模型分析多组学分子数据与患者长期生存（LTS）状态的关联，涵盖生殖系与体细胞点突变、基因表达、DNA甲基化、拷贝数变异（Copy Number Variation, CNV）及微小RNA（microRNA, miRNA）表达等多类分子特征。通过聚类分析探究GBM LTS患者肿瘤与LGG的分子关联关系。分别基于体细胞点突变、基因表达、DNA甲基化、CNV及miRNA表达数据集，通过Lasso逻辑回归筛选出13、94、43、29及1个与LTS显著相关的预测因子。单类分子特征分析中，DNA甲基化展现出最优的预测性能（曲线下面积AUC=0.84）。将多类分子特征整合至联合回归模型后，并未提升预测精度，但成功筛选出在单特征模型中未达到显著预测水平的额外基因。主成分分析（Principal Component Analysis, PCA）与聚类分析结果显示，GBM LTS患者的基因表达谱与非LTS GBM患者并无显著差异。此外，聚类分析未发现GBM LTS患者肿瘤与LGG存在紧密的分子亲缘关系，同时也未观察到LTS状态与继发性GBM之间存在显著关联。GBM LTS患者不存在独特的分子特征谱，且其肿瘤与LGG无明显分子相似性，这表明GBM患者实现长期生存的遗传与表观遗传通路具有多样性。