Genomics Research Center, Academia Sinica, Taipei, Taiwan.
Ph.D. Program in Translational Medicine, National Taiwan University and Academia Sinica, Taipei, Taiwan.
Acta Neuropathol Commun. 2024 May 18;12(1):77. doi: 10.1186/s40478-024-01790-3.
Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.
胶质母细胞瘤（GBM）是成人中最常见的恶性脑肿瘤，目前仍然无法治愈，并且在初始治疗后常常迅速复发。尽管已经进行了大量努力来揭示与 GBM 复发相关的基因组/转录组改变，但配对的原发和复发（P-R）GBM 的进化轨迹在很大程度上仍然难以捉摸。确定与复发时间（TTR）相关的基因并构建稳定有效的预测原发 GBM 患者 TTR 的预后模型仍然具有挑战性。通过整合来自 IDH 野生型（IDH-wt）患者配对纵向 GBM 的多个数据集的 RNA-seq 和基因组数据，我们在这里研究了 TTR 与配对 P-R GBM 之间基因表达谱、肿瘤突变负担（TMB）和微环境的异质性之间的关联。我们的结果表明，TTR 与配对 P-R GBM 之间的转录组/基因组差异呈正相关，与 TTR 较短的患者相比，TTR 较长的患者中非间充质到间充质转化和间充质亚型的比例更高，配对 P-R GBM 之间的基因表达谱和 TMB 之间具有高度相关性，并且这种配对 P-R GBM 相关性的拟合水平与 TTR 呈负相关。根据这些观察结果，我们确定了 55 个与 TTR 相关的基因，并使用单变量/多变量 Cox 回归分析构建了一个用于预测原发性 IDH-wt GBM 患者 TTR 的七基因（ZSCAN10、SIGLEC14、GHRHR、TBX15、TAS2R1、CDKL1 和 CD101）预后模型。该模型估计的风险评分与训练集中的 TTR 显著负相关，并且在两个独立的测试集中也具有显著差异。该模型还将 IDH-wt GBM 患者分为两组，两组之间的无进展生存期结果存在显著差异，并在所有训练和测试集中表现出预测 1 年、2 年和 3 年无进展生存率的良好性能。我们的研究结果为 GBM 复发时的分子理解提供了新的见解，并为治疗提供了潜在的治疗靶点。
