Neurosurgery Department, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510032, China.
Radiology Department, Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, 510032, China.
Sci Rep. 2024 Jul 31;14(1):17630. doi: 10.1038/s41598-024-68648-5.
Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.
胶质母细胞瘤（GBM）是一种高度侵袭性的恶性肿瘤，目前的治疗方法无法完全治愈，因此需要更精确的分子亚型特征来预测治疗反应，以实现个性化精准治疗。使用 BayesNM 从癌症基因组图谱（TCGA）中鉴定 GBM 样本的表达亚型，并与现有的 GBM 分子亚型进行比较。通过单样本基因集富集分析确定亚型的生物学特征。合并 GBM 样本的基因组和蛋白质组数据，并使用癌症基因组鉴定显著靶标（Genomic Identification of Significant Targets in Cancer，GISTIC）分析筛选具有复发性体细胞拷贝数改变现象的基因。通过评估免疫分子的表达和免疫细胞的浸润来比较亚型之间的免疫环境。根据肿瘤免疫功能障碍和排除（Tumor Immune Dysfunction and Exclusion，TIDE）评分鉴定适应免疫治疗的分子亚型。最后，在 TCGA-GBM 和 RPPA 中对 S2、S3 和 S4 的表达谱进行最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator，LASSO）逻辑回归，以确定各自相应的最佳预测模型。分类出四个新的分子亚型。具体而言，S1 表现出低增殖特征；S2 表现出高增殖、IDH1 突变、TP53 突变和缺失的特征；S3 的特征是高免疫评分、先天免疫和适应性免疫浸润评分，TIDE 评分最低，最有可能受益于免疫治疗；S4 的特征是高增殖、EGFR 扩增和高蛋白丰度，是最适合贝伐单抗治疗的亚型。LASSO 分析构建了由 S2 中的 13 个基因组成的最佳预测模型，准确率为 96.7%，由 S3 中的 17 个基因组成的预测模型准确率为 86.7%，并筛选出 14 个基因作为 S4 中最佳预测模型的组成部分，准确率为 93%。总之，本研究对 GBM 的分子亚型进行了可重复和稳健的分类，这些发现可能有助于识别对免疫治疗有反应的患者，从而改善 GBM 的预后。
