Okamoto Takanari, Mizuta Ryo, Demachi-Okamura Ayako, Muraoka Daisuke, Sasaki Eiichi, Masago Katsuhiro, Yamaguchi Rui, Teramukai Satoshi, Otani Yoshihiro, Date Isao, Tanaka Shota, Takahashi Yoshinobu, Hashimoto Naoya, Matsushita Hirokazu
Division of Translational Oncoimmunology, Aichi Cancer Center Research Institute, Nagoya, Japan; Department of Neurosurgery, Kyoto Prefectural University of Medicine Graduate School of Medical Science, Kyoto, Japan.
Division of Translational Oncoimmunology, Aichi Cancer Center Research Institute, Nagoya, Japan; Department of Neurological Surgery, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan.
Cancer Genet. 2025 Jun;294-295:32-41. doi: 10.1016/j.cancergen.2025.03.005. Epub 2025 Mar 22.
Few effective immune prognostic models based on the tumor immune microenvironment (TIME) for glioblastoma have been reported. Therefore, this study aimed to construct an immune prognostic model for glioblastoma by analyzing enriched biological processes and pathways in tumors.
A comprehensive single-sample gene set enrichment analysis (ssGSEA) of gene sets from the Molecular Signatures Database was performed using TCGA RNA sequencing data (141 glioblastoma cases). After evaluating gene sets associated with prognosis using univariable Cox regression, gene sets related to biological processes and tumor immunity in gliomas were extracted. Finally, the least absolute shrinkage and selection operator Cox regression refined the gene sets and a nomogram was constructed. The model was validated using CGGA (183 cases) and Aichi Cancer Center (42 cases) datasets.
The immune prognostic model consisted of three gene sets related to biological processes (sphingolipids, steroid hormones, and intermediate filaments) and one related to tumor immunity (immunosuppressive chemokine pathways involving tumor-associated microglia and macrophages). Kaplan-Meier curves for the training (TCGA) and validation (CGGA) cohorts showed significantly worse overall survival in the high-risk group compared to the low-risk group (p < 0.001 and p = 0.04, respectively). Furthermore, in silico cytometry revealed a significant increase in macrophages with immunosuppressive properties and T cells with effector functions in the high-risk group (p < 0.01) across all cohorts.
Construction of an immune prognostic model based on the TIME assessment using ssGSEA could potentially provide valuable insights into the prognosis and immune profiles of patients with glioblastoma and guide treatment strategies.
基于肿瘤免疫微环境（TIME）的胶质母细胞瘤有效免疫预后模型鲜有报道。因此，本研究旨在通过分析肿瘤中富集的生物学过程和通路来构建胶质母细胞瘤的免疫预后模型。
使用TCGA RNA测序数据（141例胶质母细胞瘤病例）对来自分子特征数据库的基因集进行全面的单样本基因集富集分析（ssGSEA）。在使用单变量Cox回归评估与预后相关的基因集后，提取与胶质瘤生物学过程和肿瘤免疫相关的基因集。最后，通过最小绝对收缩和选择算子Cox回归对基因集进行优化，并构建列线图。使用CGGA（183例）和爱知癌症中心（42例）数据集对该模型进行验证。
免疫预后模型由三个与生物学过程相关的基因集（鞘脂、类固醇激素和中间丝）和一个与肿瘤免疫相关的基因集（涉及肿瘤相关小胶质细胞和巨噬细胞的免疫抑制趋化因子通路）组成。训练队列（TCGA）和验证队列（CGGA）的Kaplan-Meier曲线显示，高风险组的总生存期明显低于低风险组（分别为p < 0.001和p = 0.04）。此外，计算机模拟细胞术显示，在所有队列的高风险组中，具有免疫抑制特性的巨噬细胞和具有效应功能的T细胞显著增加（p < 0.01）。
使用ssGSEA基于TIME评估构建免疫预后模型可能为胶质母细胞瘤患者的预后和免疫谱提供有价值的见解，并指导治疗策略。
