Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

Genomic studies have provided insights into molecular subgroups and oncogenic drivers of pediatric brain tumors (PBT) that may lead to novel therapeutic strategies. Participants of the cohort Pediatric Brain Tumor Atlas: CBTTC (CBTTC cohort), were randomly divided into training and validation cohorts. In the training cohort, Kaplan-Meier analysis and univariate Cox regression model were applied to preliminary screening of prognostic genes. The LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation and CBTTC cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. Also, gene set enrichment analysis (GSEA) and immune infiltrating analyses were conducted to understand function annotation and the role of the signature in the tumor microenvironment. An eight-gene signature was built, which was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen, either in the training or validation cohorts. The eight-gene signature was further proven to be independent of other clinic-pathologic parameters via the Cox regression analyses. Moreover, ROC analysis demonstrated that this signature owned a better predictive power of PBT prognosis. Furthermore, GSEA and immune infiltrating analyses showed that the signature had close interactions with immune-related pathways and was closely related to CD8 T cells and monocytes in the tumor environment. Identifying the eight-gene signature (CBX7, JADE2, IGF2BP3, OR2W6P, PRAME, TICRR, KIF4A, and PIMREG) could accurately identify patients' prognosis and the signature had close interactions with the immunodominant tumor environment, which may provide insight into personalized prognosis prediction and new therapies for PBT patients.

基因组研究已为儿童脑肿瘤（pediatric brain tumors, PBT）的分子亚型与致癌驱动因子提供了全新见解，有望催生突破性的治疗策略。本研究队列「儿童脑肿瘤图谱：CBTTC」（CBTTC队列）的受试者被随机划分为训练队列与验证队列。在训练队列中，研究采用Kaplan-Meier分析与单变量Cox回归模型对预后相关基因开展初步筛选；随后通过LASSO Cox回归模型构建多基因预后标签，并借助Kaplan-Meier分析、Cox回归分析与受试者工作特征曲线（receiver operating characteristic curve, ROC）在验证队列及CBTTC队列中对该标签进行验证。此外，本研究还实施了基因集富集分析（gene set enrichment analysis, GSEA）与免疫浸润分析，以解析该标签的功能注释及其在肿瘤微环境中的作用机制。最终构建得到一个包含8个基因的预后标签，经Kaplan-Meier分析验证显示，无论是在训练队列还是验证队列中，该标签均可显著区分患者的总生存期差异。进一步的Cox回归分析证实，该8基因预后标签独立于其他临床病理参数。此外，ROC分析表明该标签对儿童脑肿瘤预后具备更优异的预测性能。GSEA与免疫浸润分析结果进一步显示，该预后标签与免疫相关通路存在密切互作，且与肿瘤微环境中的CD8阳性T细胞及单核细胞紧密相关。该8基因预后标签包含CBX7、JADE2、IGF2BP3、OR2W6P、PRAME、TICRR、KIF4A及PIMREG，其可精准预测患者预后，且与免疫主导型肿瘤微环境存在密切关联，有望为儿童脑肿瘤患者的个性化预后预测与新型治疗方案开发提供全新思路。