Zhong Jiasheng, Liu Jie, Huang Zhilin, Zheng Yaofeng, Chen Jiawen, Ji Jingsen, Chen Taoliang, Ke Yiquan
The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Guangzhou, China.
Front Genet. 2022 Oct 24;13:1026192. doi: 10.3389/fgene.2022.1026192. eCollection 2022.
Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of "personalised medicine" remains a significant challenge. The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the "timeROC" package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the "oncoPredict" package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively. A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy. We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.
胶质瘤是颅内肿瘤中致死率最高的。此外，胶质瘤的异质性导致即使采用相同的治疗方法，治疗效果也会有所不同。开发一种新的胶质瘤特征以实现“个性化医疗”的理念仍然是一项重大挑战。检索了癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）以获取胶质瘤患者的信息。首先，进行相关性和单变量Cox回归分析以筛选与预后细胞焦亡相关的长链非编码RNA（PRL）。其次，使用特定算法选择11个PRL来构建分类器。然后通过“timeROC”软件包检测训练数据集和验证数据集的分类器效能。应用CIBERSORT和ESTIMATE软件包比较高风险组和低风险组之间免疫格局的差异。最后，分别使用“oncoPredict”软件包、生存分析和肿瘤免疫功能障碍与排除（TIDE）评分评估化疗、放疗和免疫治疗的疗效。构建了一个包含11个PRL的分类器。无论数据集是训练数据集还是验证数据集，PRL分类器对胶质瘤患者生存结果的预测能力都比临床特征更强。此外，发现低风险组和高风险组之间的肿瘤格局存在显著差异。高风险评分与更具免疫抑制性的肿瘤微环境相关。根据化疗的结果预测和分析，不同评分的患者对各种化疗药物和免疫治疗表现出不同的反应。同时，高风险组中WHO Ⅳ级或年龄>50岁的胶质瘤患者放疗后的生存率更高。我们构建了一个PRL分类器来大致预测胶质瘤患者的预后。此外，PRL分类器与胶质瘤的免疫格局相关，可能会为临床治疗提供指导。
