Dai Lin, Liu Zhihui, Zhu Yi, Ma Lixin
Department of Neurosurgery, Binzhou Medical University Hospital, Binzhou, 256603, PR China.
Department of Obstetrics and Gynecology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, PR China.
Heliyon. 2023 Mar 15;9(3):e14339. doi: 10.1016/j.heliyon.2023.e14339. eCollection 2023 Mar.
Glioblastoma (GBM) is a malignant tumor with a short survival and poor prognosis and a lack of clinically validated biomarkers for diagnosis and prognosis.
We collected cerebrospinal fluid (CSF) samples and normal CSF sample from recurrent GBM patients and paired tissue samples. Methylation profiles of CSF circulating tumor DNA (ctDNA) and transcriptional profiles of tumor tissues were analyzed. The China Glioma Genome Atlas (CGGA) database and Gene Expression Omnibus (GEO) was used for data analysis.
Lasso analysis and multiplex Cox analysis were performed using intersecting genes of differentially methylated regions and differentially expressed genes. 8 hub genes were screened to construct diagnostic and prognostic models. Based on these 8 hub genes, the diagnostic (AUC = 0.944) and prognostic (3-years, AUC = 0.876) models were accurate.
In this study, 8 hub genes were identified for the diagnosis and prognosis of recurrent GBM, providing new biomarkers for the clinical study of recurrent GBM.
胶质母细胞瘤（GBM）是一种生存期短、预后差的恶性肿瘤，且缺乏用于诊断和预后的经临床验证的生物标志物。
我们收集了复发性GBM患者的脑脊液（CSF）样本和正常CSF样本以及配对的组织样本。分析了CSF循环肿瘤DNA（ctDNA）的甲基化谱和肿瘤组织的转录谱。使用中国胶质瘤基因组图谱（CGGA）数据库和基因表达综合数据库（GEO）进行数据分析。
使用差异甲基化区域和差异表达基因的交集基因进行套索分析和多重Cox分析。筛选出8个枢纽基因以构建诊断和预后模型。基于这8个枢纽基因，诊断模型（AUC = 0.944）和预后模型（3年，AUC = 0.876）准确。
在本研究中，鉴定出8个用于复发性GBM诊断和预后的枢纽基因，为复发性GBM的临床研究提供了新的生物标志物。
