低级别神经胶质瘤 (LGG) 是中枢神经系统一级肿瘤，但随着进展，它们成为最致命的脑部肿瘤之一。对于LGG的及时和准确诊断和预后仍然非常需要。在本文中，我们旨在通过采用多种计算方法来确定与LGG相关的诊断和预测性生物标记物。为此，我们对从TCGA和GTEx中得出的LGG与相应健康脑组织的高通量转录组数据进行差异基因表达分析。检测到的差异表达基因的加权基因共表达网络分析被用来识别与LGG临床特征显着相关的共表达基因模块。这些模块中包含的基因随后用于构建基因共表达和蛋白质相互作用网络。基于网络分析，我们得出了18个中心基因的共识，即CD74、CD86、CDC25A、CYBB、HLA-DMA、ITGB2、KIF11、KIFC1、LAPTM5、LMNB1、MKI67、NCKAP1L、NUSAP1、SLC7A7、TBXAS1、TOP2A、TYROBP和WDFY4。所有检测到的中心基因在LGG中被上调，并且也与LGG患者的不良预后有关。本研究的发现可应用于临床诊断和监测LGG。© 2023 Özbek等。

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.© 2023 Özbek et al.