Table_1_Novel methylation-related long non-coding RNA clinical outcome prediction method: the clinical phenotype and immune infiltration research in low-grade gliomas.xls

BackgroundRecent studies have suggested that long non-coding RNAs (lncRNAs) may play crucial role in low-grade glioma; however, the underlying mechanisms linking them to epigenetic methylation remain unclear.MethodsWe downloaded expression level data for regulators associated with N1 methyladenosine (m1A), 5-methyladenine (m5C), and N6 methyladenosine (m6A) (M1A/M5C/M6A) methylation from the Cancer Genome Atlas-low-grade glioma (TCGA-LGG) database. We identified the expression patterns of lncRNAs, and selected methylation-related lncRNAs using Pearson correlation coefficient>0.4. Non-negative matrix dimensionality reduction was then used to determine the expression patterns of the methylation-associated lncRNAs. We constructed a weighted gene co-expression network analysis (WGCNA) network to explore the co-expression networks between the two expression patterns. Functional enrichment of the co-expression network was performed to identify biological differences between the expression patterns of different lncRNAs. We also constructed prognostic networks based on the methylation presence in lncRNAs in low-grade gliomas.ResultsWe identified 44 regulators by literature review. Using a correlation coefficient greater than 0.4, we identified 2330 lncRNAs, among which 108 lncRNAs with independent prognostic values were further screened using univariate Cox regression at P< 0.05. Functional enrichment of the co-expression networks revealed that regulation of trans-synaptic signaling, modulation of chemical synaptic transmission, calmodulin binding, and SNARE binding were mostly enriched in the blue module. The calcium and CA2 signaling pathways were associated with different methylation-related long non-coding chains. Using the Least Absolute Shrinkage Selector Operator (LASSO) regression analysis, we analyzed a prognostic model containing four lncRNAs. The model’s risk score was 1.12 *AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC. Gene set variation analysis (GSVA) revealed significant differences in mismatch repair, cell cycle, WNT signaling pathway, NOTCH signaling pathway, Complement and Cascades, and cancer pathways at different GSEC expression levels. Thus, these results suggest that GSEC may be involved in the proliferation and invasion of low-grade glioma, making it a prognostic risk factor for low-grade glioma.ConclusionOur analysis identified methylation-related lncRNAs in low-grade gliomas, providing a foundation for further research on lncRNA methylation. We found that GSEC could serve as a candidate methylation marker and a prognostic risk factor for overall survival in low-grade glioma patients. These findings shed light on the underlying mechanisms of low-grade glioma development and may facilitate the development of new treatment strategies.

近期研究指出，长非编码RNA（lncRNA）可能在低级别胶质瘤中扮演关键角色；然而，将它们与表观遗传甲基化之间的内在联系机制尚不明确。研究方法：我们从癌症基因组图谱-低级别胶质瘤（TCGA-LGG）数据库中下载了与N1甲基腺嘌呤（m1A）、5-甲基腺嘌呤（m5C）和N6甲基腺嘌呤（m6A）甲基化（M1A/M5C/M6A）相关的调节因子的表达水平数据。我们确定了lncRNA的表达模式，并利用皮尔逊相关系数大于0.4筛选出与甲基化相关的lncRNA。随后，采用非负矩阵降维法确定甲基化相关lncRNA的表达模式。我们构建了一个加权基因共表达网络分析（WGCNA）网络，以探索两种表达模式之间的共表达网络。通过共表达网络的功能富集分析，识别了不同lncRNA表达模式之间的生物学差异。此外，我们还根据低级别胶质瘤中lncRNA的甲基化状态构建了预后网络。结果：通过文献综述，我们确定了44个调节因子。利用相关系数大于0.4，我们确定了2330个lncRNA，其中通过单因素Cox回归在P<0.05水平下进一步筛选出具有独立预后价值的108个lncRNA。共表达网络的功能富集分析显示，跨突触信号调节、化学突触传递的调节、钙调蛋白结合以及SNARE结合主要富集在蓝色模块中。钙和CA2信号通路与不同的甲基化相关长非编码链相关。通过最小绝对收缩和选择算子（LASSO）回归分析，我们分析了包含四个lncRNA的预后模型。该模型的危险评分公式为：1.12 * AC012063 + 0.74 * AC022382 + 0.32 * AL049712 + 0.16 * GSEC。基因集变异分析（GSVA）揭示了在不同GSEC表达水平下，错配修复、细胞周期、WNT信号通路、NOTCH信号通路、补体和级联反应以及癌症途径存在显著差异。因此，这些结果表明GSEC可能参与低级别胶质瘤的增殖和侵袭，成为低级别胶质瘤的预后风险因素。结论：我们的分析确定了低级别胶质瘤中的甲基化相关lncRNA，为lncRNA甲基化进一步研究奠定了基础。我们发现GSEC可以作为候选甲基化标志物，并作为低级别胶质瘤患者总生存期的预后风险因素。这些发现揭示了低级别胶质瘤发展的潜在机制，并可能促进新的治疗策略的发展。