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Abstract：
This study investigates the crucial role of immune- and epithelial-mesenchymal transition (EMT)-associated genes and non-coding RNAs in glioma development and diagnosis, given the challenging 5-year survival rates associated with this prevalent CNS malignant tumor. Clinical and RNA data from glioma patients were meticulously gathered from CGGA databases, and EMT-related genes were sourced from dbEMT2.0, while immune-related genes were obtained from MSigDB. Employing consensus clustering, novel molecular subgroups were identified. Subsequent analyses, including ESTIMATE, TIMER, and MCP counter, provided insights into the tumor microenvironment (TIME) and immune status. Functional studies, embracing GO, KEGG, GSVA, and GSEA analyses, unraveled the underlying mechanisms governing these molecular subgroups. Utilizing the LASSO algorithm and multivariate Cox regression, a prognostic risk model was crafted. The study unveiled two distinct molecular subgroups with significantly disparate survival outcomes. A more favorable prognosis was linked to low immune scores, high tumor purity, and an abundance of immune infiltrating cells with differential expression of non-coding RNAs, including miRNAs. Functional analyses illuminated enrichment of immune- and EMT-associated pathways in differentially expressed genes and non-coding RNAs between these subgroups. GSVA and GSEA analyses hinted at abnormal EMT status potentially contributing to glioma-associated immune disorders. The risk model, centered on OS-EMT-ICI genes, exhibited promise in accurately predicting survival in glioma. Additionally, a nomogram integrating the risk model with clinical characteristics demonstrated notable accuracy in prognostic predictions for glioma patients. In conclusion, OS-EMT-ICI gene and non-coding RNA expression emerges as a valuable indicator intricately linked to immune microenvironment dysregulation, offering a robust tool for precise prognosis prediction in glioma patients within the OBMRC framework.
摘要：
这项研究调查了免疫和上皮间质转化（EMT）相关基因和非编码RNA在神经胶质瘤发展和诊断中的关键作用，鉴于与这种普遍的CNS恶性肿瘤相关的具有挑战性的5年生存率。神经胶质瘤患者的临床和RNA数据从CGGA数据库中精心收集，EMT相关基因来自dbEMT2.0，而免疫相关基因来自MSigDB。采用共识集群，鉴定了新的分子亚组。随后的分析，包括估计,TIMER,和MCP计数器，提供了对肿瘤微环境(TIME)和免疫状态的见解。功能研究，拥抱GO，KEGG,GSVA,和GSEA分析，揭示了控制这些分子亚群的潜在机制。利用LASSO算法和多元Cox回归，我们构建了一个预后风险模型.该研究揭示了两个不同的分子亚群，具有明显不同的生存结果。更有利的预后与低免疫评分有关，肿瘤纯度高,和丰富的非编码RNA差异表达的免疫浸润细胞，包括miRNA。功能分析揭示了这些亚组之间差异表达基因和非编码RNA中免疫和EMT相关途径的富集。GSVA和GSEA分析提示EMT异常状态可能导致神经胶质瘤相关免疫紊乱。风险模型,以OS-EMT-ICI基因为中心，在准确预测神经胶质瘤生存率方面显示出希望。此外,将风险模型与临床特征整合在一起的列线图显示了神经胶质瘤患者预后预测的显著准确性.总之,OS-EMT-ICI基因和非编码RNA表达成为与免疫微环境失调密切相关的有价值的指标。为OBMRC框架内的神经胶质瘤患者提供精确预后预测的可靠工具。
