Department of Emergency, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Department of Neurosurgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
Medicine (Baltimore). 2023 Jun 9;102(23):e33935. doi: 10.1097/MD.0000000000033935.
Gliomas have a high incidence rate in central nervous tumors. Although many breakthroughs have been made in the pathogenesis and treatment of glioma, the recurrence and metastasis rates of patients have not been improved based on the uniqueness of glioma. Glioma destroys the surrounding basement membrane (BM), leading to local infiltration, resulting in the corresponding clinical and neurological symptoms. Therefore, exploring the biological roles played by BM associated genes in glioma is particularly necessary for a comprehensive understanding of the biological processes of glioma and its treatment. Differential expression and univariate COX regression analyses were used to identify the basement membrane genes (BMGs) to be included in the model. LASSO regression was used to construct the BMG model. The Kaplan-Meier (KM) survival analysis model was used to assess the prognosis discrimination between training sets, validation sets, and clinical subgroups. Receiver-operating characteristic (ROC) analysis was used to test the prognostic efficacy of the model. Use calibration curves to verify the accuracy of nomograms. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were used to analyze the function and pathway enrichment among the model groups. ESTIMATE and other 7 algorithms including CIBERSORT were used to evaluate the immune microenvironment. "pRRophetic" was used to evaluate drug sensitivity. This study demonstrated that high-risk genes (LAMB4, MMP1, MMP7) promote glioma progression and negatively correlate with patient prognosis. In the tumor microenvironment (TME), high-risk genes have increased scores of macrophages, neutrophils, immune checkpoints, chemokines, and chemokine receptors. This study suggests that BMGs, especially high-risk-related genes, are potential sites for glioma therapy, a new prospect for comprehensively understanding the molecular mechanism of glioma.
神经胶质瘤在中枢神经系统肿瘤中发病率较高。尽管在神经胶质瘤的发病机制和治疗方面已经取得了许多突破，但基于神经胶质瘤的独特性，患者的复发和转移率并没有得到改善。神经胶质瘤破坏周围的基底膜（BM），导致局部浸润，从而导致相应的临床和神经症状。因此，探索 BM 相关基因在神经胶质瘤中的生物学作用对于全面了解神经胶质瘤的生物学过程及其治疗方法尤为必要。差异表达和单变量 COX 回归分析用于鉴定纳入模型的基底膜基因（BMGs）。LASSO 回归用于构建 BMG 模型。Kaplan-Meier（KM）生存分析模型用于评估训练集、验证集和临床亚组之间的预后判别。接收者操作特征（ROC）分析用于测试模型的预后效果。使用校准曲线验证列线图的准确性。基因本体论（GO）、京都基因与基因组百科全书（KEGG）和基因集富集分析（GSEA）用于分析模型组中的功能和途径富集。ESTIMATE 和其他 7 种算法，包括 CIBERSORT，用于评估免疫微环境。“pRRophetic”用于评估药物敏感性。本研究表明，高风险基因（LAMB4、MMP1、MMP7）促进神经胶质瘤的进展，并与患者的预后呈负相关。在肿瘤微环境（TME）中，高风险基因的巨噬细胞、中性粒细胞、免疫检查点、趋化因子和趋化因子受体的评分增加。本研究表明，BMGs，特别是与高风险相关的基因，是神经胶质瘤治疗的潜在靶点，为全面了解神经胶质瘤的分子机制提供了新的前景。
