Table_1_An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis.DOCX

BackgroundThe tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs.MethodWe downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes.ResultsNormal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine–cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC).ConclusionFor the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.

背景：肿瘤免疫微环境与胶质瘤的恶性进展和治疗抵抗密切相关。长非编码RNA（lncRNA）在此过程中发挥着调控作用。本研究旨在探究胶质瘤微环境中的病理机制以及与lncRNA相关的潜在免疫治疗耐药性。方法：我们下载了来源于胶质瘤患者的数据集，并通过对数据进行层次聚类分析。随后，我们分析了胶质瘤的免疫微环境、相关基因表达以及患者的生存情况。通过对共表达lncRNA的分析，构建了lncRNA与免疫相关基因的模型。利用生存分析和Cox回归对模型进行分析。接着，运用单因素、多因素、受试者工作特征（ROC）和主成分分析（PCA）等方法验证了模型的准确性。最后，通过基因集富集分析（GSEA）评估了与差异基因相关的功能和通路。结果：正常脑组织维持着低至中等的免疫状态，胶质瘤可明显分为三组（从低到高免疫）。随着免疫状态的提高，间质、免疫和估计评分均有所上升，而肿瘤纯度则相应下降。此外，人类白细胞抗原（HLA）、程序性细胞死亡蛋白1（PDL1）、T细胞免疫球蛋白和粘蛋白结构域3（TIM-3）、B7-H3以及细胞毒性T淋巴细胞相关抗原4（CTLA4）的表达与免疫状态呈正相关，患者的预后也随之恶化。筛选出五个与免疫基因相关的lncRNA（AP001007.1、LBX-AS1、MIR155HG、MAPT-AS1和LINC00515）构建风险模型。我们发现风险评分与患者的预后和临床特征相关，且与PDL1、TIM-3和B7-H3的表达呈正相关。这些lncRNA可能通过细胞因子-细胞因子受体相互作用、补体和凝血级联反应调节肿瘤免疫微环境，并可能在高免疫组中促进CD8+ T细胞、调节性T细胞、M1巨噬细胞和浸润性中性粒细胞的活性。在体外，通过q-PCR和免疫组化（IHC）验证了免疫相关lncRNA的异常表达以及风险评分与免疫相关指标（PDL1、CTLA4、CD3、CD8、iNOS）之间的关系。结论：本研究首次构建了免疫基因相关的lncRNA风险模型。风险评分可能成为肿瘤免疫亚型的新的生物标志物，并为胶质瘤免疫治疗提供分子靶点。