RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
基于生物信息学鉴定并验证一种新型稳健的胶质母细胞瘤预后模型
英文原题：Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics.
查看英文原题
Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics.
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研究概要
我们构建并验证了一种新的 GBM 预后模型。衰老、巨噬细胞、自噬和甲基化相关基因可能作为预后和治疗生物标志物。所开发的模型可能有助于指导 GBM 患者的治疗。我们的研究对于准确预测 GBM 预后具有重要意义，并可能为 GBM 患者的免疫治疗决策提供参考。
研究思路结论见上方概要
背景
胶质母细胞瘤（GBM）是中枢神经系统（CNS）非常常见的原发性恶性肿瘤。衰老、巨噬细胞、自噬和甲基化相关基因被假设在其发病机制中至关重要。在本研究中，我们旨在探讨这些基因在GBM预后中的作用。
方法
从癌症基因组图谱数据库（TCGA）和中国胶质瘤基因组图谱数据库（CGGA）下载了RNA序列（RNA-seq）和临床信息。我们进行了单因素和最小绝对收缩和选择算子（LASSO）多因素Cox回归分析，以识别与总生存期（OS）相关的风险特征。我们进一步开发了一个列线图来预测个体结局。此外，通过CIBERSORT分析了免疫微环境。
结果
在TCGA-GBM队列中，基于衰老、巨噬细胞、自噬和甲基化相关基因，在GBM样本与正常组织之间获得了256个差异表达基因（DEGs）。我们在TCGA-GBM队列中识别出五个具有预后价值的最优风险特征，并建立了预后风险评分模型。该模型的有效性在CGGA队列和Huanhu队列中得到了验证。最后，我们通过结合年龄、O(6)-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化状态和风险评分，构建了用于临床应用的列线图。在高风险评分组中，活化NK细胞和静息肥大细胞高表达，而记忆B细胞、浆细胞、静息NK细胞、M1巨噬细胞和中性粒细胞低表达。低风险评分的GBM患者具有更高的肿瘤免疫功能障碍和排斥（TIDE）评分。热肿瘤的风险评分高于冷肿瘤。此外，29个参与葡萄糖和脂质代谢的基因在高风险评分中高表达。31条代谢相关通路在高风险组和低风险组之间存在显著差异。
展开英文摘要原文
BACKGROUND
Glioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM.
METHODS
The RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIBERSORT.
RESULTS
256 differentially expressed genes (DEGs) were obtained based on aging, macrophage, autophagy, and methylation related genes between GBM samples and normal tissues in TCGA-GBM cohort. We identified five optimal risk signatures with prognostic values in TCGA-GBM cohort and established a prognostic risk score model. The validity of the model was verified in the CGGA cohort and Huanhu cohort. Finally, we constructed a nomogram for clinical application by combining age, O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and risk score. Activated NK cells and resting mast cells were highly expressed and memory B cells, plasma cells, resting NK cells, M1 macrophages, and neutrophils exhibited low expression in the high-risk score group. GBM patients with a low-risk score had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score. The risk score of hot tumors was higher than that of the cold tumors. Additionally, 29 genes involved in glucose and lipid metabolism were highly expressed with a high-risk score. 31 metabolism-related pathways were significantly different between high-risk and low-risk groups.
CONCLUSIONS
We constructed and validated a novel prognostic model for GBM. Aging, macrophage, autophagy, and methylation related genes may serve as prognostic and therapeutic biomarkers. The model developed may assist in guiding treatment for GBM patients. Our research had great significance in accurately predicting the prognosis of GBM and may offer reference for immunotherapy decision for GBM patients.
论文信息
- 作者
- Zhang L、Yan X、Wang Y、Wang Q、Yan H、Yan Y
- 单位
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, 300070, China.China
- 期刊
- Heliyon2024 Sep 30
- 原文标识
- PubMed 39309926 · DOI 10.1016/j.heliyon.2024.e37374
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