Department of Neurosurgery, Chengdu Second People's Hospital, Chengdu, Sichuan, China.
Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
Front Immunol. 2023 Feb 13;14:1021678. doi: 10.3389/fimmu.2023.1021678. eCollection 2023.
Glioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.
The RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.
A total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.
The risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.
脑胶质瘤是成人中最常见的原发性脑肿瘤，占脑恶性肿瘤的 70%以上。脂质是细胞生物膜和其他结构的重要组成部分。越来越多的证据支持脂质代谢在重塑肿瘤免疫微环境（TME）中的作用。然而，胶质瘤的免疫 TME 与脂质代谢之间的关系仍描述不足。
从癌症基因组图谱（TCGA）和中国脑胶质瘤基因组图谱（CGGA）下载原发性脑胶质瘤患者的 RNA-seq 数据和临床病理信息。本研究还纳入了来自华西医院（WCH）的独立 RNA-seq 数据集。首先，通过单变量 Cox 回归和 LASSO Cox 回归模型，从脂质代谢相关基因（LMRGs）中确定预后基因特征。然后，建立一个名为 LMRGs 相关风险评分（LRS）的风险评分，并根据 LRS 将患者分为高风险组和低风险组。通过构建胶质瘤风险列线图进一步验证了 LRS 的预后价值。ESTIMATE 和 CIBERSORTx 用于描绘 TME 免疫图谱。利用肿瘤免疫功能障碍和排斥（TIDE）预测胶质瘤患者免疫检查点阻断（ICB）的治疗反应。
胶质瘤与脑组织之间共有 144 个 LMRGs 存在差异表达。最终，有 11 个预后 LMRGs 被纳入 LRS 的构建。LRS 被证明是胶质瘤患者的独立预后预测因子，由 LRS、IDH 突变状态、WHO 分级和放疗组成的列线图的 C 指数为 0.852。LRS 值与基质评分、免疫评分和 ESTIMATE 评分显著相关。CIBERSORTx 表明，高风险组和低风险组患者的 TME 免疫细胞丰度存在显著差异。根据 TIDE 算法的结果，我们推测高风险组患者更有可能从免疫治疗中获益。
基于 LMRGs 的风险模型可以有效地预测胶质瘤患者的预后。风险评分还将胶质瘤患者分为具有不同 TME 免疫特征的不同组。免疫治疗可能对具有特定脂质代谢特征的胶质瘤患者有益。
