Du Peizhun, Liu Pengcheng, Patel Rajan, Chen Shiyu, Hu Cheng'en, Huang Guangjian, Liu Yi
Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, China.
A1 Legend, Privia Health, Gaithersburg, MD, United States.
Front Oncol. 2023 Jan 4;12:1019909. doi: 10.3389/fonc.2022.1019909. eCollection 2022.
As a unique feature of malignant tumors, abnormal metabolism can regulate the immune microenvironment of tumors. However, the role of metabolic lncRNAs in predicting the prognosis and immunotherapy of gastric cancer (GC) has not been explored.
We downloaded the metabolism-related genes from the GSEA website and identified the metabolic lncRNAs. Co-expression analysis and Lasso Cox regression analysis were utilized to construct the risk model. To value the reliability and sensitivity of the model, Kaplan-Meier analysis and receiver operating characteristic curves were applied. The immune checkpoints, immune cell infiltration and tumor mutation burden of low- and high-risk groups were compared. Tumor Immune Dysfunction and Exclusion (TIDE) score was conducted to evaluate the response of GC patients to immunotherapy.
Twenty-three metabolic lncRNAs related to the prognosis of GC were obtained. Three cluster patterns based on metabolic lncRNAs could distinguish GC patients with different overall survival time (OS) effectively (p<0.05). The risk score model established by seven metabolic lncRNAs was verified as an independent prognostic indicator for predicting the OS of GC. The AUC value of the risk model was higher than TNM staging. The high-risk patients were accompanied by significantly increased expression of immune checkpoint molecules (including PD-1, PD-L1 and CTLA4) and increased tumor tolerant immune cells, but significantly decreased tumor mutation burden (TMB). Consistently, TIDE values of low-risk patients were significantly lower than that of high-risk patients.
The metabolic lncRNAs risk model can reliably and independently predict the prognosis of GC. The feature that simultaneously map the immune status of tumor microenvironment and TMB gives risk model great potential to serve as an indicator of immunotherapy.
作为恶性肿瘤的一个独特特征，异常代谢可以调节肿瘤的免疫微环境。然而，代谢长链非编码RNA（lncRNA）在预测胃癌（GC）预后和免疫治疗中的作用尚未得到探索。
我们从基因集富集分析（GSEA）网站下载了与代谢相关的基因，并鉴定出代谢lncRNA。利用共表达分析和套索Cox回归分析构建风险模型。为了评估该模型的可靠性和敏感性，应用了Kaplan-Meier分析和受试者工作特征曲线。比较了低风险和高风险组的免疫检查点、免疫细胞浸润和肿瘤突变负荷。进行肿瘤免疫功能障碍和排除（TIDE）评分以评估GC患者对免疫治疗的反应。
获得了23个与GC预后相关的代谢lncRNA。基于代谢lncRNA的三种聚类模式能够有效区分总生存时间（OS）不同的GC患者（p<0.05）。由7个代谢lncRNA建立的风险评分模型被验证为预测GC患者OS的独立预后指标。风险模型的曲线下面积（AUC）值高于TNM分期。高风险患者伴有免疫检查点分子（包括程序性死亡受体1（PD-1）、程序性死亡配体1（PD-L1）和细胞毒性T淋巴细胞相关蛋白4（CTLA4））表达显著增加和肿瘤耐受免疫细胞增多，但肿瘤突变负荷（TMB）显著降低。同样，低风险患者的TIDE值显著低于高风险患者。
代谢lncRNA风险模型能够可靠且独立地预测GC的预后。同时反映肿瘤微环境免疫状态和TMB的特征使风险模型具有作为免疫治疗指标的巨大潜力。
