The Immunological Contribution of a Novel Metabolism-Related Signature to the Prognosis and Anti-Tumor Immunity in Cervical Cancer.
The Immunological Contribution of a Novel Metabolism-Related Signature to the Prognosis and Anti-Tumor Immunity in Cervical Cancer.
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DOI：
10.3390/cancers14102399
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发表时间：
2022-05-13
期刊：
影响因子：
5.2
通讯作者：
Wu, Sufang
中科院分区：
文献类型：
--
作者：
Yu, Sihui;Li, Xi;Ma, Mingjun;Yang, Rui;Zhang, Jiawen;Wu, Sufang
宫颈癌是全世界最常见的妇科恶性肿瘤。开发可靠的预后模型来预测临床结果并确定哪些患者将从不同的治疗策略中受益是至关重要的。在目前的研究中，我们确定了由ALOX12B、CA9、FAR2、F5和TDO2组成的可靠的代谢相关标志物，用于预测宫颈癌的预后和抗肿瘤免疫。高危评分患者预后明显较差，肿瘤浸润性淋巴细胞浸润程度较低。此外，基于新陈代谢的风险评分还可以基于免疫检查点的表达来预测宫颈癌患者的预后。由于这一风险评分特征在预测临床结果方面取得了良好的表现，我们真诚地期待我们的研究能够为指导宫颈癌患者的个性化治疗提供有效的预后工具。宫颈癌是女性生殖系统最常见的恶性肿瘤。基于临床病理特征的传统患者分层方法已逐渐被分子图谱策略所超越。我们的研究旨在找出一个可靠的代谢相关预测信号来预测宫颈癌的预后和抗肿瘤免疫。在本研究中，我们提取了5个与代谢相关的HUB基因，包括ALOX12B、CA9、FAR2、F5和TDO2，用于建立风险评分模型。Kaplan-Meier曲线表明，高危评分的患者在宫颈癌训练队列(TCGA，n=304，p<；0.0001)、验证队列(GSE44001，n=300，P=0.0059)和泛癌症队列(包括9个TCGA肿瘤)中的预后明显较差。利用基因集浓缩分析(GSEA)，我们观察到该模型与各种免疫调节相关的途径相关。此外，泛癌队列和免疫组织化学分析显示，高分组肿瘤浸润性淋巴细胞(TIL)的浸润率较低。此外，该模型还可以基于免疫检查点(ICP)在发现和验证队列中的表达来预测宫颈癌患者的预后。本研究建立并验证了一个与代谢相关的预后模型，可提高宫颈癌患者临床预后预测的准确性，为个体化治疗提供指导。
Cervical cancer is the most commonly diagnosed gynecological malignant carcinoma worldwide. It is crucial to develop reliable prognostic models to predict clinical outcomes and identify patients who will benefit from different treatment strategies. In current study, we identified a reliable metabolism-related signature composed of ALOX12B, CA9, FAR2, F5 and TDO2 for the prognosis and anti-tumor immunity in cervical cancer. Patients with high-risk score underwent apparently worse prognosis and displayed lower infiltration of tumor infiltrating lymphocytes. Additionally, the metabolism-based risk score could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints. Since this risk score signature achieves a good performance in predicting clinical outcome, we genuinely expect that our study could provide an effective prognostic tool for guidance of personalized treatment of cervical cancer patients. Cervical cancer is the most frequently diagnosed malignancy in the female reproductive system. Conventional stratification of patients based on clinicopathological characters has gradually been outpaced by a molecular profiling strategy. Our study aimed to identify a reliable metabolism-related predictive signature for the prognosis and anti-tumor immunity in cervical cancer. In this study, we extracted five metabolism-related hub genes, including ALOX12B, CA9, FAR2, F5 and TDO2, for the establishment of the risk score model. The Kaplan-Meier curve suggested that patients with a high-risk score apparently had a worse prognosis in the cervical cancer training cohort (TCGA, n = 304, p < 0.0001), validation cohort (GSE44001, n = 300, p = 0.0059) and pan-cancer cohorts (including nine TCGA tumors). Using a gene set enrichment analysis (GSEA), we observed that the model was correlated with various immune-regulation-related pathways. Furthermore, pan-cancer cohorts and immunohistochemical analysis showed that the infiltration of tumor infiltrating lymphocytes (TILs) was lower in the high-score group. Additionally, the model could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints (ICPs) in both the discovery and validation cohorts. Our study established and validated a metabolism-related prognostic model, which might improve the accuracy of predicting the clinical outcome of patients with cervical cancer and provide guidance for personalized treatment.
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影响因子：
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