Department of Gynecology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
J Obstet Gynaecol. 2023 Dec;43(2):2277242. doi: 10.1080/01443615.2023.2277242. Epub 2023 Nov 8.
Tumour immune microenvironment (TIME) has long been a key direction of tumour research. Understanding the occurrence, metastasis and other processes of cervical cancer (CC) is of great significance in the diagnosis and prognosis of tumours.
Here, this study applied the univariate Cox regression model to determine the prognostic association of immune and hypoxia signature genes in CC, and used Least Absolute Shrinkage and Selection Operator (LASSO) Cox method to build immune and hypoxia related risk score model to uncover the immune signature of the TIME of CC. Moreover, we used experiment to validate the expression level of signature genes. Notably, we assessed the predictive effect of anti-PD1/PDL1 immunotherapy using risk score model.
Through the LASSO Cox regression model, we obtained 12 characteristic genes associated with the prognosis of CC, and also associated with immunity and hypoxia. Interestingly, the high-risk group had the properties of high hypoxia and low immunity, while the low-risk group had the properties of low hypoxia and high immunity. In the low-risk group, patients lived longer and had a significant therapeutic advantage of anti-PD-1 immunotherapy.
Established risk scores model can help predict response to anti-PD-1 immunotherapy of CC.
肿瘤免疫微环境（TIME）一直是肿瘤研究的一个关键方向。了解宫颈癌（CC）的发生、转移等过程，对于肿瘤的诊断和预后具有重要意义。
本研究应用单因素 Cox 回归模型来确定免疫和缺氧特征基因与 CC 的预后相关性，并使用最小绝对值收缩和选择算子（LASSO）Cox 方法构建免疫和缺氧相关风险评分模型，以揭示 CC 的 TIME 的免疫特征。此外，我们使用实验验证了特征基因的表达水平。值得注意的是，我们使用风险评分模型评估了抗 PD-1/PD-L1 免疫治疗的预测效果。
通过 LASSO Cox 回归模型，我们获得了 12 个与 CC 预后相关且与免疫和缺氧相关的特征基因。有趣的是，高危组具有高缺氧和低免疫的特性，而低危组具有低缺氧和高免疫的特性。在低危组中，患者的生存期更长，并且对 PD-1 免疫治疗具有显著的治疗优势。
建立的风险评分模型有助于预测 CC 对抗 PD-1 免疫治疗的反应。
