慢性炎症对宫颈癌幸存者死亡率的持续威胁：一项使用英国生物银行和中国队列数据的孟德尔随机化和机器学习分析
The Persistent Threat of Chronic Inflammation on the Mortality Among Cervical Cancer Survivors: A Mendelian Randomization and Machine Learning Analysis Using UK Biobank and Chinese Cohort Data.
作者信息
Wang Jing, Chen Zhichao, Guan Mingfei, Ma Zebiao, Peng Lin, Chen Jiongyu, Fiori Pier Luigi, Carru Ciriaco, Capobianco Giampiero, Coradduzza Donatella, Zhou Li
机构信息
Department of Obstetrics and Gynecology, Second Affiliated Hospital of Shantou University Medical College, Shantou, People's Republic of China.
Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
出版信息
J Inflamm Res. 2025 Jul 30;18:10267-10282. doi: 10.2147/JIR.S528121. eCollection 2025.
PURPOSE
The association between inflammatory dysregulation and cervical carcinogenesis and progression has not yet been fully elucidated. We aimed to comprehensively evaluate the genetic association between inflammation and cervical cancer, and construct an accurate prognosis model based on circulating inflammatory parameters and indexes with machine learning (ML) algorithms.
PATIENTS AND METHODS
We tested the genome-wide association of circulating inflammatory molecules (CIMs) (91 circulating inflammatory cytokines and 10 inflammatory cells) and summary data retrieved from the UK biobank (cases = 1659 and controls =381,902) with two-sample Mendelian randomization (MR) and colocalization analyses. Nine ML and logistic regression (LR) integrated prognosis models were developed for 1042 subjects with cervical cancer (random allocation into training and validation cohorts at 6:4 ratio).
RESULTS
Three potential causative CIMs for cervical cancer were identified via a two-sample MR. However, neither reverse MR, nor Bayesian colocalization analyses supported shared causal variation. After feature selection with 3 algorithms (LASSO regression, Boruta and Support vector machines), the gradient boosting machine (GBM) model outperformed other models by achieving an area under the curve (AUC) of 0.930 and a Brier score of 0.027 in 1-year overall survival (OS) prediction. Similarly, the GBM model delivered the best overall performance in 5-year OS prediction with an AUC of 0.893 and a Brier score of 0.089. Following the Shapley Additive explanations (SHAP), the lymphocyte monocyte ratio, neutrophil count, platelet count, and platelet lymphocyte ratio were associated with 1-year OS, while the systemic immune-inflammation index, platelet neutrophil ratio, and monocyte count were significantly related to 5-year OS.
CONCLUSION
No substantial causal associations were observed between CIMs and cervical cancer. The cohort study findings reveal the persistent impact of inflammation on cervical cancer prognosis, highlighting the crucial role of chronic inflammation when investigating the biomarkers of cervical cancer progression and developing pharmacological interventions. The GBM model consistently achieved satisfactory performance in cervical cancer prognosis prediction with demographics and CIMs, meriting further validation and potential clinical implementation.
目的
炎症调节异常与宫颈癌发生及进展之间的关联尚未完全阐明。我们旨在全面评估炎症与宫颈癌之间的遗传关联，并基于循环炎症参数和指标，运用机器学习（ML）算法构建准确的预后模型。
患者与方法
我们通过两样本孟德尔随机化（MR）和共定位分析，测试了循环炎症分子（CIMs）（91种循环炎症细胞因子和10种炎症细胞）与从英国生物银行检索到的汇总数据（病例 = 1659例，对照 = 381,902例）之间的全基因组关联。针对1042例宫颈癌患者开发了9种ML和逻辑回归（LR）综合预后模型（以6:4的比例随机分配到训练队列和验证队列）。
结果
通过两样本MR确定了三种宫颈癌潜在的致病CIMs。然而，反向MR和贝叶斯共定位分析均不支持共享因果变异。在使用三种算法（LASSO回归、Boruta和支持向量机）进行特征选择后，梯度提升机（GBM）模型在1年总生存期（OS）预测中表现优于其他模型，曲线下面积（AUC）为0.930，布里尔评分（Brier score）为0.027。同样，GBM模型在5年OS预测中总体表现最佳，AUC为0.893，Brier评分为0.089。根据夏普利值附加解释（SHAP），淋巴细胞单核细胞比值、中性粒细胞计数、血小板计数和血小板淋巴细胞比值与1年OS相关，而全身免疫炎症指数、血小板中性粒细胞比值和单核细胞计数与5年OS显著相关。
结论
未观察到CIMs与宫颈癌之间存在实质性因果关联。队列研究结果揭示了炎症对宫颈癌预后的持续影响，突出了慢性炎症在研究宫颈癌进展生物标志物和开发药物干预措施中的关键作用。GBM模型在结合人口统计学和CIMs进行宫颈癌预后预测时始终取得令人满意的表现，值得进一步验证和潜在的临床应用。
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