结合经典和新型中性粒细胞相关生物标志物以识别非小细胞肺癌。
Combining Classic and Novel Neutrophil-Related Biomarkers to Identify Non-Small-Cell Lung Cancer.
作者信息
Ren Yunzhao, Wang Qinchuan, Xu Chenyang, Guo Qian, Dai Ruoqi, Xu Xiaohang, Zhang Yuhao, Wu Ming, Wu Xifeng, Tu Huakang
机构信息
Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics, The Second Affiliated Hospital, Zhejiang University School of Medicine, 866 Yuhangtang Rd., Hangzhou 310058, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, 866 Yuhangtang Rd., Hangzhou 310058, China.
出版信息
Cancers (Basel). 2024 Jan 25;16(3):513. doi: 10.3390/cancers16030513.
BACKGROUND
Recent studies have revealed that neutrophils play a crucial role in cancer progression. This study aimed to explore the diagnostic value of neutrophil-related biomarkers for non-small-cell lung cancer (NSCLC).
METHODS
We initially assessed the associations between classic neutrophil-related biomarkers (neutrophil-to-lymphocyte ratio (NLR), absolute neutrophil counts (NEU), absolute lymphocyte counts (LYM)) and NSCLC in 3942 cases and 6791 controls. Then, we measured 11 novel neutrophil-related biomarkers via Luminex Assays in 132 cases and 66 controls, individually matching on sex and age (±5 years), and evaluated their associations with NSCLC risk. We also developed the predictive models by sequentially adding variables of interest and assessed model improvement.
RESULTS
Interleukin-6 (IL-6) (odds ratio (OR) = 10.687, 95% confidence interval (CI): 3.875, 29.473) and Interleukin 1 Receptor Antagonist (IL-1RA) (OR = 8.113, 95% CI: 3.182, 20.689) shows strong associations with NSCLC risk after adjusting for body mass index, smoking status, NLR, and carcinoembryonic antigen. Adding the two identified biomarkers to the predictive model significantly elevated the model performance from an area under the receiver operating characteristic curve of 0.716 to 0.851 with a net reclassification improvement of 97.73%.
CONCLUSIONS
IL-6 and IL-1RA were recognized as independent risk factors for NSCLC, improving the predictive performance of the model in identifying disease.
背景
最近的研究表明，中性粒细胞在癌症进展中起关键作用。本研究旨在探讨中性粒细胞相关生物标志物对非小细胞肺癌（NSCLC）的诊断价值。
方法
我们首先在3942例病例和6791例对照中评估经典中性粒细胞相关生物标志物（中性粒细胞与淋巴细胞比值（NLR）、中性粒细胞绝对计数（NEU）、淋巴细胞绝对计数（LYM））与NSCLC之间的关联。然后，我们通过Luminex检测法在132例病例和66例对照中分别测量了11种新的中性粒细胞相关生物标志物，这些病例和对照在性别和年龄（±5岁）上进行了个体匹配，并评估了它们与NSCLC风险的关联。我们还通过依次添加感兴趣的变量来开发预测模型，并评估模型的改进情况。
结果
在校正体重指数、吸烟状况、NLR和癌胚抗原后，白细胞介素-6（IL-6）（比值比（OR）=10.687，95%置信区间（CI）：3.875，29.473）和白细胞介素1受体拮抗剂（IL-1RA）（OR = 8.113，95%CI：3.182，20.689）与NSCLC风险显示出强关联。将这两种已确定的生物标志物添加到预测模型中，显著提高了模型性能，受试者操作特征曲线下面积从0.716提高到0.851，净重新分类改善率为97.73%。
结论
IL-6和IL-1RA被认为是NSCLC的独立危险因素，提高了模型在识别疾病方面的预测性能。
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