Department of Pulmonary and Critical Care Medicine, The Fourth Affiliated Hospital of Guangxi Medical University, No. 1, Liushi Road, Liuzhou 545005, China.
Clinical Laboratory, The Fourth Affiliated Hospital of Guangxi Medical University, No. 1, Liushi Road, Liuzhou 545005, China.
Dis Markers. 2021 Feb 18;2021:6677823. doi: 10.1155/2021/6677823. eCollection 2021.
Identifying malignant pulmonary nodules and detecting early-stage lung cancer (LC) could reduce mortality. This study investigated the clinical value of a seven-autoantibody (7-AAB) panel in combination with the Mayo model for the early detection of LC and distinguishing benign from malignant pulmonary nodules (MPNs).
The concentrations of the elements of a 7-AAB panel were quantitated by enzyme-linked immunosorbent assay (ELISA) in 806 participants. The probability of MPNs was calculated using the Mayo predictive model. The performances of the 7-AAB panel and the Mayo model were analyzed by receiver operating characteristic (ROC) analyses, and the difference between groups was evaluated by chi-square tests ( ).
The combined area under the ROC curve (AUC) for all 7 AABs was higher than that of a single one. The sensitivities of the 7-AAB panel were 67.5% in the stage I-II LC patients and 60.3% in the stage III-IV patients, with a specificity of 89.6% for the healthy controls and 83.1% for benign lung disease patients. The detection rate of the 7-AAB panel in the early-stage LC patients was higher than that of traditional tumor markers. The AUC of the 7-AAB panel in combination with the Mayo model was higher than that of the 7-AAB panel alone or the Mayo model alone in distinguishing MPN from benign nodules. For early-stage MPN, the sensitivity and specificity of the combination were 93.5% and 58.0%, respectively. For advanced-stage MPN, the sensitivity and specificity of the combination were 91.4% and 72.8%, respectively. The combination of the 7-AAB panel with the Mayo model significantly improved the detection rate of MPN, but the positive predictive value (PPV) and the specificity were not improved when compared with either the 7-AAB panel alone or the Mayo model alone.
Our study confirmed the clinical value of the 7-AAB panel for the early detection of lung cancer and in combination with the Mayo model could be used to distinguish benign from malignant pulmonary nodules.
识别恶性肺结节和检测早期肺癌（LC）可降低死亡率。本研究探讨了七种自身抗体（7-AAB）联合 Mayo 模型在早期检测 LC 和区分良恶性肺结节（MPN）中的临床价值。
通过酶联免疫吸附试验（ELISA）定量检测 806 名参与者中 7-AAB 组合的元素浓度。使用 Mayo 预测模型计算 MPN 的概率。通过接收者操作特征（ROC）分析评估 7-AAB 组合和 Mayo 模型的性能，并通过卡方检验（ ）评估组间差异。
所有 7 种 AAB 的组合 ROC 曲线下面积（AUC）均高于单个 AAB。7-AAB 组合在 I-II 期 LC 患者中的灵敏度为 67.5%，在 III-IV 期患者中的灵敏度为 60.3%，对健康对照组的特异性为 89.6%，对良性肺病患者的特异性为 83.1%。7-AAB 组合在早期 LC 患者中的检出率高于传统肿瘤标志物。7-AAB 组合联合 Mayo 模型在区分 MPN 与良性结节方面的 AUC 高于单独使用 7-AAB 组合或 Mayo 模型。对于早期 MPN，组合的灵敏度和特异性分别为 93.5%和 58.0%。对于晚期 MPN，组合的灵敏度和特异性分别为 91.4%和 72.8%。7-AAB 组合联合 Mayo 模型显著提高了 MPN 的检出率，但与单独使用 7-AAB 组合或 Mayo 模型相比，阳性预测值（PPV）和特异性均未提高。
本研究证实了 7-AAB 组合在早期检测肺癌中的临床价值，与 Mayo 模型联合使用可用于区分良恶性肺结节。
