Lin Yanli, Leng Qixin, Jiang Zhengran, Guarnera Maria A, Zhou Yun, Chen Xueqi, Wang Heping, Zhou Wenxian, Cai Ling, Fang HongBin, Li Jie, Jin Hairong, Wang Linghui, Yi Shaoqiong, Lu Wei, Evers David, Fowle Carol B, Su Yun, Jiang Feng
Department of Pathology, University of Maryland School of Medicine, Baltimore, MD.
The F. Edward Hébert School of Medicine at the Uniformed Services University of the Health Sciences, Bethesda, MD.
Int J Cancer. 2017 Sep 15;141(6):1240-1248. doi: 10.1002/ijc.30822. Epub 2017 Jun 21.
Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.
肺癌主要由吸烟引起，是美国乃至全球主要的癌症杀手。通过低剂量CT（LDCT）早期检测肺癌可降低死亡率。然而，LDCT显著增加了不确定肺结节（PNs）的数量，导致过度诊断。对恶性PNs进行明确的术前诊断在临床上很重要。通过使用微阵列和液滴数字PCR直接分析135例PNs患者的血浆miRNA表达，我们鉴定出11种血浆miRNA，它们在恶性PNs患者和良性PNs患者之间表现出显著差异。通过对分子结果与临床/放射学特征进行多变量逻辑回归分析，我们开发了一种综合分类器，该分类器由两种miRNA生物标志物和一种放射学特征组成，用于区分恶性PNs和良性PNs。该分类器的灵敏度为89.9%，特异度为90.9%，与单独的生物标志物或临床/放射学特征相比显著更高（所有p < 0.05）。该分类器在两组独立患者中得到验证。我们首次表明，血浆生物标志物和放射学特征的整合可以更准确地在不确定PNs中识别肺癌。该分类器未来的应用可以使良性生长的个体免于有害的诊断程序，同时允许立即对肺癌进行有效治疗，从而降低死亡率和成本。尽管如此，仍有必要对该分类器进行进一步的前瞻性验证。
