Two plasma microRNA panels for diagnosis and subtype discrimination of lung cancer. (September 2018)

• Record Type: Journal Article
• Authors: Lu, Shaohua; Kong, Hui; Hou, Yingyong; Ge, Di; Huang, Wei; Ou, Jiaxian; Yang, Dawei; Zhang, Li; Wu, Guoming; Song, Yong; Zhang, Xiaoju; Zhai, Changwen; Wang, Qun; Zhu, Hongguang; Wu, Ying; Bai, Chunxue
• Is Part Of: Lung cancer. Volume 123(2018)
• Page Start: 44
• Page End: 51
• Publication Date: 2018-09
• Subjects: SCLC small cell lung cancer -- NSCLC non-small cell lung cancer -- PCR Polymerase Chain Reaction -- AUC Area Under Curve; Plasma microRNA -- Lung cancer -- Early diagnosis -- Subtype discrimination; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2018.06.027
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: Plasma microRNA panel built for early diagnosis of lung cancer with high accuracy. Plasma microRNA panel built for discrimination between SCLC and NSCLC. Based on a large cohort of 1132 samples collected from five medical centers. Potential clinical value in treatment of lung cancer via non-invasive blood test. Abstract: Objectives: Early and accurate diagnosis of lung cancer is crucial for effective treatment. This study aimed to identify plasma microRNAs for diagnosis of lung cancer and for further discrimination of small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC). Materials and methods: Plasma microRNA expression was investigated using three independent cohorts including 1132 participants recruited between October 2008 and September 2014 from five medical centers. The subjects were healthy individuals and patients with NSCLC or SCLC. Microarrays were used to screen 723 human microRNAs in 106 plasma samples for candidate selection. Quantitative reverse-transcriptase PCR was applied to evaluate the expression of selected microRNAs. Two logistic regression models were constructed based on a training cohort (n = 565) and then validated using an independent cohort (n = 461). The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. Results: Plasma panel A with six microRNAs (miR-17, miR-190b, miR-19a, miR-19b, miR-26b, and miR-375) provided high diagnostic accuracy in discriminating lung cancer patients from healthy individuals (AUC 0.873 and 0.868 for training and validation cohort, respectively). Moreover, plasma panel B with three microRNAs (miR-17, miR-190b, and miR-375) demonstrated high diagnostic accuracy in discriminating SCLC from NSCLC (AUC 0.878 and 0.869 for training and validation cohort, respectively). Conclusion: We constructed and validated two plasma microRNA panels that have considerable clinical value in diagnosis of lung cancer, and could play an important role in determining optimal treatment strategies based on discrimination between SCLC and NSCLC.