Shaohua Lu1, Hui Kong2, Yingyong Hou3, Di Ge4, Wei Huang5, Jiaxian Ou6, Dawei Yang6, Li Zhang7, Guoming Wu8, Yong Song9, Xiaoju Zhang10, Changwen Zhai3, Qun Wang4, Hongguang Zhu11, Ying Wu11, Chunxue Bai12. 1. Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China. Electronic address: lushaohua2010@126.com. 2. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China. 3. Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, China. 4. Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. 5. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China. 6. Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. 7. Department of Respiratory Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. 8. Institute of Respiratory Diseases, the Second Affiliated Hospital of the Third Military Medical University, Chongqing, China. 9. Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China. 10. Department of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Henan, China. 11. Department of Pathology, Shanghai Medical College, Fudan University, Shanghai, China. 12. Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. Electronic address: chunxue_bai@163.com.
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.
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 cancerpatients 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.
Authors: Lisha Ying; Lingbin Du; Ruiyang Zou; Lei Shi; Nan Zhang; Jiaoyue Jin; Chenyang Xu; Fanrong Zhang; Chen Zhu; Junzhou Wu; Kaiyan Chen; Minran Huang; Yingxue Wu; Yimin Zhang; Weihui Zheng; Xiaodan Pan; Baofu Chen; Aifen Lin; John Kit Chung Tam; Rob Martinus van Dam; David Tien Min Lai; Kee Seng Chia; Lihan Zhou; Heng-Phon Too; Herbert Yu; Weimin Mao; Dan Su Journal: Proc Natl Acad Sci U S A Date: 2020-09-17 Impact factor: 11.205
Authors: Sandra P Nunes; Francisca Diniz; Catarina Moreira-Barbosa; Vera Constâncio; Ana Victor Silva; Júlio Oliveira; Marta Soares; Sofia Paulino; Ana Luísa Cunha; Jéssica Rodrigues; Luís Antunes; Rui Henrique; Carmen Jerónimo Journal: J Clin Med Date: 2019-09-19 Impact factor: 4.241