Xia Yang1,2, Wenmei Su3, Xiuyuan Chen4, Qianqian Geng5, Jingyi Zhai6, Hu Shan1, Chunfang Guo2, Zhuwen Wang2, Han Fu6, Hui Jiang6, Jules Lin2, Kiran Hari Lagisetty2, Jie Zhang1, Yali Li1, Shuanying Yang1, Pierre P Massion7, David G Beer2, Andrew C Chang2, Nithya Ramnath8,9, Guoan Chen10. 1. Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China. 2. Section of Thoracic Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA. 3. Department of Pulmonary Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524000, China. 4. Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. 5. Department of Nuclear Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China. 6. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. 7. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA. 8. Department of Medicine, University of Michigan, Ann Arbor, MI, USA. 9. Department of Oncology, Veterans Administration Health System, Ann Arbor, MI, USA. 10. School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Abstract
BACKGROUND: Our previous studies have identified a serum-based 4-microRNA (4-miRNA) signature that may help distinguish patients with lung cancer (LC) from non-cancer controls (NCs). Here, we used an extended independent cohort of 398 subjects to further validate the diagnostic ability of this 4-miRNA signature. METHODS: Using quantitative reverse transcription polymerase chain reaction (qRT-PCR), expression of the 4-miRNAs was assessed in a total of 398 sera that included 213 LC patients and 185 NCs. A logistic regression model using training-test sets, receiver operating characteristic (ROC) curve analysis and t-test were used to test the impact of varying expression of these miRNAs on its diagnostic accuracy for LC. The cell proliferation and colony formation affected by these miRNAs, as well as gene ontology (GO) analysis of miRNA target genes were performed. RESULTS: The levels of the 4-miRNAs were significantly higher in the serum of patients with LCs as compared to NCs. Using a logistic regression prediction model based on training and test sets analysis, we obtained the area under the curve (AUC) of 0.921 [95% confidence interval (CI), 0.876-0.966] on the test set with specificity 90.6%, sensitivity 77.9%, accuracy 84.1%, positive predictive value (PPV) 89.8% and negative predictive value (NPV) 79.5%. CONCLUSIONS: We have verified that this serum 4-miRNA signature could provide a promising noninvasive biomarker for the prediction of LC, particularly in patients with indeterminate lung nodules on screening CT scans. 2019 Translational Lung Cancer Research. All rights reserved.
BACKGROUND: Our previous studies have identified a serum-based 4-microRNA (4-miRNA) signature that may help distinguish patients with lung cancer (LC) from non-cancer controls (NCs). Here, we used an extended independent cohort of 398 subjects to further validate the diagnostic ability of this 4-miRNA signature. METHODS: Using quantitative reverse transcription polymerase chain reaction (qRT-PCR), expression of the 4-miRNAs was assessed in a total of 398 sera that included 213 LC patients and 185 NCs. A logistic regression model using training-test sets, receiver operating characteristic (ROC) curve analysis and t-test were used to test the impact of varying expression of these miRNAs on its diagnostic accuracy for LC. The cell proliferation and colony formation affected by these miRNAs, as well as gene ontology (GO) analysis of miRNA target genes were performed. RESULTS: The levels of the 4-miRNAs were significantly higher in the serum of patients with LCs as compared to NCs. Using a logistic regression prediction model based on training and test sets analysis, we obtained the area under the curve (AUC) of 0.921 [95% confidence interval (CI), 0.876-0.966] on the test set with specificity 90.6%, sensitivity 77.9%, accuracy 84.1%, positive predictive value (PPV) 89.8% and negative predictive value (NPV) 79.5%. CONCLUSIONS: We have verified that this serum 4-miRNA signature could provide a promising noninvasive biomarker for the prediction of LC, particularly in patients with indeterminate lung nodules on screening CT scans. 2019 Translational Lung Cancer Research. All rights reserved.
Entities:
Keywords:
Lung cancer (LC); diagnosis; microRNA (miRNA); serum
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