Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer
Authors Sheng ML, Dong ZH, Xie YP
Received 15 June 2018
Accepted for publication 15 October 2018
Published 14 November 2018 Volume 2018:11 Pages 8143—8151
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Amy Norman
Peer reviewer comments 2
Editor who approved publication: Dr Jianmin Xu
Meiling Sheng,1,* Zhaohui Dong,2,* Yanping Xie3
1Department of Respiration, Jinhua People’s Hospital, Jinhua, Zhejiang 321000, China; 2Department of Intensive Care Unit, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China; 3Department of Respiratory Medicine, First Hospital of Huzhou, First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang 313000, China
*These authors contributed equally to this work
Background: Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.
Methods: In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.
Results: A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.
Conclusion: The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.
Keywords: tumor-educated platelet, TEP, liquid biopsy, minimal redundancy, maximal relevance, MRMR, incremental feature selection, IFS, non-small-cell lung cancer, NSCLC
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