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Application of clinical bioinformatics in lung cancer-specific biomarkers

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Abstract

The fact that lung cancer is a heterogeneous disease suggests that there is a high likelihood that effective lung cancer biomarkers will need to address patient-specific molecular defects, clinical characters, and aspects of the tumor microenvironment. In this transition, clinical bioinformatics tools and resources are the most appropriate means to improve the analysis, as major biological databases are now containing clinical data alongside genomics, proteomics, and other biological data. Clinical bioinformatics comprises a series of concepts and approaches that have been used successfully both to delineate novel biological mechanisms and to drive translational advances in individualized healthcare. In this article, we outline several of emerging clinical bioinformatics-based strategies as they apply specifically to lung cancer.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (81100534,91230204, 81270099, 81320108001, 81270131, 81300010), the Shanghai Rising Star Program (13QA1400800). The work was also supported by Zhongshan Distinguished Professor Grant (XDW), The Shanghai Committee of Science and Technology (12JC1402200, 12431900207, 11410708600, 14431905100), Zhejiang Provincial Natural Science Foundation (Z2080988), Zhejiang Provincial Science Technology Department Foundation (2010C14011), and Ministry of Education, Academic Special Science and Research Foundation for PhD Education (20130071110043). The authors have no commercial or other associations that might pose a conflict of interest in connection with the submitted material.

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  1. Zhongshan Hospital of Fudan University, Biomedical Research Center, Shanghai Institute of Clinical Bioinformatics, Fucan University Center for Clinical Bioinformatics, Shanghai, 200032, China

    Duojiao Wu & Xiangdong Wang

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  1. Duojiao Wu
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  2. Xiangdong Wang
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Correspondence to Xiangdong Wang.

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Wu, D., Wang, X. Application of clinical bioinformatics in lung cancer-specific biomarkers. Cancer Metastasis Rev 34, 209–216 (2015). https://doi.org/10.1007/s10555-015-9564-2

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