A network-based method for identifying prognostic gene modules in lung squamous carcinoma

Lin Feng; Run Tong; Xiaohong Liu; Kaitai Zhang; Guiqi Wang; Lei Zhang; Ning An; Shujun Cheng

doi:10.18632/oncotarget.7632

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Research Papers:

A network-based method for identifying prognostic gene modules in lung squamous carcinoma

Lin Feng, Run Tong, Xiaohong Liu, Kaitai Zhang, Guiqi Wang, Lei Zhang, Ning An _ and Shujun Cheng

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Oncotarget. 2016; 7:18006-18020. https://doi.org/10.18632/oncotarget.7632

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Abstract

Lin Feng1,*, Run Tong2,*, Xiaohong Liu3,*, Kaitai Zhang1, Guiqi Wang4, Lei Zhang4, Ning An1 and Shujun Cheng1

1 State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, Peking Union Medical College and Cancer Institute (Hospital), Chinese Academy of Medical Sciences, Beijing, China

2 Department of Respiratory and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China

3 Department of Gynecology and Obstetrics, Maternal and Child Health Care Hospital of Haidian, Beijing, China

4 Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China

* These authors have contributed equally to this work

Correspondence to:

Ning An, email:

Lei Zhang, email:

Keywords: lung squamous carcinoma, multi-stage carcinogenesis, network-based, greedy searching, prognostic module

Received: October 13, 2015 Accepted: February 13, 2016 Published: February 23, 2016

Abstract

Similarities in gene expression between both developing embryonic and precancerous tissues and cancer tissues may help identify much-needed biomarkers and therapeutic targets in lung squamous carcinoma. In this study, human lung samples representing ten successive time points, from embryonic development to carcinogenesis, were used to construct global gene expression profiles. Differentially expressed genes with similar expression in precancerous and cancer samples were identified. Using a network-based greedy searching algorithm to analyze the training cohort (n = 69) and three independent testing cohorts, we successfully identified a significant 22-gene module in which expression levels were correlated with overall survival in lung squamous carcinoma patients.

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Research Papers:

A network-based method for identifying prognostic gene modules in lung squamous carcinoma

Abstract