A robust gene expression-based prognostic risk score predicts overall survival of lung adenocarcinoma patients
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En-Guo Chen1,*, Pin Wang2,3,*, Haizhou Lou4,*, Yunshan Wang2, Hong Yan2, Lei Bi2, Liang Liu5, Bin Li6, Antoine M. Snijders2, Jian-Hua Mao2 and Bo Hang2
1Department of Pulmonary Medicine, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
2Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
3Department of Gastroenterology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu, China
4Department of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
5Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
6Nanjing KDRB Biotech Inc., Ltd, Jiangning District, Nanjing, Jiangsu, China
*These authors contributed equally to this work
Antoine M. Snijders, email: AMSnijders@lbl.gov
Jian-Hua Mao, email: JHMao@lbl.gov
Bo Hang, email: Bo_Hang@lbl.gov
Keywords: lung adenocarcinoma, overall survival, prognosis, gene signature, bioinformatics
Received: August 21, 2017 Accepted: October 05, 2017 Published: December 15, 2017
Identification of reliable predictive biomarkers and new therapeutic targets is a critical step for significant improvement in patient outcomes. Here, we developed a multi-step bioinformatics analytic strategy to mine large omics and clinical data to build a prognostic scoring system for predicting the overall survival (OS) of lung adenocarcinoma (LuADC) patients. In latter we first identified 1327 significantly and robustly deregulated genes, 600 of which were significantly associated with the OS of LuADC patients. Gene co-expression network analysis revealed the biological functions of these 600 genes in normal lung and LuADCs, which were found to be enriched for cell cycle-related processes, blood vessel development, cell-matrix adhesion and metabolic processes. Finally, we implemented a multiple resampling method combined with Cox regression analysis to identify a 27-gene signature associated with OS, and then created a prognostic scoring system based on this signature. This scoring system robustly predicted OS of LuADC patients in 100 sampling test sets and was further validated in four independent LuADC cohorts. In addition, in comparison to other existing prognostic gene signatures published in the literature, our signature was significantly superior in predicting OS of LuADC patients. In summary, our multi-omics and clinical data integration study created a 27-gene prognostic risk score that can predict OS of LuADC patients independent of age, gender and clinical stage. This score could guide therapeutic selection and allow stratification in clinical trials.
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