J Cancer 2019; 10(16):3706-3716. doi:10.7150/jca.32092

Research Paper

Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival

Honghua Zhang1*, Linwei Guo2,3*, Zheng Zhang1, Yunlong Sun1, Honglei Kang1, Chao Song1, Huiyong Liu1, Zhuowei Lei1, Jia Wang1, Baoguo Mi4, Qian Xu5, Hanfeng Guan1✉, Feng Li1✉

1. Department of Orthopedics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, 430030, China
2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
3. Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
4. Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University College of Medicine, No. 76 Nanguo Road, Xi'an, 710054, Shanxi, China
5. Department of Hematology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave., Wuhan, 430030, China
*Equal contribution

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Citation:
Zhang H, Guo L, Zhang Z, Sun Y, Kang H, Song C, Liu H, Lei Z, Wang J, Mi B, Xu Q, Guan H, Li F. Co-Expression Network Analysis Identified Gene Signatures in Osteosarcoma as a Predictive Tool for Lung Metastasis and Survival. J Cancer 2019; 10(16):3706-3716. doi:10.7150/jca.32092. Available from http://www.jcancer.org/v10p3706.htm

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Abstract

Osteosarcoma (OS) is the most common primary bone tumor, whose poor prognosis is mainly due to lung metastasis. The aim of this study is to build a practical and valid diagnostic test that can predict the risk of OS metastasis and progression. We performed weighted gene co-expression network analysis (WGCNA) on GSE21257 from the Gene Expression Omnibus (GEO) database, which contains microarray data of biopsies from OS patients. In these modules, the highest association was found between the blue module and metastasis stage (r = -0.52) by Pearson's correlation analysis. Based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we derived eight clinically significant genes and constructed an eight-gene signature for metastasis status. It showed great efficacy to distinguish metastasis from non-metastasis (AUC = 0.886) and the results were validated in The Cancer Genome Atlas (TCGA) database. Functional enrichment analysis of hub genes showed that their biological processes focused on immune-related pathways, suggesting the important roles of immune cells, immune pathways and the tumor microenvironment in metastasis development. In conclusion, we discovered an efficient gene signature with great efficacy to distinguish metastasis status, which may help improve early diagnosis and treatment, enhancing the clinical outcomes of OS patients. Besides we created an effective protocol to seek for several hub genes in high-throughput data by combining WGCNA and LASSO Cox regression.

Keywords: gene signature, LASSO Cox regression, lung metastasis, osteosarcoma, WGCNA