A network-based signature to predict the survival of non-smoking lung adenocarcinoma
Qixing Mao,1–4,* Louqian Zhang,1–3,* Yi Zhang,1,* Gaochao Dong,1,3 Yao Yang,4 Wenjie Xia,1–4 Bing Chen,1–3 Weidong Ma,1–3 Jianzhong Hu,4 Feng Jiang,1,3 Lin Xu1,3 1Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Re...
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doaj-a745a7f6f7bc476f8f2267b04a2aa64a2020-11-24T20:47:22ZengDove Medical PressCancer Management and Research1179-13222018-08-01Volume 102683269339894A network-based signature to predict the survival of non-smoking lung adenocarcinomaMao QZhang LZhang YDong GYang YXia WChen BMa WHu JJiang FXu LQixing Mao,1–4,* Louqian Zhang,1–3,* Yi Zhang,1,* Gaochao Dong,1,3 Yao Yang,4 Wenjie Xia,1–4 Bing Chen,1–3 Weidong Ma,1–3 Jianzhong Hu,4 Feng Jiang,1,3 Lin Xu1,3 1Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China; 2The Fourth Clinical College of Nanjing Medical University, Nanjing, China; 3Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China; 4Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA *These authors contributed equally to this work Background: A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.Materials and methods: Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.Results: Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.Conclusion: The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management. Keywords: weighted gene co-expression network analysis, WGCNA, lung adenocarcinoma, LAC, co-expressing, prognostic signaturehttps://www.dovepress.com/a-network-based-signature-to-predict-the-survival-of-non-smoking-lung--peer-reviewed-article-CMARWeighted gene co-expression network analysis (WGCNA)lung adenocarcinoma (LAC)co-expressingprognostic signature. |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mao Q Zhang L Zhang Y Dong G Yang Y Xia W Chen B Ma W Hu J Jiang F Xu L |
spellingShingle |
Mao Q Zhang L Zhang Y Dong G Yang Y Xia W Chen B Ma W Hu J Jiang F Xu L A network-based signature to predict the survival of non-smoking lung adenocarcinoma Cancer Management and Research Weighted gene co-expression network analysis (WGCNA) lung adenocarcinoma (LAC) co-expressing prognostic signature. |
author_facet |
Mao Q Zhang L Zhang Y Dong G Yang Y Xia W Chen B Ma W Hu J Jiang F Xu L |
author_sort |
Mao Q |
title |
A network-based signature to predict the survival of non-smoking lung adenocarcinoma |
title_short |
A network-based signature to predict the survival of non-smoking lung adenocarcinoma |
title_full |
A network-based signature to predict the survival of non-smoking lung adenocarcinoma |
title_fullStr |
A network-based signature to predict the survival of non-smoking lung adenocarcinoma |
title_full_unstemmed |
A network-based signature to predict the survival of non-smoking lung adenocarcinoma |
title_sort |
network-based signature to predict the survival of non-smoking lung adenocarcinoma |
publisher |
Dove Medical Press |
series |
Cancer Management and Research |
issn |
1179-1322 |
publishDate |
2018-08-01 |
description |
Qixing Mao,1–4,* Louqian Zhang,1–3,* Yi Zhang,1,* Gaochao Dong,1,3 Yao Yang,4 Wenjie Xia,1–4 Bing Chen,1–3 Weidong Ma,1–3 Jianzhong Hu,4 Feng Jiang,1,3 Lin Xu1,3 1Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China; 2The Fourth Clinical College of Nanjing Medical University, Nanjing, China; 3Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China; 4Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA *These authors contributed equally to this work Background: A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.Materials and methods: Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.Results: Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.Conclusion: The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management. Keywords: weighted gene co-expression network analysis, WGCNA, lung adenocarcinoma, LAC, co-expressing, prognostic signature |
topic |
Weighted gene co-expression network analysis (WGCNA) lung adenocarcinoma (LAC) co-expressing prognostic signature. |
url |
https://www.dovepress.com/a-network-based-signature-to-predict-the-survival-of-non-smoking-lung--peer-reviewed-article-CMAR |
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