Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling
Abstract Background Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer f...
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doaj-65ecc692743e4b70beaf67ed68e8f7752020-11-25T03:58:59ZengBMCBMC Cancer1471-24072020-10-0120111510.1186/s12885-020-07494-wIdentification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modelingDong Leng0Jiawen Yi1Maodong Xiang2Hongying Zhao3Yuhui Zhang4Clinical Laboratory, Beijing Chao-Yang Hospital, Capital Medical UniversityDepartment of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical UniversityTokyo Institute of TechnologyDepartment of Pathology, Beijing Chao-Yang Hospital, Capital Medical UniversityDepartment of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical UniversityAbstract Background Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer from healthy individuals and common genes driving the transformation from healthy to IPF and lung cancer. Methods The gene expression data for IPF and non-small cell lung cancer (NSCLC) were retrieved from the Gene Expression Omnibus (GEO) database. The DEG signatures were identified via unsupervised two-way clustering (TWC) analysis, supervised support vector machine analysis, dimensional reduction, and mutual exclusivity analysis. Gene enrichment and pathway analyses were performed to identify common signaling pathways. The most significant signature genes in common among IPF and lung cancer were further verified by immunohistochemistry. Results The gene expression data from GSE24206 and GSE18842 were merged into a super array dataset comprising 86 patients with lung disorders (17 IPF and 46 NSCLC) and 51 healthy controls and measuring 23,494 unique genes. Seventy-nine signature DEGs were found among IPF and NSCLC. The peroxisome proliferator-activated receptor (PPAR) signaling pathway was the most enriched pathway associated with lung disorders, and matrix metalloproteinase-1 (MMP-1) in this pathway was mutually exclusive with several genes in IPF and NSCLC. Subsequent immunohistochemical analysis verified enhanced MMP1 expression in NSCLC associated with IPF. Conclusions For the first time, we defined common signature genes for IPF and NSCLC. The mutually exclusive sets of genes were potential drivers for IPF and NSCLC.http://link.springer.com/article/10.1186/s12885-020-07494-wIdiopathic pulmonary fibrosisLung cancerGene expressionData miningMutual exclusivity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dong Leng Jiawen Yi Maodong Xiang Hongying Zhao Yuhui Zhang |
spellingShingle |
Dong Leng Jiawen Yi Maodong Xiang Hongying Zhao Yuhui Zhang Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling BMC Cancer Idiopathic pulmonary fibrosis Lung cancer Gene expression Data mining Mutual exclusivity |
author_facet |
Dong Leng Jiawen Yi Maodong Xiang Hongying Zhao Yuhui Zhang |
author_sort |
Dong Leng |
title |
Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
title_short |
Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
title_full |
Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
title_fullStr |
Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
title_full_unstemmed |
Identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
title_sort |
identification of common signatures in idiopathic pulmonary fibrosis and lung cancer using gene expression modeling |
publisher |
BMC |
series |
BMC Cancer |
issn |
1471-2407 |
publishDate |
2020-10-01 |
description |
Abstract Background Idiopathic pulmonary fibrosis (IPF) is associated with an increased risk for lung cancer, but the underlying mechanisms driving malignant transformation remain largely unknown. This study aimed to identify differentially expressed genes (DEGs) distinguishing IPF and lung cancer from healthy individuals and common genes driving the transformation from healthy to IPF and lung cancer. Methods The gene expression data for IPF and non-small cell lung cancer (NSCLC) were retrieved from the Gene Expression Omnibus (GEO) database. The DEG signatures were identified via unsupervised two-way clustering (TWC) analysis, supervised support vector machine analysis, dimensional reduction, and mutual exclusivity analysis. Gene enrichment and pathway analyses were performed to identify common signaling pathways. The most significant signature genes in common among IPF and lung cancer were further verified by immunohistochemistry. Results The gene expression data from GSE24206 and GSE18842 were merged into a super array dataset comprising 86 patients with lung disorders (17 IPF and 46 NSCLC) and 51 healthy controls and measuring 23,494 unique genes. Seventy-nine signature DEGs were found among IPF and NSCLC. The peroxisome proliferator-activated receptor (PPAR) signaling pathway was the most enriched pathway associated with lung disorders, and matrix metalloproteinase-1 (MMP-1) in this pathway was mutually exclusive with several genes in IPF and NSCLC. Subsequent immunohistochemical analysis verified enhanced MMP1 expression in NSCLC associated with IPF. Conclusions For the first time, we defined common signature genes for IPF and NSCLC. The mutually exclusive sets of genes were potential drivers for IPF and NSCLC. |
topic |
Idiopathic pulmonary fibrosis Lung cancer Gene expression Data mining Mutual exclusivity |
url |
http://link.springer.com/article/10.1186/s12885-020-07494-w |
work_keys_str_mv |
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