Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods The microarray...
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doaj-c66ce5e079d54022913386bf36843b352020-11-25T02:21:31ZengBMCCancer Cell International1475-28672018-12-0118111810.1186/s12935-018-0718-5Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progressionZongfu Pan0Lu Li1Qilu Fang2Yiwen Zhang3Xiaoping Hu4Yangyang Qian5Ping Huang6Department of Pharmacy, Zhejiang Cancer HospitalDepartment of Pharmacy, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityDepartment of Pharmacy, Zhejiang Cancer HospitalDepartment of Pharmacy, Zhejiang Cancer HospitalDepartment of Pharmacy, Zhejiang Cancer HospitalKey Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Zhejiang Cancer HospitalDepartment of Pharmacy, Zhejiang Cancer HospitalAbstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression. Conclusions Our study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.http://link.springer.com/article/10.1186/s12935-018-0718-5Dynamic molecular networksPancreatic ductal adenocarcinomaProgressionBioinformatics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zongfu Pan Lu Li Qilu Fang Yiwen Zhang Xiaoping Hu Yangyang Qian Ping Huang |
spellingShingle |
Zongfu Pan Lu Li Qilu Fang Yiwen Zhang Xiaoping Hu Yangyang Qian Ping Huang Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression Cancer Cell International Dynamic molecular networks Pancreatic ductal adenocarcinoma Progression Bioinformatics |
author_facet |
Zongfu Pan Lu Li Qilu Fang Yiwen Zhang Xiaoping Hu Yangyang Qian Ping Huang |
author_sort |
Zongfu Pan |
title |
Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
title_short |
Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
title_full |
Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
title_fullStr |
Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
title_full_unstemmed |
Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
title_sort |
analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression |
publisher |
BMC |
series |
Cancer Cell International |
issn |
1475-2867 |
publishDate |
2018-12-01 |
description |
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression. Conclusions Our study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention. |
topic |
Dynamic molecular networks Pancreatic ductal adenocarcinoma Progression Bioinformatics |
url |
http://link.springer.com/article/10.1186/s12935-018-0718-5 |
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