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Analysis of Dynamic Molecular Networks for Pancreatic Ductal Pan et al. Cancer Cell Int (2018) 18:214 https://doi.org/10.1186/s12935-018-0718-5 Cancer Cell International PRIMARY RESEARCH Open Access Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression Zongfu Pan1†, Lu Li2†, Qilu Fang1, Yiwen Zhang1, Xiaoping Hu1, Yangyang Qian3 and Ping Huang1* 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 diferentially expressed genes (DEGs) between normal tissue and PDAC of diferent stages were profled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression diferences 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 dif- ferent topological variation of nodes and clusters over time. The phosphosite markers of stage-specifc protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was per- formed 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 fve fundamental pathways throughout fve 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 fve 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 identifed 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 signifcantly correlated with shorter overall survival. Moreover, 15 stage-specifc protein kinases were identifed 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 interac- tion networks during PDAC progression and ofered potential targets for therapeutic intervention. *Correspondence: [email protected] †Zongfu Pan and Lu Li contributed equally to this work 1 Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou 310022, China Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat​iveco​mmons​.org/licen​ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/ publi​cdoma​in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Pan et al. Cancer Cell Int (2018) 18:214 Page 2 of 18 Keywords: Dynamic molecular networks, Pancreatic ductal adenocarcinoma, Progression, Bioinformatics Background Te molecular interactions in tumor are varying with Pancreatic ductal adenocarcinoma (PDAC) is one of the cancer staging. Construction and analysis of dynamic most malignant solid tumors arising within the ductal molecular networks provide a view to understand of the pancreas. Te lack of early diagnosis and its rapid dynamic cellular mechanisms of diferent biological pro- progression resulted in an advanced stage of PDAC cess during cancer progression and ofer opportunities patients when diagnosed. In the past several decades, for therapeutic intervention. In the present study, we many eforts have been taken to unveil the molecu- analyzed the microarray from Gene Expression Omnibus lar pathogenesis of PDAC, and to improve the patient and identifed the diferentially expressed genes between prognosis through various therapeutic strategies. How- normal tissue and PDAC of diferent stages, respectively. ever, limited advances have been made to prolong the Further, we constructed the dynamic protein–protein survival and to reduce the mortality. Te cancer stag- interaction networks and analyzed diferent topological ing is one of the most important factors in determining variation of nodes and clusters over time. Several func- treatment strategies and predicting a patient’s outcome tional molecular modules and genes were identifed as [1]. At present, surgical resection is the standard care key regulators for PDAC development. Trough visual- for only 20% of patients with localized disease, while izing the dynamic networks from early to advanced stage, most patients with surgical management will develop our study aimed at improving our understanding of the cancer recurrence and die within 2 years. Moreover, the underlying mechanism of PDAC progression and pro- median survival of advanced inoperable PDAC patient vided novel insights for precise treatment. with systemic chemotherapy is only about 8 months [2]. Terefore, exploring the molecular mechanism under- Methods lying the progression of PDAC may contribute to the Tissue samples development of precise therapeutics for PDAC patients. Human PDAC tissues with four diferent stages and non- PDAC is a complex disease driven by time and con- tumorous tissues were obtained from fve patients who text dependent alterations of multiple genes. Consid- underwent surgical resection at Zhejiang Cancer Hos- erable advances have been made in demonstrating the pital. All specimens had a pathological diagnosis at the genetic alterations involved in the progression PDAC. time of assessment. Studies were approved by the Ethics Frequent mutations in Kirsten Ras (K-RAS), TP53, Committee of Zhejiang Cancer Hospital. CDKN2A and SMAD4 has been identifed as essential drivers for PDAC development [3]. According to the Microarray data integrated genomic analysis, Bailey et al. [4] revealed Microarray dataset GSE62165 containing staging pan- diferent mechanism of molecular evolution underlying creatic ductal adenocarcinoma (PDAC) and non-tumoral four redefned pancreatic cancer subtypes, which pro- pancreatic tissue was obtained from Gene Expression vides a new insight into potential therapeutic relevance Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) and patient selection. Depending on the spatiotempo- database in the National Center for Biotechnology Infor- ral proteomic analysis of pancreas cancer progression, mation (NCBI), which was deposited by Janky and col- Mirus et al. [5] pointed out that dynamic expression league [7]. Te GSE62165 dataset included 118 surgically pattern of serine/threonine stress kinase 4 were asso- resected PDAC varying from stage I to stage IV and 13 ciated with early tumorigenic events. Additionally, control samples. Te detail PDAC patient cohort con- genome-wide transcriptome analysis by Jones et al. [6] sisted of 8 stage I samples, 30 stage IIa samples, 62 stage showed that more than 21,000 genetic altered in PDAC, IIb samples, 5 stage III samples and 13 stage IV samples. which mainly afected 12 core signaling pathways Additionally, the gene expression profle was detected including apoptosis, DNA damage repair, cell adhe- basing on HG-U219 (Afymetrix Human Genome U219 sion and invasion. Besides, Janky et al. [7] screened the Array) platform. master regulators of transcription involved in PDAC progression and highlighted the HNF1A/B as a puta- Data processing and screening of diferentially expressed tive tumor suppressor in pancreatic cancer. However, genes (DEGs) despite these important advances, the precise dynamic Te CEL fle data of GSE62165 was read using the afy landscape of molecular interaction networks during package in the R language software (version 3.2.3, PDAC progression is incompletely understood. https ://www.r-proje ct.org/). Background correction, Pan et al. Cancer Cell Int (2018) 18:214 Page 3 of 18 normalization, expression calculation of the original degree centrality (DC), betweenness centrality (BC), array data and log2 transformation were processed by local average connectivity-based method (LAC) were robust multichip average (RMA) algorithm. Empiri- used. Te top 20 DC, BC, and LAC of genes that uniquely cal Bayes method was used to identify signifcant DEGs elevated at each stage
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