scholarly journals Gene modules and non-coding RNAs involved in pancreatic tumorigenesis through acinar ductal metaplasia 

2020 ◽  
Author(s):  
Huairong Zhang ◽  
Bingyin Shi ◽  
ZU-HUA GAO ◽  
BO GAO
Keyword(s):  
Gene Expression ◽  
Intraepithelial Neoplasia ◽  
Genetic Alterations ◽  
Ductal Adenocarcinoma ◽  
Nucleosome Assembly ◽  
Go Analysis ◽  
Functional Studies ◽  
Ppi Networks ◽  
Gene Modules ◽  
Non Coding Rnas

Abstract Background: Acinar ductal metaplasia (ADM) is a recently identified precursor lesion that can progress through pancreatic ductal intraepithelial neoplasia (PanIN) to pancreatic ductal adenocarcinoma (PDAC). However, the genetic alterations and the transcriptional regulators at work during the process of ADM-driven PDAC tumorigenesis are largely unknown. We applied a multidimensional integration strategy to unveil the gene modules and non-coding RNAs that drive the ADM-PanIN-PDAC process. Methods: GSE40895 and the microarray datasets were integrated to unmask the regulators linked to ADM, PanIN and PDAC. Based on the differentially expressed genes and protein–protein interaction (PPI) networks for each stage, overlapping and crosstalk gene modules in ADM-PanIN-PDAC were identified using the search tool for the retrieval of interacting genes (STRING) and Cytoscape. The functions of these modules were elucidated by gene ontology (GO) analysis. The expression levels of hub genes and survival analysis were investigated in human PDAC via gene expression profiling interactive analysis (GEPIA). The MiRDB database was used to predict potential non-coding RNAs (ncRNAs) capable of regulating overlap and crosstalk genes.Results: We found several bridging ADM gene modules (e.g. SMARCA1 and H2AFZ), PanIN gene modules (e.g. HDAC11 and SMARCA2) and PDAC gene modules (e.g. OLFR239 and CLIP3). They were enriched in nucleosome assembly, chromatin organization and G-protein coupled receptor signalling pathways by GO analysis. MicroRNAs (e.g. mmu-miR-335-5p and mmu-miR-669n) and lncRNAs (e.g. H19 and Gm14207) took part in this ample crosstalk by regulating the gene expression. Conclusions: SMARCA1, SMARCA2 and CLIP3 were identified as novel crosstalk genes and potential prognostic biomarkers for ADM-driven PDAC carcinogenesis. After validation in clinical and functional studies, transcriptional regulatory non-coding RNAs targeting crosstalk and overlapping genes could represent effective targets for early PDAC intervention.

Gene modules and non-coding RNAs involved in pancreatic tumorigenesis through acinar ductal metaplasia

2020 ◽  
Author(s):  
Huairong Zhang ◽  
Bingyin Shi ◽  
ZU-HUA GAO ◽  
BO GAO
Keyword(s):  
Ductal Carcinoma ◽  
Genetic Alterations ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Ductal Carcinoma ◽  
Nucleosome Assembly ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Transcriptional Regulatory ◽  
Gene Modules ◽  
Non Coding Rnas

Abstract Background Acinar ductal metaplasia (ADM) can progress through pancreatic ductal carcinoma in situ (PanIN) to pancreatic ductal adenocarcinoma (PDAC). However, the genetic alterations and its transcriptional regulators during the process of ADM-driven PDAC tumorigenesis are largely unknown. Therefore, we applied a multidimensional integration strategy to unveil the gene modules and non-coding RNAs that drives the ADM-PanIN-PDAC process. Methods GSE40895 and the microarray datasets were integrated to unmask the regulators link ADM, PanIN and PDAC. Based on the differential expressed genes and protein–protein interaction (PPI) networks for each stage, Overlap and crosstalk gene modules in ADM-PanIN-PDAC were identified using STRING and Cytoscape. Function of these modules were elucidated by gene ontology analysis. Expression level of hub genes and survival analysis were investigated in human PDAC via GEPIA. MiRDB database was applied to predict potential non-coding RNAs (ncRNAs) capable of regulating overlap and crosstalk genes. Results We found several bridging ADM gene modules (e.g. SMARCA1 and H2AFZ), PanIN gene modules (e.g. HDAC11 and SMARCA2) and PDAC gene modules (e.g. OLFR239 and CLIP3). They were enriched in in nucleosome assembly, chromatin organization and G-protein coupled receptor signaling pathway by GO analysis. MicroRNAs (e.g. mmu-miR-335-5p and mmu-miR-669n) and lncRNAs (e.g. H19 and Gm14207) took part in this ample crosstalk by regulating the gene expression. Conclusions SMARCA1, SMARCA2 and CLIP3 were identified as novel crosstalk genes and significant prognostic biomarkers, providing new insights into ADM-driven PDAC carcinogenesis. Transcriptional regulatory non-coding RNAs targeting crosstalk and overlap genes appear promising for early PDAC intervention.

Effect of SMARCB1 deficiency in renal medullary carcinoma (RMC) on genes associated with nucleosome assembly and telomere organization.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 614-614 ◽  
Author(s):  
Pavlos Msaouel ◽  
Gabriel G. Malouf ◽  
Xiaoping Su ◽  
Hui Yao ◽  
Durga N Tripathi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Sickle Cell Trait ◽  
Regulation Of Gene Expression ◽  
Kidney Tissue ◽  
Rna Seq ◽  
Nucleosome Assembly ◽  
P Values ◽  
Go Analysis

614 Background: RMC is a highly aggressive tumor with close to universal fatality despite therapy. It is almost exclusively found in young African-Americans with sickle cell trait, and is characterized by complete loss of expression of SMARCB1, a major chromatin remodeler involved in regulation of gene expression. We investigated the effects of SMARCB1 loss on mutation frequency, gene expression, and cell growth in RMC. Methods: Whole exome sequencing (WES) and RNA sequencing (RNA-seq) were performed in RMC tissues from 15 and 11 patients respectively, each with matched adjacent normal kidney tissue controls. In vitro experiments were performed in a cell line (RMC2C) we established from a patient with RMC. SMARCB1 was conditionally re-expressed using a tetracycline-inducible lentivector. Gene ontology (GO) analysis was performed using DAVID. Results: WES showed that RMC harbors a low number (median of < 25/tumor sample) of non-synonymous exomic single nucleotide variants (SNVs) or small indels. GO analysis revealed that the most significant pathways upregulated in RMC compared with normal tissue were those associated with nucleosome assembly and telomere organization (p values < 0.0001). Re-expression of SMARCB1 at near-endogenous levels suppressed the growth rate of RMC2C cells. Subsequent silencing of SMARCB1 expression restored the growth rate of these cells. RNA-seq of RMC2C cells expressing SMARCB1 demonstrated that the most significant downregulated pathways compared with SMARCB1-negative RMC2C cells were those associated with nucleosome assembly and telomere organization (p values < 0.0001). Conclusions: RMC harbors a remarkably simple genome, as evidenced by our WES analysis. Therefore, consistently detected alterations, such as SMARCB1 loss, are likely to serve as drivers for this disease. Indeed, in vitro restoration of SMARCB1 expression suppressed the growth of RMC cells and repressed genes associated with nucleosome assembly and telomere organization, identifying for the first time a causal link between loss of SMARCB1 and dysregulation of these genes. These results provide the basis for future therapeutic strategies targeting SMARCB1 loss in RMC.

scholarly journals Inactivation of the Euchromatic Histone-Lysine N-Methyltransferase 2 Pathway in Pancreatic Epithelial Cells Antagonizes Cancer Initiation and Pancreatitis-Associated Promotion by Altering Growth and Immune Gene Expression Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Guillermo Urrutia ◽  
Thiago Milech de Assuncao ◽  
Angela J. Mathison ◽  
Ann Salmonson ◽  
Romica Kerketta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Ex Vivo ◽  
Mapk Pathway ◽  
Intraepithelial Neoplasia ◽  
Ductal Adenocarcinoma ◽  
Immune Gene ◽  
Protein Levels ◽  
Histone Lysine

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, painful disease with a 5-year survival rate of only 9%. Recent evidence indicates that distinct epigenomic landscapes underlie PDAC progression, identifying the H3K9me pathway as important to its pathobiology. Here, we delineate the role of Euchromatic Histone-lysine N-Methyltransferase 2 (EHMT2), the enzyme that generates H3K9me, as a downstream effector of oncogenic KRAS during PDAC initiation and pancreatitis-associated promotion. EHMT2 inactivation in pancreatic cells reduces H3K9me2 and antagonizes KrasG12D-mediated acinar-to-ductal metaplasia (ADM) and Pancreatic Intraepithelial Neoplasia (PanIN) formation in both the Pdx1-Cre and P48Cre/+KrasG12D mouse models. Ex vivo acinar explants also show impaired EGFR-KRAS-MAPK pathway-mediated ADM upon EHMT2 deletion. Notably, KrasG12D increases EHMT2 protein levels and EHMT2-EHMT1-WIZ complex formation. Transcriptome analysis reveals that EHMT2 inactivation upregulates a cell cycle inhibitory gene expression network that converges on the Cdkn1a/p21-Chek2 pathway. Congruently, pancreas tissue from KrasG12D animals with EHMT2 inactivation have increased P21 protein levels and enhanced senescence. Furthermore, loss of EHMT2 reduces inflammatory cell infiltration typically induced during KrasG12D-mediated initiation. The inhibitory effect on KrasG12D-induced growth is maintained in the pancreatitis-accelerated model, while simultaneously modifying immunoregulatory gene networks that also contribute to carcinogenesis. This study outlines the existence of a novel KRAS-EHMT2 pathway that is critical for mediating the growth-promoting and immunoregulatory effects of this oncogene in vivo, extending human observations to support a pathophysiological role for the H3K9me pathway in PDAC.

Pancreatic Intraepithelial Neoplasia and Pancreatic Tumorigenesis: Of Mice and Men

2009 ◽  
Vol 133 (3) ◽  
pp. 375-381 ◽  
Author(s):  
Niki A. Ottenhof ◽  
Anya N. A. Milne ◽  
Folkert H. M. Morsink ◽  
Paul Drillenburg ◽  
Fiebo J. W. ten Kate ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Progression ◽  
Mouse Models ◽  
Intraepithelial Neoplasia ◽  
Genetic Alterations ◽  
Genetically Engineered ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Intraepithelial Neoplasia ◽  
Pancreatic Carcinogenesis ◽  
Genetically Engineered Mouse Models

Abstract Context.—Pancreatic cancer has a poor prognosis with a 5-year survival of less than 5%. Early detection is at present the only way to improve this outlook. This review focuses on the recent advances in our understanding of pancreatic carcinogenesis, the scientific evidence for a multistaged tumor progression, and the role genetically engineered mouse models can play in recapitulating the natural course and biology of human disease. Objectives.—To illustrate the stepwise tumor progression of pancreatic cancer and genetic alterations within the different stages of progression and to review the findings made with genetically engineered mouse models concerning pancreatic carcinogenesis. Data Sources.—A review of recent literature on pancreatic tumorigenesis and genetically engineered mouse models. Conclusions.—Pancreatic cancer develops through stepwise tumor progression in which preinvasive stages, called pancreatic intraepithelial neoplasia, precede invasive pancreatic cancer. Genetic alterations in oncogenes and tumor suppressor genes underlying pancreatic cancer are also found in pancreatic intraepithelial neoplasia. These mutations accumulate during progression through the consecutive stages of pancreatic intraepithelial neoplasia lesions. Also in genetically engineered mouse models of pancreatic ductal adenocarcinoma, tumorigenesis occurs through stepwise progression via consecutive mouse pancreatic intraepithelial neoplasia, and these models provide important tools for clinical applications. Nevertheless differences between mice and men still remain.

scholarly journals Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis

2021 ◽  
Vol 27 ◽  
Author(s):  
Weiyu Zhou ◽  
Yujing Wang ◽  
Hongmei Gao ◽  
Ying Jia ◽  
Yuanxin Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Functional Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Classification Model ◽  
Ductal Adenocarcinoma ◽  
Good Survival ◽  
Support Vector ◽  
Gene Modules ◽  
Key Genes ◽  
Functional Analyses

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

scholarly journals Integrative Analysis of Genomics and Transcriptome Data to Identify Regulation Networks in Female Osteoporosis

2020 ◽  
Vol 11 ◽  
Author(s):  
Xianzuo Zhang ◽  
Kun Chen ◽  
Xiaoxuan Chen ◽  
Nikolaos Kourkoumelis ◽  
Guoyuan Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Muscle Disease ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Mineral Density ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Gene Modules ◽  
Non Coding Rnas

Background: Osteoporosis is a highly heritable skeletal muscle disease. However, the genetic mechanisms mediating the pathogenesis of osteoporosis remain unclear. Accordingly, in this study, we aimed to clarify the transcriptional regulation and heritability underlying the onset of osteoporosis.Methods: Transcriptome gene expression data were obtained from the Gene Expression Omnibus database. Microarray data from peripheral blood monocytes of 73 Caucasian women with high and low bone mineral density (BMD) were analyzed. Differentially expressed messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) were identified. Differences in BMD were then attributed to several gene modules using weighted gene co-expression network analysis (WGCNA). LncRNA/mRNA regulatory networks were constructed based on the WGCNA and subjected to functional enrichment analysis.Results: In total, 3,355 mRNAs and 999 lncRNAs were identified as differentially expressed genes between patients with high and low BMD. The WGCNA yielded three gene modules, including 26 lncRNAs and 55 mRNAs as hub genes in the blue module, 36 lncRNAs and 31 mRNAs as hub genes in the turquoise module, and 56 mRNAs and 30 lncRNAs as hub genes in the brown module. JUN and ACSL5 were subsequently identified in the modular gene network. After functional pathway enrichment, 40 lncRNAs and 16 mRNAs were found to be related to differences in BMD. All three modules were enriched in metabolic pathways. Finally, mRNA/lncRNA/pathway networks were constructed using the identified regulatory networks of lncRNAs/mRNAs and pathway enrichment relationships.Conclusion: The mRNAs and lncRNAs identified in this WGCNA could be novel clinical targets in the diagnosis and management of osteoporosis. Our findings may help elucidate the complex interactions between transcripts and non-coding RNAs and provide novel perspectives on the regulatory mechanisms of osteoporosis.

scholarly journals Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 437 ◽  
Author(s):  
Giulia Fiscon ◽  
Federica Conte ◽  
Lorenzo Farina ◽  
Paola Paci
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Detection Algorithm ◽  
Cell Phenotype ◽  
Disease Genes ◽  
Network Medicine ◽  
Topological Properties ◽  
Systematic Analysis ◽  
Ppi Networks ◽  
Non Coding Rnas

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

scholarly journals Co-Expression Network-based Analysis associated with potato initial resistance

2018 ◽  
Author(s):  
Lang Yan ◽  
Xianjun Lai ◽  
Yan Wu ◽  
Xuemei Tan ◽  
Yizheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Evolutionary Divergence ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Functional Studies ◽  
Study Gene Expression ◽  
Gene Modules ◽  
Dramatic Shift

RNA sequencing (RNA-seq) providing genome-wide expression datasets has been successfully used to study gene expression patterns and regulation mechanism among multiple samples. Gene co-expression networks (GCNs) studies within or across species showed that coordinated genes in expression patterns are often functionally related. For potatoes, a large amount of publicly available transcriptome datasets have been generated but an optimal GCN detecting expression patterns in different genotypes, tissues and environmental conditions, is lacking. We constructed a potato GCN using 16 published RNA-Seq datasets covering 11 cultivars from native habitat worldwide. The correlations of gene expression were assessed pair-wisely and biologically meaningful gene modules which are highly connected in GCN were identified. One of the primitively native-farmer-selected cultivars in the Andes, ssp.Andigena, had relative far distance in gene expression patterns with other modern varieties. GCN in further enriched 134 highly and specifically co-expressed genes in ssp.Andigena associated with potato disease and stress resistance, which underlying the dramatic shift in evolutionary pressures during potato artificial domestication. In total, the network was consisted of into 14 gene models that involves in a variety of plant processes, which sheds light on how gene modules organized intra- and inter-varieties in the context of evolutionary divergence and provides a basis of information resource for potato gene functional studies.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

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