scholarly journals Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation

2019 ◽  
Author(s):  
Kristofer Linton-Reid ◽  
Harry Clifford ◽  
Joe Sneath Thompson
Keyword(s):  
Monte Carlo ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Consensus Clustering ◽  
Survival Times ◽  
Clustering Techniques ◽  
Transcriptomics Data ◽  
Transcriptome Expression ◽  
Microarray Datasets

ABSTRACTSubtyping of tumor transcriptome expression profiles is a routine method used to distinguish tumor heterogeneity. Unsupervised clustering techniques are often combined with survival analysis to decipher the relationship between genes and the survival times of patients. However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. In this study, we introduce the issues underlying the lack of reproducibility in transcriptomic subtyping studies. This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. Our approach carefully combines multiple RNA-sequencing and microarray datasets, followed by subtyping via Monte-Carlo Consensus Clustering and creation of deep subtyping classifiers. This paper demonstrates an improved subtyping methodology by investigating pancreatic ductal adenocarcinoma. Importantly, our methodology identifies six biologically novel pancreatic ductal adenocarcinoma subtypes. Our approach also enables a degree of reproducibility, via our pancreatic ductal adenocarcinoma classifier PDACNet, which classical subtyping studies have failed to establish.

Differential alteration of TRAIL and its receptor expression profiles in patients with pancreatic ductal adenocarcinoma

2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 15607-15607
Author(s):  
A. D. Sanlioglu ◽  
E. Dirice ◽  
O. Elpek ◽  
A. F. Korcum ◽  
M. K. Balci ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Expression Profiles ◽  
Receptor Expression ◽  
Ductal Adenocarcinoma

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

scholarly journals A Network-Based Approach for Identification of Subtype-Specific Master Regulators in Pancreatic Ductal Adenocarcinoma

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 155
Author(s):  
Yuchen Zhang ◽  
Lina Zhu ◽  
Xin Wang
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Network Inference ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Regulatory Mechanism ◽  
Expression Profiles ◽  
Strong Association ◽  
Predictive Biomarkers ◽  
Integrative Analysis ◽  
Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), the predominant subtype of pancreatic cancer, has been reported with equal mortality and incidence for decades. The lethality of PDAC is largely due to its late presentation, when surgical resection is no longer an option. Similar to other major malignancies, it is now clear that PDAC is not a single disease, posing a great challenge to precise selection of patients for optimized adjuvant therapy. A representative study found that PDAC comprises four distinct molecular subtypes: squamous, pancreatic progenitor, immunogenic, and aberrantly differentiated endocrine exocrine (ADEX). However, little is known about the molecular mechanisms underlying specific PDAC subtypes, hampering the design of novel targeted agents. In this study we performed network inference that integrates miRNA expression and gene expression profiles to dissect the miRNA regulatory mechanism specific to the most aggressive squamous subtype of PDAC. Master regulatory analysis revealed that the particular subtype of PDAC is predominantly influenced by miR-29c and miR-192. Further integrative analysis found miR-29c target genes LOXL2, ADAM12 and SERPINH1, which all showed strong association with prognosis. Furthermore, we have preliminarily revealed that the PDAC cell lines with high expression of these miRNA target genes showed significantly lower sensitivities to multiple anti-tumor drugs. Together, our integrative analysis elucidated the squamous subtype-specific regulatory mechanism, and identified master regulatory miRNAs and their downstream genes, which are potential prognostic and predictive biomarkers.

scholarly journals The Role of Circular RNAs in Pancreatic Ductal Adenocarcinoma and Biliary-Tract Cancers

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3250
Author(s):  
Christopher Limb ◽  
Daniel S. K. Liu ◽  
Morten T. Veno ◽  
Eleanor Rees ◽  
Jonathan Krell ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Biliary Tract ◽  
Ex Vivo ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Circular Rnas ◽  
Specific Expression ◽  
Biliary Tract Cancers ◽  
Cerna Network

Pancreatic Ductal Adenocarcinoma (PDAC) and biliary-tract cancers (BTC) often present at a late stage, and consequently patients have poor survival-outcomes. Circular RNAs (circRNAs) are non-coding RNA molecules whose role in tumourigenesis has recently been realised. They are stable, conserved and abundant, with tissue-specific expression profiles. Therefore, significant interest has arisen in their use as potential biomarkers for PDAC and BTC. High-throughput methods and more advanced bioinformatic techniques have enabled better profiling and progressed our understanding of how circRNAs may function in the competing endogenous RNA (ceRNA) network to influence the transcriptome in these cancers. Therefore, the aim of this systematic review was to describe the roles of circRNAs in PDAC and BTC, their potential as biomarkers, and their function in the wider ceRNA network in regulating microRNAs and the transcriptome. Medline, Embase, Scopus and PubMed were systematically reviewed to identify all the studies addressing circRNAs in PDAC and BTC. A total of 32 articles were included: 22 considering PDAC, 7 for Cholangiocarcinoma (CCA) and 3 for Gallbladder Cancer (GBC). There were no studies investigating Ampullary Cancer. Dysregulated circRNA expression was associated with features of malignancy in vitro, in vivo, and ex vivo. Overall, there have been very few PDAC and BTC tissues profiled for circRNA signatures. Therefore, whilst the current studies have demonstrated some of their functions in these cancers, further work is required to elucidate their potential role as cancer biomarkers in tissue, biofluids and biopsies.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

scholarly journals Relevance of Immune Infiltration and Clinical Outcomes in Pancreatic Ductal Adenocarcinoma Subtypes

2021 ◽  
Vol 10 ◽  
Author(s):  
Rong Liu ◽  
Ya-Zhou Liao ◽  
Wei Zhang ◽  
Hong-Hao Zhou
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Clinical Outcomes ◽  
Immune Cells ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Critical Role ◽  
Cell Subset ◽  
Ductal Adenocarcinoma ◽  
Support Vector ◽  
Consensus Clustering

PurposePancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with high heterogeneity and dismal survival rates. Tumor immune microenvironment plays a critical role in sensitive to chemotherapy and prognosis. Herein, we determined the relevance of the composition of tumor-infiltrating immune cells to clinical outcomes in PDACs, and we evaluated these effects by molecular subtype.Experimental DesignData of 1,274 samples from publically available datasets were collected. Molecular subtypes were predicted with support vector machine. Twenty-two subsets of immune cells were estimated with CIBERSORTx. The associations between each cell subset and overall survival (OS), relapse free survival (RFS), and complete response (CR) to chemotherapy were evaluated, modelling cellular proportions as quartiles.ResultsAn immune-related cluster was identified with unsupervised hierarchical clustering of hallmark pathways. Of the immune cells investigated, M0 macrophages emerged as closely associated with worse OS (HR =1.23, 95% CI = 1.15–1.31, p=1.57×10-9) and RFS (HR = 1.14, 95% CI =1.04–1.25, p=2.93×10-3), regardless of molecular subtypes. The CD8+ T cells conferred favorable survival. The neutrophils conferred poor OS overall (HR=1.17, 95% CI=1.10–1.23, p=1.74×10-7) and within the classical subtype. In the basal-like subtype, activated mast cells were associated with worse OS. Consensus clustering revealed six immune subgroups with distinct survival patterns and CR rates. The higher expression of PD1 was associated with better OS.ConclusionsThe immune cellular composition infiltrate in PDAC are likely to have effects on prognosis. Further exploration of the cellular immune response has the potential to identify candidates for immunotherapy.

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