Abstract 2484: Development of a gene expression database of pancreatic ductal adenocarcinoma cases by NGS-combined HiCEP to identify tumor markers

2020 ◽  
Author(s):  
Mikiya Takao ◽  
Hirotaka Matsuo ◽  
Ryoko Araki ◽  
Seiko Shimizu ◽  
Makoto Kawaguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Markers ◽  
Ductal Adenocarcinoma ◽  
Gene Expression Database

scholarly journals Study of Clinical Survival and Gene Expression in a Sample of Pancreatic Ductal Adenocarcinoma by Parsimony Phylogenetic Analysis

2016 ◽  
Vol 20 (7) ◽  
pp. 442-447 ◽  
Author(s):  
Sinem Nalbantoglu ◽  
Mones Abu-Asab ◽  
Ming Tan ◽  
Xuemin Zhang ◽  
Ling Cai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phylogenetic Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Ductal Adenocarcinoma

IGF-II and NSC-631570 compounds affect PMP22 gene expression in Pancreatic Ductal Adenocarcinoma. Could be the new target for both chemo-resistance and neuronal invasion?

Pancreatology ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. S47
Author(s):  
Niccola Funel ◽  
Asked Nowicky ◽  
Larysa Skivka ◽  
Viktoria Romanchuck ◽  
Wassil Nowicky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Ductal Adenocarcinoma ◽  
Pmp22 Gene

scholarly journals PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

2019 ◽  
Author(s):  
Palloma Porto Almeida ◽  
Cristina Padre Cardoso ◽  
Leandro Martins de Freitas
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Artificial Neural Network ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Drug Targets ◽  
Meta Analysis ◽  
Human Protein ◽  
Ductal Adenocarcinoma ◽  
Human Protein Atlas ◽  
Artificial Neural

AbstractBackgroundAlthough the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.MethodsWe performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples.ResultsThis microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94.ConclusionThe gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.

scholarly journals Meta-analysis of 1,200 transcriptomic profiles identifies a prognostic model for pancreatic ductal adenocarcinoma

2018 ◽  
Author(s):  
Vandana Sandhu ◽  
Knut Jorgen Labori ◽  
Ayelet Borgida ◽  
Ilinca Lungu ◽  
John Bartlett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Prognostic Model ◽  
Meta Analysis ◽  
Early Death ◽  
Receiver Operating Curve ◽  
Ductal Adenocarcinoma ◽  
Survival Predictor

ABSTRACTBackgroundWith a dismal 8% median 5-year overall survival (OS), pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Only 10-20% of patients are eligible for surgery, and over 50% of these will die within a year of surgery. Identify molecular predictors of early death would enable the selection of PDAC patients at high risk.MethodsWe developed the Pancreatic Cancer Overall Survival Predictor (PCOSP), a prognostic model built from a unique set of 89 PDAC tumors where gene expression was profiled using both microarray and sequencing platforms. We used a meta-analysis framework based on the binary gene pair method to create gene expression barcodes robust to biases arising from heterogeneous profiling platforms and batch effects. Leveraging the largest compendium of PDAC transcriptomic datasets to date, we show that PCOSP is a robust single-sample predictor of early death (≤1 yr) after surgery in a subset of 823 samples with available transcriptomics and survival data.ResultsThe PCOSP model was strongly and significantly prognostic with a meta-estimate of the area under the receiver operating curve (AUROC) of 0.70 (P=1.9e-18) and hazard ratio (HR) of 1.95(1.6-2.3) (P=2.6e-16) for binary and survival predictions, respectively. The prognostic value of PCOSP was independent of clinicopathological parameters and molecular subtypes. Over-representation analysis of the PCOSP 2619 gene-pairs (1070 unique genes) unveiled pathways associated with Hedgehog signalling, epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) signalling.ConclusionPCOSP could improve treatment decision by identifying patients who will not benefit from standard surgery/chemotherapy and may benefit from alternate approaches.AbbreviationsAUROCArea under the receiver operating curveGOGene annotationOSOverall survivalPCOSPPancreatic cancer overall survival predictorPDACPancreatic ductal adenocarcinomaTSPTop scoring pairs.

scholarly journals Integrated Bioinformatics Analysis of Gene Expression Profiles for Potential Biomarker Identification Towards Early Therapeutic Intervention in Pancreatitis and Pancreatic Ductal Adenocarcinoma

2021 ◽  
Author(s):  
Manoj M Wagle ◽  
Ananya Rao Kedige ◽  
Shama P Kabekkodu ◽  
Sandeep Mallya
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Ductal Adenocarcinoma ◽  
Rapid Progression ◽  
Hub Genes ◽  
Altered Expression ◽  
Biochemical Pathways ◽  
Potential Biomarker ◽  
Key Genes

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a malignancy associated with rapid progression and an abysmal prognosis. It has been reported that chronic pancreatitis can increase the risk of developing PDAC by 16-fold. Our study aims to identify the key genes and biochemical pathways mediating pancreatitis and PDAC. The gene expression datasets were retrieved from the EMBL-EBI ArrayExpress and NCBI GEO database. A total of 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC were used in this study. The differentially expressed genes (DEGs) identified were used to perform downstream analysis for ontology, interaction, and associated pathways. Furthermore, hub gene expression was validated using the GEPIA2 tool and survival analysis using the Kaplan-Meier (KM) plotter. The potential druggability of the hub genes identified was determined using the Drug-Gene Interaction Database (DGIdb). Our study identified a total of 45 genes found to have altered expression levels in both PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption pathway, ECM-receptor interaction pathway, PI3k-Akt signaling pathway, and proteoglycans in cancer pathways as significantly enriched. Module analysis revealed 15 hub genes with 92 edges, of which 14 were found to be in the druggable genome category. Through bioinformatics analysis, we identified key genes and biochemical pathways disrupted in pancreatitis and PDAC. The results can provide new insights into targeted therapy and intervening therapeutically at an earlier stage can be used as an effective strategy to decrease the incidence and severity of PDAC.

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