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?

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.

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

10.1101/355602 ◽

2018 ◽

Cited By ~ 2

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.

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

10.21203/rs.3.rs-879048/v1 ◽

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

10.21203/rs.3.rs-16813/v1 ◽

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.

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