Dataset Information

Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma.

ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal human malignant tumor, with a dismal 5-year survival rate of less than 5%. The lack of specific symptoms at early tumor stages and the paucity of biomarkers contribute to the poor diagnosis of pancreatic ductal adenocarcinoma. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. We searched the databases of expression profiling by array on GEO, aiming at comparing gene expression profile of matched pairs of pancreatic tumor and adjacent non-tumor tissues, and we screen out 4 suitable series of gene expression microarray data ("GSE15471", "GSE18670", "GSE28735" and "GSE58561"). After carefully analyzing, 13 DEGs (MYOF, SLC6A6, S100P, HK2, IFI44L, OSBPL3, IGF2BP3, PDK4, IL1R2, ERO1A, EGLN3, PLAC8 and ACSL5) are significantly differentially expressed in four microarray databases in common. After analyzing mRNA expression data and clinical follow-up survey provided in the TCGA database and clinicopathological data of 137 pancreatic ductal adenocarcinoma patients, we carefully demonstrated that three of these differentially expressed genes (ERO1A, OSBPL3 and IFI44L) are correlated with poor prognosis of pancreatic ductal adenocarcinoma patients. In addition, we revealed that cell-matrix adhesion and extracellular matrix were top significantly regulated pathways in pancreatic ductal adenocarcinoma and depicted two protein-protein interactions networks of extracellular matrix related Genes which are dysregulated according to 4 gene expression microarray data mentioned above ("GSE15471", "GSE18670", "GSE28735" and "GSE58561"), hoping to shed light on the etiology of PDAC and mechanisms of drug resistance in PDAC in this study.

SUBMITTER: Li H

PROVIDER: S-EPMC5581051 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma.

Li Hongzhe H Wang Xinjing X Fang Yuan Y Huo Zhen Z Lu Xiongxiong X Zhan Xi X Deng Xiaxin X Peng Chenghong C Shen Baiyong B

Oncotarget 20170331 32

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal human malignant tumor, with a dismal 5-year survival rate of less than 5%. The lack of specific symptoms at early tumor stages and the paucity of biomarkers contribute to the poor diagnosis of pancreatic ductal adenocarcinoma. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. We searched the databases of expression profiling by array on GEO, aiming at comparing gene expression profile ...[more]

PMID: 28881752

Similar Datasets

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC.MethodsTo dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively.ResultsSix molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~?22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~?22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~?12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~?14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~?7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes.ConclusionsOur study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.

Project description:Dysregulated expression of the secretory protein renalase can promote pancreatic ductal adenocarcinoma (PDAC) growth in animal models. We characterized renalase expression in premalignant and malignant PDAC tissue and investigated whether plasma renalase levels corresponded to clinical PDAC characteristics. Renalase immunohistochemistry was used to determine the presence and distribution of renalase in normal pancreas, chronic pancreatitis, PDAC precursor lesions, and PDAC tissues. Associations between pretreatment plasma renalase and PDAC clinical status were assessed in patients with varied clinical stages of PDAC and included tumor characteristics, surgical resection in locally advanced/borderline resectable PDAC, and overall survival. Data were retrospectively obtained and correlated using non-parametric analysis. Little to no renalase was detected by histochemistry in the normal pancreatic head in the absence of abdominal trauma. In chronic pancreatitis, renalase immunoreactivity localized to peri-acinar spindle-shaped cells in some samples. It was also widely present in PDAC precursor lesions and PDAC tissue. Among 240 patients with PDAC, elevated plasma renalase levels were associated with worse tumor characteristics, including greater angiolymphatic invasion (80.0% vs. 58.1%, p = 0.012) and greater node positive disease (76.5% vs. 56.5%, p = 0.024). Overall survival was worse in patients with high plasma renalase levels with median follow-up of 27.70 months vs. 65.03 months (p < 0.001). Renalase levels also predicted whether patients with locally advanced/borderline resectable PDAC underwent resection (AUC 0.674; 95%CI 0.42-0.82, p = 0.04). Overall tissue renalase was increased in both premalignant and malignant PDAC tissues compared to normal pancreas. Elevated plasma renalase levels were associated with advanced tumor characteristics, decreased overall survival, and reduced resectability in patients with locally advanced/borderline resectable PDAC. These studies show that renalase levels are increased in premalignant pancreatic tissues and that its levels in plasma correspond to the clinical behavior of PDAC.

Dataset Information

Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma.

Publications

Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets