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Construction of a prognostic prediction system for pancreatic ductal adenocarcinoma to investigate the key prognostic genes.

ABSTRACT: Pancreatic cancer (PC) is associated with high mortality rates and poor prognoses. Pancreatic adenocarcinoma is the most common type of PC, and almost all cases of pancreatic adenocarcinoma are pancreatic ductal adenocarcinoma (PDAC). The aim of the current study was to reveal the genes involved in the prognosis of PDAC. Five datasets, including GSE71729 (145 PDAC samples and 46 normal samples), GSE15471 (39 PDAC samples and 39 normal samples), GSE1542 (24 PDAC samples and 25 normal samples), GSE28735 (45 PDAC samples and 45 normal samples) and GSE62452 (69 PDAC samples and 69 normal samples) were downloaded from the Gene Expression Omnibus database. Using the MetaDE.ES method in the MetaDE package, differentially expressed genes (DEGs) were identified from the five datasets. Furthermore, prognosis?associated genes were screened using the Cox regression analysis in the survival package, and co?expression network and module analyses were performed separately using Cytoscape software and GraphWeb tool, respectively. After a prognostic prediction system was constructed and validated, enrichment analysis of the signature genes was performed using the clusterProfiler package. A total of 480 DEGs were identified from the five datasets and 259 prognosis?associated genes were screened from GSE28735 and GSE62452. In addition, the prognostic prediction system composed of 67 signature genes [including basic transcription factor 3 (BTF3), serine/threonine kinase 11 (STK11), thrombospondin 1 (THBS1), ribosomal protein L38 (RPL38) and secretin receptor (SCTR)] was constructed and validated. The signature genes involved in the co?expression network were enriched in five pathways. In particular, STK11 was involved in three signaling pathways, and THBS1 was enriched in the phosphoinositide 3?kinase?Akt signaling pathway. Thus, BTF3, STK11, THBS1, RPL38 and SCTR may influence the prognosis of PDAC.

SUBMITTER: Zheng B

PROVIDER: S-EPMC5780129 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Construction of a prognostic prediction system for pancreatic ductal adenocarcinoma to investigate the key prognostic genes.

Zheng Bingli B Peng Jie J Mollayup Ablikim A Bakri Ahmat A Guo Lei L Zheng Jianjiang J Xu Hui H

Molecular medicine reports 20171020 1

Pancreatic cancer (PC) is associated with high mortality rates and poor prognoses. Pancreatic adenocarcinoma is the most common type of PC, and almost all cases of pancreatic adenocarcinoma are pancreatic ductal adenocarcinoma (PDAC). The aim of the current study was to reveal the genes involved in the prognosis of PDAC. Five datasets, including GSE71729 (145 PDAC samples and 46 normal samples), GSE15471 (39 PDAC samples and 39 normal samples), GSE1542 (24 PDAC samples and 25 normal samples), GS ...[more]

PMID: 29115420

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Project description:Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases identified differentially expressed genes (DEGs), including 159 upregulated and 53 downregulated genes. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment were performed and showed that the DEGs were mainly enriched for the PI3K-Akt signaling pathway and extracellular matrix organization. We used the cytoHubba plugin of Cytoscape to screen out the most significant ten hub genes by four different models (Degree, MCC, DMNC, and MNC). The expression and clinical relevance of these ten hub genes were validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas, respectively. High expression of nine of the hub genes was positively correlated with poor prognosis. Finally, the relationship between these hub genes and tumor immunity was analyzed using the Tumor Immune Estimation Resource. We found that the expression of SPARC, COL6A3, and FBN1 correlated positively with infiltration levels of six immune cells in the tumors. In addition, these three genes had a strong coexpression relationship with the immune checkpoints. In conclusion, our results suggest that nine upregulated biomarkers are related to poor prognosis in PDAC and may serve as potential prognostic biomarkers for PDAC therapy. Furthermore, SPARC, COL6A3, and FBN1 play an important role in tumor-related immune infiltration and may be ideal targets for immune therapy against PDAC.

Project description:BackgroundPancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC.MethodsIn this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses.ResultsSurvival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05).ConclusionsA CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.

Project description:Background: Pancreatic ductal adenocarcinoma (PDAC) leads to the majority of cancer-related deaths due to its morbidity with similar mortality. Lack of effective prognostic biomarkers are the main reason for belated post-operative intervention of recurrence which causes high mortality. Numerous systematic reviews and meta-analyses have explored the prognostic value of biomarkers in PDAC so far. In this article, we performed an umbrella review analyzing these studies to provide an overview of associations between prognostic biomarkers and PDAC survival outcome and synthesized these results to guide better clinical practice. Methods: Systematic reviews and meta-analyses investigating the associations between PDAC survival outcomes and prognostic biomarkers were acquired via the PubMed and Embase databases from inception till February 1, 2020. Associations supported by nominally statistically significant results were classified into strong, highly suggestive, suggestive, and weak based on several critical factors such as the statistical significance of summary estimates, the number of events, the estimate of the largest study included, interstudy heterogeneity, small-study effects, 95% predictive interval (PI), excess significance bias, and the results of credibility ceiling sensitivity analyses. Results: We included 41 meta-analyses containing 63 associations between PDAC survival outcomes and prognostic biomarkers. Although, none was supported by strong evidence among these associations, an association between C-reactive protein to albumin ratio (CAR) and PDAC overall survival (OS) and an association between neutrophil-lymphocyte ratio (NLR) and PDAC OS were supported by highly suggestive evidence. Otherwise, the association between lactate dehydrogenase (LDH) and PDAC OS was supported by suggestive evidence. The remaining 60 associations were supported by weak or not suggestive evidence. Conclusion: Associations between CAR or NLR and PDAC OS were supported by highly suggestive evidence. And the association between LDH and PDAC OS was supported by suggestive evidence. Although the methodological quality of the included systematic reviews and meta-analyses which were evaluated by AMSTAR2.0 is generally poor, the identification of the relatively robust prognostic biomarkers of PDAC may guide better post-operative intervention and follow-up to prolong patients' survival.

Dataset Information

Construction of a prognostic prediction system for pancreatic ductal adenocarcinoma to investigate the key prognostic genes.

Publications

Construction of a prognostic prediction system for pancreatic ductal adenocarcinoma to investigate the key prognostic genes.

Similar Datasets

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