Dataset Information

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma.

ABSTRACT: Background:Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism. Objective:This present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of PDAC and potential targets of treatment. Method:Based on the overlap genes obtained from 3 GEO datasets, the hub genes were identified using STRING and Cytoscape plugin MCODE. The enrichment and function analysis were applied using DAVID. The protein-protein interaction network was performed using cBioPortal and UCSC Xena. The Oncomine was finally used to determine the candidate gene by analyzing their expression between pancreas sample and PDAC sample. Results:25 hub genes were selected from a total of 1006 DEGs from 3 GEO datasets, consisting of 14 upregulated genes and 11 downregulated genes. The overall decline of hub gene expression enriched in G1 phase of cell cycle in other subtypes of pancreatic cancer. Oncomine database was ultimately performed to determine the 8 candidate genes, including CXCL5, CCL20, NMU, F2R, ANXA1, EDNRA, LPAR6, and GNA15. Conclusions:Conclusively, 8 candidate genes would become the potential PDAC combined biomarkers for diagnosis and therapeutic strategies.

SUBMITTER: Gu Y

PROVIDER: S-EPMC6717170 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma.

Gu Yuan Y Feng Qijin Q Liu Han H Zhou Qi Q Hu Ailing A Yamaguchi Takuji T Xia Shilin S Kobayashi Hiroyuki H

Heliyon 20190826 8

<h4>Background</h4>Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. Aberrant expression of genes plays important role in the procession of PDAC. The analysis of gene expression profile will contribute to the research of carcinoma mechanism.<h4>Objective</h4>This present study is focused to investigate the differentially expressed genes (DEGs) from 3 PDAC microarray datasets, which would provide candidate genes for putative biomarkers to understand the mechanism of ...[more]

PMID: 31489384

Similar Datasets

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.

Project description:Pancreatic ductal adenocarcinoma (PDAC), as an intractable malignancy, still causes an extremely high mortality worldwide. The ubiquitin-specific protease (USP) family constitutes the major part of deubiquitinating enzymes (DUBs) which has been reported to be involved in initiation and progression of various malignancies via the function of deubiquitination. However, the biological function and clinical values of USPs in PDAC have not been comprehensively elucidated. In this study, Gene Expression Profiling Interactive Analysis (GEPIA), Gene Expression Omnibus (GEO) datasets, UALCAN database, and the Human Protein Atlas (HPA) online tool were used to analyze the expression level and the relationship between USP expression and clinicopathological features in PDAC. Survival module of HPA and Kaplan-Meier plotter (KMP) databases was recruited to explore the prognostic value of USPs. Tumor Immune Estimation Resource (TIMER) online tool and KMP databases were utilized to elucidate tumor immune infiltration and immune-related survival of USPs. CBioPortal online tool was used to identify the gene mutation level of USPs in PDAC. Both cBioPortal and LinkedOmics were used to confirm the potential biological functions of USPs in PDAC. Our study showed that USP10, USP14, USP18, USP32, USP33, and USP39 (termed as six-USPs) expressions were significantly elevated in tumor tissues. The high expression of the four USPs (USP10, USP14, USP18, and USP39) indicated a poor prognosis. A significant relationship was indicated between the expression of six-USPs and clinicopathological features. Also, the expression of six-USPs was related to promoter methylation level. Moreover, more than 40% genetic alterations and mutations were discovered in six-USPs. Furthermore, the six-USP expression was correlated with immune infiltration and immune-related prognosis. The functional analysis found that the six-USPs were involved in various biological processes and signaling pathways, such as nucleocytoplasmic transport, choline metabolism in cancer, cell cycle, ErbB signaling pathway, RIG-I-like receptor signaling pathway, TGF-β signaling pathway, and TNF signaling pathway. In conclusion, the results showed that six-USPs are potential prognostic biomarkers and can be recruited as possible therapeutic targets of PDAC.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC.

Dataset Information

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma.

Publications

Bioinformatic evidences and analysis of putative biomarkers in pancreatic ductal adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets