Proteogenomic analysis of pancreatic cancer subtypes

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

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Proteogenomic Analysis of Pancreatic Cancer Subtypes

10.1101/2020.04.13.039834 ◽

2020 ◽

Cited By ~ 1

Author(s):

Doris Kafita ◽

Panji Nkhoma ◽

Mildred Zulu ◽

Musalula Sinkala

Keyword(s):

Pancreatic Cancer ◽

Notch Pathway ◽

Public Health Problem ◽

The Cancer Genome Atlas ◽

Molecular Processes ◽

Sequencing Data ◽

Cancer Subtypes ◽

Molecular Alterations ◽

Significant Public Health ◽

Whole Exome Sequencing Data

AbstractPancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

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Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Bioscience Reports ◽

10.1042/bsr20201087 ◽

2020 ◽

Vol 40 (12) ◽

Author(s):

Dafeng Xu ◽

Yu Wang ◽

Kailun Zhou ◽

Jincai Wu ◽

Zhensheng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Immune Cells ◽

Prognostic Value ◽

Tumor Tissue ◽

Clinical Stage ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

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6-Shogaol Mitigates Sepsis-Associated Hepatic Injury through Transcriptional Regulation

Nutrients ◽

10.3390/nu13103427 ◽

2021 ◽

Vol 13 (10) ◽

pp. 3427

Author(s):

Xiaoxuan Guo ◽

Jing Qiu ◽

Yongzhong Qian

Keyword(s):

Cell Model ◽

Public Health Problem ◽

Control Group ◽

Sequencing Data ◽

Bioactive Component ◽

Significant Public Health Problem ◽

Significant Public Health ◽

Nfκb Pathway ◽

Anti Inflammation ◽

Sequencing Data Analysis

Sepsis-associated liver dysfunction presents a significant public health problem. 6-Shogaol is the key bioactive component in dry ginger, which has antioxidant and anti-inflammation capacity. The present study aims to investigate the preventive effect of 6-shogaol on sepsis-induced liver injury. 6-Shogaol was administered to mice for 7 consecutive days before being intraperitoneally injected with lipopolysaccharide (LPS). After 24 h, mice were sacrificed, and biochemical and transcriptomic analyses were performed. Our results demonstrated that 6-shogaol prevented LPS-induced impairment in antioxidant enzymes and elevation in malondialdehyde level in the liver. The hepatic inflammatory response was significantly suppressed by 6-shogaol through suppressing the MAPK/NFκB pathway. RNA-sequencing data analysis revealed that 41 overlapped genes between the LPS vs. control group and 6-shogaol vs. LPS group were identified, among which 36 genes were upregulated, and 5 genes were downregulated for the LPS vs. control group. These overlapped genes are enriched in inflammation-related pathways, e.g., TNF and NFκB. The mRNA expression of the overlapped genes was also verified in the LPS-induced BRL-3A cell model. In summary, 6-shogaol shows great potential as a natural chemopreventive agent to treat sepsis-associated hepatic disorders.

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SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update)

Nucleic Acids Research ◽

10.1093/nar/gkaa420 ◽

2020 ◽

Vol 48 (W1) ◽

pp. W185-W192 ◽

Cited By ~ 2

Author(s):

Jorge Oscanoa ◽

Lavanya Sivapalan ◽

Emanuela Gadaleta ◽

Abu Z Dayem Ullah ◽

Nicholas R Lemoine ◽

...

Keyword(s):

Sequence Variation ◽

Molecular Processes ◽

Sequencing Data ◽

Web Based ◽

Data Annotation ◽

Cancer Driver ◽

Molecular Alterations ◽

Visualization Tools ◽

Clinically Significant ◽

Complete Overhaul

Abstract SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.

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Prognostic Biomarkers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset

Cancers ◽

10.3390/cancers11010126 ◽

2019 ◽

Vol 11 (1) ◽

pp. 126 ◽

Cited By ~ 11

Author(s):

Remy Nicolle ◽

Jerome Raffenne ◽

Valerie Paradis ◽

Anne Couvelard ◽

Aurelien de Reynies ◽

...

Keyword(s):

Pancreatic Cancer ◽

Prognostic Value ◽

Prognostic Significance ◽

The Cancer Genome Atlas ◽

Pancreatic Tumors ◽

Expression Data ◽

Web Based ◽

Molecular Alterations ◽

Cancer Genome Atlas ◽

Tcga Dataset

Data from the Cancer Genome Atlas (TCGA) are now easily accessible through web-based platforms with tools to assess the prognostic value of molecular alterations. Pancreatic tumors have heterogeneous biology and aggressiveness ranging from the deadly adenocarcinoma (PDAC) to the better prognosis, neuroendocrine tumors. We assessed the availability of the pancreatic cancer TCGA data (TCGA_PAAD) from several repositories and investigated the nature of each sample and how non-PDAC samples impact prognostic biomarker studies. While the clinical and genomic data (n = 185) were fairly consistent across all repositories, RNAseq profiles varied from 176 to 185. As a result, 35 RNAseq profiles (18.9%) corresponded to a normal, inflamed pancreas or non-PDAC neoplasms. This information was difficult to obtain. By considering gene expression data as continuous values, the expression of the 5312 and 4221 genes were significantly associated with the progression-free and overall survival respectively. Considering the cohort was not curated, only 4 and 14, respectively, had prognostic value in the PDAC-only cohort. Similarly, mutations in key genes or well-described miRNA lost their prognostic significance in the PDAC-only cohort. Therefore, we propose a web-based application to assess biomarkers in the curated TCGA_PAAD dataset. In conclusion, TCGA_PAAD curation is critical to avoid important biological and clinical biases from non-PDAC samples.

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Genetic alterations and functional networks of m6A RNA methylation regulators in pancreatic cancer based on data mining

10.21203/rs.3.rs-305217/v1 ◽

2021 ◽

Author(s):

Juan Zeng ◽

Heying Zhang ◽

Yonggang Tan ◽

Zhe Wang ◽

Yunwei Li ◽

...

Keyword(s):

Pancreatic Cancer ◽

Messenger Rna ◽

Genetic Alterations ◽

The Cancer Genome Atlas ◽

Rna Modification ◽

Functional Networks ◽

Molecular Alterations ◽

M6a Rna Methylation ◽

Molecular Therapeutic ◽

New Strategies

Abstract Background Pancreatic cancer is a fatal malignancy of the digestive system and the 5-year survival rate remains low. Therefore, new molecular therapeutic targets are required to improve treatments, prognosis, and the survival of patients. N6-methyladenosine (m6A) is the most prevalent reversible methylation in mammalian messenger RNA (mRNA) and has critical roles in the tumorigenesis and metastasis of various malignancies. However, the role of m6A in pancreatic cancer is still unclear. Exploring genetic alterations and functional networks of m6A regulators in pancreatic cancer may provide new strategies for its treatment. Methods In this study, we used data from the Cancer Genome Atlas (TCGA) database and other public databases through cBioPortal, LinkedOmics, UALCAN, GEPIA, STRING, and the database for annotation, visualization, and integrated discovery (DAVID) to systematically analyze the molecular alterations and functions of 20 main m6A regulators in pancreatic cancer. Results We found that m6A regulators had widespread genetic alterations, and that their expression levels were significantly correlated with pancreatic cancer malignancy. Moreover, m6A regulators were associated with the prognosis of pancreatic cancer patients. Conclusions m6A regulators play a crucial part in the occurrence and development of pancreatic cancer. Our study will guide further studies of m6A RNA modification in pancreatic cancer and could potentially provide new strategies for pancreatic cancer treatment.

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Universal Panel Testing of Pancreatic Cancer Cases for Cancer Predisposition

10.1101/195537 ◽

2017 ◽

Author(s):

Erin L. Young ◽

Bryony A. Thompson ◽

Deborah W. Neklason ◽

Angela K. Snow ◽

Matthew A. Firpo ◽

...

Keyword(s):

Pancreatic Cancer ◽

Genetic Testing ◽

Cancer Susceptibility ◽

Meta Analysis ◽

Gene Panel ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Cancer Screen ◽

Panel Testing ◽

Pathogenic Variants

ABSTRACTBackground and AimsGenes associated with hereditary breast and ovarian cancer (HBOC) and colorectal cancer (CRC) susceptibility have been shown to play a role in pancreatic cancer susceptibility. Germline genetic testing of pancreatic cancer cases could be beneficial for at-risk relatives with pathogenic variants in established HBOC and CRC genes, but it is unclear what proportion of pancreatic cancer cases harbor pathogenic variants in these genes.Methods66 pancreatic cancer cases, unselected for family history and diagnosed at the Huntsman Cancer Hospital (HCH), were sequenced on a custom 34-gene panel including known HBOC and CRC genes. A second set of 156 unselected HCH pancreatic cancer cases were sequenced on an expanded 59-gene panel (n=95) or with a custom 14-gene clinical panel (n=61). Sequencing data from both sets of pancreatic cancer cases, the pancreatic cancer cases of the Cancer Genome Atlas (TCGA), and an unselected pancreatic cancer screen from the Mayo Clinic were combined in a meta-analysis to estimate the proportion of carriers with pathogenic and variants of uncertain significance.ResultsApproximately 8.9% of unselected pancreatic cancer cases at the HCH carried a variant with potential HBOC or CRC screening recommendations. A meta-analysis of unselected pancreatic cancer cases revealed that approximately 10.5% carry a pathogenic variant or HiP-VUS.ConclusionWith the inclusion of both HBOC and CRC susceptibility genes in a panel test, unselected pancreatic cancer cases have a high enough percentage of carriers to rationalize genetic testing for identification of variants that could be further used in cascade testing of healthy relatives to increase HBOC and CRC surveillance measures.

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A TP53-associated immune prognostic signature for the prediction of the overall survival and therapeutic responses in pancreatic cancer

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2022010 ◽

2021 ◽

Vol 19 (1) ◽

pp. 191-208

Author(s):

Yi Liu ◽

◽

Long Cheng ◽

Xiangyang Song ◽

Chao Li ◽

...

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Tp53 Mutation ◽

Prolactin Receptor ◽

High Risk Group ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Large Sample Size ◽

Sequencing Data

<abstract> <p>Pancreatic cancer (PC) is a highly fatal disease correlated with an inferior prognosis. The tumor protein p53 (TP53) is one of the frequent mutant genes in PC and has been implicated in prognosis. We collected somatic mutation data, RNA sequencing data, and clinical information of PC samples in the Cancer Genome Atlas (TCGA) database. TP53 mutation was an independent prognostic predictor of PC patients. According to TP53 status, Gene set enrichment analysis (GSEA) suggested that TP53 mutations were related to the immunophenotype of pancreatic cancer. We identified 102 differentially expressed immune genes (DEIGs) based on TP53 mutation status and developed a TP53-associated immune prognostic model (TIPM), including Epiregulin (EREG) and Prolactin receptor (PRLR). TIPM identified the high-risk group with poor outcomes and more significant response potential to cisplatin, gemcitabine, and paclitaxel therapies. And we verified the TIPM in the International Cancer Genome Consortium (ICGC) cohort (PACA-AU) and Gene Expression Omnibus (GEO) cohort (GSE78229 and GSE28735). Finally, we developed a nomogram that reliably predicts overall survival in PC patients on the bias of TIPM and other clinicopathological factors. Our study indicates that the TIPM derived from TP53 mutation patterns might be an underlying prognostic therapeutic target. But more comprehensive researches with a large sample size is necessary to confirm the potential.</p> </abstract>

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Development of a 12-biomarkers-based prognostic model for pancreatic cancer using multi-omics integrated analysis

Acta Biochimica Polonica ◽

10.18388/abp.2020_5225 ◽

2020 ◽

Author(s):

Yanhui Jia ◽

Meiyan Shen ◽

Yan Zhou ◽

Huaiping Liu

Keyword(s):

Pancreatic Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Malignant Tumors ◽

Early Stage ◽

Prognostic Index ◽

Principal Component ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Sequencing Data

Pancreatic cancer is one of the most malignant tumors of the digestive system, with insidious, rapid onset and high mortality. The 5-year survival rate is only 10%. Therefore, in-depth exploration of the potential mechanism affecting the prognosis of pancreatic cancer, and search for biomarkers that can effectively predict the prognosis of pancreatic cancer are of practical clinical importance. The mRNA sequencing data, miRNA sequencing data, methylation data and SNP data of pancreatic cancer patients available in The Cancer Genome Atlas (TCGA) were used for analysis to identify biomarkers that significantly affect the prognosis for the patients. Finally, a prognostic prediction model was developed using principal component analysis (PCA) method. The genes that significantly affected the prognosis of pancreatic cancer were as follows: 5 DmiRNAs (hsa-mir-1179, hsa-mir-1224, hsa-mir-1251, hsa-mir-129-1 and hsa-mir-129-2), 6 DmRNAsandDMsandMethyCor database entries (MAPK8IP2, CPE, DPP6, MSI1, IL20RB and S100A2), and FMN2 gene from differential expressed mRNAs and differential single-nucleotide polymorphism (DmRNAsandDSNPs) database. Prognostic index (PI)=∑iwi xi – 0.717716. A patient was predicted as high/low risk if the PI was larger/smaller than 0.034045. Our study resulted in a comprehensive prognostic model for pancreatic cancer patients based on multi-omics analysis, which could offer better guidance for the clinical management of patients with early-stage pancreatic cancer.

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Recurrent alternative splicing isoform switches in tumor samples provide novel signatures of cancer

10.1101/006908 ◽

2014 ◽

Cited By ~ 1

Author(s):

Endre Sebestyén ◽

Michał Zawisza ◽

Eduardo Eyras

Keyword(s):

Alternative Splicing ◽

Somatic Mutations ◽

Cancer Genomics ◽

Genetic Alterations ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Low Frequencies ◽

Cancer Subtypes ◽

Alternatively Spliced

Cancer genomics has been instrumental to determine the genetic alterations that are predictive of various tumor conditions. However, the majority of these alterations occur at low frequencies, motivating the need to expand the catalogue of cancer signatures. Alternative pre-mRNA splicing alterations, which bear major importance for the understanding of cancer, have not been exhaustively studied yet in the context of recent cancer genome projects. In this article we analyze RNA sequencing data for more than 4000 samples from The Cancer Genome Atlas (TCGA) project, including paired normal samples, to detect recurrent alternative splicing isoform switches in 9 different cancer types. We first investigate whether alternative splicing isoform changes are predictive of tumors by applying a rank-based algorithm based on the reversal of the relative expression of transcript isoforms. We find that consistent alternative splicing isoform changes can separate with high accuracy tumor and normal samples, as well as some cancer subtypes. We then searched for those changes that occur in the most abundant isoform, i.e isoform switches, and are therefore more likely to have a functional impact. In total we detected 244 isoform switches, which are associated to functional pathways that are frequently altered in cancer and also separate tumor and normal samples accurately. We further assessed whether these isoform changes are associated to somatic mutations. Surprisingly, only a few cases appear to have association, including the putative tumor suppressor FBLN2 and the tumor driver MYH11, which show association of an isoform switch to mutations and indels on the alternatively spliced exon. However, the number of observed mutations is in general not sufficient to explain the frequency of the found isoform switches, suggesting that recurrent isoform switching in cancer is mostly independent of somatic mutations. In summary, we present an effective approach to detect novel alternative splicing signatures that are predictive of tumors. Moreover, the same methodology has led to uncover recurrent isoform switches in tumors, which may provide novel prognostic and therapeutic targets. Software and data are available at: https://bitbucket.org/regulatorygenomicsupf/iso-ktsp and http://dx.doi.org/10.6084/m9.figshare.1061917

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