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Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues.

ABSTRACT: A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that "ECM-receptor interaction", "PI3K-Akt signaling pathway", and "focal adhesion" are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

SUBMITTER: Atay S

PROVIDER: S-EPMC7597628 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues.

Atay Sevcan S

PeerJ 20201027

A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed ...[more]

PMID: 33194391

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Project description:BackgroundOne of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.ObjectivesThis study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.Materials and methodsUtilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.ResultsFive significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.ConclusionsThe protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as "pathways in cancer (hsa05200)", "protein processing in the endoplasmic reticulum (hsa04141)", "antigen processing and presentation (hsa04612)", "dopaminergic synapse (hsa04728)", and "measles (hsa05162)"; in which these pathways, the "protein processing in endoplasmic reticulum (hsa04141)", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.

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:Background: Cellular senescence plays a critical role in the occurrence and development, and immune modulation of cancer. This research primarily investigated the role of senescence-associated genes (SAGs) in the survival and tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC). Methods: From the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database, the gene expression profiles and clinical data of PDAC samples were downloaded. SAGs in the TCGA cohort were used to build a novel prognostic model and validated in the ICGC cohort. The relationship of signature with the immune landscape, tumor mutational burden (TMB), as well as the sensitivity of different therapies, was explored. Moreover, a nomogram was developed to predict the overall survival of PDAC patients. Results: A prognostic signature was constructed on basis of three SAGs, and patients in the low-risk score group had a longer survival time. The accuracy of the signature to distinguish different score groups was confirmed through principal component analysis (PCA) and the Receiver operator curves curve. The mRNA expression of the three signature genes was also verified in normal pancreatic and PDAC cell lines by RT-qPCR. The signature could independently predict the prognosis of PDAC patients and had broad applicability. Meanwhile, the nomogram predicted that 1- and 3-years survival rates were in good agreement with the observed overall survival rates. Low-risk patients had lower tumor mutational burden, and low-TMB patients had a better prognosis. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the low-risk group were more responsive to immunotherapy and a variety of commonly used chemotherapeutic drugs. Conclusion: The prognostic signature can well predict the prognosis and assess the possibility of immunotherapy in personalized PDAC treatment.

Project description:PurposePancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data.MethodsSeveral transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified.ResultsAfter applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis.ConclusionWe identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.

Project description:Adenocarcinomas of the pancreatic duct (PDAC) are characteristically aggressive tumors that are extremely challenging to treat as curative surgical resection, the definitive treatment, is seldom possible. Regretably, most patients are diagnosed with metastatic disease at the time of initial presentation. In addition, current chemotherapeutic concepts that are used for advanced disease stages show frustrating results. Thus, there is an urgent need to identify novel therapeutic molecular targets that are associated with PDAC disease. Recently, the chemokine receptor CXCR4 has been demonstrated to be highly expressed in metastatic PDAC. However, the results of the published data on CXCR4 and its association with clinicopathological variables and prognosis in PDAC seem to be heterogeneous. Consequently, to clarify the relevance of CXCR4 as a biomarker in PDAC we performed a comprehensive literature search by using PubMed and Web of Science databases to identify articles that focused on the expression of CXCR4 in PDAC by using immunohistochemistry. Subsequently, data from nine relevant studies, encompassing 1183 patients were extracted, qualitatively assessed, and entered into a meta-analysis. By using a random effects model, the pooled hazard ratio of the seven studies that reported on patients overall survival revealed a correlation between expression of CXCR4 and poor prognosis (HR 1.49; 95% CI: 1.04-2.14; P = 0.03; I2 = 74%). Although heterogeneity became evident, subgroup analyses confirmed the prognostic value of CXCR4 in PDAC, especially in high-quality studies that performed multivariate analysis. In addition, meta-analysis revealed a strong association of CXCR4 expression with the UICC stage (OR: 3.40; 95% CI: 1.67-6.92; P = 0.0007; I2 = 0%) and metastatic disease (N-status: OR: 2.55; 95% CI: 1.56-4.15; P = 0.0002; I2 = 26%; recurrence to the liver: OR: 2.80; 95% CI: 1.48-5.29; P = 0.001; I2 = 0%). Taken together, our meta-analysis suggests that CXCR4 represents a useful prognostic biomarker in PDAC and might therefore be evaluated as a potential therapeutic target in the treatment of metastatic cancer disease of the pancreas.

Dataset Information

Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues.

Publications

Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues.

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