Dataset Information

Analysis of Methylation‐driven Genes in Pancreatic Ductal Adenocarcinoma for Predicting Prognosis

ABSTRACT: Purpose: Considerable variations in methylation profile have been found in various cancers to modulate tumorigenesis and affect prognosis. To provide a theoretical basis for early detection, prognosis evaluation and targeted treatment for patients with pancreatic ductal adenocarcinoma: PDAC, this study identified methylation-driven genes in PDAC and explored their prognostic performance. Methods: The methylation, expression and clinical data of PDAC patients were extracted from TCGA database. Based on the β-mixture model of the MethylMix R package, the differential methylation status and connection between methylation and expression degree were examined to screen out methylation-driven genes in PDAC. COX analyses and lasso regressions were applied to construct a linear risk model based on methylation-driven genes. Univariate and multivariate analyses were performed to ensure the risk model was an independent prognostic factor. Joint survival analyses of methylation and gene expression were conducted to explore the prognostic value of component genes. The methylation sites in the key genes were also investigated. Results: A total of 118 methylation-driven genes in PDAC were identified, and two genes (FOXI2, MYEOV) constituted the risk model whose AUC was 0.722 at one year of overall survival rate, displaying a better performance on survival prediction than other clinical features. Further survival analyses demonstrated that the expression of MYEOV and combined methylation and expression levels of the genes MYEOV and FOXI2 can be potential biomarkers for survival prediction and targets of drug manipulation of PDAC patients. Close relationships were discovered between two sites in MYEOV and one site in FOXI2 and the prognosis of PDAC patients. Conclusion: Concentrating on DNA methylation, our study identified potential biomarkers and developed a reliable short-term predictive model for prognosis of PDAC patients.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC8489123 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies and is not a clinically homogeneous disease, but subsets of patients with distinct prognosis and response to therapy can be identified by genome-wide analyses. Mutations in major PDAC driver genes were associated with poor survival. By bioinformatics analysis, we identified protocadherins among the most frequently mutated genes in PDAC suggesting an important role of these genes in the biology of this tumor. Promoter methylation of protocadherins has been suggested as a prognostic marker in different tumors, but in PDAC this epigenetic modification has not been extensively studied. Thus, we evaluated whether promoter methylation of three frequently mutated protocadherins, PCDHAC2, PCDHGC5 and PCDH10 could be used as survival predictors in PDAC patients.MethodsDNA extracted from 23 PDACs and adjacent non-neoplastic pancreatic tissues were bisulfite treated. Combined Bisulfite Restriction Analysis (COBRA) coupled to denaturing high-performance liquid chromatography (dHPLC) detection and bisulfite genomic sequencing (BGS) were used to determine the presence of methylated CpG dinucleotides in the promoter amplicons analyzed.ResultsIn an exploratory analysis, two protocadherins showed the same pattern of CpG methylation in PDAC and adjacent non-neoplastic pancreatic tissues: lack of methylation for PCDHAC2, complete methylation for PCDHGC5. Conversely, the third protocadherin analyzed, PCDH10, showed a variable degree of CpG methylation in PDAC and absence of methylation in adjacent non-neoplastic pancreatic tissues. At Kaplan-Meier analysis, high levels of PCDH10 methylation defined according to the receiver operating characteristic (ROC) curve analysis were significantly associated with worse progression-free survival (PFS) rates (P = 0.008), but not with overall survival (OS). High levels of PCDH10 methylation were a prognostic factor influencing PFS (HR = 4.0: 95% CI, 1.3-12.3; P = 0.016), but not the OS.ConclusionsIn this study, we show for the first time that the methylation status of PCDH10 can predict prognosis in PDAC patients with a significant impact on the outcome in terms of progression-free survival. High levels of PCDH10 promoter methylation could be useful to identify patients at high risk of disease progression, contributing to a more accurate stratification of PDAC patients for personalized clinical management.

Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.MethodsWe developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.ResultsOwing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.ConclusionsThis study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein-protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan-Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA-DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.

Project description:Pancreatic adenocarcinoma (PAAD) is a painful and fatal disease that undoubtedly remains a health care priority and offers significant therapeutic challenges. The significance of epigenetic modifications, including DNA methylation in tumor development, has gained the attention of researchers. Identifying DNA methylation-driven genes and investigating the mechanisms underlying the tumorigenesis of PAAD are of substantial importance for developing methods of physiological evaluation, treatment planning and prognostic prediction for PAAD. In the present study, a comprehensive analysis of DNA methylation and gene expression data from 188 clinical samples was performed to identify DNA methylation-driven genes in PAAD. In addition, the diagnostic and prognostic value of DNA methylation-driven genes was evaluated using receiver operating characteristic curve, survival and recurrence analyses. A total of 7 DNA methylation-driven genes, namely zinc finger protein 208 (ZNF208), eomesodermin (EOMES), prostaglandin D2 receptor (PTGDR), chromosome 12 open reading frame 42 (C12orf42), integrin subunit ? 4 (ITGA4), dedicator of cytokinesis 8 and protein phosphatase 1 regulatory inhibitor subunit 14D (PPP1R14D), were identified. All of them may be used to diagnose PAAD with excellent specificity and sensitivity (area under curve, >0.8). Of the 7 DNA methylation-driven genes, 6 were significantly associated with overall survival (OS) and recurrence-free survival (RFS) P<0.05). Among them, ZNF208, EOMES, PTGDR, C12orf42 and ITGA4 were significantly negatively associated with the OS rate and positively associated with the recurrence rate, while PPP1R14D was significantly positively associated with the OS rate and negatively associated with the recurrence rate. The present study provides novel insight into the epigenetic alterations associated with the occurrence and progression of PAAD, thereby increasing the mechanistic understanding of this disease, offering potential novel molecular biomarkers and contributing to the development of therapeutic targets for PAAD.

Project description:Pancreatic Ductal Adenocarcinoma (PDAC) is an aggressive form of pancreatic cancer that typically manifests itself at an advanced stage and does not respond to most treatment modalities. The survival rate of a PDAC patient is less than 5%, with a median survival of just a couple of months. A better understanding of the molecular pathology of PDAC is needed to guide research for the development of better clinical treatment modalities for PDAC patients. Gene expression studies performed to date have identified different subtypes of PDAC with prognostic and clinical relevance. Subtypes identified to date are highly heterogeneous since pancreatic cancer is heterogeneous cancer. Tumor microenvironment and stroma constitute a major chunk of PDAC and contribute to the heterogeneity. Better subtyping methods are need of the hour for better prognosis and classification of PDAC for future personalized treatment. In this work, we have performed an integrated analysis of DNA methylation and gene expression datasets to provide better mechanistic and molecular insights into Pancreatic cancers, especially PDAC. The use of varied and diverse datasets has provided valuable insights into different cancer types and can play an integral role in revealing the complex nature of underlying biological mechanisms. We performed subtyping of TCGA-PAAD gene expression and methylation datasets into different subtypes using state-of-the-art normalization methods and unsupervised clustering methods that reveal latent hidden factors, leading to additional insights for subtyping. Differential expression and differential methylation were performed for each of the subtypes obtained from clustering. Our analysis gave a consensus of five cluster solution with relevant pathways like MAPK, MET. The five subtypes corresponded to the tumor and stromal subtypes. This analysis helps in distinguishing and identifying different subtypes based on enriched putative genes. These results help propose novel experimentally-verifiable PDAC subtyping and demonstrate that using varied data sets and integrated methods can contribute to disease prognostication and precision medicine in PDAC treatment.

Dataset Information

Analysis of Methylation‐driven Genes in Pancreatic Ductal Adenocarcinoma for Predicting Prognosis

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets