Genome-wide DNA methylation analysis reveals molecular subtypes of pancreatic cancer

Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations' precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

Epigenetic Landscape of DNA Methylation in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, characterized by its aggressive progression and dismal prognosis. Advances in epigenetic profiling, specifically DNA methylation analysis, have significantly deepened our understanding of PDAC pathogenesis. This review synthesizes findings from recent genome-wide DNA methylation studies, which have delineated a complex DNA methylation landscape differentiating between normal and cancerous pancreatic tissues, as well as across various stages and molecular subtypes of PDAC. These studies identified specific differentially methylated regions (DMRs) that not only enhance our grasp of the epigenetic drivers of PDAC but also offer potential biomarkers for early diagnosis and prognosis, enabling the customization of therapeutic approaches. The review further explores how DNA methylation profiling could facilitate the development of subtype-tailored therapies, potentially improving treatment outcomes based on precise molecular characterizations. Overall, leveraging DNA methylation alterations as functional biomarkers holds promise for advancing our understanding of disease progression and refining PDAC management strategies, which could lead to improved patient outcomes and a deeper comprehension of the disease's underlying biological mechanisms.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Effect of DNA methylation at the CTLA4 gene on the clinical status of autoimmune thyroid diseases

The pathogenesis and manifestation of autoimmune thyroid diseases (AITDs), Graves' disease (GD), and Hashimoto's disease (HD) are associated with T cell activation. Cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) plays a crucial role in the regulation of T cell activation. DNA methylation levels of eight CpG sites in the CTLA4 gene and expression levels of soluble CTLA-4 were examined. Methylation levels of +22 CpG and CT60 CpG-SNPs in patients with GD and HD with the CT60 GG genotype were lower than those in control subjects. Methylation levels of the-15 CpG sites were lower in patients with intractable GD than those in GD patients in remission. These results suggest that demethylation of +22 CpG and CT60 CpG-SNPs may be associated with susceptibility to GD and HD in subjects with the CTLA4 CT60 GG genotype, and that demethylation of -15 CpG may be associated with the intractability of GD.

Andrographolide suppresses the malignancy of pancreatic cancer via alleviating DNMT3B-dependent repression of tumor suppressor gene ZNF382

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer type that urgently requires effective therapeutic strategies. Andrographolide, a labdane diterpenoid compound abundant in Andrographis paniculata, has anticancer effects against various cancer types, but its anticancer activity and mechanism against PDAC remain largely uncharacterized.

PURPOSE

This study explores novel drug target(s) and underlying molecular mechanism of andrographolide against PDAC.

STUDY DESIGN AND METHODS

The malignant phenotypes of PDAC cells, PANC-1 and MIA PaCa-2 cells, were measured using MTT, clonogenic assays, and Transwell migration assays. A PDAC xenograft animal model was used to evaluate tumor growth in vivo. Western blot, immunofluorescence and immunohistochemistry were used for measuring protein expression. The TCGA database was analyzed to evaluate promoter methylation status, gene expression, and their relationship with patient survival rates. RT-qPCR was used for detecting mRNA expression. Reporter assays were used for detecting signal transduction pathways. Promoter DNA methylation was determined by sodium bisulfite treatment and methylation-specific PCR (MSP). The biological function and role of specific genes involved in drug effects were measured through gene overexpression.

RESULTS

Andrographolide treatment suppressed the proliferation and migration of PDAC cells and impaired tumor growth in vivo. Furthermore, andrographolide induced the mRNA and protein expression of zinc finger protein 382 (ZNF382) in PDAC cells. Overexpression of ZNF382 inhibited malignant phenotypes and cancer-associated signaling pathways (AP-1, NF-κB and β-catenin) and oncogenes (ZEB-1, STAT-3, STAT-5, and HIF-1α). Overexpression of ZNF382 delayed growth of PANC-1 cells in vivo. ZNF382 mRNA and protein expression was lower in tumor tissues than in adjacent normal tissues of pancreatic cancer patients. Analysis of the TCGA database found the ZNF382 promoter is hypermethylated in primary pancreatic tumors which correlates with its low expression. Furthermore, andrographolide inhibited the expression of DNA methyltransferase 3 beta (DNMT3B) and increased the demethylation of the ZNF382 promoter in PDAC cells. Overexpression of DNMT3B attenuated the andrographolide-suppressed proliferation and migration of PDAC cells.

CONCLUSION

Our finding revealed that ZNF382 acts as a tumor suppressor gene in pancreatic cancer and andrographolide restores ZNF382 expression to suppress pancreatic cancer, providing a novel molecular target and a promising therapeutic approach for treating pancreatic cancer.

Epigenetic regulation of major histocompatibility complexes in gastrointestinal malignancies and the potential for clinical interception

BACKGROUND

Gastrointestinal malignancies encompass a diverse group of cancers that pose significant challenges to global health. The major histocompatibility complex (MHC) plays a pivotal role in immune surveillance, orchestrating the recognition and elimination of tumor cells by the immune system. However, the intricate regulation of MHC gene expression is susceptible to dynamic epigenetic modification, which can influence functionality and pathological outcomes.

MAIN BODY

By understanding the epigenetic alterations that drive MHC downregulation, insights are gained into the molecular mechanisms underlying immune escape, tumor progression, and immunotherapy resistance. This systematic review examines the current literature on epigenetic mechanisms that contribute to MHC deregulation in esophageal, gastric, pancreatic, hepatic and colorectal malignancies. Potential clinical implications are discussed of targeting aberrant epigenetic modifications to restore MHC expression and 0 the effectiveness of immunotherapeutic interventions.

CONCLUSION

The integration of epigenetic-targeted therapies with immunotherapies holds great potential for improving clinical outcomes in patients with gastrointestinal malignancies and represents a compelling avenue for future research and therapeutic development.

The Effect of Intratumor Heterogeneity in Pancreatic Ductal Adenocarcinoma Progression and Treatment

BACKGROUND AND OBJECTIVES

Pancreatic cancer is one of the most lethal malignancies. Even though many substantial improvements in the survival rates for other major cancer forms were made, pancreatic cancer survival rates have remained relatively unchanged since the 1960s. Even more, no standard classification system for pancreatic cancer is based on cellular biomarkers. This review will discuss and provide updates about the role of stem cells in the progression of PC, the genetic changes associated with it, and the promising biomarkers for diagnosis.

MATERIALS AND METHODS

The search process used PubMed, Cochrane Library, and Scopus databases to identify the relevant and related articles. Articles had to be published in English to be considered.

RESULTS

The increasing number of studies in recent years has revealed that the diversity of cancer-associated fibroblasts is far greater than previously acknowledged, which highlights the need for further research to better understand the various cancer-associated fibroblast subpopulations. Despite the huge diversity in pancreatic cancer, some common features can be noted to be shared among patients. Mutations involving CDKN2, P53, and K-RAS can be seen in a big number of patients, for example. Similarly, some patterns of genes and biomarkers expression and the level of their expression can help in predicting cancer behavior such as metastasis and drug resistance. The current trend in cancer research, especially with the advancement in technology, is to sequence everything in hopes of finding disease-related mutations.

CONCLUSION

Optimizing pancreatic cancer treatment requires clear classification, understanding CAF roles, and exploring stroma reshaping approaches.

Whole-genome bisulfite sequencing identifies stage- and subtype-specific DNA methylation signatures in pancreatic cancer

In pancreatic ductal adenocarcinoma (PDAC), no recurrent metastasis-specific mutation has been found, suggesting that epigenetic mechanisms, such as DNA methylation, are the major contributors of late-stage disease progression. Here, we performed the first whole-genome bisulfite sequencing (WGBS) on mouse and human PDAC organoid models to identify stage-specific and molecular subtype-specific DNA methylation signatures. With this approach, we identified thousands of differentially methylated regions (DMRs) that can distinguish between the stages and molecular subtypes of PDAC. Stage-specific DMRs are associated with genes related to nervous system development and cell-cell adhesions, and are enriched in promoters and bivalent enhancers. Subtype-specific DMRs showed hypermethylation of GATA6 foregut endoderm transcriptional networks in the squamous subtype and hypermethylation of EMT transcriptional networks in the progenitor subtype. These results indicate that aberrant DNA methylation contributes to both PDAC progression and subtype differentiation, resulting in significant and reoccurring DNA methylation patterns with diagnostic and prognostic potential.

E3 ubiquitin ligase casitas B-lineage lymphoma-b modulates T-cell anergic resistance via phosphoinositide 3-kinase signaling in patients with immune thrombocytopenia

BACKGROUND

The E3 ubiquitin ligase casitas B-lineage lymphoma-b (CBLB) is a newly identified component of the ubiquitin-dependent protein degradation system and is considered an important negative regulator of immune cells. CBLB is essential for establishing a threshold of T-cell activation and regulating peripheral T-cell tolerance through various mechanisms. However, the involvement of CBLB in the pathogenesis of immune thrombocytopenia (ITP) is unknown.

OBJECTIVES

We aimed to investigate the expression and role of CBLB in CD4+ T cells obtained from patients with ITP through quantitative proteomics analyses.

METHODS

CD4+ T cells were transfected with adenoviral vectors overexpressing CBLB to clarify the effect of CBLB on anergic induction of T cells in patients with ITP. DNA methylation levels of the CBLB promoter and 5' untranslated region (UTR) in patient-derived CD4+ T cells were detected via MassARRAY EpiTYPER assay (Agena Bioscience).

RESULTS

CD4+ T cells from patients with ITP showed resistance to anergic induction, highly activated phosphoinositide 3-kinase-protein kinase B (AKT) signaling, decreased CBLB expression, and 5' UTR hypermethylation of CBLB. CBLB overexpression in T cells effectively attenuated the elevated phosphorylated protein kinase B level and resistance to anergy. Low-dose decitabine treatment led to significantly elevated levels of CBLB expression in CD4+ T cells from 7 patients showing a partial or complete response.

CONCLUSION

These results indicate that the 5' UTR hypermethylation of CBLB in CD4+ T cells induces resistance to T-cell anergy in ITP. Thus, the upregulation of CBLB expression by low-dose decitabine treatment may represent a potential therapeutic approach to ITP.

Genetic Signature of Human Pancreatic Cancer and Personalized Targeting

Pancreatic cancer is a highly lethal disease with a 5-year survival rate of around 11-12%. Surgery, being the treatment of choice, is only possible in 20% of symptomatic patients. The main reason is that when it becomes symptomatic, IT IS the tumor is usually locally advanced and/or has metastasized to distant organs; thus, early diagnosis is infrequent. The lack of specific early symptoms is an important cause of late diagnosis. Unfortunately, diagnostic tumor markers become positive at a late stage, and there is a lack of early-stage markers. Surgical and non-surgical cases are treated with neoadjuvant and/or adjuvant chemotherapy, and the results are usually poor. However, personalized targeted therapy directed against tumor drivers may improve this situation. Until recently, many pancreatic tumor driver genes/proteins were considered untargetable. Chemical and physical characteristics of mutated KRAS are a formidable challenge to overcome. This situation is slowly changing. For the first time, there are candidate drugs that can target the main driver gene of pancreatic cancer: KRAS. Indeed, KRAS inhibition has been clinically achieved in lung cancer and, at the pre-clinical level, in pancreatic cancer as well. This will probably change the very poor outlook for this disease. This paper reviews the genetic characteristics of sporadic and hereditary predisposition to pancreatic cancer and the possibilities of a personalized treatment according to the genetic signature.

DNA-methylation signature accurately differentiates pancreatic cancer from chronic pancreatitis in tissue and plasma

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Differentiation from chronic pancreatitis (CP) is currently inaccurate in about one-third of cases. Misdiagnoses in both directions, however, have severe consequences for patients. We set out to identify molecular markers for a clear distinction between PDAC and CP.

DESIGN

Genome-wide variations of DNA-methylation, messenger RNA and microRNA level as well as combinations thereof were analysed in 345 tissue samples for marker identification. To improve diagnostic performance, we established a random-forest machine-learning approach. Results were validated on another 48 samples and further corroborated in 16 liquid biopsy samples.

RESULTS

Machine-learning succeeded in defining markers to differentiate between patients with PDAC and CP, while low-dimensional embedding and cluster analysis failed to do so. DNA-methylation yielded the best diagnostic accuracy by far, dwarfing the importance of transcript levels. Identified changes were confirmed with data taken from public repositories and validated in independent sample sets. A signature of six DNA-methylation sites in a CpG-island of the protein kinase C beta type gene achieved a validated diagnostic accuracy of 100% in tissue and in circulating free DNA isolated from patient plasma.

CONCLUSION

The success of machine-learning to identify an effective marker signature documents the power of this approach. The high diagnostic accuracy of discriminating PDAC from CP could have tremendous consequences for treatment success, once the result from still a limited number of liquid biopsy samples would be confirmed in a larger cohort of patients with suspected pancreatic cancer.

Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study

BACKGROUND

Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC.

METHODS

The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis.

RESULTS

In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed.

CONCLUSION

Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

The diagnostic significance of the ZNF gene family in pancreatic cancer: a bioinformatics and experimental study

Background: Pancreatic adenocarcinoma (PAAD) is among the most devastating of all cancers with a poor survival rate. Therefore, we established a zinc finger (ZNF) protein-based prognostic prediction model for PAAD patients. Methods: The RNA-seq data for PAAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differentially expressed ZNF protein genes (DE-ZNFs) in PAAD and normal control tissues were screened using the "lemma" package in R. An optimal risk model and an independent prognostic value were established by univariate and multivariate Cox regression analyses. Survival analyses were performed to assess the prognostic ability of the model. Results: We constructed a ZNF family genes-related risk score model that is based on the 10 DE-ZNFs (ZNF185, PRKCI, RTP4, SERTAD2, DEF8, ZMAT1, SP110, U2AF1L4, CXXC1, and RMND5B). The risk score was found to be a significant independent prognostic factor for PAAD patients. Seven significantly differentially expressed immune cells were identified between the high- and low-risk patients. Then, based on the prognostic genes, we constructed a ceRNA regulatory network that includes 5 prognostic genes, 7 miRNAs and 35 lncRNAs. Expression analysis showed ZNF185, PRKCI and RTP4 were significantly upregulated, while ZMAT1 and CXXC1 were significantly downregulated in the PAAD samples in all TCGA - PAAD, GSE28735 and GSE15471 datasets. Moreover, the upregulation of RTP4, SERTAD2, and SP110 were verified by the cell experiments. Conclusion: We established and validated a novel, Zinc finger protein family - related prognostic risk model for patients with PAAD, that has the potential to inform patient management.

Expression of GADD45G and CAPRIN1 in Human Nucleus Pulposus: Implications for Intervertebral Disc Degeneration

Marked cellular changes occur in human intervertebral disc (IVD) degeneration during disc degeneration with biochemical changes. Genome-wide analysis of the DNA methylation profile has identified 220 differentially methylated loci associated with human IVD degeneration. Among these, two cell-cycle-associated genes, growth arrest and DNA damage 45 gamma (GADD45G) and cytoplasmic activation/proliferation-associated protein-1 (CAPRIN1), were focused on. The expression of GADD45G and CAPRIN1 in human IVDs remains unknown. We aimed to examine the expression of GADD45G and CAPRIN1 in human nucleus pulposus (NP) cells and evaluate those in human NP tissues in the early and advanced stages of degeneration according to Pfirrmann magnetic resonance imaging (MRI) and histological classifications. Human NP cells were cultured as monolayers after isolation from NP tissues by sequential enzyme digestion. Total RNA was isolated, and the mRNA expression of GADD45G and CAPRIN1 was quantified using real-time polymerase chain reaction. To examine the effects of pro-inflammatory cytokines on mRNA expression, human NP cells were cultured in the presence of IL-1β. Protein expression was evaluated using Western blotting and immunohistochemistry. GADD45G and CAPRIN1 expression was identified in human NP cells at both mRNA and protein levels. The percentage of cells immunopositive for GADD45G and CAPRIN1 significantly increased according to the Pfirrmann grade. A significant correlation between the histological degeneration score and the percentage of GADD45G-immunopositive cells was identified, but not with that of CAPRIN1-immunopositive cells. The expression of cell-cycle-associated proteins (GADD45G and CAPRIN1) was enhanced in human NP cells at an advanced stage of degeneration, suggesting that it may be regulated during the progression of IVD degeneration to maintain the integrity of human NP tissues by controlling cell proliferation and apoptosis under epigenetic alteration.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease for which potent therapies have limited efficacy. Several studies have described the transcriptomic landscape of PDAC tumors to provide insight into potentially actionable gene expression signatures to improve patient outcomes. Despite centralization efforts from multiple organizations and increased transparency requirements from funding agencies and publishers, analysis of public PDAC data remains difficult. Bioinformatic pitfalls litter public transcriptomic data, such as subtle inclusion of low-purity and non-adenocarcinoma cases. These pitfalls can introduce non-specificity to gene signatures without appropriate data curation, which can negatively impact findings. To reduce barriers to analysis, we have created pdacR ( http://pdacR.bmi.stonybrook.edu , github.com/rmoffitt/pdacR), an open-source software package and web-tool with annotated datasets from landmark studies and an interface for user-friendly analysis in clustering, differential expression, survival, and dimensionality reduction. Using this tool, we present a multi-dataset analysis of PDAC transcriptomics that confirms the basal-like/classical model over alternatives.

A Bayesian model for identifying cancer subtypes from paired methylation profiles

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cancers worldwide and has a poor survival, with a 5-year survival rate of only 8.5%. In this study we investigated altered DNA methylation associated with PDAC severity and prognosis.

METHODS

Methylome data, generated using 450 K bead array, was compared between paired PDAC and normal samples in the TCGA cohort (n = 9) and our Indian cohort (n = 7). The total Indian Cohort (n = 75) was split into cohort 1 (n = 7), cohort 2 (n = 22), cohort 3 (n = 26) and cohort 4 (n = 20).Validation of differential methylation (6 selected CpG loci) and associated gene expression for differentially methylated genes (10 selected gDMs) were carried out in separate validation cohorts, using MSP, RT-PCR and IHC correlations between methylation and gene expression were observed in TCGA, GTEx cohorts and in validation cohorts. Kaplan-Meier survival analysis was done to study differential prognosis, during 2-5 years of follow-up.

RESULTS

We identified 156 DMPs, mapped to 91 genes (gDMs), in PDAC; 68 (43.5%) DMPs were found to be differentially methylated both in TCGA cohort and our cohort, with significant concordance at hypo- and hyper-methylated loci. Enrichments of "regulation of ion transport", "Interferon alpha/beta signalling", "morphogenesis and development" and "transcriptional dysregulation" pathways were observed among 91 gDMs. Hyper-methylation of NPY and FAIM2 genes with down-regulated expression in PDAC, were significantly associated with poor prognosis in the Indian patient cohort.

CONCLUSIONS

Ethnic variations among populations may determine the altered epigenetic landscape in the PDAC patients of the Indian cohort. Our study identified novel differentially methylated genes (mainly NPY and FAIM2) and also validated the previously identified differentially methylated CpG sites associated with PDAC cancer patient's survival. Comparative analysis of our data with TCGA and CPTAC cohorts showed that both NPY and FAIM2 hyper-methylation and down-regulations can be novel epigenetically regulated genes in the Indian patient population, statistically significantly associated with poor survival and advanced tumour stages.

Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information

Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.

Regulation of DNA methylation during the testicular development of Shaziling pigs

DNA methylation is one of the key epigenetic regulatory mechanisms in development and spermatogenesis. However, the dynamic regulatory mechanisms of genome-wide DNA methylation during testicular development remain largely unknown. Herein, we generated a single-base resolution DNA methylome and transcriptome atlas of precocious porcine testicular tissues across three developmental stages (1, 75, and 150 days old). The results showed that the dynamic methylation patterns were directly related to the expression of the DNMT3A gene. Conjoint analysis revealed a negative regulatory pattern between promoter methylation and the positive regulation of 3'-untranslated region (3'UTR) methylation. Mechanistically, the decrease in promoter methylation affected the upregulation of meiosis-related genes, such as HORMAD1, SPO11, and SYCE1. Demethylation in the 3'UTR induced the downregulation of the INHBA gene and knockdown of INHBA inhibited cell proliferation by reducing the synthesis of activin A. These findings contribute to exploring the regulatory mechanisms of DNA methylation in testicular development.

Genome wide exploration of the methylome in aggressive B-cell lymphoma in Golden Retrievers reveals a conserved hypermethylome

Few recurrent DNA mutations are seen in aggressive canine B cell lymphomas (cBCL), suggesting other frequent drivers. The methylated island recovery assay (MIRA-seq) or methylated CpG-binding domain sequencing (MBD-seq) was used to define the genome-wide methylation profiles in aggressive cBCL in Golden Retrievers to determine if cBCL can be better defined by epigenetic changes than by DNA mutations. DNA hypermethylation patterns were relatively homogenous within cBCL samples in Golden Retrievers, in different breeds and in geographical regions. Aberrant hypermethylation is thus suspected to be a central and early event in cBCL lymphomagenesis. Distinct subgroups within cBCL in Golden Retrievers were not identified with DNA methylation profiles. In comparison, the methylome profile of human DLBCL (hDLBCL) is relatively heterogeneous. Only moderate similarity between hDLBCL and cBCL was seen and cBCL likely cannot be accurately classified into the subtypes seen in hDLBCL. Genes with hypermethylated regions in the promoter-TSS-first exon of cBCL compared to normal B cells often also had additional hyper- and hypomethylated regions distributed throughout the gene suggesting non-randomized repeat targeting of key genes by epigenetic mechanisms. The prevalence of hypermethylation in transcription factor families in aggressive cBCL may represent a fundamental step in lymphomagenesis.

Oncogenomic Changes in Pancreatic Cancer and Their Detection in Stool

Pancreatic cancer (PC) is an aggressive malignancy with a dismal prognosis. To improve patient survival, the development of screening methods for early diagnosis is pivotal. Oncogenomic alterations present in tumor tissue are a suitable target for non-invasive screening efforts, as they can be detected in tumor-derived cells, cell-free nucleic acids, and extracellular vesicles, which are present in several body fluids. Since stool is an easily accessible source, which enables convenient and cost-effective sampling, it could be utilized for the screening of these traces. Herein, we explore the various oncogenomic changes that have been detected in PC tissue, such as chromosomal aberrations, mutations in driver genes, epigenetic alterations, and differentially expressed non-coding RNA. In addition, we briefly look into the role of altered gut microbiota in PC and their possible associations with oncogenomic changes. We also review the findings of genomic alterations in stool of PC patients, and the potentials and challenges of their future use for the development of stool screening tools, including the possible combination of genomic and microbiota markers.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

Simple Summary

The diagnostic and treatment strategies of cancer remain generally suboptimal resulting in over-diagnosis or under-treatment. Though many attempts on optimizing treatment decisions by early prediction of disease progression have been undertaken, these efforts yielded only modest success so far due to the heterogeneity of cancer with multifactorial etiology. Here, we propose a deep-learning based data integration model capable of predicting disease progression by integrating collective information available through multiple studies with different cohorts and heterogeneous data types. The results have shown that the proposed data integration pipeline is able to identify disease progression with higher accuracy and robustness compared to using a single cohort, by offering a more complete picture of the specific disease on patients with brain, blood, and pancreatic cancers.

Abstract

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

CpG Island Methylator Phenotype Modulates the Immune Response of the Tumor Microenvironment and Influences the Prognosis of Pancreatic Cancer Patients

Background

CpG island methylator phenotype (CIMP), featured with concurrent and widespread hypermethylation of a cluster of CpGs, has been reported to play an important role in carcinogenesis. Limited studies have investigated the role of CIMP in pancreatic cancer (PC). The aim of this study was to explore the CIMP in PC patients and its impact on the immune response of the tumor microenvironment and prognosis.

Methods

DNA methylation, somatic mutation, mRNA, and corresponding clinical data of PC patients were downloaded from TCGA (184 patients) and the ICGC (264 patients). Univariate and multivariate regression analyses were used to identify prognosis-related CpGs. Consensus clustering analysis was used for identification of the CIMP in PC patients. ESTIMATE and CIBORORT were used for estimation of the tumor microenvironment (TME) in PC patients.

Results

In the TCGA PC cohort, 22,450 differential CpGs, including 12,937 hypermethylated CpGs and 9,513 hypomethylated CpGs, were identified between 184 PC patients and 10 normal controls. Univariate and multivariate Cox analysis further screened out 72 OS-related CpGs, and three distinct CIMP groups with distinctly different prognosis and molecular features, including the CIMP-L subgroup, CIMP-M subgroup, and CIMP-H subgroup, were identified based on unsupervised consensus clustering analysis of these CpGs. Patients of the CIMP-H subgroup had poorer OS and RFS, while patients of the CIMP-L subgroup had better OS and RFS. The CIMP status was also an independent prognostic factor for OS and PFS. In molecular features, significantly higher somatic mutation burden and tumor mutational burden were found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. Besides, lower stromal score, immune score, and higher cancer stemness indices and tumor purity were also found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. Correspondingly, significant total T cells, total B cells, CD8 T cells, memory CD4 T cells, and higher regulatory T cells were found in patients of the CIMP-H subgroup. Moreover, significantly lower expression of immune checkpoint genes, such as PD-1, CTLA4, CD86, VTCN1, and LAG-3, was also found in patients of the CIMP-H subgroup compared to those of the CIMP-L subgroup. In the end, we validated the CIMP status in PC patients of the ICGC dataset.

Conclusion

The CIMP may modulate the immune response of the tumor microenvironment and influence the prognosis of pancreatic cancer patients, which may help to make an assertion to provide specific and efficient treatment options for patients of different subtypes.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

Nonnegative matrix factorization-based bioinformatics analysis reveals that TPX2 and SELENBP1 are two predictors of the inner sub-consensuses of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a heterogeneous disease. However the inner sub‐groups of LUAD have not been fully studied. Markers predicted the sub‐groups and prognosis of LUAD are badly needed.

Aims

To identify biomarkers associated with the sub‐groups and prognosis of LUAD.

Materials and Methods

Using nonnegative matrix factorization (NMF) clustering, LUAD patients from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) datasets and LUAD cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) dataset were divided into different sub‐consensuses based on the gene expression profiling. The overall survival of LUAD patients in each sub‐consensus was determined by Kaplan‐Meier survival analysis. The common genes which were differentially expressed in each sub‐consensus of LUAD patients and LUAD cell lines were identified using TBtools. The predictive accuracy of TPX2 and SELENBP1 for theinner sub‐consensuses of LUAD was determined by Receiver operator characteristic (ROC) analysis. The Kaplan‐Meier survival analysis was also used to test the prognostic significance of TPX2 and SELENBP1 in LUAD patients.

Results

Using nonnegative matrix factorization clustering, LUAD patients in The Cancer Genome Atlas (TCGA), GSE30219, GSE42127, GSE50081, GSE68465, and GSE72094 datasets were divided into three sub‐consensuses. Sub‐consensus3 LUAD patients were with low overall survival and were with high TP53 mutations. Similarly, LUAD cell lines were also divided into three sub‐consensuses by NMF method, and sub‐consensus2 cell lines were resistant to EGFR inhibitors. Identification of the common genes which were differentially expressed in different sub‐consensuses of LUAD patients and LUAD cell lines revealed that TPX2 was highly expressed in sub‐consensus3 LUAD patients and sub‐consensus2 LUAD cell lines. On the contrary, SELENBP1 was highly expressed in sub‐consensus1 LUAD patients and sub‐consensus1 LUAD cell lines. The expression levels of TPX2 and SELENBP1 could distinguish sub‐consensus3 LUAD patients or sub‐consensus2 LUAD cell lines from other sub‐consensuses of LUAD patients or cell lines. Moreover, compared with normal lung tissues, TPX2 was highly expressed, while, SELENBP1 was lowly expressed in LUAD tissues. Furthermore, the higher expression levels of TPX2 were associated with the lower relapse‐free survival and the lower overall survival of LUAD patients. While, the higher expression levels of SELENBP1 were associated with the higher relapse‐free survival and higher overall survival. At last, we showed that TP53 mutant LUAD patients were with higher TPX2 and lower SELENBP1 expressions.

Discussion

Both iCluster and NMF method are proved to be robust LUAD classification systems. However, the LUAD patients in different iclusters had no significant clinical overall survival, while, sub‐consensus3 LUAD patients from NMF classification were with lower overall survival than other sub‐consensuses.

Conclusions

By integrated analysis of 1765 LUAD patients and 64 LUAD cell lines, we showed that NMF was a robust inner sub‐consensuses classification method of LUAD. TPX2 and SELENBP1 were differentially expressed in different LUAD sub‐ consensuses, and predicted the inner sub‐consensuses of LUAD with high accuracy. TPX2 was an unfavorable prognostic biomarker of LUAD which was up‐regulated in LUAD tissues and associated with the low overall survival of LUAD. SELENBP1 was a favorable prognostic biomarker of LUAD which was down‐regulated in LUAD tissues and associated with the prolonged overall survival of LUAD.

Evaluation of biomarkers, genetic mutations, and epigenetic modifications in early diagnosis of pancreatic cancer

BACKGROUND

Pancreatic cancer (PC) is one of the deadliest malignancies with an alarming mortality rate. Despite significant advancement in diagnostics and therapeutics, early diagnosis remains elusive causing poor prognosis, marred by mutations and epigenetic modifications in key genes which contribute to disease progression.

AIM

To evaluate the various biological tumor markers collectively for early diagnosis which could act as prognostic biomarkers and helps in future therapeutics of PC in Kashmir valley.

METHODS

A total of 50 confirmed PC cases were included in the study to evaluate the levels of carbohydrate antigen 19-9 (CA 19-9), tissue polypeptide specific antigen (TPS), carcinoembryonic antigen (CEA), vascular endothelial growth factor-A (VEGF-A), and epidermal growth factor receptor (EGFR). Mutational analysis was performed to evaluate the mutations in Kirsten rat sarcoma (KRAS), Breast cancer type 2 (BRCA-2), and deleted in pancreatic cancer-4 (DPC-4) genes. However, epigenetic modifications (methylation of CpG islands) were performed in the promoter regions of cyclin-dependent kinase inhibitor 2A (p16; CDKN2A), MutL homolog 1 (hMLH1), and Ras association domain-containing protein 1(RASSF1A) genes.

RESULTS

We found significantly elevated levels of biological markers CA 19-9 (P ≤ 0.05), TPS (P ≤ 0.05), CEA (P ≤ 0.001), and VEGF (P ≤ 0.001). Molecular genetic analysis revealed that KRAS gene mutation is predominant in codon 12 (16 subjects, P ≤ 0.05), and 13 (12 subjects, P ≤ 0.05). However, we did not find a mutation in DPC-4 (1203G > T) and BRCA-2 (617delT) genes. Furthermore, epigenetic modification revealed that CpG methylation in 21 (P ≤ 0.05) and 4 subjects in the promoter regions of the p16 and hMLH1 gene, respectively.

CONCLUSION

In conclusion, CA 19-9, TPS, CEA, and VEGF levels were significantly elevated and collectively have potential as diagnostic and prognostic markers in PC. Global data of mutation in the KRAS gene commonly in codon 12 and rare in codon 13 could augment the predisposition towards PC. Additionally, methylation of the p16 gene could also modulate transcription of genes thereby increasing the predisposition and susceptibility towards PC.

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.

Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

Maternal opioid use disorder: Placental transcriptome analysis for neonatal opioid withdrawal syndrome

Excessive prenatal opioid exposure may lead to the development of Neonatal Opioid Withdrawal Syndrome (NOWS). RNA-seq was done on 64 formalin-fixed paraffin-embedded placental tissue samples from 32 mothers with opioid use disorder, with newborns with NOWS that required treatment, and 32 prenatally unexposed controls. We identified 93 differentially expressed genes in the placentas of infants with NOWS compared to unexposed controls. There were 4 up- and 89 downregulated genes. Among these, 7 genes CYP1A1, APOB, RPH3A, NRXN1, LINC01206, AL157396.1, UNC80 achieved an FDR p-value of <0.01. The remaining 87 genes were significant with FDR p-value <0.05. The 4 upregulated, CYP1A1, FP671120.3, RAD1, RN7SL856P, and the 10 most significantly downregulated genes were RNA5SP364, GRIN2A, UNC5D, DMBT1P1, MIR3976HG, LINC02199, LINC02822, PANTR1, AC012178.1, CTNNA2. Ingenuity Pathway Analysis identified the 7 most likely to play an important role in the etiology of NOWS. Our study expands insights into the genetic mechanisms of NOWS development.

Epigenetic Alterations in Pancreatic Cancer Metastasis

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Pancreatic ductal adenocarcinoma (PDA) is the most common (90%) and aggressive type of pancreatic cancer. Genomic analyses of PDA specimens have identified the recurrent genetic mutations that drive PDA initiation and progression. However, the underlying mechanisms that further drive PDA metastasis remain elusive. Despite many attempts, no recurrent genetic mutation driving PDA metastasis has been found, suggesting that PDA metastasis is driven by epigenetic fluctuations rather than genetic factors. Therefore, establishing epigenetic mechanisms of PDA metastasis would facilitate the development of successful therapeutic interventions. In this review, we provide a comprehensive overview on the role of epigenetic mechanisms in PDA as a critical contributor on PDA progression and metastasis. In particular, we explore the recent advancements elucidating the role of nucleosome remodeling, histone modification, and DNA methylation in the process of cancer metastasis.

NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer

Pancreatic cancer (PC) is a highly fatal disease, yet its causes remain unclear. Comprehensive analysis of different types of PC genetic data plays a crucial role in understanding its pathogenic mechanisms. Currently, non-negative matrix factorization (NMF)-based methods are widely used for genetic data analysis. Nevertheless, it is a challenge for them to integrate and decompose different types of genetic data simultaneously. In this paper, a non-NMF network analysis method, NMFNA, is proposed, which introduces a graph-regularized constraint to the NMF, for identifying modules and characteristic genes from two-type PC data of methylation (ME) and copy number variation (CNV). Firstly, three PC networks, i.e., ME network, CNV network, and ME-CNV network, are constructed using the Pearson correlation coefficient (PCC). Then, modules are detected from these three PC networks effectively due to the introduced graph-regularized constraint, which is the highlight of the NMFNA. Finally, both gene ontology (GO) and pathway enrichment analyses are performed, and characteristic genes are detected by the multimeasure score, to deeply understand biological functions of PC core modules. Experimental results demonstrated that the NMFNA facilitates the integration and decomposition of two types of PC data simultaneously and can further serve as an alternative method for detecting modules and characteristic genes from multiple genetic data of complex diseases.

Desmoglein 3 contributes to tumorigenicity of pancreatic ductal adenocarcinoma through activating Src-FAK signaling

Desmogleins (DSGs), with the ability to link adjacent cells, have been shown to participate in the development of malignancy. DSG3 was up-regulated in various cancers, including lung, head and neck, and esophagus squamous cell carcinoma, which contributed to the tumor progression. The role of DSG3 in pancreatic ductal adenocarcinoma (PDAC) still remains elusive. Here, the expression of DSG3 was found to be enhanced in pancreatic cancer cell lines in vitro. Functional assays showed that shRNA-mediated knockdown of DSG3 decreased cell viability of pancreatic cancer cells and retarded the cell proliferation, migration and invasion. However, pcDNA-mediated over-expression of DSG3 exhibited reversed effect on pancreatic cancer cell progression. In addition, the in vivo assay demonstrated that transfection of shDSG3 lentiviruses into pancreatic cancer cells repressed the tumorigenicity of PDAC after the cancer cells were transplanted into mice subcutaneously. Elevated DSG3 expression promoted the phosphorylation of Src (p-Src), focal adhesion kinase (p-FAK) and AKT (p-AKT) in vitro, while silence of DSG3 reduced the expression of p-Src, p-FAK and p-AKT both in vitro and in vivo. In conclusion, DSG3, as an oncogene, contributed to the tumorigenicity of PDAC through activating Src-FAK signaling.

Estrogen exposure causes the progressive growth of SK-Hep1-derived tumor in ovariectomized mice

Liver cancer, one of the leading death causes, has different incidence and mortality rates in men and women. The influencing factor is considered to estrogen. However, the role of estrogen in liver cancer remains controversial. In this study, we investigated the effects of estrogen on tumor progression. Total RNA sequencing was analyzed in SK-Hep1-derived tumor tissues, and 15 genes were expressed only in female mice. Among the differentially expressed genes, matrix metalloprotease 7 (MMP7), germ cell associated 1 (GSG1), and chromosome 6 open reading frame 15 (C6orf15) were associated with significantly different overall survival rates based on their expression level in liver cancer patients. Interestingly, exogenous estrogen aggravated SK-Hep1-derived tumor growth in ovariectomized (OVX) mice. When OVX mice were treated with exogenous estrogen, SK-Hep1-derived tumor tissues exhibited high MMP7 expression levels and low GSG1 and C6orf15 expression levels. These expression patterns were consistent with those of liver cancer patients with low overall survival rates. These results suggest that these genes are expected to be prognostic biomarkers of liver cancer. In conclusion, our results suggest that continuous estrogen exposure may promote tumor growth in OVX mice.

Identification of prognosis-related molecular subgroups based on DNA methylation in pancreatic cancer

Background

Pancreatic cancer (PC) is one of the most lethal and aggressive cancer malignancies. The lethality of PC is associated with delayed diagnosis, presence of distant metastasis, and its easy relapse. It is known that clinical treatment decisions are still mainly based on the clinical stage and pathological grade, which are insufficient to determine an appropriate treatment. Considering the significant heterogeneity of PC biological characteristics, the current clinical classificatory pattern relying solely on classical clinicopathological features identification needs to be urgently improved. In this study, we conducted in-depth analyses to establish prognosis-related molecular subgroups based on DNA methylation signature.

Results

DNA methylation, RNA sequencing, somatic mutation, copy number variation, and clinicopathological data of PC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. A total of 178 PC samples were used to develop distinct molecular subgroups based on the 4227 prognosis-related CpG sites. By using consensus clustering analysis, four prognosis-related molecular subgroups were identified based on DNA methylation. The molecular characteristics and clinical features analyses based on the subgroups offered novel insights into the development of PC. Furthermore, we built a risk score model based on the expression data of five CpG sites to predict the prognosis of PC patients by using Lasso regression. Finally, the risk score model and other independent prognostic clinicopathological information were integrative utilised to construct a nomogram model.

Conclusion

Novel prognosis-related molecular subgroups based on the DNA methylation signature were established. The specific five CpG sites model for PC prognostic prediction and the derived nomogram model are effective and intuitive tools. Moreover, the construction of molecular subgroups based on the DNA methylation data is an innovative complement to the traditional classification of PC and may contribute to precision medicine development, therapeutic efficacy prediction, and clinical decision guidance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01090-w.

Effect of DNA methylation on gene transcription is associated with the distribution of methylation sites across the genome in osteoarthritis

Genetics and epigenetics are important subjects in the field of osteoarthritis (OA) research. DNA methylation may affect gene transcription, but the specific mechanisms have remained to be fully elucidated. In the present study, the ChAMP methylation analysis package was used to identify differentially methylated genes (DMGs) from the dataset GSE63695 from the Gene Expression Omnibus (GEO) database. The distribution of differentially methylated sites (DMS) and the total array sites across the genome were analyzed by enrichment analysis. Subsequently, two mRNA expression profiling datasets, GSE114007 and GSE113825, were obtained from the GEO database and common differentially expressed genes (DEGs) were identified using the Limma package. Key genes were screened by analyzing the distribution of DMS across the genome consisting of DEGs and DMGs. A total of 1,662 and 1,986 DEGs were identified between OA and normal human cartilage from the GSE113825 and GSE114007 dataset, respectively. A further screening revealed 292 genes with common differences between the two datasets. A total of 574 DMS containing 394 DMGs were observed between OA and normal cartilage. Integrative analysis revealed a corresponding subset of 15 genes. Of these, 6 genes were verified by reverse transcription-quantitative PCR, confirming that the mRNA expression of 5 genes (MAP1B, FNDC1, ANLN, SCNN1A and STC2) in OA cartilage was consistent with the mRNA expression from the analysis of the datasets. Upon treatment with the DNA methylation inhibitor 5-aza-2'-deoxycytidine, the mRNA levels of FNDC1 and SCNN1A were decreased, and no significant alteration in the mRNA levels of MAP1B, ANLN, KCNN4 and STC2 was observed. The incidence of differential methylation varied in subregions of the genome and the effects on transcription were associated with the distribution of DEGs across the genome. The regulation of this appears more complex than initially postulated. Combining the data on epigenetic differences of OA with the genome or transcriptome data for analysis may improve the understanding of the pathophysiological processes of OA. FNDC1 and SCNN1A may potentially be valuable biomarkers for OA.

Systematic Investigation of DNA Methylation Associated With Platinum Chemotherapy Resistance Across 13 Cancer Types

Background: Platinum resistance poses a significant problem for oncology clinicians. As a result, the role of epigenetics and DNA methylation in platinum-based chemoresistance has gained increasing attention from researchers in recent years. A systematic investigation of aberrant methylation patterns related to platinum resistance across various cancer types is urgently needed.

Methods: We analyzed the platinum chemotherapy response-related methylation patterns from different perspectives of 618 patients across 13 cancer types and integrated transcriptional and clinical data. Spearman’s test was used to evaluate the correlation between methylation and gene expression. Cox analysis, the Kaplan-Meier method, and log-rank tests were performed to identify potential risk biomarkers based on differentially methylated positions (DMPs) and compare survival based on DMP values. Support vector machines and receiver operating characteristic curves were used to identify the platinum-response predictive DMPs.

Results: A total of 3,703 DMPs (p value < 0.001 and absolute delta beta >0.10) were identified, and the DMP numbers of each cancer type varied. A total of 39.83% of DMPs were hypermethylated and 60.17% were hypomethylated in platinum-resistant patients. Among them, 405 DMPs (Benjamini and Hochberg adjusted p value < 0.05) were found to be associated with prognosis in tumor patients treated with platinum-based regimens, and 664 DMPs displayed the potential to predict platinum chemotherapy response. In addition, we defined six DNA DMPs consisting of four gene members (mesothelin, protein kinase cAMP-dependent type II regulatory subunit beta, msh homeobox 1, and par-6 family cell polarity regulator alpha) that may have favorable prognostic and predictive values for platinum chemotherapy.

Conclusion: The methylation-transcription axis exists and participates in the complex biological mechanism of platinum resistance in various cancers. Six DMPs and four associated genes may have the potential to serve as promising epigenetic biomarkers for platinum-based chemotherapy and guide clinical selection of optimal treatment.

Analysis of mRNA Expression and DNA Methylation Datasets According to the Genomic Distribution of CpG Sites in Osteoarthritis

Objectives Transcriptional changes in cartilage can impact function by causing degradation such as that which occurs during the development of osteoarthritis (OA). Epigenetic regulation may be a key factor leading to transcriptional changes in OA. In this study, we performed a combined analysis of DNA methylation and gene expression microarray datasets and identified key transcription factors (TFs) central to the regulation of gene expression in OA. Methods A DNA methylation profile dataset (GSE63106) and a gene expression profiling dataset (GSE114007) were extracted from the Gene Expression Omnibus (GEO). We used ChAMP methylation analysis and the Limma package to identify differentially methylation genes (DMGs) and differentially expressed genes (DEGs) from normal and human knee cartilage samples in OA. Function enrichment analysis of DMGs was conducted using the DAVID database. A combined analysis of DEGs and DMGs was conducted to identify key TFs in OA. We then validated the mRNA expression of selected TFs in normal and OA cartilage by RT-qPCR. Primary chondrocytes were cultured and treated with the DNA methylation inhibitor 5-Aza-2-deoxycytidine (5-Aza) for functional validation. Results We identified 2,170 differential methylation sites (DMS) containing 1005 genes and 1986 DEGs between normal human and OA cartilage. Functional analysis of DMGs revealed that focal adhesion, extracellular matrix (ECM)-receptor interactions, the PI3K-Akt signaling pathway, and the FoxO signaling pathway were involved in OA. Integrated analysis showed a subset of 17 TFs. Four TFs (ELF3, SOX11, RARA, and FOXD2) were validated. RT-qPCR results showed the mRNA expression of SOX11, RARA, and FOXD2 were consistent with the results from the mRNA expression data. However, the expression of ELF3 could not be validated. Upon 5-Aza-2'-deoxycytidine (5-Aza) treatment, the mRNA levels of ELF3 and SOX11 were down-regulated, whilst RARA was up-regulated, and FOXD2 showed no significant change in expression level. Conclusions the effect of DNA methylation on the transcriptional regulation is related to the distribution of methylated sites across the genome. Epigenetic studies on the positions of DMS in transcriptional units can inform a better understanding of the function of DNA methylation and its transcription regulation.

Aberrantly DNA Methylated-Differentially Expressed Genes in Pancreatic Cancer Through an Integrated Bioinformatics Approach

Pancreatic cancer remains one of the chief contributors to cancer related deaths on a global scale, with its diagnosis often associated with poor prognosis and high mortality. Accumulating literature continues to highlight the role of aberrant DNA methylation in relation to pancreatic cancer progression. Integrated bioinformatics approaches in the characterization of methylated-differentially expressed genes (MeDEGs) in pancreatic cancer were employed to enhance our understanding of the potential underlying molecular mechanisms of this cancer. We initially identified differentially expressed genes (DEGs) between 178 pancreatic cancer samples and 4 normal samples and differentially methylated genes (DMGs) based on 185 pancreatic cancer samples as well as 10 normal samples by analyzing RNA sequencing data in the TCGA database. Eventually, 31 MeDEGs including 5 hypomethylated/upregulated genes and 26 hypermethylated/downregulated genes were identified. Univariate Cox model and Kaplan–Meier method revealed that, among 31 MeDEGs, 5 hypermethylated/downregulated genes (ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42) were correlated with poor survival of patients with pancreatic cancer. KEGG pathway enrichment analysis by GSEA 3.0 and the protein–protein interaction (PPI) network revealed that these 5 MeDEGs were enriched in numerous cancer-related pathways in addition to interacting with each other, highlighting a significant role in the development of pancreatic cancer. Taken together, the key findings of the current study demonstrate that ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42 are hypermethylated/downregulated genes in pancreatic cancer and may be associated, through their modulation of specific pathways, with unfavorable pancreatic cancer prognosis.

Epigenetic Alterations in the Gastrointestinal Tract: Current and Emerging Use for Biomarkers of Cancer

Colorectal cancer, liver cancer, stomach cancer, pancreatic cancer, and esophageal cancer are leading causes of cancer-related deaths worldwide. A fundamental trait of virtually all gastrointestinal cancers is genomic and epigenomic DNA alterations. Cancer cells acquire genetic and epigenetic alterations that drive the initiation and progression of the cancers by altering the molecular and cell biological processes of the cells. These alterations, as well as other host and microenvironment factors, ultimately mediate the clinical behavior of the precancers and cancers and can be used as biomarkers for cancer risk determination, early detection of cancer and precancer, determination of the prognosis of cancer and prediction of the response to therapy. Epigenetic alterations have emerged as one of most robust classes of biomarkers and are the basis for a growing number of clinical tests for cancer screening and surveillance.

Unsupervised subtyping and methylation landscape of pancreatic ductal adenocarcinoma

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.

Next-generation sequencing of residual cytologic fixative preserved DNA from pancreatic lesions: A pilot study

BACKGROUND

Endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) is a sensitive and specific tool in the risk stratification of pancreatic lesions, including cysts. The sensitivity and specificity of EUS-FNA has been shown to improve when cytology is combined with next-generation sequencing (NGS). Ideally, fresh cyst fluid is used for NGS. In this pilot study, we explore the possibility of sequencing DNA derived from residual alcohol-fixed pancreatic aspirates.

METHODS

Residual cytologic fixatives (n = 42) from 39 patients who underwent EUS-FNA for pancreatic lesions were collected along with demographics, imaging, and laboratory studies. Samples were designated as nonneoplastic/nonmucinous benign (NB), mucinous cyst (MC), pancreatic ductal adenocarcinoma (PDAC), or well-differentiated neuroendocrine tumor (NET) on the basis of cytopathologic evaluation and sequenced on the Oncomine platform (ThermoFisher Scientific, Waltham, Massachusetts).

RESULTS

Ten of 14 (71.4%) MCs exhibited clinically significant variants, including KRAS, GNAS, and TP53. Ten of 15 (66.7%) PDACs had KRAS alterations, and 9 of 15 (60%) showed variants in TP53. No variants were detected in any NETs. Only 1 of 9 (11.1%) NB aspirates showed variants in KRAS and MAP2K. Sequencing of formalin-fixed, paraffin-embedded tissue revealed variants identical to those detected in fixative-derived DNA in 4 of 5 cases (80%).

CONCLUSION

Residual DNA from alcohol-fixed aspirates are an underutilized source for NGS. Sequencing residual fixative-derived DNA has the potential to be integrated into the workup of pancreatic aspirates, possibly impacting management.

Identification of Prognostic Markers in Cholangiocarcinoma Using Altered DNA Methylation and Gene Expression Profiles

Background

Cholangiocarcinoma (CCA) is a rare disease, but it is amongst the most lethal cancers with a median survival under 1 year. Variations in DNA methylation and gene expression have been extensively studied in other cancers for their role in pathogenesis and disease prognosis, but these studies are very limited in CCA. This study focusses on the identification of DNA methylation and gene expression prognostic biomarkers using multi-omics data of CCA tumors from The Cancer Genome Atlas (TCGA).

Method

We have conducted a genome-wide analysis of differential DNA methylation and gene/miRNA expression using data from 36 CCA tumor and 9 normal samples from TCGA. The impact of DNA methylation in promoters and long-range distal enhancers on the regulation and expression of CCA-associated genes was examined using linear regression. Next, we conducted network analyses on genes which are regulated by DNA methylation as well as by miRNA. Finally, we performed Kaplan–Meier and Cox proportional hazards regression analyses in order to identify the role of selected methylation sites and specific genes and miRNAs in patient survival. We also performed real-time quantitative PCR (qPCR) to confirm the change in gene expression in CCA patients’ tumor and adjacent normal samples.

Results

Altered DNA methylation was observed on 12,259 CpGs across all chromosomes, of which 78% were hypermethylated. We observed a strong negative relationship between promoter hypermethylation and corresponding gene expression in 92% of the CpGs. Differential expression analyses revealed altered expression patterns in 3,305 genes and 101 miRNAs. Finally, we identified 17 differentially methylated promoter CpGs, 72 differentially expressed genes, and two miRNAs that are likely associated with patient survival. Pathway analysis suggested that cell division, bile secretion, amino acid metabolism, PPAR signaling, hippo signaling were highly affected by gene expression and DNA methylation alterations. The qPCR analysis further confirmed that MDK, HNF1B, PACS1, and GLUD1 are differentially expressed in CCA.

Conclusion

Based on the survival analysis, we conclude that DEPDC1, FUT4, MDK, PACS1, PIWIL4 genes, miR-22, miR-551b microRNAs, and cg27362525 and cg26597242 CpGs can strongly support their use as prognostic markers of CCA.

MYEOV increases HES1 expression and promotes pancreatic cancer progression by enhancing SOX9 transactivity

Emerging evidence indicates that myeloma overexpressed (MYEOV) is an oncogene and plays crucial roles in multiple human cancers. However, its roles in the development of pancreatic ductal adenocarcinoma (PDAC) remain elusive. Here, we provide evidence of essential roles of MYEOV in the development and progression of PDAC. In tumor specimens derived from pancreatic cancer patients, MYEOV was overexpressed and associated with poor prognosis. In addition, MYEOV expression in PDAC was upregulated through promoter hypomethylation. MYEOV depletion impaired metastatic ability and proliferation of PDAC cells both in vitro and in vivo, whereas its overexpression had the opposite effect. Mechanistic investigations revealed that MYEOV interacted with SRY-Box Transcription Factor 9 (SOX9), a well-known oncogenic transcription factor in PDAC. This interaction occurred mainly in the nuclei of PDAC cells and increased transcriptional activity of SOX9. Furthermore, MYEOV promoted the expression of Hairy and enhancer of split homolog-1 (HES1), a SOX9 target gene, by enhancing SOX9 DNA-binding ability to the HES1 enhancer without affecting the protein level and subcellular localization of SOX9. HES1 knockdown partly abrogated the oncogenic effect of MYEOV. Our findings suggest that MYEOV could be a potential prognostic biomarker and therapeutic target for PDAC.

Multivariate analysis reveals differentially expressed genes among distinct subtypes of diffuse astrocytic gliomas: diagnostic implications

Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers. Here we interrogated microarray gene expression profiles (GEP) of 268 diffuse astrocytic gliomas-33 diffuse astrocytomas (DA), 52 anaplastic astrocytomas (AA) and 183 primary glioblastoma (GBM)-based on multivariate analysis, to identify discriminatory GEP that might support precise histopathological tumor stratification, particularly among inconclusive cases with II-III grade diagnosed, which have different prognosis and treatment strategies. Microarrays based GEP was analyzed on 155 diffuse astrocytic gliomas (discovery cohort) and validated in another 113 tumors (validation set) via sequential univariate analysis (pairwise comparison) for discriminatory gene selection, followed by nonnegative matrix factorization and canonical biplot for identification of discriminatory GEP among the distinct histological tumor subtypes. GEP data analysis identified a set of 27 genes capable of differentiating among distinct subtypes of gliomas that might support current histological classification. DA + AA showed similar molecular profiles with only a few discriminatory genes overexpressed (FSTL5 and SFRP2) and underexpressed (XIST, TOP2A and SHOX2) in DA vs AA and GBM. Compared to DA + AA, GBM displayed underexpression of ETNPPL, SH3GL2, GABRG2, SPX, DPP10, GABRB2 and CNTN3 and overexpression of CHI3L1, IGFBP3, COL1A1 and VEGFA, among other differentially expressed genes.

Calcium Signaling Alterations Caused by Epigenetic Mechanisms in Pancreatic Cancer: From Early Markers to Prognostic Impact

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with high mortality rates. PDAC initiation and progression are promoted by genetic and epigenetic dysregulation. Here, we aimed to characterize the PDAC DNA methylome in search of novel altered pathways associated with tumor development. We examined the genome-wide DNA methylation profile of PDAC in an exploratory cohort including the comparative analyses of tumoral and non-tumoral pancreatic tissues (PT). Pathway enrichment analysis was used to choose differentially methylated (DM) CpGs with potential biological relevance. Additional samples were used in a validation cohort. DNA methylation impact on gene expression and its association with overall survival (OS) was investigated from PDAC TCGA (The Cancer Genome Atlas) data. Pathway analysis revealed DM genes in the calcium signaling pathway that is linked to the key pathways in pancreatic carcinogenesis. DNA methylation was frequently correlated with expression, and a subgroup of calcium signaling genes was associated with OS, reinforcing its probable phenotypic effect. Cluster analysis of PT samples revealed that some of the methylation alterations observed in the Calcium signaling pathway seemed to occur early in the carcinogenesis process, a finding that may open new insights about PDAC tumor biology.

A novel sensitive detection method for DNA methylation in circulating free DNA of pancreatic cancer

Despite recent advances in clinical treatment, pancreatic cancer remains a highly lethal malignancy. In order to improve the survival rate of patients with pancreatic cancer, the development of non-invasive diagnostic methods using effective biomarkers is urgently needed. Here, we developed a highly sensitive method to detect DNA methylation in cell-free (cf)DNA samples based on the enrichment of methyl-CpG binding (MBD) protein coupled with a digital PCR method (MBD–ddPCR). Five DNA methylation markers for the diagnosis of pancreatic cancer were identified through DNA methylation microarray analysis in 37 pancreatic cancers. The sensitivity and specificity of the five markers were validated in another independent cohort of pancreatic cancers (100% and 100%, respectively; n = 46) as well as in The Cancer Genome Atlas data set (96% and 90%, respectively; n = 137). MBD–ddPCR analysis revealed that DNA methylation in at least one of the five markers was detected in 23 (49%) samples of cfDNA from 47 patients with pancreatic cancer. Further, a combination of DNA methylation markers and the KRAS mutation status improved the diagnostic capability of this method (sensitivity and specificity, 68% and 86%, respectively). Genome-wide MBD-sequencing analysis in cancer tissues and corresponding cfDNA revealed that more than 80% of methylated regions were overlapping; DNA methylation profiles of cancerous tissues and cfDNA significantly correlated with each other (R = 0.97). Our data indicate that newly developed MBD–ddPCR is a sensitive method to detect cfDNA methylation and that using five marker genes plus KRAS mutations may be useful for the detection of pancreatic cancers.

ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Motivation

DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set.

Results

We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer.

Availability and implementation

ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/.

Supplementary information

Supplementary data are available at Bioinformatics online.