The identification of specific methylation patterns across different cancers

Comprehensive in silico CpG methylation analysis in hepatocellular carcinoma identifies tissue- and tumor-type specific marks disconnected from gene expression

DNA methylation is crucial for chromatin structure, transcription regulation and genome stability, defining cellular identity. Aberrant hypermethylation of CpG-rich regions is common in cancer, influencing gene expression. However, the specific contributions of individual epigenetic modifications to tumorigenesis remain under investigation. In hepatocellular carcinoma (HCC), DNA methylation alterations are documented as in other tumor types. We aimed to identify hypermethylated CpGs in HCC, assess their specificity across other tumor types, and investigate their impact on gene expression. To this end, public methylomes from HCC, other liver diseases, and 27 tumor types as well as expression data from TCGA-LIHC and GTEx were analyzed. This study identified 39 CpG sites that were hypermethylated in HCC compared to control liver tissue, and were located within promoter, gene bodies, and intergenic CpG islands. Notably, these CpGs were predominantly unmethylated in healthy liver tissue and other normal tissues. Comparative analysis with 27 other tumors revealed both common and HCC-specific hypermethylated CpGs. Interestingly, the HCC-hypermethylated genes showed minimal expression in the different healthy tissues, with marginal changes in the level of expression in the corresponding tumors. These findings confirm previous evidence on the limited influence of DNA hypermethylation on gene expression regulation in cancer. It also highlights the existence of mechanisms that allow the selection of tissue-specific methylation marks in normally unexpressed genes during carcinogenesis. Overall, our study contributes to demonstrate the complexity of cancer epigenetics, emphasizing the need of better understanding the interplay between DNA methylation, gene expression dynamics, and tumorigenesis.

The Role of Methylation in Chronic Lymphocytic Leukemia and Its Prognostic and Therapeutic Impacts in the Disease: A Systematic Review

Epigenetic regulation has been thoroughly investigated in recent years and has emerged as an important aspect of chronic lymphocytic leukemia (CLL) biology. Characteristic aberrant features such as methylation patterns and global DNA hypomethylation were the early findings of the research during the last decades. The investigation in this field led to the identification of a large number of genes where methylation features correlated with important clinical and laboratory parameters. Gene-specific analyses investigated methylation in the gene body enhancer regions as well as promoter regions. The findings included genes and proteins involved in key pathways that play central roles in the pathophysiology of the disease. Τhe application of these findings beyond the theoretical understanding can not only lead to the creation of prognostic and predictive models and scores but also to the design of novel therapeutic agents. The following is a review focusing on the present knowledge about single gene/gene promoter methylation or mRNA expression in CLL cases as well as records of older data that have been published in past papers.

DNA hypomethylation mediates immune response in pan-cancer

Abnormal DNA methylation is a fundamental characterization of epigenetics in cancer. Here we demonstrate that aberrant DNA methylating can modulate the tumour immune microenvironment in 16 cancer types. Differential DNA methylation in promoter region can regulate the transcriptomic pattern of immune-related genes and DNA hypomethylation mainly participated in the processes of immunity, carcinogenesis and immune infiltration. Moreover, many cancer types shared immune-related functions, like activation of innate immune response, interferon gamma response and NOD-like receptor signalling pathway. DNA methylation can further help identify molecular subtypes of kidney renal clear cell carcinoma. These subtypes are characterized by DNA methylation pattern, major histocompatibility complex, cytolytic activity and cytotoxic t lymphocyte and tumour mutation burden, and subtype with hypomethylation pattern shows unstable immune status. Then, we investigate the DNA methylation pattern of exhaustion-related marker genes and further demonstrate the role of hypomethylation in tumour immune microenvironment. In summary, our findings support the use of hypomethylation as a biomarker to understand the mechanism of tumour immune environment.

In Silico Gene Prioritization Highlights the Significance of Bone Morphogenetic Protein 4 (BMP4) Promoter Methylation across All Methylation Clusters in Colorectal Cancer

The cytosine-phosphate-guanine (CpG) island methylator phenotype (CIMP) represents one of the pathways involved in the development of colorectal cancer, characterized by genome-wide hypermethylation. To identify samples exhibiting hypermethylation, we used unsupervised hierarchical clustering on genome-wide methylation data. This clustering analysis revealed the presence of four distinct subtypes within the tumor samples, namely, CIMP-H, CIMP-L, cluster 3, and cluster 4. These subtypes demonstrated varying levels of methylation, categorized as high, intermediate, and very low. To gain further insights, we mapped significant probes from all clusters to Ensembl Regulatory build 89, with a specific focus on those located within promoter regions or bound regions. By intersecting the methylated promoter and bound regions across all methylation subtypes, we identified a total of 253 genes exhibiting aberrant methylation patterns in the promoter regions across all four subtypes of colorectal cancer. Among these genes, our comprehensive genome-wide analysis highlights bone morphogenic protein 4 (BMP4) as the most prominent candidate. This significant finding was derived through the utilization of various bioinformatics tools, emphasizing the potential role of BMP4 in colorectal cancer development and progression.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Consistent DNA Hypomethylations in Prostate Cancer

With approximately 1.4 million men annually diagnosed with prostate cancer (PCa) worldwide, PCa remains a dreaded threat to life and source of devastating morbidity. In recent decades, a significant decrease in age-specific PCa mortality has been achieved by increasing prostate-specific antigen (PSA) screening and improving treatments. Nevertheless, upcoming, augmented recommendations against PSA screening underline an escalating disproportion between the benefit and harm of current diagnosis/prognosis and application of radical treatment standards. Undoubtedly, new potent diagnostic and prognostic tools are urgently needed to alleviate this tensed situation. They should allow a more reliable early assessment of the upcoming threat, in order to enable applying timely adjusted and personalized therapy and monitoring. Here, we present a basic study on an epigenetic screening approach by Methylated DNA Immunoprecipitation (MeDIP). We identified genes associated with hypomethylated CpG islands in three PCa sample cohorts. By adjusting our computational biology analyses to focus on single CpG-enriched 60-nucleotide-long DNA probes, we revealed numerous consistently differential methylated DNA segments in PCa. They were associated among other genes with NOTCH3, CDK2AP1, KLK4, and ADAM15. These can be used for early discrimination, and might contribute to a new epigenetic tumor classification system of PCa. Our analysis shows that we can dissect short, differential methylated CpG-rich DNA fragments and combinations of them that are consistently present in all tumors. We name them tumor cell-specific differential methylated CpG dinucleotide signatures (TUMS).

Epigenetically regulated PCDHB15 impairs aggressiveness of metastatic melanoma cells

The protocadherin proteins are cell adhesion molecules at the crossroad of signaling pathways playing a major role in neuronal development. It is now understood that their role as signaling hubs is not only important for the normal physiology of cells but also for the regulation of hallmarks of cancerogenesis. Importantly, protocadherins form a cluster of genes that are regulated by DNA methylation. We have identified for the first time that PCDHB15 gene is DNA-hypermethylated on its unique exon in the metastatic melanoma-derived cell lines and patients' metastases compared to primary tumors. This DNA hypermethylation silences the gene, and treatment with the DNA demethylating agent 5-aza-2'-deoxycytidine reinduces its expression. We explored the role of PCDHB15 in melanoma aggressiveness and showed that overexpression impairs invasiveness and aggregation of metastatic melanoma cells in vitro and formation of lung metastasis in vivo. These findings highlight important modifications of the methylation of the PCDHβ genes in melanoma and support a functional role of PCDHB15 silencing in melanoma aggressiveness.

KRAS Promoter Methylation Status and miR-18a-3p and miR-143 Expression in Patients With Wild-type KRAS Gene in Colorectal Cancer

BACKGROUND/AIM

Although some mutations of KRAS proto-oncogene, GTPase (KRAS) have been associated with the prognosis and therapeutic management of colorectal cancer (CRC), the epigenetic mechanisms (DNA methylation and microRNA expression) that regulate wild-type KRAS expression in patients with CRC are poorly known. The aim of this study was to establish whether there is a relationship between the expression of the wild-type KRAS gene, the methylation status of its distal promoter, and miR-143 and miR-18a-3p levels in samples of sporadic CRC.

PATIENTS AND METHODS

A total of 51 cases of sporadic CRC with wild-type KRAS were analyzed. The expression levels of KRAS mRNA, miR-18a-3p, miR-143, and KRAS protein, as well as methylation in the distal promoter of the KRAS gene were evaluated.

RESULTS

In the analyzed cases, KRAS mRNA expression was detected in 51.1%; wild-type KRAS protein was found in the membrane in 31.4% and in the cytoplasm in 98% of cases. An inverse relationship of marginal significance was observed between miR-18a-3p and KRAS protein expression in the cytoplasm (odds ratio=0.14, 95% confidence interval=0.012-1.092; p=0.08). The methylation status of the distal promoter of KRAS at four CpG islands was analyzed in 30 cases (58.8%): partial methylation of the four CpG islands evaluated was observed in two cases (6.7%). In these cases, KRAS protein expression was not evidenced at the membrane level; miR-18a-3p expression was not detected either but high expression of miR-143 was observed.

CONCLUSION

No association was found between the expression levels of KRAS mRNA, miR-18a-3p, miR-143 and methylation status. Methylation status was detected with low frequency, thus being the first report of methylation in wild-type KRAS.

DNA Methylation of HOXA11 Gene as Prognostic Molecular Marker in Human Gastric Adenocarcinoma

Hypermethylation of tumor suppressor genes and hypomethylation of oncogenes might be identified as possible biomarkers in gastric cancer (GC). We aimed to assess the DNA methylation status of selected genes in GC tissue samples and evaluate these genes' prognostic importance on patient survival. Patients (99) diagnosed with GC and who underwent gastrectomy were included. We selected a group of genes (RAD51B, GFRA3, AKR7A3, HOXA11, TUSC3, FLI1, SEZ6L, GLDC, NDRG) which may be considered as potential tumor suppressor genes and oncogenes. Methylation of the HOXA11 gene promoter was significantly more frequent in GC tumor tissue (p = 0.006) than in healthy gastric mucosa. The probability of surviving longer (71.2 months (95% CI 57-85.3) vs. 44.3 months (95% CI 34.8-53.9)) was observed with unmethylated HOXA11 promoter in cancer tissues. Survival in patients with a methylation of HOXA11 promoter either in healthy gastric mucosa or gastric cancer tissue was twice as high as in patients with a methylation of HOXA11 promoter in both healthy gastric mucosa and cancer tissue (61.2 months (95% CI 50.9-71.4) vs. 28.5 months (95% CI 20.8-36.2)). Multivariate Cox analysis revealed the HOXA11 methylation as significantly associated with patients' survival (HR = 2.4, 95% CI 1.19-4.86). Our results suggest that the HOXA11 gene might be a potential prognostic molecular marker in patients with gastric adenocarcinoma.

PCDHB15 as a potential tumor suppressor and epigenetic biomarker for breast cancer

Breast cancer is among the most frequently diagnosed cancer types and the leading cause of cancer-related death in women. The mortality rate of patients with breast cancer is currently increasing, perhaps due to a lack of early screening tools. In the present study, using The Cancer Genome Atlas (TCGA) breast cancer dataset (n=883), it was determined that methylation of the protocadherin β15 (PCDHB15) promoter was higher in breast cancer samples than that in normal tissues. A negative association between promoter methylation and expression of PCDHB15 was observed in the TCGA dataset and breast cancer cell lines. In TCGA cohort, lower PCDHB15 expression was associated with shorter relapse-free survival times. Treatment with the DNA methyltransferase inhibitor restored PCDHB15 expression in a breast cancer cell line; however, overexpression of PCDHB15 was shown to suppress colony formation. PCDHB15 methylation detected in circulating cell-free DNA (cfDNA) isolated from serum samples was higher in patients with breast cancer (40.8%) compared with that in patients with benign tumors (22.4%). PCDHB15 methylation was not correlated with any clinical parameters. Taken together, PCDHB15 is a potential tumor suppressor in cases of breast cancer, which can be epigenetically silenced via promoter methylation. PCDHB15 methylation using cfDNA is a novel minimally invasive epigenetic biomarker for the diagnosis and prognosis of breast cancer.

SALL Proteins; Common and Antagonistic Roles in Cancer

Simple Summary

Transcription factors play essential roles in regulating gene expression, impacting the cell phenotype and function, and in the response of cells to environmental conditions. Alterations in transcription factors, including gene amplification or deletion, point mutations, and expression changes, are implicated in carcinogenesis, cancer progression, metastases, and resistance to cancer treatments. Not surprisingly, transcription factor activity is altered in numerous cancers, representing a unique class of cancer drug targets. This review updates and integrates information on the SALL family of transcription factors, highlighting the synergistic and/or antagonistic functions they perform in various cancer types.

Abstract

SALL proteins are a family of four conserved C2H2 zinc finger transcription factors that play critical roles in organogenesis during embryonic development. They regulate cell proliferation, survival, migration, and stemness; consequently, they are involved in various human genetic disorders and cancer. SALL4 is a well-recognized oncogene; however, SALL1–3 play dual roles depending on the cancer context and stage of the disease. Current reviews of SALLs have focused only on SALL2 or SALL4, lacking an integrated view of the SALL family members in cancer. Here, we update the recent advances of the SALL members in tumor development, cancer progression, and therapy, highlighting the synergistic and/or antagonistic functions they perform in similar cancer contexts. We identified common regulatory mechanisms, targets, and signaling pathways in breast, brain, liver, colon, blood, and HPV-related cancers. In addition, we discuss the potential of the SALL family members as cancer biomarkers and in the cancer cells’ response to therapies. Understanding SALL proteins’ function and relationship will open new cancer biology, clinical research, and therapy perspectives.

Charting differentially methylated regions in cancer with Rocker-meth

Differentially DNA methylated regions (DMRs) inform on the role of epigenetic changes in cancer. We present Rocker-meth, a new computational method exploiting a heterogeneous hidden Markov model to detect DMRs across multiple experimental platforms. Through an extensive comparative study, we first demonstrate Rocker-meth excellent performance on synthetic data. Its application to more than 6,000 methylation profiles across 14 tumor types provides a comprehensive catalog of tumor type-specific and shared DMRs, and agnostically identifies cancer-related partially methylated domains (PMD). In depth integrative analysis including orthogonal omics shows the enhanced ability of Rocker-meth in recapitulating known associations, further uncovering the pan-cancer relationship between DNA hypermethylation and transcription factor deregulation depending on the baseline chromatin state. Finally, we demonstrate the utility of the catalog for the study of colorectal cancer single-cell DNA-methylation data.

Matteo Benelli et al. present Rocker-meth, a new Hidden Markov Model (HMM)-based method, to robustly identify differentially methylated regions (DMRs). They use Rocker-meth to analyse more than 6000 methylation profiles across 14 cancer types, providing a catalog of tumor-specific and shared DMRs.

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization

High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.

Automatic Detection of the Circulating Cell-Free Methylated DNA Pattern of GCM2, ITPRIPL1 and CCDC181 for Detection of Early Breast Cancer and Surgical Treatment Response

The early detection of cancer can reduce cancer-related mortality. There is no clinically useful noninvasive biomarker for early detection of breast cancer. The aim of this study was to develop accurate and precise early detection biomarkers and a dynamic monitoring system following treatment. We analyzed a genome-wide methylation array in Taiwanese and The Cancer Genome Atlas (TCGA) breast cancer (BC) patients. Most breast cancer-specific circulating methylated CCDC181, GCM2 and ITPRIPL1 biomarkers were found in the plasma. An automatic analysis process of methylated ccfDNA was established. A combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was performed in R using Recursive Partitioning and Regression Trees to establish a new prediction model. Combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was found to have a sensitivity level of 97% and an area under the curve (AUC) of 0.955 in the training set, and a sensitivity level of 100% and an AUC of 0.961 in the test set. The circulating methylated CCDC181, GCM2 and ITPRIPL1 was also significantly decreased after surgery (all p < 0.001). The aberrant methylation patterns of the CCDC181, GCM2 and ITPRIPL1 genes means that they are potential biomarkers for the detection of early BC and can be combined with breast imaging data to achieve higher accuracy, sensitivity and specificity, facilitating breast cancer detection. They may also be applied to monitor the surgical treatment response.

Different methylation signatures at diagnosis in patients with high-risk myelodysplastic syndromes and secondary acute myeloid leukemia predict azacitidine response and longer survival

BACKGROUND

Epigenetic therapy, using hypomethylating agents (HMA), is known to be effective in the treatment of high-risk myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) patients who are not suitable for intensive chemotherapy and/or allogeneic stem cell transplantation. However, response rates to HMA are low and there is an unmet need in finding prognostic and predictive biomarkers of treatment response and overall survival. We performed global methylation analysis of 75 patients with high-risk MDS and secondary AML who were included in CETLAM SMD-09 protocol, in which patients received HMA or intensive treatment according to age, comorbidities and cytogenetic.

RESULTS

Unsupervised analysis of global methylation pattern at diagnosis did not allow patients to be differentiated according to the cytological subtype, cytogenetic groups, treatment response or patient outcome. However, after a supervised analysis we found a methylation signature defined by 200 probes, which allowed differentiating between patients responding and non-responding to azacitidine (AZA) treatment and a different methylation pattern also defined by 200 probes that allowed to differentiate patients according to their survival. On studying follow-up samples, we confirmed that AZA decreases global DNA methylation, but in our cohort the degree of methylation decrease did not correlate with the type of response. The methylation signature detected at diagnosis was not useful in treated samples to distinguish patients who were going to relapse or progress.

CONCLUSIONS

Our findings suggest that in a subset of specific CpGs, altered DNA methylation patterns at diagnosis may be useful as a biomarker for predicting AZA response and survival.

Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.

TMEFF2: A Transmembrane Proteoglycan with Multifaceted Actions in Cancer and Disease

Transmembrane protein with an EGF-like and two Follistatin-like domains 2 (TMEFF2) is a 374-residue long type-I transmembrane proteoglycan which is proteolytically shed from the cell surface. The protein is involved in a range of functions including metabolism, neuroprotection, apoptosis, embryonic development, onco-suppression and endocrine function. TMEFF2 is methylated in numerous cancers, and an inverse correlation with the stage, response to therapy and survival outcome has been observed. Moreover, TMEFF2 methylation increases with breast, colon and gastric cancer progression. TMEFF2 is methylated early during oncogenesis in breast and colorectal cancer, and the detection of methylated free-circulating TMEFF2 DNA has been suggested as a potential diagnostic tool. The TMEFF2 downregulation signature equals and sometimes outperforms the Gleason and pathological scores in prostate cancer. TMEFF2 is downregulated in glioma and cotricotropinomas, and it impairs the production of adrenocorticotropic hormone in glioma cells. Interestingly, through binding the amyloid β protein, its precursor and derivatives, TMEFF2 provides neuroprotection in Alzheimer's disease. Despite undergoing extensive investigation over the last two decades, the primary literature regarding TMEFF2 is incoherent and offers conflicting information, in particular, the oncogenic vs. onco-suppressive role of TMEFF2 in prostate cancer. For the first time, we have compiled, contextualised and critically analysed the vast body of TMEFF2-related literature and answered the apparent discrepancies regarding its function, tissue expression, intracellular localization and oncogenic vs. onco-suppressive role.

Identification and validation of smoking-related genes in lung adenocarcinoma using an in vitro carcinogenesis model and bioinformatics analysis

Background

Lung cancer is one of the most common carcinomas in the world, and lung adenocarcinoma (LUAD) is the most lethal and most common subtype of lung cancer. Cigarette smoking is the most leading risk factor of lung cancer, but it is still unclear how normal lung cells become cancerous in cigarette smokers. This study aims to identify potential smoking-related biomarkers associated with the progression and prognosis of LUAD, as well as their regulation mechanism using an in vitro carcinogenesis model and bioinformatics analysis.

Results

Based on the integration analysis of four Gene Expression Omnibus (GEO) datasets and our mRNA sequencing analysis, 2 up-regulated and 11 down-regulated genes were identified in both S30 cells and LUAD. By analyzing the LUAD dataset in The Cancer Gene Analysis (TCGA) database, 3 of the 13 genes, viz., glycophorin C (GYPC), NME/NM23 nucleoside diphosphate kinase 1 (NME1) and slit guidance ligand 2 (SLIT2), were found to be significantly correlated with LUAD patients’ smoking history. The expression levels of GYPC, NME1 and SLIT2 in S30 cells and lung cancer cell lines were validated by quantitative PCR, immunofluorescence, and western blot assays. Besides, these three genes are associated with tumor invasion depth, and elevated expression of NME1 was correlated with lymph node metastasis. The enrichment analysis suggested that these genes were highly correlated to tumorigenesis and metastasis-related biological processes and pathways. Moreover, the increased expression levels of GYPC and SLIT2, as well as decreased expression of NME1 were associated with a favorable prognosis in LUAD patients. Furthermore, based on the multi-omics data in the TCGA database, these genes were found to be regulated by DNA methylation.

Conclusion

In conclusion, our observations indicated that the differential expression of GYPC, NME1 and SLIT2 may be regulated by DNA methylation, and they are associated with cigarette smoke-induced LUAD, as well as serve as prognostic factors in LUAD patients.

SurvivalMeth: a web server to investigate the effect of DNA methylation-related functional elements on prognosis

Aberrant DNA methylation is a fundamental characterization of epigenetics for carcinogenesis. Abnormality of DNA methylation-related functional elements (DMFEs) may lead to dysfunction of regulatory genes in the progression of cancers, contributing to prognosis of many cancers. There is an urgent need to construct a tool to comprehensively assess the impact of DMFEs on prognosis. Therefore, we developed SurvivalMeth (http://bio-bigdata.hrbmu.edu.cn/survivalmeth) to explore the prognosis-related DMFEs, which documented many kinds of DMFEs, including 309,465 CpG island-related elements, 104,748 transcript-related elements, 77,634 repeat elements, as well as cell-type specific 1,689,653 super enhancers (SE) and 1,304,902 CTCF binding regions for analysis. SurvivalMeth is a convenient tool which collected DNA methylation profiles of 36 cancers and allowed users to query their genes of interest in different datasets for prognosis. Furthermore, SurvivalMeth not only integrated different combinations, including single DMFE, multiple DMFEs, SEs and clinical data, to perform survival analysis on preupload data but also allowed for uploading customized DNA methylation profile of DMFEs from various diseases to analyze. SurvivalMeth provided a comprehensive resource and automated analysis for prognostic DMFEs, including DMFE methylation level, correlation analysis, clinical analysis, differential analysis, DMFE annotation, survival-related detailed result and visualization of survival analysis. In summary, we believe that SurvivalMeth will facilitate prognostic research of DMFEs in diverse cancers.

Protocadherins at the Crossroad of Signaling Pathways

Protocadherins (Pcdhs) are cell adhesion molecules that belong to the cadherin superfamily, and are subdivided into clustered (cPcdhs) and non-clustered Pcdhs (ncPcdhs) in vertebrates. In this review, we summarize their discovery, expression mechanisms, and roles in neuronal development and cancer, thereby highlighting the context-dependent nature of their actions. We furthermore provide an extensive overview of current structural knowledge, and its implications concerning extracellular interactions between cPcdhs, ncPcdhs, and classical cadherins. Next, we survey the known molecular action mechanisms of Pcdhs, emphasizing the regulatory functions of proteolytic processing and domain shedding. In addition, we outline the importance of Pcdh intracellular domains in the regulation of downstream signaling cascades, and we describe putative Pcdh interactions with intracellular molecules including components of the WAVE complex, the Wnt pathway, and apoptotic cascades. Our overview combines molecular interaction data from different contexts, such as neural development and cancer. This comprehensive approach reveals potential common Pcdh signaling hubs, and points out future directions for research. Functional studies of such key factors within the context of neural development might yield innovative insights into the molecular etiology of Pcdh-related neurodevelopmental disorders.

A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates

In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before applying a more refined method for model selection and statistical analysis. This paper proposes a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that with any fixed number of refinement steps, the BJASS procedure retains all important variables with probability tending to 1. Simulation results are presented to illustrate its performance in comparison with some marginal screening methods. Real data examples are provided using a diffuse large-B-cell lymphoma (DLBCL) data and a breast cancer data. We have implemented the BJASS method using Matlab and made it available to readers through Github https://github.com/yiucla/BJASS .

Genome-Wide Tiling Array Analysis of HPV-Induced Warts Reveals Aberrant Methylation of Protein-Coding and Non-Coding Regions

The human papillomaviruses (HPV) are a group of double-stranded DNA viruses that exhibit an exclusive tropism for squamous epithelia. HPV can either be low- or high-risk depending on its ability to cause benign lesions or cancer, respectively. Unsurprisingly, the majority of epigenetic research has focused on the high-risk HPV types, neglecting the low-risk types in the process. Therefore, the main objective of this study is to better understand the epigenetics of wart formation by investigating the differences in methylation between HPV-induced cutaneous warts and normal skin. A number of clear and very significant differences in methylation patterns were found between cutaneous warts and normal skin. Around 55% of the top-ranking 100 differentially methylated genes in warts were protein coding, including the EXOC4, KCNU, RTN1, LGI1, IRF2, and NRG1 genes. Additionally, non-coding RNA genes, such as the AZIN1-AS1, LINC02008, and MGC27382 genes, constituted 11% of the top-ranking 100 differentially methylated genes. Warts exhibited a unique pattern of methylation that is a possible explanation for their transient nature. Since the genetics of cutaneous wart formation are not completely known, the findings of the present study could contribute to a better understanding of how HPV infection modulates host methylation to give rise to warts in the skin.

Construction of Novel DNA Methylation-Based Prognostic Model to Predict Survival in Glioblastoma

Glioblastoma (GBM) is a most aggressive primary cancer in brain with poor prognosis. This study aimed to identify novel tumor biomarkers with independent prognostic values in GBMs. The DNA methylation profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. Differential methylated genes (DMGs) were screened from recurrent GBM samples using limma package in R software. Functional enrichment analysis was performed to identify major biological processes and signaling pathways. Furthermore, critical DMGs associated with the prognosis of GBM were screened according to univariate and multivariate cox regression analysis. A risk score-based prognostic model was constructed for these DMGs and prediction ability of this model was validated in training and validation data set. In total, 495 DMGs were identified between recurrent samples and disease-free samples, including 356 significantly hypermethylated and 139 hypomethylated genes. Functional and pathway items for these DMGs were mainly related to sensory organ development, neuroactive ligand-receptor interaction, pathways in cancer, etc. Five genes with abnormal methylation level were significantly correlated with prognosis according to survival analysis, such as ALX1, KANK1, NUDT12, SNED1, and SVOP. Finally, the risk model provided an effective ability for prognosis prediction both in training and validation data set. We constructed a novel prognostic model for survival prediction of GBMs. In addition, we identified five DMGs as critical prognostic biomarkers in GBM progression.

DNA Methylation Status in Cancer Disease: Modulations by Plant-Derived Natural Compounds and Dietary Interventions

The modulation of the activity of DNA methyltransferases (DNMTs) represents a crucial epigenetic mechanism affecting gene expressions or DNA repair mechanisms in the cells. Aberrant modifications in the function of DNMTs are a fundamental event and part of the pathogenesis of human cancer. Phytochemicals, which are biosynthesized in plants in the form of secondary metabolites, represent an important source of biomolecules with pleiotropic effects and thus provide a wide range of possible clinical applications. It is well documented that phytochemicals demonstrate significant anticancer properties, and in this regard, rapid development within preclinical research is encouraging. Phytochemicals affect several epigenetic molecular mechanisms, including DNA methylation patterns such as the hypermethylation of tumor-suppressor genes and the global hypomethylation of oncogenes, that are specific cellular signs of cancer development and progression. This review will focus on the latest achievements in using plant-derived compounds and plant-based diets targeting epigenetic regulators and modulators of gene transcription in preclinical and clinical research in order to generate novel anticancer drugs as sensitizers for conventional therapy or compounds suitable for the chemoprevention clinical setting in at-risk individuals. In conclusion, indisputable anticancer activities of dietary phytochemicals linked with proper regulation of DNA methylation status have been described. However, precisely designed and well-controlled clinical studies are needed to confirm their beneficial epigenetic effects after long-term consumption in humans.

Construction of prognostic risk prediction model of oral squamous cell carcinoma based on co-methylated genes

This study aimed to identify DNA methylation markers in oral squamous cell carcinoma (OSCC) and to construct a prognostic prediction model of OSCC. For this purpose, the methylation data of patients with OSCC downloaded from The Cancer Genome Atlas were considered as a training dataset. The methylation profiles of GSE37745 for OSCC samples were downloaded from Gene Expression Omnibus and considered as validation dataset. Differentially methylated genes (DMGs) were screened from the TCGA training dataset, followed by co-methylation analysis using weighted correlation network analysis (WGCNA). Subsequently, the methylation and gene expression levels of DMGs involved in key modules were extracted for correlation analysis. Prognosis-related methylated genes were screened using the univariate Cox regression analysis. Finally, the risk prediction model was constructed and validated through GSE52793. The results revealed that a total of 948 DMGs with CpGs were screened out. Co-methylation gene analysis obtained 2 (brown and turquoise) modules involving 380 DMGs. Correlation analysis revealed that the methylation levels of 132 genes negatively correlated with the gene expression levels. By combining with the clinical survival prognosis of samples, 5 optimized prognostic genes [centromere protein V (CENPV), Tubby bipartite transcription factor (TUB), synaptotagmin like 2 (SYTL2), occludin (OCLN) and CAS1 domain containing 1 (CASD1)] were selected for constructing a risk prediction model. It was consistent in the training dataset and GSE52793 that low-risk samples had a better survival prognosis. On the whole, this study indicates that the constructed risk prediction model based on CENPV, SYTL2, OCLN, CASD1, and TUB may have the potential to be used for predicting the survival prognosis of patients with OSCC.

Putative tumor suppressor cytoglobin promotes aryl hydrocarbon receptor ligand-mediated triple negative breast cancer cell death

Nearly 40 000 women die annually from breast cancer in the United States. Clinically available targeted breast cancer therapy is largely ineffective in triple negative breast cancer (TNBC), characterized by tumors that lack expression of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her2). TNBC is associated with a poor prognosis. Previous reports show that aryl hydrocarbon receptor (AhR) partial agonist 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole (5F 203) selectively inhibits the growth of breast cancer cells, including those of the TNBC subtype. We previously demonstrated that 5F 203 induced the expression of putative tumor suppressor gene cytoglobin (CYGB) in breast cancer cells. In the current study, we determined that 5F 203 induces apoptosis and caspase-3 activation in MDA-MB-468 TNBC cells and in T47D ER+ PR + Her2 - breast cancer cells. We also show that caspases and CYGB promote 5F 203-mediated apoptosis in MDA-MB-468 cells. 5F 203 induced lysosomal membrane permeabilization (LMP) and cathepsin B release in MDA-MB-468 and T47D cells. In addition, silencing CYGB attenuated the ability of 5F 203 to induce caspase-3/-7 activation, proapoptotic gene expression, LMP, and cathepsin B release in MDA-MB-468 cells. Moreover, 5F 203 induced CYGB protein expression, proapoptotic protein expression, and caspase-3 cleavage in MDA-MB-468 cells and in MDA-MB-468 xenograft tumors grown orthotopically in athymic mice. These data provide a basis for the development of AhR ligands with the potential to restore CYGB expression as a novel strategy to treat TNBC.

Microarray-based analysis of whole-genome DNA methylation profiling in early detection of breast cancer

Emerging evidence indicated that changes in DNA methylation early in breast cancer (BC) development might be clinically relevant for therapeutic decisions. Through analysis of whole-genome gene expression microarray and DNA methylation microarray, we explored genes with abnormal DNA methylation in BC for early detection. Firstly, human BC tissues and adjacent non-cancerous tissues were collected from nine BC patients. Gene expression microarray sequencing was conducted for identifying differentially expressed genes and DNA methylation microarray sequencing for differentially methylated genes in BC. Differentially expressed genes and methylated genes in BC were further explored using the Cancer Genome Atlas database. The correlation between DNA methylation and gene expression was illustrated by multiple comparisons. In other 60 clinical samples, methylation specific polymerase chain reaction (PCR) and reverse transcription quantitative PCR were applied for the methylation of HOXA4 and IGF1 genes in BC and adjacent non-cancerous tissues. In total, 1680 upregulated genes and 1249 downregulated genes were determined in BC. Chromosome 16 and 17 showed more differentially methylated genes, and DNA methylation level was increased in BC tissues in each gene region. Chromosome 19 showed more differentially methylated genes, and DNA methylation level was increased in BC tissues in the exoniensis 1, untranslated region-5 and transcriptional start site 200 gene regions. In other 60 clinical samples, HOXA4 and IGF1 in BC tissues presented increased DNA methylation and decreased gene expression in BC. MCF7 cells treated with RG108 showed decreased HOXA4 and IGF1 expressions. It was estimated that HOXA4 and IGF1 were identified with increased DNA methylation and decreased gene expression in BC, which may serve as biomarkers in early BC detection.

pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data

Motivation

DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time.

Results

In this article, we proposed a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer-related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals.

Availability and Implementation

The R package 'pETM' is publicly available through CRAN: http://cran.r-project.org .

Contact

sw2206@columbia.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identifying prognostic biomarkers based on aberrant DNA methylation in kidney renal clear cell carcinoma

The outcome of kidney renal clear cell carcinoma (KIRC) differs even among individuals with similar clinical characteristics. DNA methylation is regarded as a regulator of gene expression in cancers, which may be a molecular marker of prognosis. In this study, we aimed to mine novel methylation markers of the prognosis of KIRC. We revealed a total of 2793 genes differentially methylated in their promoter regions (DMGs) and 2979 differentially expressed genes (DEGs) in KIRC tissues compared with normal tissues using The Cancer Genome Atlas datasets. Then, we detected 57 and 34 subpathways enriched among the DMGs and DEGs, respectively, using the R package iSubpathwayMiner. We retained 56 subpathways related to both aberrant methylation and expression based on a hypergeometric test for further analysis. An integrated gene regulatory network was constructed using the regulatory relationships between genes in the subpathways. Using the top 15% of the nodes from the network ranked by degree, survival analysis was performed. We validated four DNA methylation signatures (RAC2, PLCB2, VAV1, and PARVG) as being highly correlated with prognosis in KIRC. These findings suggest that DNA methylation might become a prognostic predictor in KIRC and could supplement histological prognostic prediction.

Candidate genes and pathways downstream of PAX8 involved in ovarian high-grade serous carcinoma

Understanding the biology and molecular pathogenesis of ovarian epithelial cancer (EOC) is key to developing improved diagnostic and prognostic indicators and effective therapies. Although research has traditionally focused on the hypothesis that high-grade serous carcinoma (HGSC) arises from the ovarian surface epithelium (OSE), recent studies suggest that additional sites of origin exist and a substantial proportion of cases may arise from precursor lesions located in the Fallopian tubal epithelium (FTE). In FTE cells, the transcription factor PAX8 is a marker of the secretory cell lineage and its expression is retained in 96% of EOC. We have recently reported that PAX8 is involved in the tumorigenic phenotype of ovarian cancer cells. In this study, to uncover genes and pathways downstream of PAX8 involved in ovarian carcinoma we have determined the molecular profiles of ovarian cancer cells and in parallel of Fallopian tube epithelial cells by means of a silencing approach followed by an RNA-seq analysis. Interestingly, we highlighted the involvement of pathways like WNT signaling, epithelial-mesenchymal transition, p53 and apoptosis. We believe that our analysis has led to the identification of candidate genes and pathways regulated by PAX8 that could be additional targets for the therapy of ovarian carcinoma.

Detection of prognostic methylation markers by methylC-capture sequencing in acute myeloid leukemia

Clinical and genetic features incompletely predict outcome in acute myeloid leukemia (AML). The value of clinical methylation assays for prognostic markers has not been extensively explored. We assess the prognostic implications of methylC-capture sequencing (MCC-Seq) in patients with de novo AML by integrating DNA methylation and genetic risk stratification. MCC-Seq assessed DNA methylation level in 44 samples. The differentially methylated regions associated with prognostic genetic information were identified. The selected prognostic DNA methylation markers were independently validated in two sets. MCC-Seq exhibited good performance in AML patients. A panel of 12 differentially methylated genes was identified with promoter hyper-differentially methylated regions associated with the outcome. Compared with a low M-value, a high M-value was associated with failure to achieve complete remission (p = 0.024), increased hazard for disease-free survival in the study set (p = 0.039) and poor overall survival in The Cancer Genome Atlas set (p = 0.038). Hematopoietic stem cell transplantation and survival outcomes were not adversely affected by a high M-value (p = 0.271). Our study establishes that MCC-Seq is a stable, reproducible, and cost-effective methylation assay in AML. A 12-gene M-value encompassing epigenetic and genetic prognostic information represented a valid prognostic marker for patients with AML.

Whole‑genome scale identification of methylation markers specific for cerebral palsy in monozygotic discordant twins

Cerebral palsy (CP) is a severe type of brain disease affecting movement and posture. Although CP has strong genetic and environmental components, considerable differences in the methylome between monozygotic (MZ) twins discordant for CP implicates epigenetic contributors as well. In order to determine the differences in methylation in patients with CP without interference of the interindividual genomic variation, four pairs of MZ twins discordant for CP were profiled for DNA methylation changes using reduced representation bisulfite sequencing on the genomic-scale. Similar DNA methylation patterns were observed in all samples. However, MZ twins demonstrated higher correlations and closer evolutionary associations compared with the other samples, indicating a stable methylome of MZ twins. A total of 190 differentially methylated genes (DMGs) were identified using Student's t-test, of which 37 genes were hypermethylated in the CP group while the remainders were hypomethylated compared with control group. The identified DMGs were enriched in several cerebral abnormalities, including cerebral cortical atrophy and cerebral atrophy, suggesting that the occurrence of CP may be associated with the methylation alterations. The neighboring genes of DMGs in the protein-protein interaction network were enriched in numerous important functions in essential processes. The results of the present study identified important genes that may epigenetically contribute to the occurrence and development of CP in MZ twins, suggesting that the different prevalence of CP in identical twins may be associated with DNA methylation alterations.

Large-scale DNA methylation expression analysis across 12 solid cancers reveals hypermethylation in the calcium-signaling pathway

Tumorigenesis is linked to the role of DNA methylation in gene expression regulation. Yet, cancer is a highly heterogeneous disease in which the global pattern of DNA methylation and gene expression, especially across diverse cancers, is not well understood. We investigated DNA methylation status and its association with gene expressions across 12 solid cancer types obtained from The Cancer Genome Atlas. Results showed that global hypermethylation was an important characteristic across all 12 cancer types. Moreover, there were more epigenetically silenced than epigenetically activated genes across the cancers. Further analysis identified epigenetically silenced genes shared in the calcium-signaling pathway across the different cancer types. Reversing the aberrant DNA methylation of genes involved in the calcium-signaling pathway could be an effective strategy for suppressing cancers and developing anti-cancer drugs.

AGTR1 promoter hypermethylation in lung squamous cell carcinoma but not in lung adenocarcinoma

Aberrant DNA methylation is associated with non-small cell lung cancer (NSCLC), suggesting that gene promoter methylation may be a potential biomarker for the detection or risk prediction of NSCLC. The present study aimed to evaluate the potential usage of angiotensin II receptor type 1 (AGTR1) methylation in two major pathologic subtypes: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Quantitative methylation-specific polymerase chain reaction was used to investigate the effect of AGTR1 promoter methylation in the tumor and the paired adjacent non-tumor tissue samples from 42 patients with LUSC, and 69 with LUAD. The percentage of methylated reference was calculated and presented as the median (interquartile range 25th-75th percentile). The results of the current study revealed that there was significantly increased AGTR1 promoter methylation in the tumor tissues compared with the paired adjacent non-tumor tissue [97.4 (57.22-130.5) vs. 85 (48.25-123); P=0.024]. Furthermore, higher AGTR1 promoter methylation was observed in patients with LUSC compared with LUAD (odds ratio=2.483; 95% confidence interval=1.125-5.480; P=0.023). Significant differences were identified in AGTR1 methylation between non-tumor and the tumor tissues in LUSC [113.5 (68.33-148.73) vs. 93.04 (45.94-140); P=0.008]. In addition, the Cancer Genome Atlas data of 378 patients with LUSC and 477 with LUAD revealed an inverse correlation between gene expression and the methylation status of AGTR1 promoter.. These data suggest that AGTR1 hypermethylation is a promising biomarker to assist in LUSC detection and diagnosis.

The landscape of DNA methylation-mediated regulation of long non-coding RNAs in breast cancer

Although systematic studies have identified a host of long non-coding RNAs (lncRNAs) which are involved in breast cancer, the knowledge about the methyla-tion-mediated dysregulation of those lncRNAs remains limited. Here, we integrated multi-omics data to analyze the methylated alteration of lncRNAs in breast invasive carcinoma (BRCA). We found that lncRNAs showed diverse methylation patterns on promoter regions in BRCA. LncRNAs were divided into two categories and four subcategories based on their promoter methylation patterns and expression levels be-tween tumor and normal samples. Through cis-regulatory analysis and gene ontology network, abnormally methylated lncRNAs were identified to be associated with can-cer regulation, proliferation or expression of transcription factors. Competing endog-enous RNA network and functional enrichment analysis of abnormally methylated lncRNAs showed that lncRNAs with different methylation patterns were involved in several hallmarks and KEGG pathways of cancers significantly. Finally, survival analysis based on mRNA modules in networks revealed that lncRNAs silenced by high methylation were associated with prognosis significantly in BRCA. This study enhances the understanding of aberrantly methylated patterns of lncRNAs and pro-vides a novel insight for identifying cancer biomarkers and potential therapeutic tar-gets in breast cancer.

DNA methylation dynamics: identification and functional annotation

DNA methylation is an epigenetic modification of cytosines that undergoes dynamic changes in a temporal, spatial and cell-type-specific manner. Recent advances in technology have permitted the profiling of high-throughput methylomes in large numbers of biological samples. Various computational tools have been developed to identify and analyze DNA methylation dynamics in a variety of critical biological processes. As DNA methylation is becoming increasingly viewed as a dynamic process, the mechanisms governing DNA methylation dynamics and its roles in the transcriptional regulatory network are of great interest. It has been reported that DNA methylation dynamics plays essential roles in multiple biological processes, including development and cancer. As a functional event, the dynamics of DNA methylation have become increasingly relevant to many researchers. Here, we review state-of-the-art advances at three levels (genome-wide identification, regulatory mechanism investigation and the functional annotation) in the field of DNA methylation dynamics, as well as the future perspective of DNA methylation dynamics.

The identification of age-associated cancer markers by an integrative analysis of dynamic DNA methylation changes

As one of the most widely studied epigenetic modifications, DNA methylation has an important influence on human traits and cancers. Dynamic variations in DNA methylation have been reported in malignant neoplasm and aging; however, the mechanisms remain poorly understood. By constructing an age-associated and cancer-related weighted network (ACWN) based on the correlation of the methylation level and the protein-protein interaction, we found that DNA methylation changes associated with age were closely related to the occurrence of cancer. Additional analysis of 102 module genes mined from the ACWN revealed discrimination based on two main patterns. One pattern involved methylation levels that increased with aging and were higher in cancer patients compared with normal controls (HH pattern). The other pattern involved methylation levels that decreased with aging and were lower in cancer compared with normal (LL pattern). Upon incorporation with gene expression levels, 25 genes were filtered based on negative regulation by DNA methylation. These genes were regarded as potential cancer risk markers that were influenced by age in the process of carcinogenesis. Our results will facilitate further studies regarding the impact of the epigenetic effects of aging on diseases and will aid in the development of tailored cancer preventive strategies.

Analysis of TMEFF2 allografts and transgenic mouse models reveals roles in prostate regeneration and cancer

BACKGROUND

Previous results from our lab indicate a tumor suppressor role for the transmembrane protein with epidermal growth factor and two follistatin motifs 2 (TMEFF2) in prostate cancer (PCa). Here, we further characterize this role and uncover new functions for TMEFF2 in cancer and adult prostate regeneration.

METHODS

The role of TMEFF2 was examined in PCa cells using Matrigel(TM) cultures and allograft models of PCa cells. In addition, we developed a transgenic mouse model that expresses TMEFF2 from a prostate specific promoter. Anatomical, histological, and metabolic characterizations of the transgenic mouse prostate were conducted. The effect of TMEFF2 in prostate regeneration was studied by analyzing branching morphogenesis in the TMEFF2-expressing mouse lobes and alterations in branching morphogenesis were correlated with the metabolomic profiles of the mouse lobes. The role of TMEFF2 in prostate tumorigenesis in whole animals was investigated by crossing the TMEFF2 transgenic mice with the TRAMP mouse model of PCa and analyzing the histopathological changes in the progeny.

RESULTS

Ectopic expression of TMEFF2 impairs growth of PCa cells in Matrigel or allograft models. Surprisingly, while TMEFF2 expression in the TRAMP mouse did not have a significant effect on the glandular prostate epithelial lesions, the double TRAMP/TMEFF2 transgenic mice displayed an increased incidence of neuroendocrine type tumors. In addition, TMEFF2 promoted increased branching specifically in the dorsal lobe of the prostate suggesting a potential role in developmental processes. These results correlated with data indicating an alteration in the metabolic profile of the dorsal lobe of the transgenic TMEFF2 mice.

CONCLUSIONS

Collectively, our results confirm the tumor suppressor role of TMEFF2 and suggest that ectopic expression of TMEFF2 in mouse prostate leads to additional lobe-specific effects in prostate regeneration and tumorigenesis. This points to a complex and multifunctional role for TMEFF2 during PCa progression.

Aberrant Hypermethylation of SALL3 with HPV Involvement Contributes to the Carcinogenesis of Cervical Cancer

OBJECTIVE

This study aimed to investigate the methylation status of the promoter region of spalt-like transcription factor 3 (SALL3) and the expression of SALL3 in cervical cancer to explore the function of this gene in cervical cancer carcinogenesis.

METHODS

The methylation status of SALL3 was detected by methylation-specific PCR, and SALL3 gene expression was assessed by real-time quantitative PCR in the cervical cancer cell lines, SiHa, HeLa and C33A, as well as in cervical cancer tissue samples (n = 23), matched pericarcinomatous tissue samples (n = 23) and normal cervix tissue samples (n = 17). MTT was used to measure the cell viability and proliferation capacity of SiHa and HeLa cells.

RESULTS

The SALL3 promoter was completely methylated in SiHa cells, unmethylated in C33A cells and partially methylated in HeLa cells. After treatment of SiHa and HeLa cells with 5 μM and 10 μM of 5-Azacytidine (5-Aza), respectively, the methylation level of the SALL3 promoter decreased and observed increase in the degree of unmethylation in a dose-dependent manner. Moreover, the relative expression of SALL3 mRNA increased as the concentration of 5-Aza increased in SiHa (p<0.05) and HeLa (p<0.05) cells. This above-mentioned increase in SALL3 mRNA in SiHa cells was more remarkable than that observed in HeLa cells. Cell proliferation capacity also decreased after administration of 5-Aza to SiHa and HeLa cells (p<0.05). Methylation of the SALL3 promoter was observed in 15 of 23 (65.21%) cervical cancer tissue samples, 15 of 23 (65.21%) matched pericarcinomatous tissue samples and 5 of 17 (29.41%) normal cervical tissue samples (p<0.05). SALL3 mRNA expression was significantly lower in cervical cancer and pericarcinomatous tissues compared with normal cervical tissues (p<0.05). In all cervix tissue samples, HPV infection was positively associated with hypermethylation of the promoter region of SALL3 (p<0.05, r = 0.408), and the expression of SALL3 mRNA in HPV-positive tissues was lower than that in HPV-negative tissues (p<0.05).

CONCLUSION

The aberrant hypermethylation of SALL3 together with HPV involvement inactivated its function as a tumor suppressor and contributed to carcinogenesis in cervical cancer.