Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes

Neural-net-based cell deconvolution from DNA methylation reveals tumor microenvironment associated with cancer prognosis

DNA methylation is a pivotal epigenetic modification that defines cellular identity. While cell deconvolution utilizing this information is considered useful for clinical practice, current methods for deconvolution are limited in their accuracy and resolution. In this study, we collected DNA methylation data from 945 human samples derived from various tissues and tumor-infiltrating immune cells and trained a neural network model with them. The model, termed MEnet, predicted abundance of cell population together with the detailed immune cell status from bulk DNA methylation data, and showed consistency to those of flow cytometry and histochemistry. MEnet was superior to the existing methods in the accuracy, speed, and detectable cell diversity, and could be applicable for peripheral blood, tumors, cell-free DNA, and formalin-fixed paraffin-embedded sections. Furthermore, by applying MEnet to 72 intrahepatic cholangiocarcinoma samples, we identified immune cell profiles associated with cancer prognosis. We believe that cell deconvolution by MEnet has the potential for use in clinical settings.

Identification of Distinct Brain MRI Phenotypes and Their Association With Long-Term Dementia Risk in Community-Dwelling Older Adults

BACKGROUND AND OBJECTIVES

Individual brain MRI markers only show at best a modest association with long-term occurrence of dementia. Therefore, it is challenging to accurately identify individuals at increased risk for dementia. We aimed to identify different brain MRI phenotypes by hierarchical clustering analysis based on combined neurovascular and neurodegenerative brain MRI markers and to determine the long-term dementia risk within the brain MRI phenotype subgroups.

METHODS

Hierarchical clustering analysis based on 32 combined neurovascular and neurodegenerative brain MRI markers in community-dwelling individuals of the Age-Gene/Environment Susceptibility Reykjavik Study was applied to identify brain MRI phenotypes. A Cox proportional hazards regression model was used to determine the long-term risk for dementia per subgroup.

RESULTS

We included 3,056 participants and identified 15 subgroups with distinct brain MRI phenotypes. The phenotypes ranged from limited burden, mostly irregular white matter hyperintensity (WMH) shape and cerebral atrophy, mostly irregularly WMHs and microbleeds, mostly cortical infarcts and atrophy, mostly irregularly shaped WMH and cerebral atrophy to multiburden subgroups. Each subgroup showed different long-term risks for dementia (min-max range hazard ratios [HRs] 1.01-6.18; mean time to follow-up 9.9 ± 2.6 years); especially the brain MRI phenotype with mainly WMHs and atrophy showed a large increased risk (HR 6.18, 95% CI 3.37-11.32).

DISCUSSION

Distinct brain MRI phenotypes can be identified in community-dwelling older adults. Our results indicate that distinct brain MRI phenotypes are related to varying long-term risks of developing dementia. Brain MRI phenotypes may in the future assist in an improved understanding of the structural correlates of dementia predisposition.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data

Motivation

Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches.

Results

iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets.

Availability and implementation

Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

Progression Risk Score Estimation Based on Immunostaining Data in Oral Cancer Using Unsupervised Hierarchical Clustering Analysis: A Retrospective Study in Taiwan

This study aimed to investigate whether the progression risk score (PRS) developed from cytoplasmic immunohistochemistry (IHC) biomarkers is available and applicable for assessing risk and prognosis in oral cancer patients. Participants in this retrospective case-control study were diagnosed between 2012 and 2014 and subsequently underwent surgical intervention. The specimens from surgery were stained by IHC for 16 cytoplasmic target markers. We evaluated the results of IHC staining, clinical and pathological features, progression-free survival (PFS), and overall survival (OS) of 102 oral cancer patients using a novel estimation approach with unsupervised hierarchical clustering analysis. Patients were stratified into high-risk (52) and low-risk (50) groups, according to their PRS; a metric consisting of cytoplasmic PLK1, PhosphoMet, SGK2, and SHC1 expression. Moreover, PRS could be extended for use in the Cox proportional hazard regression model to estimate survival outcomes with associated clinical parameters. Our study findings revealed that the high-risk patients had a significantly increased risk in cancer progression compared with low-risk patients (hazard ratio (HR) = 2.20, 95% confidence interval (CI) = 1.10-2.42, p = 0.026). After considering the influences of demographics, risk behaviors, and tumor characteristics, risk estimation with PRS provided distinct PFS groups for patients with oral cancer (p = 0.017, p = 0.019, and p = 0.020). Our findings support that PRS could serve as an ideal biomarker for clinical use in risk stratification and progression assessment in oral cancer.

Phenomapping of Patients with Primary Breast Cancer Using Machine Learning-Based Unsupervised Cluster Analysis

Primary breast cancer (PBC) is a heterogeneous disease at the clinical, histopathological, and molecular levels. The improved classification of PBC might be important to identify subgroups of the disease, relevant to patient management. Machine learning algorithms may allow a better understanding of the relationships within heterogeneous clinical syndromes. This work aims to show the potential of unsupervised learning techniques for improving classification in PBC. A dataset of 712 women with PBC is used as a motivating example. A set of variables containing biological prognostic parameters is considered to define groups of individuals. Four different clustering methods are used: K-means, self-organising maps, hierarchical agglomerative (HAC), and Gaussian mixture models clustering. HAC outperforms the other clustering methods. With an optimal partitioning parameter, the methods identify two clusters with different clinical profiles. Patients in the first cluster are younger and have lower values of the oestrogen receptor (ER) and progesterone receptor (PgR) than patients in the second cluster. Moreover, cathepsin D values are lower in the first cluster. The three most important variables identified by the HAC are: age, ER, and PgR. Unsupervised learning seems a suitable alternative for the analysis of PBC data, opening up new perspectives in the particularly active domain of dissecting clinical heterogeneity.

Methylated biomarkers for breast cancer identified through public database analysis and plasma target capture sequencing

Background

Aberrant methylation is common during the early stage of cancer development. This study was designed to investigate DNA methylation as biomarker for breast cancer.

Methods

Public database analysis and methylation-specific whole-gene sequencing were conducted to identify methylated biomarkers that would enable early non-invasive diagnosis of breast cancer. Firstly, the data was obtained from the TCGA Database and the Blueprint Epigenome Database. Secondly, methylation-specific whole-gene sequencing was conducted in 10 female patients with early-stage breast cancer and 10 healthy female volunteers from Nanfang Hospital of Southern Medical University between March 2018 and July 2018. Thirdly, the R language was used for data analysis, and KEGG and DAVID online tool was used for annotations.

Results

We found that methylation levels at 13 cytosine-phosphate-guanine (CpG) sites (cg04066177, cg04281344, cg05995576, cg06221609, cg08642731, cg11388802, cg12665414, cg14557216, cg19404723, cg19457909, cg24570211, cg25818763, and cg26215982) in the malignant tissue DNA were highly comparable to those of circulating cell-free DNA (cfDNA) of breast cancer patients, but were significantly different from those of normal tissue DNA, cfDNA of healthy women, and leukocyte DNA. In addition, three CpG sites (cg04281344, cg24570211, and cg26215982) were confirmed in clinical research, which showed that the sensitivity and specificity of these CpGs as biomarkers for breast cancer were 69.4–83.7% and 85.7–88.6%, respectively.

Conclusions

New biomarkers were identified and confirmed for breast cancer by comparing the methylation of tumour tissues, leukocytes, and non-plasma DNA.

Accurate and efficient detection of gene fusions from RNA sequencing data

The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.

Identification of ZG16B as a prognostic biomarker in breast cancer

Zymogen granule protein 16B (ZG16B) has been identified in various cancers, while so far the association between ZG16B and breast cancer hasn’t been explored. Our aim is to confirm whether it can serve as a prognostic biomarker in breast cancer. In this study, Oncomine, Cancer Cell Line Encyclopedia (CCLE), Ualcan, and STRING database analyses were conducted to detect the expression level of ZG16B in breast cancer with different types. Kaplan–Meier plotter was used to analyze the prognosis of patients with high or low expression of ZG16B. We found that ZG16B was significantly upregulated in breast cancer. Moreover, ZG16B was closely associated with foregone biomarkers and crucial factors in breast cancer. In the survival analysis, high expression of ZG16B represents a favorable prognosis in patients. Our work demonstrates the latent capacity of ZG16B to be a biomarker for prognosis of breast cancer.

GABC: A comprehensive resource and Genome Atlas for Breast Cancer

We developed the Genome Atlas of Breast Cancer (GABC), a global map of noncoding events in the human genome associated with breast cancer that provides a valuable reference resource for users to investigate the underlying genome abnormalities in breast cancer patients. Although significant progress has been made in breast cancer treatment, its morbidity and recurrence rates in women are still high worldwide. Curation and integration of breast cancer-related dysregulations from multiple aspects is essential for disease prevention and diagnosis. In this study, we developed the GABC, which contains 10 172 aberrant noncoding events occurring at multiomics levels, including the genome (single nucleotide polymorphism and somatic mutation), transcriptome (long noncoding RNA and microRNA) and epigenome (DNA methylation, enhancer and superenhancer). Each event entry provides descriptions of detailed biological mechanisms specific to the region or element. Users can also check the genome locations and relationships of functional regulators. The GABC provides a flexible and user-friendly interface for users to search, browse and download data. In addition, the GABC provides an interface to submit newly discovered noncoding events that can be included in the database. Therefore, the GABC aims to constantly enhance our understanding of noncoding genomic events in breast cancer.

MRI phenotypes of the brain are related to future stroke and mortality in patients with manifest arterial disease: The SMART-MR study

Neurodegenerative and neurovascular diseases lead to heterogeneous brain abnormalities. A combined analysis of these abnormalities by phenotypes of the brain might give a more accurate representation of the underlying aetiology. We aimed to identify different MRI phenotypes of the brain and assessed the risk of future stroke and mortality within these subgroups. In 1003 patients (59 ± 10 years) from the Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study, different quantitative 1.5T brain MRI markers were used in a hierarchical clustering analysis to identify 11 distinct subgroups with a different distribution in brain MRI markers and cardiovascular risk factors, and a different risk of stroke (Cox regression: from no increased risk compared to the reference group with relatively few brain abnormalities to HR = 10.34; 95% CI 3.80↔28.12 for the multi-burden subgroup) and mortality (from no increased risk compared to the reference group to HR = 4.00; 95% CI 2.50↔6.40 for the multi-burden subgroup). In conclusion, within a group of patients with manifest arterial disease, we showed that different MRI phenotypes of the brain can be identified and that these were associated with different risks of future stroke and mortality. These MRI phenotypes can possibly classify individual patients and assess their risk of future stroke and mortality.

Circulating plasma lncRNAs as novel markers of EGFR mutation status and monitors of epidermal growth factor receptor-tyrosine kinase inhibitor therapy

Background

Epidermal growth factor receptor (EGFR) gene mutations predict tumor response to EGFR tyrosine kinase inhibitors (EGFR‐TKIs) in non‐small cell lung cancer (NSCLC). However, even patients with EGFR‐sensitive mutations in NSCLC have limited efficacy with EGFR‐TKI. Studies have shown that long noncoding RNA (lncRNA) is related to diagnosis and prognosis with NSCLC. This study aimed to explore the correlation between lncRNA in NSCLC patients with EGFR mutation status and EGFR‐TKI efficacy.

Methods

The amplification‐refractory mutation system method was used to test the EGFR mutation status in tumor tissues and pleural effusions of NSCLC patients. Three EGFR‐mutant patients and three EGFR wild‐type patients were selected. Differential lncRNA was performed on the pleural effusions of the two selected groups of patients using Clariom D Human chip technology. Five lncRNAs significantly associated with EGFR mutation status were screened by FC value and GO analysis, and then evaluated by real‐time quantitative polymerase chain reaction in NSCLC patients' pleural effusions. Three were further analyzed in NSCLC patients' plasma.

Results

There were 61 significant differences in lncRNA between EGFR mutation‐positive and wild‐type patients. Among them, SCARNA7, MALAT1, NONHSAT017369, NONHSAT051892, and FTH1P2 were significantly associated with EGFR mutation status. SCARNA7, MALAT1, and NONHSAT017369 showed consistent results with plasma in pleural effusions compared to EGFR wild‐type, all upregulated in the EGFR mutation group.

Conclusion

This study shows that lncRNAs can be used not only as potential biomarkers for predicting the mutation status of EGFR and the efficacy of EGFR‐TKI, but also for monitoring the efficacy of EGFR‐TKI.

High-Resolution Bisulfite-Sequencing of Peripheral Blood DNA Methylation in Early-Onset and Familial Risk Breast Cancer Patients

PURPOSE

Understanding and explaining hereditary predisposition to cancer has focused on the genetic etiology of the disease. However, mutations in known genes associated with breast cancer, such as BRCA1 and BRCA2, account for less than 25% of familial cases of breast cancer. Recently, specific epigenetic modifications at BRCA1 have been shown to promote hereditary breast cancer, but the broader potential for epigenetic contribution to hereditary breast cancer is not yet well understood.

EXPERIMENTAL DESIGN

We examined DNA methylation through deep bisulfite sequencing of CpG islands and known promoter or regulatory regions in peripheral blood DNA from 99 patients with familial or early-onset breast or ovarian cancer, 6 unaffected BRCA mutation carriers, and 49 unaffected controls.

RESULTS

In 9% of patients, we observed altered methylation in the promoter regions of genes known to be involved in cancer, including hypermethylation at the tumor suppressor PTEN and hypomethylation at the proto-oncogene TEX14. These alterations occur in the form of allelic methylation that span up to hundreds of base pairs in length.

CONCLUSIONS

Our observations suggest a broader role for DNA methylation in early-onset, familial risk breast cancer. Further studies are warranted to clarify these mechanisms and the benefits of DNA methylation screening for early risk prediction of familial cancers.

HOPX homeobox methylation in differentiated thyroid cancer and its clinical relevance

BACKGROUND

The inactivation of the tumor-suppressor homeodomain-only protein X (HOPX) usually involves promoter methylation in several cancer types. This study aimed to investigate the HOPX-β mRNA expression and promoter methylation and their clinical relevance in differentiated thyroid cancer (DTC).

PATIENTS AND METHODS

Clinicopathological data and paraffin-embedded thyroid tumor tissues from 21 patients with DTC and 6 with benign tumors (T) and their non-tumor parenchyma (NT) were investigated. Tumor cell lines (FTC238, FTC236 and WRO) were treated with demethylating agent. HOPX-β mRNA expression was assessed by qRT-PCR and methylation status by Q-MSP. Thyroid cancer data from Cancer Genome Atlas (TCGA) was also collected.

RESULTS

HOPX-β mRNA re-expression in two cell lines treated with demethylating agent was observed concomitantly with reduced promoter methylation. Reduced mRNA expression in T group compared to their NT was observed, and reduced protein expression in T compared to NT was observed in three cases. Low mRNA expression with high methylation status was detected in 6/14 DTC samples. High methylation status was associated with older age at diagnosis, recurrent or progressive disease and with the presence of new neoplasm event post initial therapy while hyper-methylation correlated with worse overall survival, worse disease-free status and older age.

CONCLUSION

A moderate coupling of downregulation of HOPX-β mRNA expression in DTC followed by high HOPX-β promoter methylation was observed however; high HOPX promoter methylation status was associated with the worse prognosis of DTC patients.

A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains

Background

Partially methylated domains are extended regions in the genome exhibiting a reduced average DNA methylation level. They cover gene-poor and transcriptionally inactive regions and tend to be heterochromatic. We present a comprehensive comparative analysis of partially methylated domains in human and mouse cells, to identify structural and functional features associated with them.

Results

Partially methylated domains are present in up to 75% of the genome in human and mouse cells irrespective of their tissue or cell origin. Each cell type has a distinct set of partially methylated domains, and genes expressed in such domains show a strong cell type effect. The methylation level varies between cell types with a more pronounced effect in differentiating and replicating cells. The lowest level of methylation is observed in highly proliferating and immortal cancer cell lines. A decrease of DNA methylation within partially methylated domains tends to be linked to an increase in heterochromatic histone marks and a decrease of gene expression. Characteristic combinations of heterochromatic signatures in partially methylated domains are linked to domains of early and middle S-phase and late S-G2 phases of DNA replication.

Conclusions

Partially methylated domains are prominent signatures of long-range epigenomic organization. Integrative analysis identifies them as important general, lineage- and cell type-specific topological features. Changes in partially methylated domains are hallmarks of cell differentiation, with decreased methylation levels and increased heterochromatic marks being linked to enhanced cell proliferation. In combination with broad histone marks, partially methylated domains demarcate distinct domains of late DNA replication.

Electronic supplementary material

The online version of this article (10.1186/s13059-018-1510-5) contains supplementary material, which is available to authorized users.

Decreased methylation in the SNAI2 and ADAM23 genes associated with de-differentiation and haematogenous dissemination in breast cancers

BACKGROUND

In breast cancer (BC), deregulation of DNA methylation leads to aberrant expressions and functions of key regulatory genes. In our study, we investigated the relationship between the methylation profiles of genes associated with cancer invasivity and clinico-pathological parameters. In detail, we studied differences in the methylation levels between BC patients with haematogenous and lymphogenous cancer dissemination.

METHODS

We analysed samples of primary tumours (PTs), lymph node metastases (LNMs) and peripheral blood cells (PBCs) from 59 patients with sporadic disseminated BC. Evaluation of the DNA methylation levels of six genes related to invasivity, ADAM23, uPA, CXCL12, TWIST1, SNAI1 and SNAI2, was performed by pyrosequencing.

RESULTS

Among the cancer-specific methylated genes, we found lower methylation levels of the SNAI2 gene in histologic grade 3 tumours (OR = 0.61; 95% CI, 0.39-0.97; P = 0.038) than in fully or moderately differentiated cancers. We also evaluated the methylation profiles in patients with different cancer cell dissemination statuses (positivity for circulating tumour cells (CTCs) and/or LNMs). We detected the significant association between reduced DNA methylation of ADAM23 in PTs and presence of CTCs in the peripheral blood of patients (OR = 0.45; 95% CI, 0.23-0.90; P = 0.023).

CONCLUSION

The relationships between the decreased methylation levels of the SNAI2 and ADAM23 genes and cancer de-differentiation and haematogenous dissemination, respectively, indicate novel functions of those genes in the invasive processes. After experimental validation of the association between the lower values of SNAI2 and ADAM23 methylation and clinical features of aggressive BCs, these methylation profiles could improve the management of metastatic disease.

Large-scale analysis of DFNA5 methylation reveals its potential as biomarker for breast cancer

Background

Breast cancer is the most frequent cancer among women worldwide. Biomarkers for early detection and prognosis of these patients are needed. We hypothesized that deafness, autosomal dominant 5 (DFNA5) may be a valuable biomarker, based upon strong indications for its role as tumor suppressor gene and its function in regulated cell death. In this study, we aimed to analyze DFNA5 methylation and expression in the largest breast cancer cohort to date using publicly available data from TCGA, in order to further unravel the role of DFNA5 as detection and/or prognostic marker in breast cancer. We analyzed Infinium HumanMethylation450k data, covering 22 different CpGs in the DFNA5 gene (668 breast adenocarcinomas and 85 normal breast samples) and DFNA5 expression (Agilent 244K Custom Gene Expression: 476 breast adenocarcinomas and 56 normal breast samples; RNA-sequencing: 666 breast adenocarcinomas and 71 normal breast samples).

Results

DFNA5 methylation and expression were significantly different between breast cancer and normal breast samples. Overall, breast cancer samples showed higher DFNA5 methylation in the putative gene promoter compared to normal breast samples, whereas in the gene body and upstream of the putative gene promoter, the opposite is true. Furthermore, DFNA5 methylation, in 10 out of 22 CpGs, and expression were significantly higher in lobular compared to ductal breast cancers. An important result of this study was the identification of a combination of one CpG in the gene promoter (CpG07504598) and one CpG in the gene body (CpG12922093) of DFNA5, which was able to discriminate between breast cancer and normal breast samples (AUC = 0.93). This model was externally validated in three independent datasets. Moreover, we showed that estrogen receptor state is associated with DFNA5 methylation and expression. Finally, we were able to find a significant effect of DFNA5 gene body methylation on a 5-year overall survival time.

Conclusions

We conclude that DFNA5 methylation shows strong potential as detection and prognostic biomarker for breast cancer.

Xist reduction in breast cancer upregulates AKT phosphorylation via HDAC3-mediated repression of PHLPP1 expression

Long noncoding RNAs (lncRNAs) dysregulated in cancer potentially play oncogenic or tumor-suppressive roles. While the X inactivate-specific transcript (Xist) lncRNA is important for X-chromosome inactivation in female cells, very little is known about the role of Xist in human breast cancer in modulating cellular pathway(s). Here, we show that Xist expression is significantly reduced in breast tumor samples and cancer cell lines. Xist knockdown or overexpression resulted in increased or decreased levels, respectively, of AKT phosphorylation and cell viability. Further studies revealed an inverse correlation between Xist and phospho-AKT levels in breast cancer samples. Additionally, Xist knockdown-elicited increase of cell viability was attenuated by AKT inhibitor. These results suggest that Xist negatively regulates cell viability via inhibition of AKT activation. Interestingly, decreased Xist expression in breast cancer samples was associated with reduced levels of Jpx RNA, an lncRNA that positively regulates Xist promoter activity. Accordingly, Jpx knockdown enhanced AKT activation and cell viability. We also demonstrate that knockdown of Xist or SPEN, an intermediator protein to link Xist, SMRT co-repressor and HDAC3 complexes for X-chromosome inactivation, decreased expression of PHLPP1, a phosphatase to remove AKT phosphorylation, via increased HDAC3 recruitment to the PHLPP1 promoter, correlating with increased AKT phosphorylation. Our findings elucidate the tumor suppressor role of Xist in breast cancer and provide the molecular basis of Xist in downregulating AKT activation.

Multiplex bioimaging of piRNA molecular pathway-regulated theragnostic effects in a single breast cancer cell using a piRNA molecular beacon

Recently, PIWI-interacting small non-coding RNAs (piRNAs) have emerged as novel cancer biomarkers candidate because of their high expression level in various cancer types and role in the control of tumor suppressor genes. In this study, a novel breast cancer theragnostics probe based on a single system targeting the piRNA-36026 (piR-36026) molecular pathway was developed using a piR-36026 molecular beacon (MB). The piR-36026 MB successfully visualized endogenous piR-36026 biogenesis, which is highly expressed in MCF7 cells (a human breast cancer cell line), and simultaneously inhibited piR-36026-mediated cancer progression in vitro and in vivo. We discovered two tumor suppressor proteins, SERPINA1 and LRAT, that were directly regulated as endogenous piR-36026 target genes in MCF7 cells. Furthermore, multiplex bioimaging of a single MCF7 cell following treatment with piR-36026 MB clearly visualized the direct molecular interaction of piRNA-36026 with SERPINA1 or LRAT and subsequent molecular therapeutic responses including caspase-3 and PI in the nucleus.

The impact of next-generation sequencing on the DNA methylation-based translational cancer research

Epigenetics is currently in an exponential phase of growth, constituting one of the most promising fields in science, particularly in cancer research. Impaired epigenetic processes can lead to abnormal gene activity or inactivity, causing cellular disorders that are closely associated with tumor initiation and progression. Thus, there is a pivotal role of massive sequencing techniques for epigenetics, which aim to find novel biomarkers, factors of prognosis and prediction, and targets for achieving personalized treatments. We present a brief description of the evolution of next-generation sequencing technologies and its coupling with DNA methylation analysis techniques, highlighting its future in translational medicine and presenting significant findings in several malignancies. We also expose critical topics related to the implementation of these approaches, which is expected to be affordable for most research centers in the near future.

X chromosome gain in male breast cancer

Male breast cancer (MBC) is an uncommon disease whose molecular profile is not well known. X chromosome gain has been described as a marker of aggressive behavior in female breast cancer. The aim of this study is to investigate the role of the X chromosome in male breast cancer. Twenty cases of male breast invasive ductal carcinoma were retrieved and compared with 10 cases of gynecomastia. Cases were tested by fluorescence in situ hybridization to assess a cytogenetic profile for the X chromosome. The X chromosome status was compared with histopathologic features and stage at presentation. All MBC cases harbored an X chromosome gain (100%) in a variable percentage of neoplastic cells, ranging from 31% to 85% (mean, 59%). On the contrary, all cases of gynecomastia showed wild X chromosome asset. The patients' age at surgery and tumor grading showed a statistically significant correlation (P = .0188-.04), with the percentages of neoplastic cells showing an X chromosome gain. These data suggest that this X chromosome gain plays a role in the neoplastic transformation of male breast epithelial cells.