Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Widespread redundancy in -omics profiles of cancer mutation states

BACKGROUND

In studies of cellular function in cancer, researchers are increasingly able to choose from many -omics assays as functional readouts. Choosing the correct readout for a given study can be difficult, and which layer of cellular function is most suitable to capture the relevant signal remains unclear.

RESULTS

We consider prediction of cancer mutation status (presence or absence) from functional -omics data as a representative problem that presents an opportunity to quantify and compare the ability of different -omics readouts to capture signals of dysregulation in cancer. From the TCGA Pan-Cancer Atlas that contains genetic alteration data, we focus on RNA sequencing, DNA methylation arrays, reverse phase protein arrays (RPPA), microRNA, and somatic mutational signatures as -omics readouts. Across a collection of genes recurrently mutated in cancer, RNA sequencing tends to be the most effective predictor of mutation state. We find that one or more other data types for many of the genes are approximately equally effective predictors. Performance is more variable between mutations than that between data types for the same mutation, and there is little difference between the top data types. We also find that combining data types into a single multi-omics model provides little or no improvement in predictive ability over the best individual data type.

CONCLUSIONS

Based on our results, for the design of studies focused on the functional outcomes of cancer mutations, there are often multiple -omics types that can serve as effective readouts, although gene expression seems to be a reasonable default option.

HCNM: Heterogeneous Correlation Network Model for Multi-level Integrative Study of Multi-omics Data for Cancer Subtype Prediction

Integrative analysis of multi-omics data is important for biomedical applications, as it is required for a comprehensive understanding of biological function. Integrating multi-omics data serves multiple purposes, such as, an integrated data model, dimensionality reduction of omic features, patient clustering, etc. For oncological data, patient clustering is synonymous to cancer subtype prediction. However, there is a gap in combining some of the widely used integrative analyses to build more powerful tools. To bridge the gap, we propose a multi-level integration algorithm to identify representative integrative subspace and use it for cancer subtype prediction. The three integrative approaches we implement on multi-omics features are, (1) multivariate multiple (linear) regression of the features from a cohort of patients/samples, (2) network construction using different omics features, and (3) fusion of sample similarity networks across the features. We use a type of multilayer network, called heterogeneous network, as a data model to transition between a network-free (NF) regression model and a network-based (NB) model, which uses correlation networks. The heterogeneous networks consist of intra- and inter-layer graphs. Our proposed heterogeneous correlation network model, HCNM, is central to our algorithm for gene-ranking, integrative subspace identification, and tumor-specific subtypes prediction. The genes of our representative integrative subspace have been enriched with gene-ontology and found to exhibit significant gene-disease association (GDA) scores. The subspace in genes which is less than 5% of the total gene-set of each genomic feature is used with NB fusion integrative model to predict sample subtypes. As the identified integrative subspace data of multi-omics is less prone to noise, bias, and outliers, our experiments show that the subtypes in our results agree with previous benchmark studies and exhibit better classification between poor and good survival of patient cohorts.Clinical relevance: Finding significant cancer-specific genes and subtypes of cancer is vital for early prognosis, and personalized treatment; therefore, improves survival probability of a patient.

Epigenetic interplay between methylation and miRNA in bladder cancer: focus on isoform expression

BACKGROUND

Various epigenetic factors are responsible for the non-genetic regulation on gene expression. The epigenetically dysregulated oncogenes or tumor suppressors by miRNA and/or DNA methylation are often observed in cancer cells. Each of these epigenetic regulators has been studied well in cancer progressions; however, their mutual regulatory relationship in cancer still remains unclear. In this study, we propose an integrative framework to systematically investigate epigenetic interactions between miRNA and methylation at the alternatively spliced mRNA level in bladder cancer. Each of these epigenetic regulators has been studied well in cancer progressions; however, their mutual regulatory relationship in cancer still remains unclear.

RESULTS

The integrative analyses yielded 136 significant combinations (methylation, miRNA and isoform). Further, overall survival analysis on the 136 combinations based on methylation and miRNA, high and low expression groups resulted in 13 combinations associated with survival. Additionally, different interaction patterns were examined.

CONCLUSIONS

Our study provides a higher resolution of molecular insight into the crosstalk between two epigenetic factors, DNA methylation and miRNA. Given the importance of epigenetic interactions and alternative splicing in cancer, it is timely to identify and understand the underlying mechanisms based on epigenetic markers and their interactions in cancer, leading to alternative splicing with primary functional impact.

A systematic view of computational methods for identifying driver genes based on somatic mutation data

Abnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile-Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.

Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer

MOTIVATION

To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW, and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene-gene graph using pathway information by assigning interactions between genes in multiple layers of networks.

RESULTS

: As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene-gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets.

AVAILABILITY

iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integration of Proteomics and Other Omics Data

In recent biomedical studies, multidimensional profiling, which collects proteomics as well as other types of omics data on the same subjects, is getting increasingly popular. Proteomics, transcriptomics, genomics, epigenomics, and other types of data contain overlapping as well as independent information, which suggests the possibility of integrating multiple types of data to generate more reliable findings/models with better classification/prediction performance. In this chapter, a selective review is conducted on recent data integration techniques for both unsupervised and supervised analysis. The main objective is to provide the "big picture" of data integration that involves proteomics data and discuss the "intuition" beneath the recently developed approaches without invoking too many mathematical details. Potential pitfalls and possible directions for future developments are also discussed.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

BACKGROUND

Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.

METHODS

In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.

RESULTS

Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.

CONCLUSION

In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer

MicroRNAs (miRNAs) are small endogenous regulatory molecules that modulate gene expression post-transcriptionally. Although differential expression of miRNAs have been implicated in many diseases (including cancers), the underlying mechanisms of action remain unclear. Because each miRNA can target multiple genes, miRNAs may potentially have functional implications for the overall behavior of entire pathways. Here, we investigate the functional consequences of miRNA dysregulation through an integrative analysis of miRNA and mRNA expression data using a novel approach that incorporates pathway information a priori. By searching for miRNA-pathway associations that differ between healthy and tumor tissue, we identify specific relationships at the systems level which are disrupted in cancer. Our approach is motivated by the hypothesis that if an miRNA and pathway are associated, then the expression of the miRNA and the collective behavior of the genes in a pathway will be correlated. As such, we first obtain an expression-based summary of pathway activity using Isomap, a dimension reduction method which can articulate non-linear structure in high-dimensional data. We then search for miRNAs that exhibit differential correlations with the pathway summary between phenotypes as a means of finding aberrant miRNA-pathway coregulation in tumors. We apply our method to cancer data using gene and miRNA expression datasets from The Cancer Genome Atlas and compare ∼10⁵ miRNA-pathway relationships between healthy and tumor samples from four tissues (breast, prostate, lung and liver). Many of the flagged pairs we identify have a biological basis for disruption in cancer.

LSCDFS-MKL: A multiple kernel based method for lung squamous cell carcinomas disease-free survival prediction with pathological and genomic data

Lung squamous cell carcinoma (SCC) is a fatal disease in both male and female, for which current treatments are inadequate. Surgical resection is regarded as the cornerstone of treatment for patients with lung SCC, but even for the same stage patients, the wide spectrum of disease-free survival (DFS) times exits. Therefore, how to improve the DFS prediction performance of lung SCC becomes one major research area. In this study, we proposed a novel method called LSCDFS-MKL, which was on the basis of multiple kernel learning to predict DFS of lung SCC. In LSCDFS-MKL, we first efficiently integrated pathological images and genomic data (copy number aberration, gene expression, protein expression) from lung SCC. The results of LSCDFS-MKL between different types of data show that the features extracted from pathological images play an important role in DFS prediction of lung SCC. Then we compared our method LSCDFS-MKL with other existing methods and performance analysis indicates that LSCDFS-MKL has a significantly better performance than other prediction methods. After that, we applied the proposed method on different stage stratums and the performance demonstrates that LSCDFS-MKL remains efficient in DFS prediction of lung SCC patients. Finally, we performed LSCDFS-MKL on an independent validation dataset and the accuracy of DFS prediction achieves 100%, which is promising.

Preparing next-generation scientists for biomedical big data: artificial intelligence approaches

Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.

Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images

Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). We synthesize a workflow which (a) intelligently samples the training data by automatically selecting only image areas that display visible disease-relevant tissue state and (b) isolates regions most pertinent to the trained CNN prediction and translates them to observable and qualitative features such as color, intensity, cell and tissue morphology and texture. We use the Cancer Genome Atlas’s Breast Invasive Carcinoma (TCGA-BRCA) histology dataset to build a model predicting patient attributes (disease stage and node status) and the tumor proliferation challenge (TUPAC 2016) breast cancer histology image repository to help identify disease-relevant tissue state (mitotic activity). We find that our enhanced CNN based workflow both increased patient attribute predictive accuracy (~2% increase for disease stage and ~10% increase for node status) and experimentally proved that a data-driven CNN histology model predicting breast invasive carcinoma stages is highly sensitive to features such as color, cell size, and shape, granularity, and uniformity. This work summarizes the need for understanding the widely trusted models built using deep learning and adds a layer of biological context to a technique that functioned as a classification only approach till now.

Data integration by multi-tuning parameter elastic net regression

Background

To integrate molecular features from multiple high-throughput platforms in prediction, a regression model that penalizes features from all platforms equally is commonly used. However, data from different platforms are likely to differ in effect sizes, the proportion of predictive features, and correlations structures. Subtle but important features may be missed by shrinking all features equally.

Results

We propose an Elastic net (EN) model with separate tuning parameter penalties for each platform that is fit using standard software. In a comprehensive simulation study, we evaluated the performance of EN logistic regression with multiple tuning penalties. We found that when the number of informative features differs among the platforms, and when there is no notable correlation between the features from different platforms, the multi-tuning parameter EN yields more predictive models. Moreover, the multi-tuning parameter EN is robust, in the sense that there is no loss of predictivity relative to a single tuning parameter EN when features across all platforms have similar effects. We also investigated the performance of multi-tuning parameter EN using real cancer datasets.

Conclusion

The proposed multi-tuning parameter EN model, fit using standard penalized regression software, can achieve better prediction in sample classification when integrating multiple genomic platforms, compared to the traditional method where a single penalty parameter is used for all features in different platforms.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2401-1) contains supplementary material, which is available to authorized users.

Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

Background

Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.

Methods

Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.

Results

The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.

Conclusions

In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

Large-Scale Analysis of Genetic and Clinical Patient Data

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

Mixomics analysis of breast cancer: Long non-coding RNA linc01561 acts as ceRNA involved in the progression of breast cancer

OBJECTIVE

This study aimed at finding the long non-coding RNA (lncRNA), miRNA and mRNA which played critical roles in breast cancer (BrCa) by using mixOmics R package.

METHOD

The BrCa dataset were obtained from TCGA and then analyzed using "DESeq2" R package. Multivariate analyses were performed with the "mixOmics" R package and the first component of the stacked partial least-Squares discriminant analysis results were used for searching the interested lncRNA, miRNA and mRNA. qRT-PCR was applied to identify the bioinformatics results in four BrCa cell lines (MCF7, BT-20, ZR-75-1, and MX-1) and the breast epithelial cell line MCF-10 A. Then cells (MCF-1 and MX-1) were transfected with si-linc01561, miR-145-5p mimics and si-MMP11 to further investigate the effects of linc01561, miR-145-5p and MMP11 on the BrCa cells proliferation and apoptosis.

RESULTS

MixOmics results showed that linc01561, miR-145-5p and MMP11 might play important roles in BrCa. qRT-PCR results identified that in BrCa cell lines, linc01561 and MMP11 were higher expressed while miR-145-5p was lower expressed compared with those in epithelial cell line. The linc01561 inhibition elevated miR-145-5p expression and then suppressed MMP11 expression. Moreover, linc01561 inhibition suppressed the BrCa cells proliferation and promoted the apoptosis, which was realized by up-regulating expression of miR-145-5p and down-regulating expression of MMP11.

CONCLUSION

In summary, the findings of this study, based on ceRNA theory, combining the research foundation of miR-145-5p and MMP11, and taking linc01561 as a new study point, provide new insight into molecular-level reversing proliferation and apoptosis of BrCa.

Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma

It is common that cancer patients have different molecular signatures even though they have similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge that identifies knowledge-driven genomic interactions associated with outcomes of interest. However, no systematic approach has been proposed to identify interaction models between pathways based on multi-omics data. Here we have proposed such a novel methodological framework, called metadimensional knowledge-driven genomic interactions (MKGIs). To test the utility of the proposed framework, we applied it to an ovarian cancer dataset including multi-omics profiles from The Cancer Genome Atlas to predict grade, stage, and survival outcome. We found that each knowledge-driven genomic interaction model, based on different genomic datasets, contains different sets of pathway features, which suggests that each genomic data type may contribute to outcomes in ovarian cancer via a different pathway. In addition, MKGI models significantly outperformed the single knowledge-driven genomic interaction model. From the MKGI models, many interactions between pathways associated with outcomes were found, including the mitogen-activated protein kinase (MAPK) signaling pathway and the gonadotropin-releasing hormone (GnRH) signaling pathway, which are known to play important roles in cancer pathogenesis. The beauty of incorporating biological knowledge into the model based on multi-omics data is the ability to improve diagnosis and prognosis and provide better interpretability. Thus, determining variability in molecular signatures based on these interactions between pathways may lead to better diagnostic/treatment strategies for better precision medicine.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

BACKGROUND

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

METHODS

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

RESULTS

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

CONCLUSIONS

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer

Background

One of the fundamental challenges in cancer is to detect the regulators of gene expression changes during cancer progression. Through transcriptional silencing of critical cancer-related genes, epigenetic change such as DNA methylation plays a crucial role in cancer. In addition, miRNA, another major component of epigenome, is also a regulator at the post-transcriptional levels that modulate transcriptome changes. However, a mechanistic role of synergistic interactions between DNA methylation and miRNA as epigenetic regulators on transcriptomic changes and its association with clinical outcomes such as survival have remained largely unexplored in cancer.

Methods

In this study, we propose an integrative framework to identify epigenetic interactions between methylation and miRNA associated with transcriptomic changes. To test the utility of the proposed framework, the bladder cancer data set, including DNA methylation, miRNA expression, and gene expression data, from The Cancer Genome Atlas (TCGA) was analyzed for this study.

Results

First, we found 120 genes associated with interactions between the two epigenomic components. Then, 11 significant epigenetic interactions between miRNA and methylation, which target E2F3, CCND1, UTP6, CDADC1, SLC35E3, METRNL, TPCN2, NACC2, VGLL4, and PTEN, were found to be associated with survival. To this end, exploration of TCGA bladder cancer data identified epigenetic interactions that are associated with survival as potential prognostic markers in bladder cancer.

Conclusions

Given the importance and prevalence of these interactions of epigenetic events in bladder cancer it is timely to understand further how different epigenetic components interact and influence each other.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0269-y) contains supplementary material, which is available to authorized users.

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.

Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature

Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.