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

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

BACKGROUND AND OBJECTIVE

Breast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes.

METHODS

NNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers.

RESULTS

The SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes.

CONCLUSIONS

The proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Epigenetic alterations fuel brain metastasis via regulating inflammatory cascade

Brain metastasis (BrM) is a major threat to the survival of melanoma, breast, and lung cancer patients. Circulating tumor cells (CTCs) cross the blood-brain barrier (BBB) and sustain in the brain microenvironment. Genetic mutations and epigenetic modifications have been found to be critical in controlling key aspects of cancer metastasis. Metastasizing cells confront inflammation and gradually adapt in the unique brain microenvironment. Currently, it is one of the major areas that has gained momentum. Researchers are interested in the factors that modulate neuroinflammation during BrM. We review here various epigenetic factors and mechanisms modulating neuroinflammation and how this helps CTCs to adapt and survive in the brain microenvironment. Since epigenetic changes could be modulated by targeting enzymes such as histone/DNA methyltransferase, deacetylases, acetyltransferases, and demethylases, we also summarize our current understanding of potential drugs targeting various aspects of epigenetic regulation in BrM.

Identification of prognostically significant DNA methylation signatures in patients with various breast cancer types

Breast cancer (BC) is the most frequently diagnosed cancer and one of the major causes of female mortality. The development of prognostic models based on multiomics data is the main goal of precision oncology. Aberrant DNA methylation in BC is a diagnostic marker of carcinogenesis. Despite the existing factors of BC prognosis, introduction of methylation markers would make it possible to obtain more accurate prognostic scores. The study was aimed to assess DNA methylation signatures in various BC subtypes for clinical endpoints and patients' clinicopathological characteristics. The data on methylation of CpG dinucleotides (probes) and clinical characteristics of BC samples were obtained from The Cancer Genome Atlas Breast Cancer database. CpG dinucleotides associated with the selected endpoints were chosen by univariate Cox regression method. The LASSO method was used to search for stable probes, while further signature construction and testing of the clinical characteristics independence were performed using multivariate Cox regression. The dignostic and prognostic potential of the signatures was assessed using ROC analysis and Kaplan–Meier curves. It has been shown that the signatures of selected probes have a significant diagnostic (AUC 0.76–1) and prognostic (p < 0.05) potential. This approach has made it possible to identify 47 genes associated with good and poor prognosis, among these five genes have been described earlier. If the genome-wide DNA analysis results are available, the research approach applied can be used to study molecular pathogenesis of BC and other disorders.

ProTICS reveals prognostic impact of tumor infiltrating immune cells in different molecular subtypes

Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS.

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.

Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

Background

Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis.

Methods

In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed.

Results

We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed.

Conclusions

Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

Electronic supplementary material

The online version of this article (10.1007/s12282-020-01191-z) contains supplementary material, which is available to authorized users.

Perspectives on the Role of Histone Modification in Breast Cancer Progression and the Advanced Technological Tools to Study Epigenetic Determinants of Metastasis

Metastasis is a complex process that involved in various genetic and epigenetic alterations during the progression of breast cancer. Recent evidences have indicated that the mutation in the genome sequence may not be the key factor for increasing metastatic potential. Epigenetic changes were revealed to be important for metastatic phenotypes transition with the development in understanding the epigenetic basis of breast cancer. Herein, we aim to present the potential epigenetic drivers that induce dysregulation of genes related to breast tumor growth and metastasis, with a particular focus on histone modification including histone acetylation and methylation. The pervasive role of major histone modification enzymes in cancer metastasis such as histone acetyltransferases (HAT), histone deacetylases (HDACs), DNA methyltransferases (DNMTs), and so on are demonstrated and further discussed. In addition, we summarize the recent advances of next-generation sequencing technologies and microfluidic-based devices for enhancing the study of epigenomic landscapes of breast cancer. This feature also introduces several important biotechnologists for identifying robust epigenetic biomarkers and enabling the translation of epigenetic analyses to the clinic. In summary, a comprehensive understanding of epigenetic determinants in metastasis will offer new insights of breast cancer progression and can be achieved in the near future with the development of innovative epigenomic mapping tools.

Multi-Omic Regulation of the PAM50 Gene Signature in Breast Cancer Molecular Subtypes

Breast cancer is a disease that exhibits heterogeneity that goes from the genomic to the clinical levels. This heterogeneity is thought to be captured (at least partially) by the so-called breast cancer molecular subtypes. These molecular subtypes were initially defined based on the unsupervised clustering of gene expression and its correlate with histological, morphological, phenotypic and clinical features already known. Later, a 50-gene signature, PAM50, was defined in order to identify the biological subtype of a given sample within the clinical setting. The PAM50 signature was obtained by the use of unsupervised statistical methods, and therefore no limitation was set on the biological relevance (or lack of) of the selected genes beyond its predictive capacity. An open question that remains is what are the regulatory elements that drive the various expression behaviors of this set of genes in the different molecular subtypes. This question becomes more relevant as the measurement of more biological layers of regulation becomes accessible. In this work, we analyzed the gene expression regulation of the 50 genes in the PAM50 signature, in terms of (a) gene co-expression, (b) transcription factors, (c) micro-RNAs, and (d) methylation. Using data from the Cancer Genome Atlas (TCGA) for the Luminal A and B, Basal, and HER2-enriched molecular subtypes as well as normal tumor adjacent tissue, we identified predictors for gene expression through the use of an elastic net model. We compare and contrast the sets of identified regulators for the gene signature in each molecular subtype, and systematically compare them to current literature. We also identified a unique set of predictors for the expression of genes in the PAM50 signature associated with each of the molecular subtypes. Most selected predictors are exclusive for a PAM50 gene and predictors are not shared across subtypes. There are only 13 coding transcripts and 2 miRNAs selected for the four subtypes. MiR-21 and miR-10b connect almost all the PAM50 genes in all the subtypes and normal tissue, but do it in an exclusive manner, suggesting a cancer switch from miR-10b coordination in normal tissue to miR-21. The PAM50 gene sets of selected predictors that enrich for a function across subtypes, support that different regulatory molecular mechanisms are taking place. With this study we aim to a wider understanding of the regulatory mechanisms that differentiate the expression of the PAM50 signature, which in turn could perhaps help understand the molecular basis of the differences between the molecular subtypes.

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.

Gene expression and DNA methylation analyses suggest that immune process-related ADCY6 is a prognostic factor of luminal-like breast cancer

Breast cancer is a malignant tumor that seriously threatens women's health, and luminal-like cancer subtypes account for the majority of the cases. The purpose of this study was to investigate the relationships among DNA methylation, gene expression profile, and the tumor-immune microenvironment of luminal-like breast cancer, and to identify the potential key genes that regulate immune cell infiltration in luminal-like breast cancer. The ESTIMATE algorithm was applied to calculate immune scores and stromal scores of patients with breast cancer. Kaplan-Meier curves were generated for survival analysis. The clusterProfile package was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. The protein-protein interaction (PPI) network was constructed using the STRING database and Cytoscape software. Correlations between ADCY6 expression and immune cell infiltration-related pathways were analyzed by gene set variation analysis. R software was used for the statistical analysis and figure generation. Disease-free survival was higher in the immune score-high group than it was in the immune score-low group, while the stromal score had no correlation with prognosis. There were 515 genes that differed in both gene expression and DNA methylation levels, and these genes were mainly enriched in immune process-related pathways. ADCY6 was enriched in module A of the PPI network. Patients with downregulation and hypermethylation of ADCY6 associated with a better prognosis. ADCY6 expression was negatively correlated with the activation of immune process-related signaling pathways, immune checkpoint receptors, and ligands, except for CLEC4G. DNA methylation was found to be involved in the regulation of the key cellular pathways of luminal-like breast cancer immune cell infiltration. Additionally, ADCY6 was identified as a prognostic factor involved in the DNA methylation-regulated immune processes in luminal-like breast cancer.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

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.

Methotrexate combined with methylprednisolone for the recovery of motor function and differential gene expression in rats with spinal cord injury

Methylprednisolone is a commonly used drug for the treatment of spinal cord injury, but high doses of methylprednisolone can increase the incidence of infectious diseases. Methotrexate has anti-inflammatory activity and immunosuppressive effects, and can reduce inflammation after spinal cord injury. To analyze gene expression changes and the molecular mechanism of methotrexate combined with methylprednisolone in the treatment of spinal cord injury, a rat model of spinal cord contusion was prepared using the PinPoint™ precision cortical impactor technique. Rats were injected with methylprednisolone 30 mg/kg 30 minutes after injury, and then subcutaneously injected with 0.3 mg/kg methotrexate 1 day after injury, once a day, for 2 weeks. TreadScan gait analysis found that at 4 and 8 weeks after injury, methotrexate combined with methylprednisolone significantly improved hind limb swing time, stride time, minimum longitudinal deviation, instant speed, footprint area and regularity index. Solexa high-throughput sequencing was used to analyze differential gene expression. Compared with methylprednisolone alone, differential expression of 316 genes was detected in injured spinal cord treated with methotrexate and methylprednisolone. The 275 up-regulated genes were mainly related to nerve recovery, anti-oxidative, anti-inflammatory and anti-apoptotic functions, while 41 down-regulated genes were mainly related to proinflammatory and pro-apoptotic functions. These results indicate that methotrexate combined with methylprednisolone exhibited better effects on inhibiting the activity of inflammatory cytokines and enhancing antioxidant and anti-apoptotic effects and thereby produced stronger neuroprotective effects than methotrexate alone. The 316 differentially expressed genes play an important role in the above processes.