Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer

Integrated molecular-network analysis reveals infertility-associated key genes and transcription factors in the non-obstructive azoospermia

BACKGROUND

Male infertility is a multifactorial reproductive health problem with complex causes. Non-obstructive azoospermia (NOA) is characterized by failure of spermatogenesis, leading to the absence of spermatozoa in ejaculates. The molecular mechanism underlying the NOA is still not well understood.

OBJECTIVES

This study aims to identify the key genes involved in male infertility that could be a potential biomarker in the diagnosis and prognosis of azoospermia.

STUDY DESIGN

The microarray expression profiles dataset GSE45885 and GSE45887 were downloaded from the NCBI's Gene Expression Omnibus (GEO) database and analyzed for male infertility-associated differentially expressed genes (DEGs) using the GEO2R tool. The common DEGs between the two datasets were combined and their protein-protein interaction (PPI) network was constructed using Cytoscape to reveal the hub genes by topology and module analysis. In addition, transcription factors (TFs) and protein kinases regulating the hub genes were identified using the X2K tool. Then, the expression of the hub genes was validated by analyzing the GSE190752 microarray dataset. Further, the PPI network was screened for biological roles and enriched pathways using DAVID software.

RESULTS

About 256 DEGs associated with NOA were identified and constructed the PPI network to find the infertility-associated proteins. The biological processes linked with these proteins were spermatogenesis, cell differentiation, flagellated sperm motility, and spermatid development. The topology and module analysis of the infertility-associated protein network identified the hub genes TEX38, FAM71F, PRR30, FAM166A, LYZL6, TPPP2, ARMC12, SPACA4, and FAM205A, which were found to be upregulated in the non-obstructive azoospermia. In addition, a total of 23 transcription factors and 3 protein kinases that are regulating these key hub genes were identified. Further these hub genes expression was validated using the microarray data and found that their expression was increased in the testicular biopsies obtained from NOA subjects, compared to healthy individuals.

CONCLUSION

The identified key genes and its associated transcription factors are known to regulate the infertility-related processes in the non-obstructive azoospermia. Also, the clinical sample-based microarray data validation for the expression of these key hub genes indicates their potentiality to develop them as diagnostic or prognostic biomarkers for NOA.

Proteomic characterization of post-translational modifications in drug discovery

Protein post-translational modifications (PTMs), which are usually enzymatically catalyzed, are major regulators of protein activity and involved in almost all celluar processes. Dysregulation of PTMs is associated with various types of diseases. Therefore, PTM regulatory enzymes represent as an attractive and important class of targets in drug research and development. Inhibitors against kinases, methyltransferases, deacetyltransferases, ubiquitin ligases have achieved remarkable success in clinical application. Mass spectrometry-based proteomics technologies serve as a powerful approach for system-wide characterization of PTMs, which facilitates the identification of drug targets, elucidation of the mechanisms of action of drugs, and discovery of biomakers in personalized therapy. In this review, we summarize recent advances of proteomics-based studies on PTM targeting drugs and discuss how proteomics strategies facilicate drug target identification, mechanism elucidation, and new therapy development in precision medicine.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Applying graph database technology for analyzing perturbed co-expression networks in cancer

Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

cDNA expression library screening revealed novel functional genes involved in clear cell carcinogenesis of the ovary in vitro

In order to identify genes involved in the pathogenesis of clear cell carcinoma of the ovary (CCC), functional screening using a cDNA expression library was performed. We extracted mRNA from a CCC cell line (RMG-1), established a cDNA library using a retroviral vector, transfected that library into mouse NIH3T3 cells and sequenced the resultant foci. The tissue-type specific expression of isolated genes and their transforming activities were evaluated. Seven genes were isolated. Of these genes, the mRNA expression of SEC61B and DVL1 is significantly stronger in CCC than in other histological types (p < .05). Immunohistochemical staining reveals the stronger expression of SEC61B and C1ORF38 than normal ovarian tissues (p < .05). Focus formation is confirmed by the transfection of SEC61B, C1ORF38, and DVL1 into NIH3T3 cells. The present study identified novel genes including SEC61B, C1ORF38, and DVL1, involved in the pathogenesis of CCC. These genes may be additional therapeutic targets for CCC.Impact statementWhat is already known on this subject? Several important genetic abnormalities, including ARID1A and PIK3CA mutations, have been reported in ovarian clear cell carcinoma (CCC).What the results of this study add? SEC61B, C1ORF38, and DVL1 were newly detected as candidate genes involved in ovarian clear cell carcinogenesis.What the implications are of these findings for clinical practice and/or further research? Functional screening using a cDNA expression library may be a useful technique to identify functional genes for pathogenesis. The information obtained using this technique may provide new therapeutic targets of CCC.

Systematic analysis of the intersection of disease mutations with protein modifications

BACKGROUND

Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus.

RESULTS

Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP.

CONCLUSIONS

We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers.

Standard treatment and emerging drugs for managing synovial sarcoma: adult's and pediatric oncologist perspective

INTRODUCTION

in this review we discuss the standard of care for both pediatric and adult synovial sarcoma (SS), the prognostic differences between them, and the treatments available for localized and advanced diseases. We also overview the biology and the recent drugs under consideration in clinical trials on SS. Areas covered: we focus on new targeted therapies being investigated for advanced SS, especially anti-angiogenic drugs, and immunotherapy. We review all the published data and ongoing trials dedicated to SS or to soft tissue sarcoma in general, paying particular attention to the results obtained in SS patients. Expert opinion: we expect new treatment strategies to become available for SS in the near future. The ongoing and published trials on targeted therapies and immunotherapy mainly concern adult patients, but the somatic biology of pediatric SS has some similarities as in adult disease. A stronger cooperation between adult and pediatric oncologists in recent years has led to a more shared effort to find new treatment strategies for advanced SS patients, regardless of their age.

Epigenomic Reordering Induced by Polycomb Loss Drives Oncogenesis but Leads to Therapeutic Vulnerabilities in Malignant Peripheral Nerve Sheath Tumors

Malignant peripheral nerve sheath tumor (MPNST) is an aggressive sarcoma with recurrent loss-of-function alterations in polycomb-repressive complex 2 (PRC2), a histone-modifying complex involved in transcriptional silencing. To understand the role of PRC2 loss in pathogenesis and identify therapeutic targets, we conducted parallel global epigenomic and proteomic analysis of archival formalin-fixed, paraffin-embedded (FFPE) human MPNST with and without PRC2 loss (MPNST_LOSS vs. MPNST_RET). Loss of PRC2 resulted in increased histone posttranslational modifications (PTM) associated with active transcription, most notably H3K27Ac and H3K36me2, whereas repressive H3K27 di- and trimethylation (H3K27me2/3) marks were globally lost without a compensatory gain in other repressive PTMs. Instead, DNA methylation globally increased in MPNST_LOSS. Epigenomic changes were associated with upregulation of proteins in growth pathways and reduction in IFN signaling and antigen presentation, suggesting a role for epigenomic changes in tumor progression and immune evasion, respectively. These changes also resulted in therapeutic vulnerabilities. Knockdown of NSD2, the methyltransferase responsible for H3K36me2, restored MHC expression and induced interferon pathway expression in a manner similar to PRC2 restoration. MPNST_LOSS were also highly sensitive to DNA methyltransferase and histone deacetylase (HDAC) inhibitors. Overall, these data suggest that global loss of PRC2-mediated repression renders MPNST differentially dependent on DNA methylation to maintain transcriptional integrity and makes them susceptible to therapeutics that promote aberrant transcription initiation. SIGNIFICANCE: Global profiling of histone PTMs and protein expression in archival human MPNST illustrates how PRC2 loss promotes oncogenesis but renders tumors vulnerable to pharmacologic modulation of transcription.See related commentary by Natarajan and Venneti, p. 3172.

The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine

The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic.

Genomic characterization of genes encoding histone acetylation modulator proteins identifies therapeutic targets for cancer treatment

A growing emphasis in anticancer drug discovery efforts has been on targeting histone acetylation modulators. Here we comprehensively analyze the genomic alterations of the genes encoding histone acetylation modulator proteins (HAMPs) in the Cancer Genome Atlas cohort and observe that HAMPs have a high frequency of focal copy number alterations and recurrent mutations, whereas transcript fusions of HAMPs are relatively rare genomic events in common adult cancers. Collectively, 86.3% (63/73) of HAMPs have recurrent alterations in at least 1 cancer type and 16 HAMPs, including 9 understudied HAMPs, are identified as putative therapeutic targets across multiple cancer types. For example, the recurrent focal amplification of BRD9 is observed in 9 cancer types and genetic depletion of BRD9 inhibits tumor growth. Our systematic genomic analysis of HAMPs across a large-scale cancer specimen cohort may facilitate the identification and prioritization of potential drug targets and selection of suitable patients for precision treatment.

Targeting histone acetylation modulators (HAMPs) is a promising avenue of drug discovery in cancer research. Here, the authors integrate multi-dimensional genomic profiles to systematically investigate recurrent genomic alterations in HAMPs, identifying potential therapeutic targets for precision epigenetic treatment.

15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms

For 15 years the mission of PhosphoSitePlus® (PSP, https://www.phosphosite.org) has been to provide comprehensive information and tools for the study of mammalian post-translational modifications (PTMs). The number of unique PTMs in PSP is now more than 450 000 from over 22 000 articles and thousands of MS datasets. The most important areas of growth in PSP are in disease and isoform informatics. Germline mutations associated with inherited diseases and somatic cancer mutations have been added to the database and can now be viewed along with PTMs and associated quantitative information on novel 'lollipop' plots. These plots enable researchers to interactively visualize the overlap between disease variants and PTMs, and to identify mutations that may alter phenotypes by rewiring signaling networks. We are expanding the sequence space to include over 30 000 human and mouse isoforms to enable researchers to explore the important but understudied biology of isoforms. This represents a necessary expansion of sequence space to accommodate the growing precision and depth of coverage enabled by ongoing advances in mass spectrometry. Isoforms are aligned using a new algorithm. Exploring the worlds of PTMs and disease mutations in the entire isoform space will hopefully lead to new biomarkers, therapeutic targets, and insights into isoform biology.

Comprehensive molecular biomarker identification in breast cancer brain metastases

Background

Breast cancer brain metastases (BCBM) develop in about 20–30% of breast cancer (BC) patients. BCBM are associated with dismal prognosis not at least due to lack of valuable molecular therapeutic targets. The aim of the study was to identify new molecular biomarkers and targets in BCBM by using complementary state-of-the-art techniques.

Methods

We compared array expression profiles of three BCBM with 16 non-brain metastatic BC and 16 primary brain tumors (prBT) using a false discovery rate (FDR) p < 0.05 and fold change (FC) > 2. Biofunctional analysis was conducted on the differentially expressed probe sets. High-density arrays were employed to detect copy number variations (CNVs) and whole exome sequencing (WES) with paired-end reads of 150 bp was utilized to detect gene mutations in the three BCBM.

Results

The top 370 probe sets that were differentially expressed between BCBM and both BC and prBT were in the majority comparably overexpressed in BCBM and included, e.g. the coding genes BCL3, BNIP3, BNIP3P1, BRIP1, CASP14, CDC25A, DMBT1, IDH2, E2F1, MYCN, RAD51, RAD54L, and VDR. A number of small nucleolar RNAs (snoRNAs) were comparably overexpressed in BCBM and included SNORA1, SNORA2A, SNORA9, SNORA10, SNORA22, SNORA24, SNORA30, SNORA37, SNORA38, SNORA52, SNORA71A, SNORA71B, SNORA71C, SNORD13P2, SNORD15A, SNORD34, SNORD35A, SNORD41, SNORD53, and SCARNA22. The top canonical pathway was entitled, role of BRCA1 in DNA damage response. Network analysis revealed key nodes as Akt, ERK1/2, NFkB, and Ras in a predicted activation stage. Downregulated genes in a data set that was shared between BCBM and prBT comprised, e.g. BC cell line invasion markers JUN, MMP3, TFF1, and HAS2. Important cancer genes affected by CNVs included TP53, BRCA1, BRCA2, ERBB2, IDH1, and IDH2. WES detected numerous mutations, some of which affecting BC associated genes as CDH1, HEPACAM, and LOXHD1.

Conclusions

Using complementary molecular genetic techniques, this study identified shared and unshared molecular events in three highly aberrant BCBM emphasizing the challenge to detect new molecular biomarkers and targets with translational implications. Among new findings with the capacity to gain clinical relevance is the detection of overexpressed snoRNAs known to regulate some critical cellular functions as ribosome biogenesis.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1370-x) contains supplementary material, which is available to authorized users.

Epigenetic Alterations in Bone and Soft Tissue Tumors

Human malignancies are driven by heritable alterations that lead to unchecked cellular proliferation, invasive growth and distant spread. Heritable changes can arise from changes in DNA sequence, or, alternatively, through altered gene expression rooted in epigenetic mechanisms. In recent years, high-throughput sequencing of tumor genomes has revealed a central role for mutations in epigenetic regulatory complexes in oncogenic processes. Through interactions with or direct modifications of chromatin, these proteins help control the accessibility of genes, and thus the transcriptional profile of a cell. Dysfunction in these proteins can lead to activation of oncogenic pathways or silencing of tumor suppressors. Although epigenetic regulators are altered across a broad spectrum of human malignancies, they play a particularly central role in tumors of mesenchymal and neuroectodermal origin. This review will focus on recent advances in the understanding of the molecular pathogenesis of a subset of tumors in which alterations in the polycomb family of chromatin modifying complexes, the SWI/SNF family of nucleosome remodelers, and histones play a central role in disease pathogenesis. Although this review will focus predominantly on the molecular mechanisms underlying these tumors, each section will also highlight areas in which an understanding of the molecular pathogenesis of these diseases has led to the adoption of novel immunohistochemical and molecular markers.

Genetic Variants in Epigenetic Pathways and Risks of Multiple Cancers in the GAME-ON Consortium

Background: Epigenetic disturbances are crucial in cancer initiation, potentially with pleiotropic effects, and may be influenced by the genetic background.Methods: In a subsets (ASSET) meta-analytic approach, we investigated associations of genetic variants related to epigenetic mechanisms with risks of breast, lung, colorectal, ovarian and prostate carcinomas using 51,724 cases and 52,001 controls. False discovery rate-corrected P values (q values < 0.05) were considered statistically significant.Results: Among 162,887 imputed or genotyped variants in 555 candidate genes, SNPs in eight genes were associated with risk of more than one cancer type. For example, variants in BABAM1 were confirmed as a susceptibility locus for squamous cell lung, overall breast, estrogen receptor (ER)-negative breast, and overall prostate, and overall serous ovarian cancer; the most significant variant was rs4808076 [OR = 1.14; 95% confidence interval (CI) = 1.10-1.19; q = 6.87 × 10-5]. DPF1 rs12611084 was inversely associated with ER-negative breast, endometrioid ovarian, and overall and aggressive prostate cancer risk (OR = 0.93; 95% CI = 0.91-0.96; q = 0.005). Variants in L3MBTL3 were associated with colorectal, overall breast, ER-negative breast, clear cell ovarian, and overall and aggressive prostate cancer risk (e.g., rs9388766: OR = 1.06; 95% CI = 1.03-1.08; q = 0.02). Variants in TET2 were significantly associated with overall breast, overall prostate, overall ovarian, and endometrioid ovarian cancer risk, with rs62331150 showing bidirectional effects. Analyses of subpathways did not reveal gene subsets that contributed disproportionately to susceptibility.Conclusions: Functional and correlative studies are now needed to elucidate the potential links between germline genotype, epigenetic function, and cancer etiology.Impact: This approach provides novel insight into possible pleiotropic effects of genes involved in epigenetic processes. Cancer Epidemiol Biomarkers Prev; 26(6); 816-25. ©2017 AACR.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

The epigenomic basis of common diseases

A report of the 6th Epigenomics of Common Diseases Conference held at the Wellcome Genome Campus in Hinxton, Cambridge, UK, on 1-4 November 2016.

A cloud-based workflow to quantify transcript-expression levels in public cancer compendia

Public compendia of sequencing data are now measured in petabytes. Accordingly, it is infeasible for researchers to transfer these data to local computers. Recently, the National Cancer Institute began exploring opportunities to work with molecular data in cloud-computing environments. With this approach, it becomes possible for scientists to take their tools to the data and thereby avoid large data transfers. It also becomes feasible to scale computing resources to the needs of a given analysis. We quantified transcript-expression levels for 12,307 RNA-Sequencing samples from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas. We used two cloud-based configurations and examined the performance and cost profiles of each configuration. Using preemptible virtual machines, we processed the samples for as little as $0.09 (USD) per sample. As the samples were processed, we collected performance metrics, which helped us track the duration of each processing step and quantified computational resources used at different stages of sample processing. Although the computational demands of reference alignment and expression quantification have decreased considerably, there remains a critical need for researchers to optimize preprocessing steps. We have stored the software, scripts, and processed data in a publicly accessible repository (https://osf.io/gqrz9).

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.

CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms

The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA–miRNA and gene–transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial. We developed CrossHub software, which enables two-way identification of most possible TF–gene interactions: on the basis of ENCODE ChIP-Seq binding evidence or Jaspar prediction and co-expression according to the data of The Cancer Genome Atlas (TCGA) project, the largest cancer omics resource. Similarly, CrossHub identifies mRNA–miRNA pairs with predicted or validated binding sites (TargetScan, mirSVR, PicTar, DIANA microT, miRTarBase) and strong negative expression correlations. We observed partial consistency between ChIP-Seq or miRNA target predictions and gene–TF/miRNA co-expression, demonstrating a link between these indicators. Additionally, CrossHub expression-methylation correlation analysis can be used to identify hypermethylated CpG sites or regions with the greatest potential impact on gene expression. Thus, CrossHub is capable of outlining molecular portraits of a specific gene and determining the three most common sources of expression regulation: promoter/enhancer methylation, miRNA interference and TF-mediated activation or repression. CrossHub generates formatted Excel workbooks with the detailed results. CrossHub is freely available at https://sourceforge.net/projects/crosshub/.

Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.