CancerMA: a web-based tool for automatic meta-analysis of public cancer microarray data

Methodology to identify a gene expression signature by merging microarray datasets

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

BACKGROUND

Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

METHODS

We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

RESULTS

We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

CONCLUSIONS

The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

TEX19 promotes ovarian carcinoma progression and is a potential target for epitope vaccine immunotherapy

AIMS

Testis Expressed 19 (TEX19) is one of cancer/testis antigens identified in recent years and is related to the oncogenesis and progress of several cancers. This study aimed to reveal the role of TEX19 in ovarian cancer (OC) and searched for potential candidate epitope peptides of TEX19 to facilitate clinical application.

MAIN METHODS

TEX19 levels were evaluated by immunohistochemistry (IHC) in 98 human ovarian tissue samples. The correlation of TEX19 levels with patients' clinicopathological features was assessed. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting analysis were utilized to detect TEX19 levels in ovarian cell lines and TEX19-deficient cells. The level of TEX19 in OVCAR-3 and A2780 was knocked down by small interfering RNA (siRNA), and loss-of-function assays were used to determine the biological effects of TEX19 on the proliferation, migration, and invasion of OC cells. Subsequently, candidate epitope peptides from TEX19 were predicted and verified by the IEDB database, pepsite2 website, MOE software, and T2 cell binding assay.

KEY FINDINGS

TEX19 was significantly upregulated in OC which correlated to higher TNM stage, lymph node involvement, and invasiveness. Knockdown of TEX19 inhibited proliferation, migration, and invasion of OC cells. Additionally, we screened four peptides derived from TEX19 and found TL to be the dominant peptide with the greatest affinity with HLA-A*0201.

SIGNIFICANCE

Our data indicated a cancer-promoting effect of TEX19 in OC and demonstrated that TL could be a potential candidate for an anti-tumor epitope vaccine of OC, suggesting that TEX19 is a promising biomarker and immunotherapeutic target for OC.

Epigenetic alterations of CYLD promoter modulate its expression in gastric adenocarcinoma: A footprint of infections

Gastric cancer (GC) is one of the most common causes of cancer‐related death in the world, with multiple genetic and epigenetic alterations involved in disease development. CYLD tumor suppressor gene encodes a multifunctional deubiquitinase which negatively regulates various signaling pathways. Deregulation of this gene has been found in different types of cancer. This study aimed to evaluate for the first time the CpG island methylation pattern of CYLD gene promoter, and its expression level in gastric adenocarcinoma. CYLD messenger RNA expression and promoter methylation in 53 tumoral and their non‐neoplastic counterpart tissues were assessed using quantitative polymerase chain reaction and bisulfite sequencing. Also, we investigated the impacts of the infectious agents including Helicobacter pylori (H. pylori), EBV, and CMV on CYLD expression and promoter methylation in GC. Results showed that the expression level of CYLD was downregulated in GC, and was significantly associated with gender (female), patient’s age (<60), high grade, and no lymph‐node metastasis (p = 0.001, 0.002, 0.03, and 0.003, respectively). Among the 31 analyzed CpG sites located in about 600 bp region within the promoter, two CpG sites were hypermethylated in GC tissues. We also found a significant inverse association between DNA promoter methylation and CYLD expression (p = 0.02). Furthermore, a direct association between H. pylori, EBV, and CMV infections with hypermethylation and reduced CYLD expression was observed (p = 0.04, 0.03, and 0.03, respectively). Our findings indicate that CYLD is downregulated in GC. Infectious agents may influence CYLD expression.

LCE: an open web portal to explore gene expression and clinical associations in lung cancer

We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource—the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

Sensitivity of Colorectal Cancer to Arginine Deprivation Therapy is Shaped by Differential Expression of Urea Cycle Enzymes

Tumors deficient in the urea cycle enzymes argininosuccinate synthase-1 (ASS1) and ornithine transcarbamylase (OTC) are unable to synthesize arginine and can be targeted using arginine-deprivation therapy. Here, we show that colorectal cancers (CRCs) display negligible expression of OTC and, in subset of cases, ASS1 proteins. CRC cells fail to grow in arginine-free medium and dietary arginine deprivation slows growth of cancer cells implanted into immunocompromised mice. Moreover, we report that clinically-formulated arginine-degrading enzymes are effective anticancer drugs in CRC. Pegylated arginine deiminase (ADI-PEG20), which degrades arginine to citrulline and ammonia, affects growth of ASS1-negative cells, whereas recombinant human arginase-1 (rhArg1peg5000), which degrades arginine into urea and ornithine, is effective against a broad spectrum of OTC-negative CRC cell lines. This reflects the inability of CRC cells to recycle citrulline and ornithine into the urea cycle. Finally, we show that arginase antagonizes chemotherapeutic drugs oxaliplatin and 5-fluorouracil (5-FU), whereas ADI-PEG20 synergizes with oxaliplatin in ASS1-negative cell lines and appears to interact with 5-fluorouracil independently of ASS1 status. Overall, we conclude that CRC is amenable to arginine-deprivation therapy, but we warrant caution when combining arginine deprivation with standard chemotherapy.

Identification of differentially expressed miRNAs associated with chronic kidney disease-mineral bone disorder

The purpose of this study is to characterize a meta-signature of differentially expressed mRNA in chronic kidney disease (CKD) to predict putative microRNA (miRNA) in CKD-mineral bone disorder (CKD-MBD) and confirm the changes in these genes and miRNA expression under uremic conditions by using a cell culture system. PubMed searches using MeSH terms and keywords related to CKD, uremia, and mRNA arrays were conducted. Through a computational analysis, a meta-signature that characterizes the significant intersection of differentially expressed mRNA and expected miRNAs associated with CKD-MBD was determined. Additionally, changes in gene and miRNA expressions under uremic conditions were confirmed with human Saos-2 osteoblast-like cells. A statistically significant mRNA meta-signature of upregulated and downregulated mRNA levels was identified. Furthermore, miRNA expression profiles were inferred, and computational analyses were performed with the imputed microRNA regulation based on weighted ranked expression and putative microRNA targets (IMRE) method to identify miRNAs associated with CKD occurrence. TLR4 and miR-146b levels were significantly associated with CKD-MBD. TLR4 levels were significantly downregulated, whereas primiR- 146b and miR-146b were upregulated in the presence of uremic toxins in human Saos-2 osteoblast-like cells. Differentially expressed miRNAs associated with CKD-MBD were identified through a computational analysis, and changes in gene and miRNA expressions were confirmed with an in vitro cell culture system.

BEX3 contributes to cisplatin chemoresistance in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) can develop cisplatin‐resistant phenotype. Research has revealed that enriched in cancer stem cell population is involved in developing cisplatin‐resistant phenotype. CD271 is a candidate stem cell maker in head and neck cancers. The CD receptor does not possess any enzymatic property. Signal transduction function of CD271 is mediated by the cellular receptor‐associated protein. Our data showed that Brain‐expressed X‐linked 3 (BEX3), a CD271 receptor‐associated protein, was overexpressed in NPC. BEX3 overexpression was a unique event in cancer developed in the head and neck regions, especially NPC. BEX3 expression was inducible by cisplatin in NPC. In cisplatin‐resistant NPC xenograft, treatment with nontoxic level of cisplatin led to a remarkable increase in BEX3 level. High BEX3 expression was accompanied with high octamer‐binding transcription factor 4 (OCT4) expression in cisplatin‐resistant NPC. To confirm the inducing role of BEX3 on OCT4 expression, we knockdown BEX3 using siRNA and compared the expression of OCT4 with mock transfectants. Suppressing BEX3 transcripts led to a significant reduction in OCT4. In addition, targeting BEX3 using shRNA could increase the sensitivity of NPC cells to cisplatin. In summary, our results indicated a unique functional role of BEX3 in mediating the sensitivity of NPC cells to cisplatin. Targeting or blocking BEX3 activity might be useful in reversing the cisplatin‐resistant phenotype in NPC.

A Database of Gene Expression Profiles of Korean Cancer Genome

Because there are clear molecular differences entailing different treatment effectiveness between Korean and non-Korean cancer patients, identifying distinct molecular characteristics of Korean cancers is profoundly important. Here, we report a web-based data repository, namely Korean Cancer Genome Database (KCGD), for searching gene signatures associated with Korean cancer patients. Currently, a total of 1,403 cancer genomics data were collected, processed and stored in our repository, an ever-growing database. We incorporated most widely used statistical survival analysis methods including the Cox proportional hazard model, log-rank test and Kaplan-Meier plot to provide instant significance estimation for searched molecules. As an initial repository with the aim of Korean-specific marker detection, KCGD would be a promising web application for users without bioinformatics expertise to identify significant factors associated with cancer in Korean.

Identification of a class of human cancer germline genes with transcriptional silencing refractory to the hypomethylating drug 5-aza-2'-deoxycytidine

Bona fide germline genes have expression restricted to the germ cells of the gonads. Testis-specific germline development-associated genes can become activated in cancer cells and can potentially drive the oncogenic process and serve as therapeutic/biomarker targets; such germline genes are referred to as cancer/testis genes. Many cancer/testis genes are silenced via hypermethylation of CpG islands in their associated transcriptional control regions and become activated upon treatment with DNA hypomethylating agents; such hypomethylation-induced activation of cancer/testis genes provides a potential combination approach to augment immunotherapeutics. Thus, understanding cancer/testis gene regulation is of increasing clinical importance. Previously studied cancer/testis gene activation has focused on X chromosome encoded cancer/testis genes. Here we find that a sub-set of non-X encoded cancer/testis genes are silenced in non-germline cells via a mechanism that is refractory to epigenetic dysregulation, including treatment with the hypomethylating agent 5-aza-2'-deoxycytidine and the histone deacetylase inhibitor tricostatin A. These findings formally indicate that there is a sub-group of the clinically important cancer/testis genes that are unlikely to be activated in clinical therapeutic approaches using hypomethylating agents and it indicates a unique transcriptional silencing mechanism for germline genes in non-germline cells that might provide a target mechanism for new clinical therapies.

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Transcriptional assessment by microarray analysis and large-scale meta-analysis of the metabolic capacity of cardiac and skeletal muscle tissues to cope with reduced nutrient availability in Gilthead Sea Bream (Sparus aurata L.)

The effects of nutrient availability on the transcriptome of cardiac and skeletal muscle tissues were assessed in juvenile gilthead sea bream fed with a standard diet at two feeding levels: (1) full ration size and (2) 70 % satiation followed by a finishing phase at the maintenance ration. Microarray analysis evidenced a characteristic transcriptomic profile for each muscle tissue following changes in oxidative capacity (heart > red skeletal muscle > white skeletal muscle). The transcriptome of heart and secondly that of red skeletal muscle were highly responsive to nutritional changes, whereas that of glycolytic white skeletal muscle showed less ability to respond. The highly expressed and nutritionally regulated genes of heart were mainly related to signal transduction and transcriptional regulation. In contrast, those of white muscle were enriched in gene ontology (GO) terms related to proteolysis and protein ubiquitination. Microarray meta-analysis using the bioinformatic tool Fish and Chips ( http://fishandchips.genouest.org/index.php ) showed the close association of a representative cluster of white skeletal muscle with some of cardiac and red skeletal muscle, and many GO terms related to mitochondrial function appeared to be common links between them. A second round of cluster comparisons revealed that mitochondria-related GOs also linked differentially expressed genes of heart with those of liver from cortisol-treated gilthead sea bream. These results show that mitochondria are among the first responders to environmental and nutritional stress stimuli in gilthead sea bream, and functional phenotyping of this cellular organelle is highly promising to obtain reliable markers of growth performance and well-being in this fish species.

Meta-analysis of expression of l(3)mbt tumor-associated germline genes supports the model that a soma-to-germline transition is a hallmark of human cancers

Evidence is starting to emerge indicating that tumorigenesis in metazoans involves a soma-to-germline transition, which may contribute to the acquisition of neoplastic characteristics. Here, we have meta-analyzed gene expression profiles of the human orthologs of Drosophila melanogaster germline genes that are ectopically expressed in l(3)mbt brain tumors using gene expression datasets derived from a large cohort of human tumors. We find these germline genes, some of which drive oncogenesis in D. melanogaster, are similarly ectopically activated in a wide range of human cancers. Some of these genes normally have expression restricted to the germline, making them of particular clinical interest. Importantly, these analyses provide additional support to the emerging model that proposes a soma-to-germline transition is a general hallmark of a wide range of human tumors. This has implications for our understanding of human oncogenesis and the development of new therapeutic and biomarker targets with clinical potential.

A novel cohort of cancer-testis biomarker genes revealed through meta-analysis of clinical data sets

The identification of cancer-specific biomolecules is of fundamental importance to the development of diagnostic and/or prognostic markers, which may also serve as therapeutic targets. Some antigenic proteins are only normally present in male gametogenic tissues in the testis and not in normal somatic cells. When these proteins are aberrantly produced in cancer they are referred to as cancer/testis (CT) antigens (CTAs). Some CTA genes have been proven to encode immunogenic proteins that have been used as successful immunotherapy targets for various forms of cancer and have been implicated as drug targets. Here, a targeted in silico analysis of cancer expressed sequence tag (EST) data sets resulted in the identification of a significant number of novel CT genes. The expression profiles of these genes were validated in a range of normal and cancerous cell types. Subsequent meta-analysis of gene expression microarray data sets demonstrates that these genes are clinically relevant as cancer-specific biomarkers, which could pave the way for the discovery of new therapies and/or diagnostic/prognostic monitoring technologies.

CancerEST: a web-based tool for automatic meta-analysis of public EST data

The identification of cancer-restricted biomarkers is fundamental to the development of novel cancer therapies and diagnostic tools. The construction of comprehensive profiles to define tissue- and cancer-specific gene expression has been central to this. To this end, the exploitation of the current wealth of 'omic'-scale databases can be facilitated by automated approaches, allowing researchers to directly address specific biological questions. Here we present CancerEST, a user-friendly and intuitive web-based tool for the automated identification of candidate cancer markers/targets, for examining tissue specificity as well as for integrated expression profiling. CancerEST operates by means of constructing and meta-analyzing expressed sequence tag (EST) profiles of user-supplied gene sets across an EST database supporting 36 tissue types. Using a validation data set from the literature, we show the functionality and utility of CancerEST. DATABASE URL: http://www.cancerest.org.uk.

INMEX--a web-based tool for integrative meta-analysis of expression data

The widespread applications of various ‘omics’ technologies in biomedical research together with the emergence of public data repositories have resulted in a plethora of data sets for almost any given physiological state or disease condition. Properly combining or integrating these data sets with similar basic hypotheses can help reduce study bias, increase statistical power and improve overall biological understanding. However, the difficulties in data management and the complexities of analytical approaches have significantly limited data integration to enable meta-analysis. Here, we introduce integrative meta-analysis of expression data (INMEX), a user-friendly web-based tool designed to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner. INMEX is freely available at http://www.inmex.ca.

On the notion of doll's eyes

CD90 is a membrane GPI-anchored protein with one Ig V-type superfamily domain that was initially described in mouse T cells. Besides the specific expression pattern and functions of CD90 that were described in normal tissues, i.e., neurons, fibroblasts and T cells, increasing evidences are currently highlighting the possible involvement of CD90 in cancer. This review first provides a brief overview on CD90 gene, mRNA and protein features and then describes the established links between CD90 and cancer. Finally, we report newly uncovered functional connections between CD90 and endoplasmic reticulum (ER) stress signaling and discuss their potential impact on cancer development.