Integrated analysis of gene expression profiles associated with response of platinum/paclitaxel-based treatment in epithelial ovarian cancer

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

SCAMP3 promotes breast cancer progression through the c-MYC-β-Catenin-SQSTM1 growth and stemness axis

The cellular trafficking protein secretory-carrier-membrane-protein 3 (SCAMP3) has been previously shown to promote hepatocellular carcinoma, melanoma, glioma and pancreatic adenocarcinoma. Moreover, previous work has shown that SCAMP3 regulates the epidermal growth factor receptor (EGFR) in triple negative breast cancer (TNBC). However, the oncogenic role of SCAMP3 in different molecular subtypes of breast cancer (BRCA) remains largely unknown. In this study, the role of SCAMP3 in different molecular subtypes of BRCA was investigated using in silico, in vitro and in vivo approaches. In silico analysis of BRCA patient samples showed that SCAMP3 is highly overexpressed in different BRCA molecular subtypes, advanced disease grades and lymph node metastatic stages. Depletion of SCAMP3 inhibited BRCA cell growth, stemness, clonogenic potential and migration and promoted autophagy and cellular senescence. The expression of stemness markers CD44 and OCT4A was reduced in SCAMP3-silenced MDA-MB-231 cells. SCAMP3 overexpression promoted cell proliferation, clonogenicity, tumor spheroid formation and migration in vitro and tumor growth in vivo. SCAMP3 promoted epithelial-mesenchymal-transition (EMT) by regulating E-cadherin expression. SCAMP3 enhanced in vivo tumor growth in MDA-MB-231 tumor xenograft mouse model. Mechanistically, SCAMP3 depletion inhibited β-Catenin, c-MYC and SQSTM1 expression, while its overexpression increased the expression of the same oncogenic proteins. Increased SCAMP3 expression associated with increased chemoresistance in BRCA cells while its depletion associated with increased sensitivity to chemotherapy. BRCA patients with high SCAMP3 expression showed poor prognosis, decreased overall survival and relapse free survival relative to counterparts with reduced SCAMP3 expression. These findings suggest that SCAMP3 exerts a wide range of oncogenic effects in different molecular subtypes of BRCA by modulating the c-MYC-β-Catenin-SQSTM1 axis that targets tumor growth, metastasis, stemness and chemoresistance.

Identification of an Immune Gene-Based Cisplatin Response Model and CD27 as a Therapeutic Target against Cisplatin Resistance for Ovarian Cancer

Objective. Evidence demonstrates that the immune microenvironment is extensively associated with chemotherapy response of ovarian cancer (OV). Herein, this study is aimed at establishing a cisplatin response prediction model for OV on the basis of immune genes. Methods. The expression profiles of cisplatin-sensitive and cisplatin-resistant OV specimens were integrated from multiple public datasets. The abundance scores of 22 immune cells were estimated with CIBERSORT algorithm. Differentially expressed immune genes (DEGs) were determined between cisplatin-sensitive and cisplatin-resistant groups. Thereafter, a cisplatin response model was constructed based on prognostic DEGs with logistic regression analysis. The prediction performance was validated in independent cohorts. The possible relationships between the model and immunotherapy were then assessed. Results. Treg scores were significantly decreased in cisplatin-resistant than cisplatin-sensitive OV specimens, with the opposite results for naïve B cells and activated dendritic cells. Fourteen prognostic DEGs were identified and used to develop a cisplatin-response model. The response scores, estimated by the model, showed favorable performance in discriminating cisplatin-response and nonresponse samples. The response scores also presented significantly negative correlations with three well-known cisplatin-resistant pathways and a positive correlation with the expression of CD274 (PD-L1). Moreover, the decreased CD27 expression was observed in cisplatin-resistant groups, and OV specimens with higher CD27 expressions were more sensitive to cisplatin treatment. Conclusion. Altogether, our findings proposed a cisplatin response prediction model and identified CD27 that might be involved in cisplatin resistance. Further investigations suggested that CD27 could be a promising immunotherapeutic target for cisplatin-resistant subset of OV.

Sialomucin CD43 Plays a Deleterious Role in the Development of Experimental Heart Failure Induced by Pressure Overload by Modulating Cardiac Inflammation and Fibrosis

Sialomucin CD43 is a transmembrane protein differentially expressed in leukocytes that include innate and adaptive immune cells. Among a variety of cellular processes, CD43 participates in T cell adhesion to vascular endothelial cells and contributes to the progression of experimental autoimmunity. Sequential infiltration of myeloid cells and T cells in the heart is a hallmark of cardiac inflammation and heart failure (HF). Here, we report that CD43-/- mice have improved survival to HF induced by transverse aortic constriction (TAC). This enhanced survival is associated with improved systolic function, decreased cardiac fibrosis, and significantly reduced T cell cardiac infiltration in response to TAC compared to control wild-type (WT) mice. Lack of CD43 did not alter the number of myeloid cells in the heart, but resulted in decreased cardiac CXCL10 expression, a chemoattractant for T cells, and in a monocyte shift to anti-inflammatory macrophages in vitro. Collectively, these findings unveil a novel role for CD43 in adverse cardiac remodeling in pressure overload induced HF through modulation of cardiac T cell inflammation.

Identifying gene expression patterns associated with drug-specific survival in cancer patients

The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions.

PRSS1 Upregulation Predicts Platinum Resistance in Ovarian Cancer Patients

Ovarian cancer is the most frequent cause of death among gynecologic malignancies. A total of 80% of patients who have completed platinum-based chemotherapy suffer from relapse and develop resistance within 2 years. In the present study, we obtained patients' complete platinum (cisplatin and carboplatin) medication information from The Cancer Genome Atlas database and then divided them into two categories: resistance and sensitivity. Difference analysis was performed to screen differentially expressed genes (DEgenes) related to platinum response. Subsequently, we annotated DEgenes into the protein–protein interaction network as seed nodes and analyzed them by random walk. Finally, second-ranking protease serine 1 gene (PRSS1) was selected as a candidate gene for verification analysis. PRSS1's expression pattern was continuously studied in Oncomine and cBio Cancer Genomic Portal databases, revealing the key roles of PRSS1 in ovarian cancer formation. Hereafter, we conducted in-depth explorations on PRSS1's platinum response to ovarian cancer through tissue and cytological experiments. Quantitative real-time polymerase chain reaction and Western blot assay results indicated that PRSS1 expression levels in platinum-resistant samples (tissue/cell) were significantly higher than in samples sensitive to platinum. By cell transfection assay, we observed that knockdown of PRSS1 reduced the resistance of ovarian cancer cells to cisplatin. Meanwhile, overexpression of PRSS1 increased the resistance to cisplatin. In conclusion, we identified a novel risk gene PRSS1 related to ovarian cancer platinum response and confirmed its key roles using multiple levels of low-throughput experiments, revealing a new treatment strategy based on a novel target factor for overcoming cisplatin resistance in ovarian cancer.

Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model

Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436⁻0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170⁻0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334⁻0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128⁻0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial.

NRP1 is targeted by miR-130a and miR-130b, and is associated with multidrug resistance in epithelial ovarian cancer based on integrated gene network analysis

Multidrug resistance (MDR) in epithelial ovarian cancer (EOC) remains a public health issue for women worldwide, and its molecular mechanisms remain to be fully elucidated. The present study aimed to predict the potential genes involved in MDR, and examine the mechanisms underlying MDR in EOC using bioinformatics techniques. In the present study, four public microarray datasets, including GSE41499, GSE33482, GSE15372 and GSE28739, available in Gene Expression Omnibus were downloaded, and 11 microRNAs (miRNA; miRs), including miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b^* and miR-200c, from previously published reports in PubMed were used to perform a comprehensive bioinformatics analysis through gene expression analysis, signaling pathway analysis, literature co-occurrence and miRNA-mRNA interaction networks. The results demonstrated that the expression of neuropilin 1 (NRP1) was upregulated, thereby acting as the most important hub gene in the integrated gene network. NRP1 was targeted by miR-130a and miR-130b at the binding site of chromosome 10: 33466864-3466870, which was involved in the axon guidance signaling pathway. These results suggested that alteration of the gene expression levels of NRP1 expression may contribute to MDR in EOC. These data provide important information for further experimental investigations of the drug resistance-associated functions of NRP1 in EOC.

Hormone receptor, HER2/NEU and EGFR expression in ovarian carcinoma--is here a prognostic phenotype?

PURPOSE

We aimed to evaluate the effects of hormone receptor, HER2, and epidermal growth factor receptor (EGFR) expression on epithelial ovarian cancer (EOC) prognosis and investigate whether or not phenotypic subtypes might exist.

MATERIALS AND METHODS

The medical records of 82 patients who were diagnosed with EOC between 2003 and 2012 and treated by platinum-based chemotherapy were retrospectively evaluated. Expression of EGFR, oestrogen (ER), progesterone (PR), and cerbB2 (HER2) receptors were assessed immunohistochemically on paraffin-embedded tissues of these patients. Three phenotypic subtypes were defined according to ER, PR, and HER2 expression and associations of these with EGFR expression, clinicopathologic features, platinum sensitivity, and survival were investigated.

RESULTS

When we classified EOC patients into three subtypes, 63.4% had hormone receptor positive (HR(+)) (considering breast cancer subtypes, luminal A), 18.3% had triple negative, and 18.3% had HER2(+) disease. EGFR positivity was observed in 37 patients (45.1%) and was significantly more frequent with advanced disease (p=0.013). However, no significant association with other clinicopathologic features and platinum sensitivity was observed. HER2(+) patients had significantly poorer outcomes than HER2(-) counterparts (triple negative and HR positive patients) (p=0.019). Multivariate analysis demonstrated that the strongest risk factor for death was residual disease after primary surgery.

CONCLUSIONS

Triple negative EOC may not be an aggressive phenotype as in breast cancer. The HER2 positive EOC has more aggressive behaviour compared to triple negative and HR(+) phenotypes. EGFR expression is more frequent in advanced tumours, but is not related with poorer outcome. Additional ovarian cancer molecular subtyping using gene expression analysis may provide more reliable data.

Inflammation-regulating factors in ascites as predictive biomarkers of drug resistance and progression-free survival in serous epithelial ovarian cancers

Background

Platinum-based combination therapy is the standard first-line treatment for women with advanced serous epithelial ovarian carcinoma (EOC). However, about 20 % will not respond and are considered clinically resistant. The availability of biomarkers to predict responses to the initial therapy would provide a practical approach to identify women who would benefit from a more appropriate first-line treatment. Ascites is an attractive inflammatory fluid for biomarker discovery as it is easy and minimally invasive to obtain. The aim of this study was to evaluate whether six selected inflammation-regulating factors in ascites could serve as diagnostic or drug resistance biomarkers in patients with advanced serous EOC.

Methods

A total of 53 women with stage III/IV serous EOC and 10 women with benign conditions were enrolled in this study. Eleven of the 53 women with serous EOC were considered clinically resistant to treatment with progression-free survival < 6 months. Ascites were collected at the time of the debulking surgery and the levels of cytokines were measured by ELISA. The six selected cytokines were evaluated for their ability to discriminate serous EOC from benign controls, and to discriminate platinum resistant from platinum sensitive patients.

Results

Median ascites levels of IL-6, IL-10 and osteoprotegerin (OPG) were significantly higher in women with advanced serous EOC than in controls (P ≤ 0.012). There were no significant difference in the median ascites levels of leptin, soluble urokinase plasminogen activator receptor (suPAR) and CCL18 among serous EOC women and controls. In Receiver Operator curve (ROC) analysis, IL-6, IL-10 and OPG had a high area under the curve value of 0.905, 0.832 and 0.825 respectively for distinguishing EOC from benign controls. ROC analysis of individual cytokines revealed low discriminating potential to stratify patients according to their sensitivity to first-line treatment. The combination of biomarkers with the highest discriminating potential was with CA125 and leptin (AUC = 0.936, 95 % CI: 0.894–0.978).

Conclusion

IL-6 was found to be strongly associated with advanced serous EOC and could be used in combination with serum CA125 to discriminate benign and EOC. Furthermore, the combination of serum CA125 and ascites leptin was a strong predictor of clinical resistance to first-line therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1511-7) contains supplementary material, which is available to authorized users.

Genetic and molecular analyses reveal G6PC as a key element connecting glucose metabolism and cell cycle control in ovarian cancer

We aimed to evaluate the role of glucose-6-phosphatase, catalytic subunit (G6PC) in ovarian cancer and to exploit its therapeutic potential. With reproduction of The Cancer Genome Atlas (TCGA) database, we studied expressions of genes in the glucose metabolism pathways in silico. The cBioPortal For Cancer Genomics was used to study the clinical, pathological and molecular profiles of G6PC. In vitro studies were performed to validate the function of G6PC and the effect of genetic and pharmaceutical G6PC inhibition. In 158 ovarian cancer (OvCa) patients with complete RNA-seq data, G6PC expression was increased in 27 patients (17 %). Both overall survival (OS) and disease-free period were significantly shorter in cases with increased G6PC level. Significantly decreased total and phosphorylated CDKN1B level was noted in OvCa with increased G6PC expression. Silenced G6PC in OvCa cells induced decreased cell proliferation, viability, invasiveness and anchorage-independent cell growth. G6PC silencing also induced enhanced cell cycle control proteins and restoration of CDKN1B level. Pharmaceutical inhibition of G6PC with specific compound showed similar effects to genetic silencing. G6PC played dual roles both in glucose metabolism and cell cycle control in OvCa, which potentiated it a promising therapeutic target.

Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1101-8) contains supplementary material, which is available to authorized users.

Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer

Ovarian cancer is the fifth leading cause of death among female cancers. Front-line therapy for ovarian cancer is platinum-based chemotherapy. However, the response of patients is highly nonuniform. The TCGA database of serous ovarian carcinomas shows that ~10% of patients respond poorly to platinum-based chemotherapy, with tumors relapsing in seven months or less. Another 10% or so enjoy disease-free survival of three years or more. The objective of the present research is to identify a small number of highly predictive biomarkers that can distinguish between the two extreme responders and then extrapolate to all patients. This is achieved using the lone star algorithm that is specifically developed for biological applications. Using this algorithm, we are able to identify biomarker panels of 25 genes (of 12,000 genes) that can be used to classify patients into one of the three groups: super responders, medium responders, and nonresponders. We are also able to determine a discriminant function that can divide the entire patient population into two classes, such that one group has a clear survival advantage over the other. These biomarkers are developed using the TCGA Agilent platform data and cross-validated on the TCGA Affymetrix platform data, as well as entirely independent data from Tothill et al. The P-values on the training data are extremely small, sometimes below machine zero, while the P-values on cross-validation are well below the widely accepted threshold of 0.05.

Treatment options in recurrent ovarian cancer: latest evidence and clinical potential

Ovarian cancer (OC) is the fifth most common cause of cancer death in women. Although significant progress has been made in the treatment of OC, the majority of patients experience disease recurrence and receive second-line and sometimes several lines of treatment. Here we review the options available for the treatment of recurrent disease and discuss how different agents are selected, combined and offered in a rationale sequence in the context of multidisciplinary care. We reviewed published work between 1990 and 2013 and meeting abstracts related to the use of chemotherapy and surgery in patients with recurrent ovarian cancer. We discuss treatment regimens, efficacy endpoints and safety profiles of the different therapies. Platinum-based drugs are the most active agents and are selected on the basis of a probability of response to retreatment. Nonplatinum-based chemotherapy regimens are usually given in the 'platinum-resistant' setting and have a modest effect on outcome. Molecular targeted therapy of ovarian cancer given alone or integrated with chemotherapy is showing promising results. Many patients are now receiving more than one line of therapy for recurrent disease, usually platinum based until platinum resistance emerges. The sequential use of chemotherapy regimens and the incorporation of molecularly targeted treatments, either alone or in combination with chemotherapy, have over the last decade significantly extended the median survival of patients with ovarian cancer.

Connexin 30 expression inhibits growth of human malignant gliomas but protects them against radiation therapy

BACKGROUND

Glioblastomas remain ominous tumors that almost invariably escape treatment. Connexins are a family of transmembrane, gap junction-forming proteins, some members of which were reported to act as tumor suppressors and to modulate cellular metabolism in response to cytotoxic stress.

METHODS

We analyzed the copy number and expression of the connexin (Cx)30 gene gap junction beta-6 (GJB6), as well as of its protein immunoreactivity in several public and proprietary repositories of glioblastomas, and their influence on patient survival. We evaluated the effect of the expression of this gap junction protein on the growth, DNA repair and energy metabolism, and treatment resistance of these tumors.

RESULTS

The GJB6 gene was deleted in 25.8% of 751 analyzed tumors and mutated in 15.8% of 158 tumors. Cx30 immunoreactivity was absent in 28.9% of 145 tumors. Restoration of Cx30 expression in human glioblastoma cells reduced their growth in vitro and as xenografts in the striatum of immunodeficient mice. Cx30 immunoreactivity was, however, found to adversely affect survival in 2 independent retrospective cohorts of glioblastoma patients. Cx30 was found in clonogenic assays to protect glioblastoma cells against radiation-induced mortality and to decrease radiation-induced DNA damage. This radioprotection correlated with a heat shock protein 90-dependent mitochondrial translocation of Cx30 following radiation and an improved ATP production following this genotoxic stress.

CONCLUSION

These results underline the complex relationship between potential tumor suppressors and treatment resistance in glioblastomas and single out GJB6/Cx30 as a potential biomarker and target for therapeutic intervention in these tumors.

Machine learning methods in the computational biology of cancer

The objectives of this Perspective paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algorithm for sparse regression is presented and is applied to predict the time to tumour recurrence in ovarian cancer. A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented.

Integrated analysis of expression profiling data identifies three genes in correlation with poor prognosis of triple-negative breast cancer

Triple-negative breast cancer (TNBC) shows more aggressive clinical behavior and poorer outcome than non-triple-negative breast cancer (NTNBC), and cannot be treated either via endocrine therapy or by Trastuzumab. For TNBC, chemotherapy is currently the mainstay of systemic medical treatment, the lack of more efficient options of treatment has been a problem in breast cancer prevention. In this study, we aimed to find genes related to prognosis in TNBC by bioinformatic analysis and to provide therapeutic candidates for TNBC treatment. We compared the differences in gene expression levels between cancer patients and healthy individuals across five breast cancer microarray databases to generate a gene cohort specifically upregulated in the NTNBC subtype, whose expression levels are ≥2-fold higher in TNBC compared to NTNBC and healthy individuals. Another two databases with clinical information were applied for following Kaplan-Meier analysis, and high expression of BIRC5, CENPA and FAM64A in this cohort were found to be related to poor survival (OS, DMFS, DFS and RFS). This correlation was also seen in patients at early stages and grades. On the other hand, the outcome of patients with synchronous upregulation of these three genes was the worst, while those with synchronous low gene level was the best. In conclusion, BIRC5, CENPA and FAM64A are specifically upregulated in TNBC, and the high expression of these three genes is associated with poor breast cancer prognosis, suggesting their clinical implication as therapeutic targets in TNBC.

Eureka-DMA: an easy-to-operate graphical user interface for fast comprehensive investigation and analysis of DNA microarray data

BACKGROUND

In the past decade, the field of molecular biology has become increasingly quantitative; rapid development of new technologies enables researchers to investigate and address fundamental issues quickly and in an efficient manner which were once impossible. Among these technologies, DNA microarray provides methodology for many applications such as gene discovery, diseases diagnosis, drug development and toxicological research and it has been used increasingly since it first emerged. Multiple tools have been developed to interpret the high-throughput data produced by microarrays. However, many times, less consideration has been given to the fact that an extensive and effective interpretation requires close interplay between the bioinformaticians who analyze the data and the biologists who generate it. To bridge this gap and to simplify the usability of such tools we developed Eureka-DMA - an easy-to-operate graphical user interface that allows bioinformaticians and bench-biologists alike to initiate analyses as well as to investigate the data produced by DNA microarrays.

RESULTS

In this paper, we describe Eureka-DMA, a user-friendly software that comprises a set of methods for the interpretation of gene expression arrays. Eureka-DMA includes methods for the identification of genes with differential expression between conditions; it searches for enriched pathways and gene ontology terms and combines them with other relevant features. It thus enables the full understanding of the data for following testing as well as generating new hypotheses. Here we show two analyses, demonstrating examples of how Eureka-DMA can be used and its capability to produce relevant and reliable results.

CONCLUSIONS

We have integrated several elementary expression analysis tools to provide a unified interface for their implementation. Eureka-DMA's simple graphical user interface provides effective and efficient framework in which the investigator has the full set of tools for the visualization and interpretation of the data with the option of exporting the analysis results for later use in other platforms. Eureka-DMA is freely available for academic users and can be downloaded at http://blue-meduza.org/Eureka-DMA.