Cancer Microarray Datasets Research Articles

Identification of differentially expressed genes by means of outlier detection

BackgroundAn important issue in microarray data is to select, from thousands of genes, a small number of informative differentially expressed (DE) genes which may be key elements for a disease. If each gene is analyzed individually, there is a big number of hypotheses to test and a multiple comparison correction method must be used. Consequently, the resulting cut-off value may be too small. Moreover, an important issue is the selection’s replicability of the DE genes. We present a new method, called ORdensity, to obtain a reproducible selection of DE genes. It takes into account the relation between all genes and it is not a gene-by-gene approach, unlike the usually applied techniques to DE gene selection.ResultsThe proposed method returns three measures, related to the concepts of outlier and density of false positives in a neighbourhood, which allow us to identify the DE genes with high classification accuracy. To assess the performance of ORdensity, we used simulated microarray data and four real microarray cancer data sets. The results indicated that the method correctly detects the DE genes; it is competitive with other well accepted methods; the list of DE genes that it obtains is useful for the correct classification or diagnosis of new future samples and, in general, it is more stable than other procedures.ConclusionsORdensity is a new method for identifying DE genes that avoids some of the shortcomings of the individual gene identification and it is stable when the original sample is changed by subsamples.

Overexpression of Rac GTPase Activating Protein 1 Contributes to Proliferation of Cancer Cells by Reducing Hippo Signaling to Promote Cytokinesis

Agents designed to block or alter cytokinesis can kill or stop proliferation of cancer cells. We aimed to identify cytokinesis-related proteins that are overexpressed in hepatocellular carcinoma (HCC) cells and might be targeted to slow liver tumor growth. Using the Oncomine database, we compared the gene expression patterns in 16 cancer microarray datasets and assessed gene enrichment sets using gene ontology. We performed immunohistochemical analysis of an HCC tissue microarray and identified changes in protein levels that are associated with patient survival times. Candidate genes were overexpressed or knocked down with small hairpin RNAs in SMMC7721, MHCC97H, or HCCLM3 cell lines; we analyzed their proliferation, viability, and clone-formation ability and their growth as subcutaneous or orthotopic xenograft tumors in mice. We performed microarray analyses to identify alterations in signaling pathways and immunoblot and immunofluorescence assays to detect and localize proteins in tissues. Yeast 2-hybrid screens and massspectrometry combined with co-immunoprecipitation experiments were used to identify binding proteins. Protein interactions were validated with co-immunoprecipitation and proximity ligation assays. Chromatin immunoprecipitation, promoter luciferase activity, and quantitative real-time polymerase chain reaction analyses were used to identify factors that regulate transcription of specific genes. The genes that were most frequently overexpressed in different types of cancer cells were involved in cell division processes. We identified 3 cytokinesis-regulatory proteins among the 10genes most frequently overexpressed by all cancer cell types. Rac GTPase activating protein 1 (RACGAP1) was the cytokinesis-regulatory protein that was most highly overexpressed in multiple cancers. Increased expression of RACGAP1 in tumor tissues was associated with shorter survival times of patients with cancer. Knockdown of RACGAP1 in HCC cells induced cytokinesis failure and cell apoptosis. In microarray analyses, we found knockdown of RACGAP1 in SMMC7721 cells to reduce expression of genes regulated by yes-associated protein (YAP) and WW domain containing transcription regulator 1 (WWTR1 or TAZ). RACGAP1 reduced activation of the Hippo pathway in HCC cells by increasing activity of RhoA and polymerization of filamentous actin. Knockdown of YAP reduced phosphorylation of RACGAP1 and redistribution at the anaphase central spindle. We found transcription of the translocated promoter region, nuclear basket protein (TPR) to be regulated by YAP and coordinately expressed with RACGAP1 to promote proliferation of HCC cells. TPR redistributed upon nuclear envelope breakdown and formed complexes with RACGAP1 during mitosis. Knockdown of TPR in HCC cells reduced phosphorylation of RACGAP1 by aurora kinase B and impaired their redistribution at the central spindle during cytokinesis. STAT3 activated transcription of RACGAP in HCCcells. In an analysis of gene expression patterns of multiple tumor types, we found RACGAP1 to be frequently overexpressed, which is associated with shorter survival times of patients. RACGAP1 promotes proliferation ofHCC cells by reducing activation of the Hippo and YAPpathways and promoting cytokinesis in coordination withTPR.

Breast cancer is associated with methylation and expression of the a disintegrin and metalloproteinase domain33 (ADAM33) gene affected by endocrine‑disrupting chemicals.

A disintegrin and metalloproteinase domain33 (ADAM33) gene is a transmembrane glycoprotein that mediates changes in cell adhesion and plays an important role in cancer progression. Since bisphenolA (BPA) and phthalates are epigenetically toxic, the purpose of this study was to examine whether BPA and phthalate metabolites, including monoethyl phthalate(MEP), mono‑n‑butyl phthalate (MBP), mono‑isobutyl phthalate(MIBP), mono(2‑ethylhexyl) phthalate(MEHP), mono(2‑ethyl‑5‑hydroxyhexyl) phthalate (MEHHP), mono(2‑ethyl‑5‑carboxypentyl) phthalate (MECPP), and mono(2‑ethyl‑5‑oxohexyl) phthalate (MEOHP), have an epigenetic impact on ADAM33 and the incidence of breast cancer. CpG islands of breast cancer microarraydatasets obtained from the Gene Expression Omnibus (GEO) were used to assess the ADAM33 methylation profile. We designed a case‑control study including 44cases and 22age‑matched controls to detect the methylation status of intron1 in ADAM33 from peripheral blood mononuclear cells (PBMCs) in blood, using BSP, nested PCR, and bisulfite sequencing, and measured the in vivo gene expression of ADAM33 and the urinary concentrations of endocrine‑disrupting chemicals(EDCs), using real‑time PCR, high‑performance liquid chromatography(HPLC) and liquid chromatography-mass spectrometry(LC‑MS). Only one dataset, GSE32393, reached significance(P=0.016). ADAM33 expression and methylation frequencies at CpG site3 in intron1 were higher in the control group. We found a positive association between intron1 methylation level and ADAM33 expression as well as urinary concentrations of MEHHP, MECPP, MEOHP and Σ4MEHP (the sum of MEHP, MECPP, MEHHP, and MEOHP) in the cases. This study suggests that metabolites of phthalate such as MEHHP, MECPP, MEOHP and Σ4MEHP may increase the intron1 methylation level to elevate ADAM33 gene expression and have a protective effect on reducing the risk of breast cancer.

An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

PurposeThe aim of this study was to investigate the diagnostic and prognostic value of human kinesin family member 4A (KIF4A) as an effective biomarker for breast cancer.Materials and methodsCancer Genome Atlas data and 12 independent public breast cancer microarray data sets were downloaded and analyzed using individual and pooled approaches.ResultsThe results of our study revealed a strong and positive correlation between KIF4A expression and malignant features of breast cancer. KIF4A had a strong prognostic value in both ER-positive and ER-negative breast cancers comparable to or even better than tumor size, lymph node invasion, and Elston grade. We also found that KIF4A might be the target gene of microRNA-335, which can suppress KIF4A expression by targeting the 3′-untranslated region of its mRNA.ConclusionKIF4A might serve as a robust prognostic predictor for breast cancer. Targeting KIF4A activity could be a promising therapeutic option in breast cancer treatment.

Optimal Bayesian Filtering for Biomarker Discovery: Performance and Robustness.

Optimal Bayesian feature filtering (OBF) is a fast and memory-efficient algorithm that optimally identifies markers with distributional differences between treatment groups under Gaussian models. Here, we study the performance and robustness of OBF for biomarker discovery. Our contributions are twofold: (1) we examine how OBF performs on data that violates modeling assumptions, and (2) we provide guidelines on how to set input parameters for robust performance. Contribution (1) addresses an important, relevant, and commonplace problem in computational biology, where it is often impossible to validate an algorithm's core assumptions. To accomplish both tasks, we present a battery of simulations that implement OBF with different inputs and challenge each assumption made by OBF. In particular, we examine the robustness of OBF with respect to incorrect input parameters, false independence, imbalanced sample size, and we address the Gaussianity assumption by considering performance on an extensive family of non-Gaussian distributions. We address advantages and disadvantages between different priors and optimization criteria throughout. Finally, we evaluate the utility of OBF in biomarker discovery using acute myeloid leukemia (AML) and colon cancer microarray datasets, and show that OBF is successful at identifying well-known biomarkers for these diseases that rank low under moderated t-test.

Fusion of stability and multi-objective optimization for solving cancer tissue classification problem

The concept of stability is one of the commonly used physical phenomena. Current paper builds on the hypothesis that the optimal number of clusters present in the dataset corresponds to that partitioning which is most stable over some small changes in the dataset. In order to quantify the degree of stability, a new measure is also proposed in the paper. Thereafter an expert clustering approach is developed in the current paper which utilizes the properties of stability for automatically detecting the number of clusters from a given dataset. Initially, several different variants of the dataset are generated by introducing small perturbations. A multi-objective based expert clustering framework is developed to automatically partition different variants of the data. A new objective function, capturing stability property of clustering solution namely ‘Agreement-index’, along with two well-known objective functions are optimized simultaneously using a multi-objective simulated annealing based process, namely AMOSA for the purpose of clustering. Finally, the problem of cancer classification is addressed as the application domain of the proposed expert framework. Results of our newly developed stability based clustering namely Stab-clustering with respect to existing approaches are shown for twelve microarray cancer datasets in terms of different cluster quality measures. The obtained results confirm the robustness of our proposed technique over state-of-the-art. A thorough biological and statistical significance tests are also conducted to prove the effectiveness of the proposed approach.

CRBP-1 over-expression is associated with poor prognosis in tongue squamous cell carcinoma

BackgroundTongue squamous cell carcinoma (TSCC) is one of the most common malignancies of oral squamous cell carcinomas. Cellular retinol binding protein-1 (CRBP-1) as a carrier protein transports retinol from the liver storage site to peripheral tissue. Up-regulated expression of CRBP-1 is associated with some tumor types such as prostate cancer, breast cancer and ovarian cancer as reported, but its role in TSCC remains uncertain.MethodsIn this study, an integrated bioinformatics analysis based on the multiple cancer microarray data sets available from Oncomine database was conducted to view the differential expression of CRBP-1 between TSCC and the adjacent non-tumorous tissues. Quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB) and immunohistochemical (IHC) assays were performed to investigate CRBP-1 expression in 101 paraffin-embeded TSCC tissues and 48 pairs of freshly frozen tissues. Kaplan-Meier curve and univariate and multivariate Cox-regression analysis were used to estimate the association between CRBP-1 expression and patients’ prognosis. Then western blotting, MTT, transwell migration and invasion assays were performed in TSCC cell lines to investigate the effects of CRBP-1 on cellular proliferation and invasion.ResultsCompared with the matched adjacent non-tumorous tissues, the expression of CRBP-1 was significantly up-regulated in TSCC tissues, which correlated with the differentiation state (P = 0.003), N classification (P = 0.048), the clinical stage (P = 0.048) and death (P = 0.001). The Kaplan-Meier curve showed that TSCC patients with higher CRBP-1 expression levels had lower overall survival rates than those with lower CRBP-1 expression levels. A univariate and multivariate analysis demonstrated that CRBP-1 was an independent prognostic factor (P < 0.05). Furthermore, we knocked down CRBP-1 expression and observed that TSCC cell proliferation and invasion in vitro were significantly blocked, as determined by MTT and transwell assays.ConclusionsUp-regulated expression of CRBP-1 is associated with poor prognosis in TSCC, so it might potentially serve as an additional prognostic marker, and the inhibition of CRBP-1 might provide new therapeutic approaches for TSCC.

An Efficient Mixed-Model for Screening Differentially Expressed Genes of Breast Cancer Based on LR-RF.

To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this paper proposed a novel method named LR-RF to select differentially expressed genes of breast cancer on microarray data by the Bonferroni test of FWER error measure. Comparing with Logistic Regression and Random Forest, our study shows that LR-FR has a great facility in selecting differentially expressed genes. The average prediction accuracy of the proposed LR-RF from replicating random test 10 times surprisingly reaches 93.11 percent with variance as low as 0.00045. The prediction accuracy rate reaches a maximum 95.57 percent when threshold value α = 0.2 in the random forest algorithm process of ranking genes' importance score, and the differentially expressed genes are relatively few in number. In addition, through analyzing the gene interaction networks, most of the top 20 genes we selected were found to involve in the development of breast cancer. All of these results demonstrate the reliability and efficiency of LR-RF. It is anticipated that LR-RF would provide new knowledge and method for biologists, medical scientists, and cognitive computing researchers to identify disease-related genes of breast cancer.

Meta-Analysis Of Brain And Central Nervous System Microarray Datasets

Brain and CNS cancer are rare in comparison to other types of cancer. Currently there are no effective therapies for their treatment. In this study, meta-analysis of microarray datasets of Brain and CNS cancer was done to obtain significantly upregulated genes with increased statistical power and generalizability. A total of 130 significantly up-regulated genes were obtained. Some of the genes found during analysis have not yet been associated with this cancer. Different biological networks were created and analyzed using the significantly up-regulated genes as input. For each network, the most significant pathways have also been identified computationally.

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster.

The KMT1A-GATA3-STAT3 Circuit Is a Novel Self-Renewal Signaling of Human Bladder Cancer Stem Cells.

Purpose: Bladder cancer is one of the most common urinary malignancies worldwide characterized by a high rate of recurrence and no targeted therapy method. Bladder cancer stem cells (BCSCs) play a crucial role in tumor initiation, metastasis, and drug resistance. However, the regulatory signaling and self-renewal mechanisms of BCSCs remain largely unknown. Here, we identified a novel signal, the KMT1A-GATA3-STAT3 circuit, which promoted the self-renewal and tumorigenicity of human BCSCs.Experimental Design: In a discovery step, human BCSCs and bladder cancer non-stem cells (BCNSCs) isolated from primary bladder cancer samples #1 and #2, and the bladder cancer cell line EJ were analyzed by transcriptome microarray. In a validation step, 10 paired bladder cancer and normal tissues, different tumor cell lines, the public microarray datasets of human bladder cancer, and The Cancer Genome Atlas database were applied for the verification of gene expression.Results: KMT1A was highly expressed and responsible for the increase of tri-methylating lysine 9 of histone H3 (H3K9me3) modification in BCSCs compared with either BCNSCs or normal bladder tissue. GATA3 bound to the -1710∼-1530 region of STAT3 promoter and repressed its transcription. H3K9me3 modification on the -1351∼-1172bp region of the GATA3 promoter mediated by KMT1A repressed the transcription of GATA3 and upregulated the expression of STAT3. In addition, the activated STAT3 triggered self-renewal of BCSCs. Furthermore, depletion of KMT1A or STAT3 abrogated the formation of BCSC tumorspheres and xenograft tumors.Conclusions: KMT1A positively regulated the self-renewal and tumorigenicity of human BCSCs via KMT1A-GATA3-STAT3 circuit, in which KMT1A could be a promising target for bladder cancer therapy. Clin Cancer Res; 23(21); 6673-85. ©2017 AACR.

Gene selection for tumor classification using a novel bio-inspired multi-objective approach

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.

Abstract 326: Baseline IL-17 receptor signaling is essential for controlling aberrant JNK-dependent cellular proliferation via maintenance of endogenous level of ubiquitin-editing enzyme A20

Abstract The molecular mechanisms underlying aberrant activation of NF-κB and JNK in cancer remain incompletely understood. Here, we demonstrate that baseline IL-17 receptor (IL-17R) signaling is essential for controlling aberrant NF-κB and JNK activation, and restraining JNK-dependent homeostatic cellular proliferation. Using a shRNA knockdown approach, we demonstrated in B16 melanoma and 4T1 breast carcinoma murine cell lines that IL-17RA silencing markedly enhanced tumor cell growth in vitro and in vivo. Through mapping IL-17R signaling pathways, we further demonstrated that baseline IL-17A/IL-17R signaling actively restrained JNK phosphorylation in vitro and in vivo via the maintenance of basal expression of the ubiquitin-editing enzyme A20, a negative regulator of NF-κB and JNK. Remarkably, IL-17RA reconstitution evidently restored the A20 level, and suppressed cell proliferation and JNK activity in tumor cells. The reconstitution of A20 in IL-17RA knockdown subclones was able to restore the normal rate of cellular proliferation and associated JNK/c-Jun activity. Finally, meta-analysis of human cancer microarray and RNA-Seq datasets confirmed significant co-expression of IL-17RA and A20. Furthermore, alterations (mutation, upregulation or downregulation) of IL-17RA level in melanoma, ER+ breast cancer and colorectal cancer patients were associated with poorer overall survival compared to the respective patients with normal baseline IL-17RA expression. Together, our data demonstrates a previously unrecognized molecular mechanism underlying aberrant activation of NF-κB and JNK in cancer cells. This work highlights the unique biological role of proinflammatory IL-17R signaling in the maintenance of A20 to regulate the pathogenesis of human cancer, which draws caution on the utility of IL-17A neutralizing antibody in cancer therapy. CY is a PhD student supported by the graduate student scholarship from the IWK Health Centre and received a trainee award from the Beatrice Hunter Cancer Research Institute with funds provided by the Cancer Research Training Program as part of The Terry Fox Foundation Strategic Health Research Training Program in Cancer Research in CIHR. Citation Format: Chi Yan, Yang Lei, Anna L. Greenshields, David W. Hoskin, Tong-Jun Lin, Jun Wang. Baseline IL-17 receptor signaling is essential for controlling aberrant JNK-dependent cellular proliferation via maintenance of endogenous level of ubiquitin-editing enzyme A20 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 326. doi:10.1158/1538-7445.AM2017-326

Abstract 3497: Network-based systematic inference of lncRNA activity in breast cancer

Abstract The production of lncRNA is widespread across the human genome, yet the functions of lncRNAs and the phenotypes resulting from their activation have remained largely unstudied. In cancer, several lncRNAs have been found to associate with disease development and poor patient prognosis including HOTAIR and MALAT1. Recent studies have been performed using lncRNA microarrays or re-annotated probes from coding gene microarrays to systematically investigate their roles in cancer. However, datasets from lncRNA microarrays are typically not accompanied by comprehensive clinical information that can be used to draw associations between lncRNA expression and clinical phenotypes. Furthermore, lncRNA stability and cellular localization is highly variable suggesting that expression-based inference of lncRNA activity may not always be an accurate measure of its activity. To address this issue, we introduce an activity-based approach whereby we analyze the expression of lncRNA target genes to infer lncRNA activity. Specifically, we construct a breast cancer specific lncRNA-target gene network from RNA-seq data using a network reconstruction algorithm. We then apply this network to several breast cancer microarray datasets to systematically investigate lncRNA activity and their association with clinical phenotypes. Notably, we find that certain lncRNAs are active in specific molecular subtypes of breast cancer even though their expression levels do not reveal significant differences. Lastly, we performed survival analysis to systematically screen for novel lncRNAs whose activity associate with patient prognosis. Our results suggest that activity-based inference of lncRNAs can identify novel lncRNAs that may serve as therapeutic targets or biomarkers in breast cancer. Citation Format: Matthew Ung, Daniel Mattox, George Wang, Chao Cheng. Network-based systematic inference of lncRNA activity in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3497. doi:10.1158/1538-7445.AM2017-3497

Gene expression profiling to predict recurrence of advanced squamous cell carcinoma of the tongue: discovery and external validation.

ObjectivesTo establish a prognostic signature for locally advanced tongue squamous cell carcinoma (TSCC) patients treated with surgery.ResultsIn the discovery study, unsupervised hierarchical clustering analysis identified two clusters which differentiated the Kaplan-Meier curves of RFS [median RFS, 111 days vs. not reached; log-rank test, P = 0.023]. The 30 genes identified were combined into a dichotomous PI. In the validation cohort, classification according to the PI was associated with RFS [median RFS, 754 days vs. not reached; log-rank test, P = 0.026 in GSE31056] and DSS [median DSS, 540 days vs. not reached; log-rank test, P = 0.046 in GSE42743 and 443 days vs. not reached; P < 0.001 in GSE41613]. Among genes, positive immunohistochemical staining of cytokeratin 4 was associated with favorable prognostic values for RFS (hazard ratio (HR), 0.591, P = 0.045) and DSS (HR, 0.333, P = 0.004).Materials and methodsWe conducted gene expression profiling of 26 clinicopathologically homogeneous advanced TSCC tissue samples using cDNA microarray as a discovery study. Candidate genes were screened using clustering analysis and univariate Cox regression analysis for relapse-free survival (RFS). These were combined into a prognostic index (PI), which was validated using three public microarray datasets of tongue and oral cancer (123 patients). Some genes identified in discovery were immunohistochemically examined for protein expression in another 127 TSCC patients.ConclusionWe identified robust molecular markers that showed significant associations with prognosis in TSCC patients. Gene expression profiling data were successfully converted to protein expression profiling data.

Hybrid Feature Selection Approach Based on GRASP for Cancer Microarray Data

Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm based on the tabu search as local search procedures to a Greedy Randomized Adaptive Search Procedure (GRASP) for high dimensional microarray data sets. The proposed Tabu based Greedy Randomized Adaptive Search Procedure algorithm is named as TGRASP. In TGRASP, a new parameter has been introduced named as Tabu Tenure and the existing parameters, NumIter and size have been modified. We observed that different parameter settings affect the quality of the optimum. The second proposed algorithm known as FFGRASP (Firefly Greedy Randomized Adaptive Search Procedure) uses a firefly optimization algorithm in the local search optimzation phase of the greedy randomized adaptive search procedure (GRASP). Firefly algorithm is one of the powerful algorithms for optimization of multimodal applications. Experimental results show that the proposed TGRASP and FFGRASP algorithms are much better than existing algorithm with respect to three performance parameters viz. accuracy, run time, number of a selected subset of features. We have also compared both the approaches with a unified metric (Extended Adjusted Ratio of Ratios) which has shown that TGRASP approach outperforms existing approach for six out of nine cancer microarray datasets and FFGRASP performs better on seven out of nine datasets.

Analysis of long non-coding RNA expression profiles in ovarian cancer.

Ovarian cancer is one of the major threats to female health. Identifying cancer cases at an early stage and selecting effective therapeutic drugs for patients is challenging. The number of studies concerning long non-coding RNAs (lncRNAs) is increasing rapidly; there is a large body of evidence indicating that lncRNAs are crucial in oncogenic and tumor-suppression mechanisms. Therefore, in the present study, lncRNA expression in ovarian cancer was considered. All of the existing ovarian cancer microarray datasets in the Gene Expression Omnibus database were assessed and two met the criteria for the present study; these were designated the training and validation sets. A re-annotation pipeline method was established to annotate lncRNAs from existing probe sets. When comparing ovarian cancer with normal ovarian tissues, seven lncRNAs from the RefSeq database, based on their combined ability to classify tissue in the training set, were identified and validated with the validation set. Research into the molecular functions of the seven identified lncRNAs may contribute to the understanding of ovarian cancer oncogenesis; they may also be candidates for novel ovarian cancer biomarkers.

A discrete bacterial algorithm for feature selection in classification of microarray gene expression cancer data

When mining in high dimensional data, the curse of dimensionality is one of the major difficulty to overcome. In this paper, a weighted feature selection strategy is developed and embedded in bacterial based algorithms to reduce the feature dimension in classification. The proposed weighted feature selection strategy distinguishes the features by their classification performances as well as the occurrence frequency in population according to the two matrices. The objectives of minimizing the number of features, maximizing the performance, and minimizing the computational cost are all considered. Regarding the drawback of bacterial based algorithms, Bacterial Colony Optimization based feature selection algorithm is proposed to decrease the computational complexity as well as improve the search ability even in discrete optimization problems. To test the effectiveness of the proposed feature selection method, four bacterial based methods with the weighted strategy embedded have been compared with four classical feature selection methods and three well-known population based algorithms using 15 cancer micro-array datasets with different numbers of features and classes. The results show that the weighted feature selection strategies embedded have improved the feature selection capability of bacterial algorithms. The new proposed mechanisms embedded in Bacterial Colony Optimization method can overcome the limitation of the traditional bacterial based algorithms using premature termination to decrease the computational time, and provide comparable or in most cases better solutions than other feature selection methods considered in the comparison.

Abstract A20: Baseline IL-17 receptor signaling is essential for controlling aberrant JNK-dependent cellular proliferation via maintenance of endogenous level of ubiquitin-editing enzyme A20

Abstract The molecular mechanisms underlying aberrant activation of NF-κB and JNK in cancer remain incompletely understood. Here, we demonstrate that baseline IL-17 receptor (IL-17R) signaling is essential for controlling aberrant NF-κB and JNK activation, and restraining JNK-dependent homeostatic cellular proliferation. Using a shRNA knockdown approach, we demonstrated in two murine tumor cell lines that IL-17RA silencing markedly enhanced tumor cell growth in vitro and in vivo. Through mapping IL-17R signaling pathways, we further demonstrated that baseline IL-17A/IL-17R signaling actively restrained JNK phosphorylation in vitro and in vivo via maintenance of basal expression of the ubiquitin-editing enzyme A20, a negative regulator of NF-κB and JNK. Remarkably, IL-17RA reconstitution evidently restored the A20 level, and suppressed cell proliferation and JNK activity in tumor cells. The reconstitution of A20 in IL-17RA knockdown subclones is able to restore the normal rate of cellular proliferation and associated JNK/c-Jun activity. Finally, meta-analysis of human cancer microarray and RNA sequencing datasets confirmed close co-expression of IL-17RA and A20. Furthermore, alterations (mutation, upregulation or downregulation) of IL-17RA level in melanoma and ER+ breast cancer patients showed poor survival rate compared to the respective patients with normal baseline IL-17RA expression. Together, our data demonstrates a previously unrecognized molecular mechanism underlying aberrant activation of NF-κB and JNK in cancer cells. This work has significantly advanced our understanding of the biological role of the proinflammatory IL-17R signaling and highlights its unique role in the maintenance of A20 to regulate the pathogenesis of human cancer at translational level. Citation Format: Chi Yan, Yang Lei, Anna L. Greenshields, David W. Hoskin, Tong-Jun Lin, Jun Wang. Baseline IL-17 receptor signaling is essential for controlling aberrant JNK-dependent cellular proliferation via maintenance of endogenous level of ubiquitin-editing enzyme A20. [abstract]. In: Proceedings of the AACR Special Conference on Translational Control of Cancer: A New Frontier in Cancer Biology and Therapy; 2016 Oct 27-30; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2017;77(6 Suppl):Abstract nr A20.

Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization

PurposeThe categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification.Design/methodology/approachThe proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.FindingsExamples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures.Originality/valueThe novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.