Sequence databases and microarrays as tools for identifying prostate cancer biomarkers

The genome is now accessible to the endoscopist

Biological procedures within cells are carried out by genes that can be studied using microarray technology. Gene expression data from a cell is stored and features of each cell are described by mercury. Microarray data is one of the latest breakthroughs used for selecting relevant biological information from thousands of genes simultaneously expressed in a distinct cell. Understanding the level of gene expression in a cell can provide valuable information in terms of identifying viral infection and determining cancer susceptibility. In a sample tissue, many genes are expressed. In that most of the genes are unrelated or irrelevant for clinical diagnosis. An accurate classification of diseases and phenotypes is selecting a small set of genes from thousands of genes. This paper concentrates on common gene expression classification techniques with MapReduce framework and related methods to find differentially expressed genes and select the significant genes in the specific gene expression.

A proposal for direct-ordering gene expression data by self-organising maps

Background: CXXC5 encodes a retinoid-inducible nuclear factor that plays an essential role in human hematopoiesis (1, 2). We have previously identified the differential expression of this gene in rat cancer cell lines with different cancer phenotypes (3). However, knowledge on the expression of this gene in human prostate cell lines or tissue is lacking, both at the transcript and protein level. Methods: Prospective study conducted between January-2012 through June-2015 at the University of Cartagena, Colombia. Expression of CXXC5 was evaluated by quantitative PCR, both in prostate cancer cell lines and tissue biopsies from patients undergoing transrectal ultrasound-guided prostate needle biopsy for suspected prostate cancer (PCa). A total of 65 prostate tissue samples, consisting of 24 benign prostatic tissue biopsies and 41 prostate cancer tissue biopsies were included in the study. Transcript levels in individual biopsies from each group were normalized to transcript levels of beta-actin of the corresponding sample and compared. The protein expression level of CXXC5 was analyzed by immunohistochemistry in benign and malignant prostate tissue. A final score was determined by multiplying the highest intensity of staining by the mean percentage of positive cells stained to yield a value between 0 and 300 for each case. For statistical analysis we compared parameters between group of cases with PCa and without PCa. This study was approved by the Ethical Review Board of the academic institution. Results: CXXC5 transcript expression levels were significantly lower in immortalized non-tumoral prostate cell line PWR-1E compared to PCa cell lines: LNCaP (Fold change: 10.5, p = 0.001), and PC3 (Fold change: 2.8, p = 0.014). In prostate tissue we observed a significant higher expression in low grade prostate cancer compared to benign prostate tissue (p = 0.034). IHC evaluation showed that CXXC5 staining was higher in tissue samples from patients with prostate cancer compared to patients with benign disease (p = <0.001). In the prostate cancer group, immunostaining was also stronger in malignant acini than in adjacent, benign acini, within the same patient sample. Conclusions: CXXC5 exhibits differential expression in prostate cell lines with different cancer phenotypes and between cancer and benign prostate tissue. Additional studies are required to determine the biological and clinical significance of CXXC5 in prostate cancer.

Feature Selection From Gene Expression Data Using Simulated Annealing and Partial Least Squares Regression Coefficients

Microarrays are capable of determining the expression levels of thousands of genes simultaneously. One important application of gene expression data is classification of samples into categories. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. Standard statistic methodologies in classification or prediction do not work well when the number of variables p (genes) far too exceeds the number of samples n. So, modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis to model the gene expression data, then apply optimal scoring algorithm to classify them. Further speaking, this approach can first make full use of the high-order statistical information contained in the gene expression data. Second, this approach also employs regularized regression models to handle the situation of large numbers of correlated predictor variables. Finally, the predictive models are developed for classifying tumors based on the entire gene expression profile. To show the validity of the proposed method, we apply it to classify four DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible. Matlab scripts are available on request.

BackgroundInferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell; (2) complex dynamic and nonlinear relationships exist among genes; (3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules.ResultsThis study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information.ConclusionThe reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e.g. normalization and missing value imputation) to reduce the data noise; (2) clustering genes based on gene expression data and gene functional category information to identify biologically meaningful modules, thereby reducing the dimensionality of the data; (3) modeling GRNs with the PSO-RNN method between the modules to capture their nonlinear and dynamic relationships. The method is shown to lead to biologically meaningful modules and networks among the modules.

In order to understand even the simplest cellular processes, we need to integrate proteomic, gene expression and other biomolecular data. To date, most computational approaches aimed at integrating proteomics and gene expression data use direct gene/protein correlation measures. However, due to post-transcriptional and translational regulations, the correspondence between the expression of a gene and its protein is complicated. We apply a multivariate statistical method, co-inertia analysis (CIA), to visualise gene and proteomic expression data stemming from the same biological samples. Principal components analysis or correspondence analysis can be used for data exploration on single datasets. CIA is then used to explore the relationships between two or more datasets. We further explore the data by projecting gene ontology (GO) information onto these plots to describe the cellular processes in action. We apply these techniques to gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI-60 cancer cell lines. In each case, we visualise gene expression, protein abundance and GO classes in the same low dimensional projections and identify GO classes that are likely to be of biological importance.

A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a finer and more informative classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification. This article compares the performance of different discrimination methods for the classification of tumors based on gene expression data. The methods include nearest-neighbor classifiers, linear discriminant analysis, and classification trees. Recent machine learning approaches, such as bagging and boosting, are also considered. The discrimination methods are applied to datasets from three recently published cancer gene expression studies.

Background: The Hippo TEAD-transcriptional regulators YAP1 and TAZ play essential role in cancer cell growth and metastasis. However, the function of YAP1 and TAZ in prostate cancer is not well characterized. We discovered that expression of PDEF is decreased during prostate cancer progression and re-expression of PDEF limits prostate cancer metastasis in part by promoting luminal epithelial phenotype, but mechanisms of PDEF action are not completely understood. In the present study, we evaluated the effects of the SPDEF on YAP1/TAZ levels and expression of YAP1/TAZ regulated genes in prostate cancer (PCa) cells. We also evaluated expression of SPDEF, YAP1, TAZ and YAPTAZZ-Tead regulated genes in clinical cohorts of prostate cancer patients. Material & Method: Prostate cancer (PC3 and DU145) cells were transfected with PDEF or respective vector control. shRNA was used to knock down SPDEF in AR positive and androgen responsive LNCaP cells. Protein levels were analyzed by western blotting. Gene expression was monitored by microarray analysis/RNA seq and confirmed by RTPCR assays. Cell migration and invasion were measured by scratch wound healing and by trans well migration though Matrigel assays respectively. We analyzed publicly available gene expression data in several prostate cancer cohorts. Results: We observed that the two well-characterized metastatic PCa cells (PC3 and DU145 cells) express both YAP1 and TAZ, but do not express SPDEF. Expression of PDEF in PC3 and DU145 cells resulted in an increased phospho-YAP1 and phospho-TAZ protein levels and inhibition of YAP1/TAZ target genes, as compared to the respective vector control, directly demonstrating that PDEF plays a critical role in modulating YAP1/TAZ-TEAD transcriptional activity, and by extension in the regulation of the Hippo pathway. Analysis of publicly available PCa data sets revealed a significant increase in TAZ mRNA levels in prostate cancer tissues as compared to normal prostate tissues (p=4.95E-08). Furthermore, we also found a significant gradual increase in TAZ mRNA expression and concomitant decrease in YAP1 mRNA during disease progression and metastasis and in neuroendocrine PCa. Conclusions: Taken together these data suggest that SPDEF limits prostate cancer metastasis in part by targeting YAP1/TAZ driven transcriptional output. Citation Format: Hari K. Koul, Praveen K. Jaiswal, Suman Mohajan, Mousa Vatanmakanian, Fengtian Wang, Sweaty Koul. PDEF restricts prostate cancer cellular plasticity in part by modulating YAP1/TAZ-TEAD transcriptional network [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1457.

Breast cancer (BCa) and prostate cancer (PCa) are both hormone driven cancers with similar etiology and tumor microenvironment inflammation promotes BCa and PCa progression. Interleukin-1 (IL-1) is an inflammatory cytokine present in the tumor microenvironment and IL-1 is elevated in BCa and PCa patient tumor and/or blood serum and correlates with poor prognosis. We have shown that IL-1 represses the BCa and PCa therapeutic targets, Estrogen Receptor Alpha (ERa) and Androgen Receptor (AR), respectively, in PCa and BCa cell lines; yet the cells remain viable. Thus, IL-1 may promote treatment resistance and disease progression. The pro-survival protein Sequestome-1 (SQSMT1/p62) is also upregulated in both BCa and PCa cell lines exposed to IL-1, suggesting that BCa and PCa cells have evolved a shared response to IL-1 and hormone receptor loss. RNA sequencing of an IL-1-treated PCa cell line revealed an IL-1-modulated gene suite predicted to confer AR-independent tumorigenicity. We performed RT-qPCR in PCa cell lines for several select genes to confirm the RNA sequencing results and given the similar etiology and IL-1 regulation of ERa, AR, and p62 expression in BCa and PCa cell lines, we presumed that our select genes would be similarly regulated by IL-1 in BCa cells. However, outside of ERa, AR, and p62 expression, our select genes did not show similar IL-1 regulation in BCa cell lines, suggesting that IL-1 regulates a unique set of genes that could contribute to ERa-independent tumorigenicity in BCa cells. Therefore, to identify a BCa-specific IL-1-modulated gene suite, we performed RNA sequencing on an IL-1-treated ERα+ BCa cell line and compared gene expression changes with 1) basal gene expression in an ERa- BCa cell line and 2) our IL-1-modulated PCa-specific gene suite. Our bioinformatics analysis revealed pathways that are predicted to promote ERa-independent tumorigenicity in BCa cells and investigations are underway to demonstrate the functional significance of our gene expression data. Taken together, our studies provide insight into the mechanistic function of IL-1 in PCa and BCa resistance to hormone receptor-targeted therapies and tumor progression. Citation Format: Afshan Fathima Nawas, Mohammed Kanchwala, Shayna Thomas-Jardin, Vanessa Anunobi, Ally Wong, Chao Xing, Nikki Delk. Identification of Interleukin-1 (IL-1) induced gene expression pattern in breast cancer (BCa) cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3444.

The androgen receptor (AR) is a transcription factor that drives prostate cancer (PCa) by modulating the expression of thousands of genes to promote proliferation and survival and to reprogram metabolism. However, how AR activation affects alternative splicing is mostly unknown. Our objective was to define its role in the transcriptome-wide regulation of alternative splicing. Three human PCa models–LNCaP, LAPC4, and 22Rv1 cells–were treated with and without androgens, and RNA was purified for deep-sequencing analyses (RNA-seq). Several bio-informatic tools were then used to study alternative splicing. We demonstrate that in the absence of androgens, alternative splicing complexity is similar among AR-positive PCa cells, with 48% of all transcripts having various levels of alternative splicing. We also describe splicing differences among cell lines, such as specific splicing of AR, REST, TSC2, and CTBP1. Interestingly, AR activation changed the alternative splicing of thousands of transcripts in all the cell lines tested. Overlap between AR-sensitive alternative splicing events revealed that genes linked to PCa metabolism are major targets for this specific modulation. These genes encode metabolic enzymes such as the prostate-specific membrane antigen (FOLH1), malate dehydrogenase 1 (MDH1), and malic enzyme 2 (ME2). Enzymatic assays revealed that AR alters their activities without changing their total gene expression levels, demonstrating that AR-driven regulation of alternative splicing has a direct effect on cancer cell metabolism. Overall, our study presents a comprehensive analysis of PCa cell transcriptome and its modulation by AR, revealing cell metabolism as a key target for AR-dependent regulation of alternative splicing. Citation Format: Lucas Germain, Camille Lafront, Jolyane Beaudette, Raghavendra Tejo Karthik Poluri, Cindy Weidmann, Etienne Audet-Walsh. Alternative splicing regulation by the androgen receptor in prostate cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2083.

Researchers are interested in genomic level cancer classification because it offers a thorough, precise, and reliable diagnosis for various cancer types. The gene expression data set is made up of hundreds of samples, each containing thousands of genes. As a result, accurately and effectively classifying such samples is difficult. To analyze gene expression patterns efficiently, recent studies have used artificial intelligence, data mining, and statistical techniques. Furthermore, machine learning methods have been extensively used to construct robust and accurate classification frameworks. In this paper, we examined the effectiveness of machine learning-based classification techniques such as Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Tree, K-Nearest Neighbors, and Random Forest methods. In a total of 72 patients, 7129 distinct gene expressions and their degrees were evaluated to determine their relevance. Features have been normalized and PCA was used to decrease the dataset's dimensionality and complexity. The dataset was divided into training and test sets, with 90% of the occurrences in the training set and 10% in the test set. And finally, SMOTE was employed to oversample the minority class. In this study, gene expression data was utilized to differentiate Acute Myeloid Leukemia (AML) from Acute Lymphoblastic Leukemia (ALL). To evaluate the performance of the six machine learning models we used, we examined their associated confusion matrices and calculated nine performance evaluation metrics for each model's prediction on the test dataset. Results demonstrate that SVM, Logistic Regression, and Naïve Bayes outperformed the other ML techniques with an accuracy of 100%.

Induction of comprehensible models for gene expression datasets by subgroup discovery methodology

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.