Housekeeping genes in prostate tumorigenesis

CHD6 eviction of promoter nucleosomes maintains housekeeping transcriptional program in prostate cancer

CHD6, a member of the chromodomain helicase DNA-binding protein family, has been implicated in various diseases and tumors. However, its precise binding model of CHD6 on regulatory functional genes remains poorly understood. In this study, we discovered sharp peaks of CHD6, as the first member of CHD family for housekeeping process, binding only to the promoter region of genes in the C4-2 cell line. These genes, with conserved sharp CHD6 peaks across tumor cells, likely represent housekeeping genes ADNP and GOLGA5. Genes with sharp CHD6 peaks exhibit stable and low expression levels, sharing epigenetic features similar to housekeeping genes. Furthermore, this regulatory model also exists in both HEK293 cells and cardiomyocytes. Overall, the results of this study demonstrate that CHD6 binds to the promoter regions of housekeeping genes, regulating their histone modifications, chromatin structure, and gene expression.

Differential expression analyses reveal extensive transcriptional plasticity induced by temperature in New Zealand silver trevally (Pseudocaranx georgianus)

Ectotherm species, such as marine fishes, depend on environmental temperature to regulate their vital functions. In finfish aquaculture production, being able to predict physiological responses in growth and other economic traits to temperature is crucial to address challenges inherent in the selection of grow-out locations. This will become an even more significant issue under the various predicted future climate change scenarios. In this study, we used the marine teleost silver trevally (Pseudocaranx georgianus), a species currently being explored as a candidate for aquaculture in New Zealand, as a model to study plasticity in gene expression patterns and growth in response to different temperatures. Using a captive study population, temperature conditions were experimentally manipulated for 1 month to mimic seasonal extremes. Phenotypic differences in growth were measured in 400 individuals, and gene expression patterns of pituitary gland and liver were determined in a subset of 100 individuals. Results showed that growth increased 50% in the warmer compared with the colder condition, suggesting that temperature has a large impact on metabolic activities associated with growth. A total of 265,116,678 single-end RNA sequence reads were aligned to the trevally genome, and 28,416 transcript models were developed (27,887 of these had GenBank accessions, and 17,980 unique gene symbols). Further filtering reduced this set to 8597 gene models. 39 and 238 differentially expressed genes (DEGs) were found in the pituitary gland and the liver, respectively (|log2FC| > 0.26, p-value < 0.05). Of these, 6 DEGs showed a common expression pattern between both tissues, all involved in housekeeping functions. Temperature-modulated growth responses were linked to major pathways affecting metabolism, cell regulation and signalling, previously shown to be important for temperature tolerance in other fish species. An interesting finding of this study was that genes linked to the reproductive system were up-regulated in both tissues in the high treatment, indicating the onset of sexual maturation. Few studies have investigated the thermal plasticity of the gene expression in the main organs of the somatotropic axis simultaneously. Our findings indicate that trevally exhibit substantial growth differences and predictable plastic regulatory responses to different temperature conditions. We identified a set of genes that provide a list of candidates for further investigations for selective breeding objectives and how populations may adapt to increasing temperatures.

Identification of ACTB Gene as a Potential Safe Harbor Locus in Pig Genome

Transgenic pigs play an important role in biomedicine and agriculture. The "safe harbor" locus maintains consistent foreign gene expression in cells and is important for transgenic pig generation. However, as only several safe harbor loci(Rosa26, pH11 and Pifs501) have been identified in pigs, meeting the needs of the insertion of various foreign genes is difficult. In this study, we develop a novel strategy for the efficient knock-in of gene-of-interest fragments into endogenous beta-actin(ACTB) gene via CRISPR/Cas9 mediated homologous recombination with normal expression of ACTB. Thus, we provide an alternative strategy to integrate exogenous genes into the pig genome that can be applied to agricultural breeding and biomedical models.

Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer

BACKGROUND

Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these "housekeeping genes" (HKGs) could separate one normal human tissue type from another. Current focus on identifying "specific disease markers" is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers.

METHODS

Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER.

RESULTS

This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients.

DISCUSSION

Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway

Genome-wide association studies (GWASs) identified over 500 single nucleotide polymorphisms (SNPs) influencing cancer risk. It is logical to expect the cancer-associated genes to cluster in pathways directly involved in carcinogenesis, e.g. cell cycle. Nevertheless, analyses of the GWAS-detected cancer risk genes usually show no or weak enrichment by known cancer genes.We hypothesized that GWAS-detected cancer risk-associated genes function as upstream regulators of the genes directly involved in carcinogenesis. We have analyzed four common cancers: breast, colon, lung, and prostate. To identify downstream targets of GWAS-detected cancer risk genes we used MedScan, which is a text mining tool offered by PathwayStudio. We also used data on protein/protein interactions reported by BioGRID database. Among all identified targets we have selected common downstream targets. A gene was considered a common downstream target if it was a downstream target for at least three GWAS-detected genes for a given cancer type. Common downstream targets were identified separately for each cancer type. We found that common downstream targets for all four cancer types were enriched by cell cycle genes, more specifically, the genes involved in G1/S transition. Common downstream targets for bipolar disorder, Crohn's disease, and type 2 diabetes did not show G1/S transition enrichment.The results of this analysis suggest that many cancer risk genes function as upstream regulators of the genes directly involved in G1/S transition and exert their risk effects by reducing threshold for G1/S transition, elevating the background level of cell proliferation and cancer risk.

Overexpression of miR-145-5p inhibits proliferation of prostate cancer cells and reduces SOX2 expression

We aimed to perform functional analysis of miR-145-5p in prostate cancer (PCa) cells and to identify targets of miR-145-5p for understanding its role in PCa pathogenesis. PC3, DU145, LNCaP PCa, and PNT1a nontumorigenic prostate cell lines were utilized for functional analysis of miR-145-5p. Its overexpression caused inhibition of proliferation through apoptosis and reduced migration in PCa cells. SOX2 expression was significantly decreased in both mRNA and protein level in miR-145-5p-overexpressed PCa cells. We proposed that miR-145-5p, being an important regulator of SOX2, carries a crucial role in PCa tumorigenesis.

ErbB2 receptor immunoreactivity in prostate cancer: relationship to the androgen receptor, disease severity at diagnosis and disease outcome

Background

ErbB2 is a member of the epidermal growth factor family of tyrosine kinases that is centrally involved in the pathogenesis of prostate cancer and several studies have reported that a high expression of this protein has prognostic value. In the present study, we have investigated whether tumour ErbB2 immunoreactivity (ErbB2-IR) has clinically useful prognostic value, i.e. that it provides additional prognostic information to that provided by routine clinical tests (Gleason score, tumour stage).

Methodology/Principal Findings

ErbB2-IR was measured in a well-characterised tissue microarray of tumour and non-malignant samples obtained at diagnosis. Additionally, mRNA levels of ErbB2-IR in the prostate were determined in the rat following manipulation of circulating androgen levels. Tumour ErbB2-IR was significantly associated with the downstream signalling molecule phosphorylated-Akt and with the cell proliferation marker Ki-67. The significant association of tumour ErbB2-IR with the Gleason score at diagnosis was lost when controlled for the association of both parameters with Ki-67. In the rat prostate, mRNA for ErbB2 was inversely associated with circulating androgen levels. There was no association between ErbB2-IR and the androgen receptor (AR)-IR in the tumours, but an interaction between the two parameters was seen with respect to their association with the tumour stage. Tumour ErbB2-IR was confirmed to be a prognostic marker for disease-specific survival, but it did not provide significant additive information to the Gleason score or to Ki-67.

Conclusions/Significance

It is concluded that tumour ErbB2-IR is of limited clinical value as a prognostic marker to aid treatment decisions, but could be of pathophysiological importance in prostate cancer.

Building a statistical model for predicting cancer genes

More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were significant in the model. We found that a typical PCa gene is a prostate-specific transcription factor, kinase, or phosphatase with high interindividual variance of the expression level in adjacent normal prostate tissue and differential expression between normal prostate tissue and primary tumor. PCa genes are likely to have an antiapoptotic effect and to play a role in cell proliferation, angiogenesis, and cell adhesion. Their proteins are likely to be ubiquitinated or sumoylated but not acetylated. A number of novel PCa candidates have been proposed. Functional annotations of novel candidates identified antiapoptosis, regulation of cell proliferation, positive regulation of kinase activity, positive regulation of transferase activity, angiogenesis, positive regulation of cell division, and cell adhesion as top functions. We provide the list of the top 200 predicted PCa genes, which can be used as candidates for experimental validation. The model may be modified to predict genes for other cancer sites.

Comprehensive literature review and statistical considerations for microarray meta-analysis

With the rapid advances of various high-throughput technologies, generation of ‘-omics’ data is commonplace in almost every biomedical field. Effective data management and analytical approaches are essential to fully decipher the biological knowledge contained in the tremendous amount of experimental data. Meta-analysis, a set of statistical tools for combining multiple studies of a related hypothesis, has become popular in genomic research. Here, we perform a systematic search from PubMed and manual collection to obtain 620 genomic meta-analysis papers, of which 333 microarray meta-analysis papers are summarized as the basis of this paper and the other 249 GWAS meta-analysis papers are discussed in the next companion paper. The review in the present paper focuses on various biological purposes of microarray meta-analysis, databases and software and related statistical procedures. Statistical considerations of such an analysis are further scrutinized and illustrated by a case study. Finally, several open questions are listed and discussed.

Selection of reference genes for gene expression studies in rats

Selection of the most stable reference gene is critical for a reliable interpretation of gene expression data using RT-PCR. In order so, 17 commonly used genes were analyzed in Wistar rat duodenum, jejunum, ileum and liver following a fat gavage and at two time periods. These reference genes were also tested in liver from Zucker (fa/fa) on a long-term dietary trial. Four strategies were used to select the most suitable reference gene for each tissue: ranking according to biological coefficient of variation and further validation by statistical comparison among groups, geNorm, NormFinder and BestKeeper programs. No agreement was observed among these approaches for a particular gene, nor a common gene for all tissues. Furthermore we demonstrated that normalising using an inadequate reference conveyed into false negative and positive results. The selection of genes provided by BestKeeper resulted in more reliable results than the other statistical packages. According to this program, Tbp, Ubc, Hprt and Rn18s were the best reference genes for duodenum, jejunum, ileum and liver, respectively following a fat gavage in Wistar rats and Rn18s for liver in another rat strain on a long-term dietary intervention. Therefore, BestKeeper is highly recommendable to select the most stable gene to be used as internal standard and the selection of a specific reference expression gene requires a validation for each tissue and experimental design.

Prioritizing genes associated with prostate cancer development

Background

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.

Methods

A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.

Results

Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.

Conclusions

By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.

The synthetic caged garcinia xanthone cluvenone induces cell stress and apoptosis and has immune modulatory activity

Several caged Garcinia xanthone natural products have potent bioactivity and a documented value in traditional Eastern medicine. Previous synthesis and structure activity relationship studies of these natural products resulted in the identification of the pharmacophore represented by the structure of cluvenone. In the current study, we examined the anticancer activity of cluvenone and conducted gene expression profiling and pathway analyses. Cluvenone was found to induce apoptosis in T-cell acute lymphoblastic leukemia cells (EC₅₀ = 0.25 μmol/L) and had potent growth-inhibitory activity against the NCI60 cell panel, including those that are multidrug-resistant, with a GI₅₀ range of 0.1 to 2.7 μmol/L. Importantly, cluvenone was approximately 5-fold more potent against a primary B-cell acute lymphoblastic leukemia compared with peripheral blood mononuclear cells from normal donors, suggesting that it has significant tumor selectivity. Comparison of cluvenone's growth-inhibitory profile to those in the National Cancer Institute database revealed that compounds with a similar profile to cluvenone were mechanistically unlike known agents, but were associated with cell stress and survival signaling. Gene expression profiling studies determined that cluvenone induced the activation of mitogen-activated protein kinase and NrF2 stress response pathways. Furthermore, cluvenone was found to induce intracellular reactive oxygen species formation. Lastly, the modulation in the expression of several genes associated with T cell and natural killer cell activation and function by cluvenone suggests a role as an immune-modulator. The current work highlights the potential of cluvenone as a chemotherapeutic agent and provides support for further investigation of these intriguing molecules with regard to mechanism and targets.

Heat shock proteins in prostate cancer: from tumorigenesis to the clinic

The heat shock proteins (HSP) constitute a superfamily of chaperone proteins present in all cells and in all cell compartments, operating in a complex interplay with synergistic/overlapping multiplicity of functions, even though the common effect is cell protection. Several reasons explain the need for investigating HSP in prostate cancer: (1) these molecules function as chaperones of tumorigenesis accompanying the emergence of prostate cancer cells, (2) they appear as useful molecular markers associated with disease aggressiveness and with resistance to anticancer therapies including hormone therapy, radiotherapy, chemotherapy and hyperthermia, and (3) they can be used as targets for therapies. The latter can be accomplished by: (i) interrupting the interaction of HSP (mainly HSPC1) with various client proteins that are protected from degradation when chaperoned by the HSP; (ii) using the chaperone and adjuvant capabilities of certain HSP to present antigenic peptides to the immune system, so this system can recognise the prostate tumour cells as foreign to mount an effective antitumoral response; and (iii) using treatment planning models taking into account the HSP expression levels to obtain more effective therapies. In summary, the study of the HSP during tumorigenesis as well as during cancer progression, and the inclusion of treatment designs targeting HSP combined with other treatment modalities, should improve prostate cancer survival in the near future.

Robust multi-tissue gene panel for cancer detection

Background

We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types.

Methods

Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models.

Results

We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets.

Conclusions

Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.