Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays

Cellular functions of heat shock protein 20 (HSPB6) in cancer: A review

Heat shock proteins (HSP) are a large family of peptide proteins that are widely found in cells. Studies have shown that the expression and function of HSPs in cells are very complex, and they can participate in cellular physiological and pathological processes through multiple pathways. Multiple heat shock proteins are associated with cancer cell growth, proliferation, metastasis, and resistance to anticancer drugs, and they play a key role in cancer development by ensuring the correct folding or degradation of proteins in cancer cells. As research hotspots, HSP90, HSP70 and HSP27 have been extensively studied in cancer so far. However, HSP20, also referred to as HSPB6, as a member of the small heat shock protein family, has been shown to play an important role in the cardiovascular system, but little research has been conducted on HSP20 in cancer. This review summarizes the current cellular functions of HSP20 in different cancer types, as well as its effects on cancer proliferation, progression, prognosis, and its other functions in cancer, to illustrate the close association between HSP20 and cancer. We show that, unlike most HSPs, HSP20 mainly plays an active anticancer role in cancer development, which is expected to provide new ideas and help for cancer diagnosis and treatment and research.

Whole-genome sequencing of glioblastoma reveals enrichment of non-coding constraint mutations in known and novel genes

BACKGROUND

Glioblastoma (GBM) has one of the worst 5-year survival rates of all cancers. While genomic studies of the disease have been performed, alterations in the non-coding regulatory regions of GBM have largely remained unexplored. We apply whole-genome sequencing (WGS) to identify non-coding mutations, with regulatory potential in GBM, under the hypothesis that regions of evolutionary constraint are likely to be functional, and somatic mutations are likely more damaging than in unconstrained regions.

RESULTS

We validate our GBM cohort, finding similar copy number aberrations and mutated genes based on coding mutations as previous studies. Performing analysis on non-coding constraint mutations and their position relative to nearby genes, we find a significant enrichment of non-coding constraint mutations in the neighborhood of 78 genes that have previously been implicated in GBM. Among them, SEMA3C and DYNC1I1 show the highest frequencies of alterations, with multiple mutations overlapping transcription factor binding sites. We find that a non-coding constraint mutation in the SEMA3C promoter reduces the DNA binding capacity of the region. We also identify 1776 other genes enriched for non-coding constraint mutations with likely regulatory potential, providing additional candidate GBM genes. The mutations in the top four genes, DLX5, DLX6, FOXA1, and ISL1, are distributed over promoters, UTRs, and multiple transcription factor binding sites.

CONCLUSIONS

These results suggest that non-coding constraint mutations could play an essential role in GBM, underscoring the need to connect non-coding genomic variation to biological function and disease pathology.

The imbalance in the complement system and its possible physiological mechanisms in patients with lung cancer

BACKGROUND

The clinical and experimental evidences for complement-cancer relationships are solid, whereas an epidemiological study reporting the imbalance of complement system in patients is still lacking.

METHODS

Using publicly available databases, we jointly compared the levels of complement components in plasma and lung cancer tissues. With iTRAQ proteomics, quantitative RT-PCR and western blotting, we analysed the differences in complement levels in lung cancer tissues and normal control tissues. Complement components are mainly synthesized by the liver and secreted into the blood. Using paired co-cultures of human normal QSG-7701 hepatocytes with lung cancer cells (A549, LTEP-α-2 or NCI-H1703) or human normal bronchial epithelial (HBE) cells, we examined the effects of lung cancer cells on complement synthesis and secretion in QSG-7701 hepatocytes.

RESULTS

An integrated analysis of transcriptome and proteome datasets from 43 previous studies revealed lower mRNA and protein levels of 25 complement and complement-related components in lung cancer tissues than those in normal control tissues; conversely, higher levels of complement proteins were detected in plasma from patients than those in healthy subjects. Our iTRAQ proteome study identified decreased and increased levels of 31 and 2 complement and complement-related proteins, respectively, in lung cancer tissues, of which the reduced levels of 10 components were further confirmed using quantitative RT-PCR and western blotting. Paired co-cultures of QSG-7701 hepatocytes with A549, LTEP-α-2, NCI-H1703 or HBE cells indicated that lung cancer cells increased complement synthesis and secretion in QSG-7701 cells compared to HBE cells.

CONCLUSIONS

The opposite associations between the levels of complement and complement-related components in lung cancer tissues and plasma from patients that have been repeatedly reported by independent publications may indicate the prevalence of an imbalance in the complement system of lung cancer patients. The possible mechanism of the imbalance may be associated not only with the decreased complement levels in lung cancer tissues but also the concurrent lung cancer tissue-induced increase in hepatocyte complement synthesis and plasma secretion in patients. And the imbalance should be accompanied by a suppression of complement-dependent immunity in lung cancer tissues coupled with a burden of complement immunity in the circulation of patients.

Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes

In many cancers, significantly down- or upregulated genes are found within chromosomal regions with DNA copy number alteration opposite to the expression changes. Generally, this paradox has been overlooked as noise, but can potentially be a consequence of interference of epigenetic regulatory mechanisms, including microRNA-mediated control of mRNA levels.

To explore potential associations between microRNAs and paradoxes in non-small-cell lung cancer (NSCLC) we curated and analyzed lung adenocarcinoma (LUAD) data, comprising gene expressions, copy number aberrations (CNAs) and microRNA expressions. We integrated data from 1,062 tumor samples and 241 normal lung samples, including newly-generated array comparative genomic hybridization (aCGH) data from 63 LUAD samples.

We identified 85 “paradoxical” genes whose differential expression consistently contrasted with aberrations of their copy numbers. Paradoxical status of 70 out of 85 genes was validated on sample-wise basis using The Cancer Genome Atlas (TCGA) LUAD data. Of these, 41 genes are prognostic and form a clinically relevant signature, which we validated on three independent datasets. By meta-analysis of results from 9 LUAD microRNA expression studies we identified 24 consistently-deregulated microRNAs. Using TCGA-LUAD data we showed that deregulation of 19 of these microRNAs explains differential expression of the paradoxical genes.

Our results show that deregulation of paradoxical genes is crucial in LUAD and their expression pattern is maintained epigenetically, defying gene copy number status.

Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm

Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.

Elevated C1orf63 expression is correlated with CDK10 and predicts better outcome for advanced breast cancers: a retrospective study

BACKGROUND

Chromosome 1 open reading frame 63 (C1orf63) is located on the distal short arm of chromosome 1, whose allelic loss has been observed in several human cancers. C1orf63 has been reported to be up-regulated in IL-2-starved T lymphocytes, which suggests it might be involved in cell cycle control, a common mechanism for carcinogenesis. Here we investigated the expression and clinical implication of C1orf63 in breast cancer.

METHODS

Paraffin-embedded specimens, clinicopathological features and follow-up data of the breast cancer patients were collected. Publicly available microarray and RNA-seq datasets used in this study were downloaded from ArrayExpress of EBI and GEO of NCBI. KM plotter tool was also adopted. The expression of C1orf63 and CDK10, one known cell cycle-dependent tumor suppressor in breast cancer, was assessed by immunohistochemistry. Western blotting was performed to detect C1orf63 protein in human breast cancer cell lines, purchased from the Culture Collection of the Chinese Academy of Sciences, Shanghai.

RESULTS

In a group of 12 human breast tumors and their matched adjacent non-cancerous tissues, C1orf63 expression was observed in 7 of the 12 breast tumors, but not in the 12 adjacent non-cancerous tissues (P < 0.001). Similar results were observed of C1orf63 mRNA expression both in breast cancer and several other cancers, including lung cancer, prostate cancer and hepatocellular carcinoma. In another group of 182 breast cancer patients, C1orf63 expression in tumors was not correlated with any clinicopathological features collected in this study. Survival analyses showed that there was no significant difference of overall survival (OS) rates between the C1orf63 (+) group and the C1orf63 (-) group (P = 0.145). However, the analyses of KM plotter displayed a valid relationship between C1orf63 and RFS (relapse free survival)/OS (P < 0.001; P = 0.007). Notablely, in breast cancers with advanced TNM stages (III ~ IV) among these 182 patients, C1orf63 expression was an independent prognostic factor predicting better clinical outcome (HR: 0.41; 95 % CI: 0.17 ~ 0.97; P = 0.042). Additionally, we found that CDK10 mRNA expression was positively correlated with C1orf63, which was consistent with the relationship of protein expression between C1orf63 and CDK10 (r s = 0.391; P < 0.001).

CONCLUSIONS

Compared to adjacent non-cancerous tissues, C1orf63 expression was elevated in tumor tissues. However, C1orf63 predicts better prognosis for breast cancers with advanced TNM stage, and the underlying mechanism is unknown. In addition, C1orf63 is correlated with the cell cycle related gene, CDK10.

Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods

BACKGROUND

With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies.

OBJECTIVES

We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods.

METHODS

Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity.

RESULTS

Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01).

CONCLUSIONS

Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research.

Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue

Objective: Novel statistical methods and increasingly more accurate gene annotations can transform “old” biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches.

Methods: The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways.

Results: Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93–100% (AUC = 0.93–1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue.

Conclusion: Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.

'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine

Background

The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery.

Method

‘N-of-1-pathways’ is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient.

Results

Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03).

Conclusions

The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.

Software

http://lussierlab.org/publications/N-of-1-pathways

Concordance of deregulated mechanisms unveiled in underpowered experiments: PTBP1 knockdown case study

Background

Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge.

Methods

"N-of-1-pathways" translates gene expression data profiles into mechanism-level profiles on single pairs of samples (one p-value per geneset). It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples (e.g. patients, cell-lines, tissues, etc.), which helps the interpretation of the underpinning biology.

Results

In the context of underpowered experiments, N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets (Odds-Ratios≥13, p-values≤1 × 10⁻⁵), such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples.

Conclusions

N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.

Multiscale in situ analysis of the role of dyskerin in lung cancer cells

Dyskerin is one of the three subunits of the telomerase ribonucleoprotein (RNP) complex. Very little is known about the role of dyskerin in the biology of the telomeres in cancer cells. In this study, we use a quantitative, multiscale 3D image-based in situ method and several molecular techniques to show that dyskerin is overexpressed in lung cancer cell lines. Furthermore, we show that dyskerin expression correlates with telomere length both at the cell population level--cells with higher dyskerin expression have short telomeres--and at the single cell level--the shortest telomeres of the cell are spatially associated with areas of concentration of dyskerin proteins. Using this in vitro model, we also show that exogenous increase in dyskerin expression confers resistance to telomere shortening caused by a telomerase inactivating drug. Finally, we show that resistance is achieved by the recovery of telomerase activity associated with dyskerin. In summary, using a novel multiscale image-based in situ method, we show that, in lung cancer cell lines, dyskerin responds to continuous telomere attrition by increasing the telomerase RNP activity, which in turn provides resistance to telomere shortening.

Identification of novel deregulated RNA metabolism-related genes in non-small cell lung cancer

Lung cancer is a leading cause of cancer death worldwide. Several alterations in RNA metabolism have been found in lung cancer cells; this suggests that RNA metabolism-related molecules are involved in the development of this pathology. In this study, we searched for RNA metabolism-related genes that exhibit different expression levels between normal and tumor lung tissues. We identified eight genes differentially expressed in lung adenocarcinoma microarray datasets. Of these, seven were up-regulated whereas one was down-regulated. Interestingly, most of these genes had not previously been associated with lung cancer. These genes play diverse roles in mRNA metabolism: three are associated with the spliceosome (ASCL3L1, SNRPB and SNRPE), whereas others participate in RNA-related processes such as translation (MARS and MRPL3), mRNA stability (PCBPC1), mRNA transport (RAE), or mRNA editing (ADAR2, also known as ADARB1). Moreover, we found a high incidence of loss of heterozygosity at chromosome 21q22.3, where the ADAR2 locus is located, in NSCLC cell lines and primary tissues, suggesting that the downregulation of ADAR2 in lung cancer is associated with specific genetic losses. Finally, in a series of adenocarcinoma patients, the expression of five of the deregulated genes (ADAR2, MARS, RAE, SNRPB and SNRPE) correlated with prognosis. Taken together, these results support the hypothesis that changes in RNA metabolism are involved in the pathogenesis of lung cancer, and identify new potential targets for the treatment of this disease.

Small interfering RNA against transcription factor STAT6 leads to increased cholesterol synthesis in lung cancer cell lines

STAT6 transcription factor has become a potential molecule for therapeutic intervention because it regulates broad range of cellular processes in a large variety of cell types. Although some target genes and interacting partners of STAT6 have been identified, its exact mechanism of action needs to be elucidated. In this study, we sought to further characterize the molecular interactions, networks, and functions of STAT6 by profiling the mRNA expression of STAT6 silenced human lung cells (NCI-H460) using microarrays. Our analysis revealed 273 differentially expressed genes after STAT6 silencing. Analysis of the gene expression data with Ingenuity Pathway Analysis (IPA) software revealed Gene expression, Cell death, Lipid metabolism as the functions associated with highest rated network. Cholesterol biosynthesis was among the most enriched pathways in IPA as well as in PANTHER analysis. These results have been validated by real-time PCR and cholesterol assay using scrambled siRNA as a negative control. Similar findings were also observed with human type II pulmonary alveolar epithelial cells, A549. In the present study we have, for the first time, shown the inverse relationship of STAT6 with the cholesterol biosynthesis in lung cancer cells. The present findings are potentially significant to advance the understanding and design of therapeutics for the pathological conditions where both STAT6 and cholesterol biosynthesis are implicated viz. asthma, atherosclerosis etc.

The emerging role of HSP20 as a multifunctional protective agent

The small heat shock proteins (sHSPs) are a highly conserved family of molecular chaperones that are ubiquitously expressed throughout nature. They are transiently upregulated in many tissue types following stressful stimuli. Recently, one member of the sHSP family, HSP20 (HspB6), has been shown to be highly effective as a protective mediator against a number of debilitating pathological conditions, including cardiac hypertrophy and Alzheimer's disease. Hsp20 is also an important modulator of vital physiological processes, such as smooth muscle relaxation and cardiac contractility. This review focuses on the molecular mechanisms employed by HSP20 that allow it to act as an innate protector in the context of cardiovascular and neurological diseases. Emerging evidence for a possible role as an anti-cancer agent is also presented.

Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background

Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples.

Results

We focused on five solid cancer types (colon, kidney, liver, lung, and pancreas), where available microarray data allowed us to compare meta-analysis and integrated approaches. Our results from the merged SAM significantly overlapped gene lists from the validated inverse-variance method. Both meta-analysis and merged SAM approaches successfully captured the aberrances in the cell cycle that commonly occur in the different cancer types. However, the integrated SAM analysis replicated the known cancer literature (excluding microarray studies) with much more accuracy than the meta-analysis.

Conclusion

The merged SAM test is a powerful, robust approach for combining data from similar platforms and for analyzing asymmetric datasets, including those with only normal or only cancer samples that cannot be utilized by meta-analysis methods. The integrated SAM approach can also be used in comparing global gene expression between various subtypes of cancer arising from the same tissue.

Gene expression profiling and network analysis reveals lipid and steroid metabolism to be the most favored by TNFalpha in HepG2 cells

Background

The proinflammatory cytokine, TNFα, is a crucial mediator of the pathogenesis of several diseases, more so in cases involving the liver wherein it is critical in maintaining liver homeostasis since it is a major determiner of hepatocyte life and death. Gene expression profiling serves as an appropriate strategy to unravel the underlying signatures to envisage such varied responses and considering this, gene transcription profiling was examined in control and TNFα treated HepG2 cells.

Methods and Findings

Microarray experiments between control and TNFα treated HepG2 cells indicated that TNFα could significantly alter the expression profiling of 140 genes; among those up-regulated, several GO (Gene Ontology) terms related to lipid and fat metabolism were significantly (p<0.01) overrepresented indicating a global preference of fat metabolism within the hepatocyte and those within the down-regulated dataset included genes involved in several aspects of the immune response like immunoglobulin receptor activity and IgE binding thereby indicating a compromise in the immune defense mechanism(s). Conserved transcription factor binding sites were identified in identically clustered genes within a common GO term and SREBP-1 and FOXJ2 depicted increased occupation of their respective binding elements in the presence of TNFα. The interacting network of “lipid metabolism, small molecule biochemistry” was derived to be significantly overrepresented that correlated well with the top canonical pathway of “biosynthesis of steroids”.

Conclusions

TNFα alters the transcriptome profiling within HepG2 cells with an interesting catalog of genes being affected and those involved in lipid and steroid metabolism to be the most favored. This study represents a composite analysis of the effects of TNFα in HepG2 cells that encompasses the altered transcriptome profiling, the functional analysis of the up- and down- regulated genes and the identification of conserved transcription factor binding sites. These could possibly determine TNFα mediated alterations mainly the phenotypes of hepatic steatosis and fatty liver associated with several hepatic pathological states.

A benchmark for statistical microarray data analysis that preserves actual biological and technical variance

BACKGROUND

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.

RESULTS

Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.

CONCLUSIONS

Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better.

AVAILABILITY

The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Identification of importin 8 (IPO8) as the most accurate reference gene for the clinicopathological analysis of lung specimens

Background

The accurate normalization of differentially expressed genes in lung cancer is essential for the identification of novel therapeutic targets and biomarkers by real time RT-PCR and microarrays. Although classical "housekeeping" genes, such as GAPDH, HPRT1, and beta-actin have been widely used in the past, their accuracy as reference genes for lung tissues has not been proven.

Results

We have conducted a thorough analysis of a panel of 16 candidate reference genes for lung specimens and lung cell lines. Gene expression was measured by quantitative real time RT-PCR and expression stability was analyzed with the softwares GeNorm and NormFinder, mean of |ΔCt| (= |Ct Normal-Ct tumor|) ± SEM, and correlation coefficients among genes. Systematic comparison between candidates led us to the identification of a subset of suitable reference genes for clinical samples: IPO8, ACTB, POLR2A, 18S, and PPIA. Further analysis showed that IPO8 had a very low mean of |ΔCt| (0.70 ± 0.09), with no statistically significant differences between normal and malignant samples and with excellent expression stability.

Conclusion

Our data show that IPO8 is the most accurate reference gene for clinical lung specimens. In addition, we demonstrate that the commonly used genes GAPDH and HPRT1 are inappropriate to normalize data derived from lung biopsies, although they are suitable as reference genes for lung cell lines. We thus propose IPO8 as a novel reference gene for lung cancer samples.

Identification of transcription factor and microRNA binding sites in responsible to fetal alcohol syndrome

This is a first report, using our MotifModeler informatics program, to simultaneously identify transcription factor (TF) and microRNA (miRNA) binding sites from gene expression microarray data. Based on the assumption that gene expression is controlled by combinatorial effects of transcription factors binding in the 5'-upstream regulatory region and miRNAs binding in the 3'-untranslated region (3'-UTR), we developed a model for (1) predicting the most influential cis-acting elements under a given biological condition, and (2) estimating the effects of those elements on gene expression levels. The regulatory regions, TF and miRNA, which mediate the differential genes expression in fetal alcohol syndrome were unknown; microarray data from alcohol exposure paradigm was used. The model predicted strong inhibitory effects of 5' cis-acting elements and stimulatory effects of 3'-UTR under alcohol treatment. Current predictive model derived a key hypothesis for the first time a novel role of miRNAs in gene expression changes associated with abnormal mouse embryo development after alcohol exposure. This suggests that disturbance of miRNA functions may contribute to the alcohol-induced developmental deficiencies.

CRSD: a comprehensive web server for composite regulatory signature discovery

Transcription factors (TFs) and microRNAs play important roles in the regulation of human gene expression, and the study of their combinatory regulations of gene expression is a new research field. We constructed a comprehensive web server, the composite regulatory signature database (CRSD), that can be applied in investigating complex regulatory behaviors involving gene expression signatures (GESs), microRNA regulatory signatures (MRSs) and TF regulatory signatures (TRSs). Six well-known and large-scale databases, including the human UniGene, mature microRNAs, putative promoter, TRANSFAC, pathway and Gene Ontology (GO) databases, were integrated to provide the comprehensive analysis in CRSD. Two new genome-wide databases, of MRSs and TRSs, were also constructed and further integrated into CRSD. To accomplish the microarray data analysis at one go, several methods, including microarray data pretreatment, statistical and clustering analysis, iterative enrichment analysis and motif discovery, were closely integrated in the web server, which has not been the case in previous studies. Our implementation showed that the published literature could demonstrate the results of genome-wide enrichment analysis. We conclude that CRSD is a powerful and useful bioinformatic web server and may provide new insights into gene regulation networks. CRSD and the online tutorial are publicly available at .

Topology-based cancer classification and related pathway mining using microarray data

Cancer classification is the critical basis for patient-tailored therapy, while pathway analysis is a promising method to discover the underlying molecular mechanisms related to cancer development by using microarray data. However, linking the molecular classification and pathway analysis with gene network approach has not been discussed yet. In this study, we developed a novel framework based on cancer class-specific gene networks for classification and pathway analysis. This framework involves a novel gene network construction, named ordering network, which exhibits the power-law node-degree distribution as seen in correlation networks. The results obtained from five public cancer datasets showed that the gene networks with ordering relationship are better than those with correlation relationship in terms of accuracy and stability of the classification performance. Furthermore, we integrated the ordering networks, classification information and pathway database to develop the topology-based pathway analysis for identifying cancer class-specific pathways, which might be essential in the biological significance of cancer. Our results suggest that the topology-based classification technology can precisely distinguish cancer subclasses and the topology-based pathway analysis can characterize the correspondent biochemical pathways even if there are subtle, but consistent, changes in gene expression, which may provide new insights into the underlying molecular mechanisms of tumorigenesis.

Gfi1 coordinates epigenetic repression of p21Cip/WAF1 by recruitment of histone lysine methyltransferase G9a and histone deacetylase 1

The growth factor independent 1 (Gfi1) transcriptional regulator oncoprotein plays a crucial role in hematopoietic, inner ear, and pulmonary neuroendocrine cell development and governs cell processes as diverse as self-renewal of hematopoietic stem cells, proliferation, apoptosis, differentiation, cell fate specification, and oncogenesis. However, the molecular basis of its transcriptional functions has remained elusive. Here we show that Gfi1 recruits the histone lysine methyltransferase G9a and the histone deacetylase 1 (HDAC1) in order to modify the chromatin of genes targeted for repression by Gfi1. G9a and HDAC1 are both in a repressive complex assembled by Gfi1. Endogenous Gfi1 colocalizes with G9a, HDAC1, and K9-dimethylated histone H3. Gfi1 associates with G9a and HDAC1 on the promoter of the cell cycle regulator p21Cip/WAF1, resulting in an increase in K9 dimethylation at histone H3. Silencing of Gfi1 expression in myeloid cells reverses G9a and HDAC1 recruitment to p21Cip/WAF1 and elevates its expression. These findings highlight the role of epigenetics in the regulation of development and oncogenesis by Gfi1.