Integrated analysis of DNA copy number and gene expression microarray data using gene sets

Background  

Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes.     

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.     

CpG island methylation pattern in different human tissues and its correlation with gene expression

The study on DNA methylation pattern in different human tissues attracts increasing interest nowadays, but a systematic analysis of CpG island methylation pattern between both somatic tissues and gametocyte is still lacking. In this work, we analyzed the CpG island methylation data of sperm and other 11 somatic tissues from Human Epigenome Project, and found that the CpG island methylation profiles are highly correlated between somatic tissues, while the methylation profile in sperm is quite distinct. Furthermore, we observed that in the six tissues investigated, there is no obvious correlation between the methylation level of promoter CpG islands and corresponding gene expression across different tissues.     

Integrated analyses of copy number variations and gene expression in lung adenocarcinoma

Numerous efforts have been made to elucidate the etiology and improve the treatment of lung cancer, but the overall five-year survival rate is still only 15%. Identification of prognostic biomarkers for lung cancer using gene expression microarrays poses a major challenge in that very few overlapping genes have been reported among different studies. To address this issue, we have performed concurrent genome-wide analyses of copy number variation and gene expression to identify genes reproducibly associated with tumorigenesis and survival in non-smoking female lung adenocarcinoma. The genomic landscape of frequent copy number variable regions (CNVRs) in at least 30% of samples was revealed, and their aberration patterns were highly similar to several studies reported previously. Further statistical analysis for genes located in the CNVRs identified 475 genes differentially expressed between tumor and normal tissues (p<10⁻⁵). We demonstrated the reproducibility of these genes in another lung cancer study (p = 0.0034, Fisher''s exact test), and showed the concordance between copy number variations and gene expression changes by elevated Pearson correlation coefficients. Pathway analysis revealed two major dysregulated functions in lung tumorigenesis: survival regulation via AKT signaling and cytoskeleton reorganization. Further validation of these enriched pathways using three independent cohorts demonstrated effective prediction of survival. In conclusion, by integrating gene expression profiles and copy number variations, we identified genes/pathways that may serve as prognostic biomarkers for lung tumorigenesis.     

Jointly analyzing gene expression and copy number data in breast cancer using data reduction models

With the growing surge of biological measurements, the problem of integrating and analyzing different types of genomic measurements has become an immediate challenge for elucidating events at the molecular level. In order to address the problem of integrating different data types, we present a framework that locates variation patterns in two biological inputs based on the generalized singular value decomposition (GSVD). In this work, we jointly examine gene expression and copy number data and iteratively project the data on different decomposition directions defined by the projection angle /spl theta/ in the GSVD. With the proper choice of /spl theta/, we locate similar and dissimilar patterns of variation between both data types. We discuss the properties of our algorithm using simulated data and conduct a case study with biologically verified results. Ultimately, we demonstrate the efficacy of our method on two genome-wide breast cancer studies to identify genes with large variation in expression and copy number across numerous cell line and tumor samples. Our method identifies genes that are statistically significant in both input measurements. The proposed method is useful for a wide variety of joint copy number and expression-based studies. Supplementary information is available online, including software implementations and experimental data.     

Integrated analysis of gene copy number, copy neutral LOH, and microRNA profiles in adult acute lymphoblastic leukemia

We adopted an integrated analysis of gene copy number alterations (CNAs), copy number neutral loss of heterozygosity (CNN LOH), and microRNA (miRNA) profiling in 21 adult acute lymphoblastic leukemia (ALL) patients. This study revealed the most frequent CNAs to be at chromosomes 9p, 7, and 17 and recurrent CNN LOH at 5p, 9p, and Xq. As for the most differentially expressed miRNAs, they included 8 upregulated and 14 downregulated miRNAs, of which miR-148a at 7p15.2, miR-22 at 17p13.3, miR-223 at Xq12, as well as miR-101-2 at 9p24.1 exhibited recurrent CNAs or CNN LOH. miR-101-2 was recurrently downregulated, and although the related CNN LOH was detected only in BCR-ABL1 negative cases (2/14), deletions of miR-101-2 were observed solely in BCR-ABL1 positive cases (4/7). Finally, BCR-ABL1 positive cases, in contrast to negative ones, were characterized by slightly, but still significantly, higher expression levels of miR-29b.     

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.     

Rare germline copy number variants in colorectal cancer predisposition characterized by exome sequencing analysis

正Colorectal cancer(CRC)is one of the most common neoplasms and an important cause of mortality worldwide(http://globocan.iarc.fr/).Approximately 35%of the variation in CRC susceptibility is likely due to heritable factors(Lichtenstein et al.,2000).Genetic variations in the human genome include single nucleotide variants(SNVs),short insertions and deletions,and larger structural vari-     

Integrated analyses of copy number variations and gene differential expression in lung squamous-cell carcinoma

Background

Although numerous efforts have been made, the pathogenesis underlying lung squamous-cell carcinoma (SCC) remains unclear. This study aimed to identify the CNV-driven genes by an integrated analysis of both the gene differential expression and copy number variation (CNV).

Results

A higher burden of the CNVs was found in 10–50 kb length. The 16 CNV-driven genes mainly located in chr 1 and chr 3 were enriched in immune response [e.g. complement factor H (CFH) and Fc fragment of IgG, low affinity IIIa, receptor (FCGR3A)], starch and sucrose metabolism [e.g. amylase alpha 2A (AMY2A)]. Furthermore, 38 TFs were screened for the 9 CNV-driven genes and then the regulatory network was constructed, in which the GATA-binding factor 1, 2, and 3 (GATA1, GATA2, GATA3) jointly regulated the expression of TP63.

Conclusions

The above CNV-driven genes might be potential contributors to the development of lung SCC.     

Novel markers of gene methylation and expression in breast cancer

An optimized methylation-sensitive restriction fingerprinting technique was used to search for differentially methylated CpG islands in the tumor genome and detected seven genes subject to abnormal epigenetic regulation in breast cancer: SEMA6B, BIN1, VCPIP1, LAMC3, KCNH2, CACNG4, and PSMF1. For each gene, the rate of promoter methylation and changes in expression were estimated in tumor and morphologically intact paired specimens of breast tissue (N = 100). Significant methylation rates of 38, 18, and 8% were found for SEMA6B, BIN1, and LAMC3, respectively. The genes were not methylated in morphologically intact breast tissue. The expression of SEMA6B, BIN1, VCPIP1, LAMC3, KCNH2, CACNG4, and PSMF1 was decreased in 44–94% of tumor specimens by the real-time RT-PCR assay. The most profound changes in SEMA6B and LAMC3 suggest that these genes can be included in biomarker panels for breast cancer diagnosis. Fine methylation mapping of the most frequently methylated CpG islands (SEMA6B, BIN1, and LAMC3) provides a fundamental basis for developing efficient methylation tests for these genes.     

Functional profiling and gene expression analysis of chromosomal copy number alterations

Contrarily to the traditional view in which only one or a few key genes were supposed to be the causative factors of diseases, we discuss the importance of considering groups of functionally related genes in the study of pathologies characterised by chromosomal copy number alterations. Recent observations have reported the existence of regions in higher eukaryotic chromosomes (including humans) containing genes of related function that show a high degree of coregulation. Copy number alterations will consequently affect to clusters of functionally related genes, which will be the final causative agents of the diseased phenotype, in many cases. Therefore, we propose that the functional profiling of the regions affected by copy number alterations must be an important aspect to take into account in the understanding of this type of pathologies. To illustrate this, we present an integrated study of DNA copy number variations, gene expression along with the functional profiling of chromosomal regions in a case of multiple myeloma.     

Individual and combined effects of DNA methylation and copy number alterations on miRNA expression in breast tumors

Background

The global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer.

Results

We identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein.

Conclusions

Our data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.     

Integrative analysis of gene expression and copy number alterations using canonical correlation analysis

Background  

With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia.     

rSW-seq: Algorithm for detection of copy number alterations in deep sequencing data

Background  

Recent advances in sequencing technologies have enabled generation of large-scale genome sequencing data. These data can be used to characterize a variety of genomic features, including the DNA copy number profile of a cancer genome. A robust and reliable method for screening chromosomal alterations would allow a detailed characterization of the cancer genome with unprecedented accuracy.     

CpG island methylation in precursors of gastrointestinal malignancies

Gastrointestinal malignancies account for about 20% of all cancers worldwide. It is widely accepted that cancer evolves through several stepwise morphological stages such as the adenoma-carcinoma and hyperplastic polyp-serrated adenoma-carcinoma sequences in colorectal cancers, and the metaplasia-dysplasia-carcinoma sequences in esophageal and gastric cancers. The morphological progression is associated with the accumulation of multiple genetic and epigenetic events. It is now recognized that epigenetic silencing of gene expression by CpG island methylation is an important alternative mechanism of inactivating tumor suppressor genes. Inflammatory conditions of the gastrointestinal and pancreaticobiliary tracts and liver such as Barrett esophagus, Helicobacter pylori gastritis, inflammatory bowel disease and viral hepatitis, are associated with increased frequency of malignancies and CpG methylation. In addition, CpG methylation is present in aberrant crypt foci and pancreatic intraepithelial neoplasia that are considered putative precursors of colon and pancreatic carcinomas, respectively. Understanding of these early genetic and epigenetic changes allows for the discoveries of potential screening, monitoring and therapeutic strategies. Targeting of the epigenetic changes that occur before the development of frank malignancy offers a potential chemopreventive strategy.