Identifying pathways affected by cancer mutations

Mutations in 15 cancers, sourced from the COSMIC Whole Genomes database, and 297 human pathways, arranged into pathway groups based on the processes they orchestrate, and sourced from the KEGG pathway database, have together been used to identify pathways affected by cancer mutations. Genes studied in ≥ 15, and mutated in ≥ 10 samples of a cancer have been considered recurrently mutated, and pathways with recurrently mutated genes have been considered affected in the cancer. Novel doughnut plots have been presented which enable visualization of the extent to which pathways and genes, in each pathway group, are targeted, in each cancer. The ‘organismal systems’ pathway group (including organism-level pathways; e.g., nervous system) is the most targeted, more than even the well-recognized signal transduction, cell-cycle and apoptosis, and DNA repair pathway groups. The important, yet poorly-recognized, role played by the group merits attention. Pathways affected in ≥ 7 cancers yielded insights into processes affected.     

Comprehensive gene expression analysis reveals multiple signal pathways associated with prostate cancer

Prostate cancer (PC) depends on androgenic signaling for growth and survival. To data, the exact molecular mechanism of hormone controlling proliferation and tumorigenesis in the PC remains unclear. Therefore, in this study, we explored the differentially expressed genes (DEGs) and identified featured genes related to hormone stimulus from PC. Two sets of gene expression data, including PC and normal control sample, were downloaded from Gene Expression Omnibus (GEO) database. The t-test was used to identify DEGs between PC and controls. Gene ontology (GO) functional annotation was applied to analyze the function of DEGs and screen hormone-related DEGs. Then these hormone-related DEGs were further analyzed in constructed cancer network and Human Protein Reference Database to screen important signaling pathways they participated in. A total of 912 DEGs were obtained which included 326 up-regulated genes and 586 down-regulated genes. GO functional enrichment analysis identified 50 hormone-related DEGs associated with PC. After pathway and PPI network analysis, we found these hormone-related DEGs participated in several important signaling pathways including TGF-β (TGFB2, TGFB3 and TGFBR2), MAPK (TGFB2, TGFB3 and TGFBR2), insulin (PIK3R3, SHC1 and EIF4EBP1), and p53 signaling pathways (CCND2 and CDKN1A). In addition, a total of five hormone-related DEGs (SHC1, CAV1, RXRA, CDKN1A and SRF) were located in the center of PPI network and 12 hormone-related DEGs formed six protein modules. These important signal pathways and hormone-related DEGs may provide potential therapeutic targets for PC.     

Identification of cancer genomic markers via integrative sparse boosting

In high-throughput cancer genomic studies, markers identified from the analysis of single data sets often suffer a lack of reproducibility because of the small sample sizes. An ideal solution is to conduct large-scale prospective studies, which are extremely expensive and time consuming. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple data sets is challenging because of the high dimensionality of genomic measurements and heterogeneity among studies. In this article, we propose a sparse boosting approach for marker identification in integrative analysis of multiple heterogeneous cancer diagnosis studies with gene expression measurements. The proposed approach can effectively accommodate the heterogeneity among multiple studies and identify markers with consistent effects across studies. Simulation shows that the proposed approach has satisfactory identification results and outperforms alternatives including an intensity approach and meta-analysis. The proposed approach is used to identify markers of pancreatic cancer and liver cancer.     

Identifying genetic marker sets associated with phenotypes via an efficient adaptive score test

In recent years, genome-wide association studies (GWAS) and gene-expression profiling have generated a large number of valuable datasets for assessing how genetic variations are related to disease outcomes. With such datasets, it is often of interest to assess the overall effect of a set of genetic markers, assembled based on biological knowledge. Genetic marker-set analyses have been advocated as more reliable and powerful approaches compared with the traditional marginal approaches (Curtis and others, 2005. Pathways to the analysis of microarray data. TRENDS in Biotechnology 23, 429-435; Efroni and others, 2007. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS One 2, 425). Procedures for testing the overall effect of a marker-set have been actively studied in recent years. For example, score tests derived under an Empirical Bayes (EB) framework (Liu and others, 2007. Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 63, 1079-1088; Liu and others, 2008. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC bioinformatics 9, 292-2; Wu and others, 2010. Powerful SNP-set analysis for case-control genome-wide association studies. American Journal of Human Genetics 86, 929) have been proposed as powerful alternatives to the standard Rao score test (Rao, 1948. Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Mathematical Proceedings of the Cambridge Philosophical Society, 44, 50-57). The advantages of these EB-based tests are most apparent when the markers are correlated, due to the reduction in the degrees of freedom. In this paper, we propose an adaptive score test which up- or down-weights the contributions from each member of the marker-set based on the Z-scores of their effects. Such an adaptive procedure gains power over the existing procedures when the signal is sparse and the correlation among the markers is weak. By combining evidence from both the EB-based score test and the adaptive test, we further construct an omnibus test that attains good power in most settings. The null distributions of the proposed test statistics can be approximated well either via simple perturbation procedures or via distributional approximations. Through extensive simulation studies, we demonstrate that the proposed procedures perform well in finite samples. We apply the tests to a breast cancer genetic study to assess the overall effect of the FGFR2 gene on breast cancer risk.     

Comparing statistical methods for constructing large scale gene networks

The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.     

Identification of messenger and long noncoding RNAs associated with gallbladder cancer via gene expression profile analysis

The present study aimed to investigate the long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in the progression of gallbladder cancer and explore the potential physiopathologic mechanisms of gallbladder cancer in terms of competing endogenous RNAs (ceRNAs). The original lncRNA and mRNA expression profile data (nine gallbladder cancer tissues samples and nine normal gallbladder samples) in GSE76633 was downloaded from the Gene Expression Omnibus database. Differentially expressed mRNAs and lncRNAs between gallbladder cancer tissue and normal control were selected and the pathways in which they are involved were analyzed using bioinformatics analyses. MicroRNAs (miRNAs) were also predicted based on the differentially expressed mRNAs. Finally, the co-expression relation between lncRNA and mRNA was analyzed and the ceRNA network was constructed by combining the lncRNA-miRNA, miRNA-mRNA, and lncRNA-mRNA pairs. Overall, 373 significantly differentially expressed mRNAs and 47 lncRNAs were identified between cancer and normal tissue samples. The upregulated genes were significantly enriched in the extracellular matrix (ECM)-receptor interaction pathway, while the downregulated genes were involved in the complement and coagulation cascades. Altogether, 128 co-expression relations between lncRNA and mRNA were obtained. In addition, 196 miRNA-mRNA regulatory relations and 145 miRNA-lncRNA relation pairs were predicted. Finally, the lncRNA-miRNA-gene ceRNA network was constructed by combining the three types of relation pairs, such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6. mRNAs and lncRNAs may be involved in gallbladder cancer progression via ECM-receptor interaction pathways and the complement and coagulation cascades. Moreover, ceRNAs such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6 can also be implicated in the pathogenesis of gallbladder cancer.     

Detection of gene pathways with predictive power for breast cancer prognosis

Background  

Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.     

基于共进化基因功能模块发现候选癌基因

癌基因组的体细胞突变扫查数据为研究人员发现新的癌基因提供了大量的信息。已有的通过基因突变频率寻找候选癌基因的方法倾向于发现突变频率较高的癌基因,但是部分低频率突变的基因也可能在癌症发生过程中发挥重要作用。具有相似系统发生谱并且具有蛋白互作关系的基因可能具有相似的功能,它们的损伤可能会导致相同或相似的疾病表型。基于这一假设,文章提出了一种发现候选癌基因的新方法。首先,寻找具有相似系统发生谱的蛋白质互作子网,定义为共进化基因模块;然后,在癌基因组中发生至少一次非同义体细胞突变的基因中,筛选出与已知癌基因在同一共进化模块并具有直接相互作用的基因,预测为候选癌基因。据此,文章共预测了15个候选癌基因,其中只有2个基因在以往的工作中通过基于高突变频率的方法被识别为癌基因。因此,该方法可以有效地发现突变频率低的候选癌基因。     

GPR30 gene polymorphisms are associated with progesterone receptor status and histopathological characteristics of breast cancer patients

G-protein coupled receptor GPR30 has been demonstrated to mediate estrogenic effects on essential features of human breast cancer cells. Polymorphisms in GPR30 gene might therefore affect breast cancer susceptibility or tumor characteristics. This is the first study examining allele and genotype frequencies of GPR30 single nucleotide polymorphisms (SNPs) in breast cancer patients. A total of 257 sporadic breast cancer cases and 247 age-matched controls were genotyped for three GPR30 polymorphisms by means of allele-specific tetra-primer PCR. Comparison of the breast cancer case and the control group with regard to the SNP allele, genotype and haplotype frequencies did not show significant differences. In contrast, the GPR30 SNPs tested were significantly associated with tumor size, histological grading, nodal status and progesterone receptor (PR) status. The A allele of SNP rs3808351 was significantly less frequent in patients with large or G3 tumors, T allele of SNP rs11544331 less frequently occurred in patients with positive nodal status, suggesting that both SNPs might exert protective effects regarding aggressive breast cancer entities. Both homozygous GG genotype of promoter SNP rs3808350 and T allele of missense SNP rs11544331 were inversely associated with PR-negativity, suggesting that they might exert protective effects regarding development of PR-negative cancer. In conclusion, the results of this study support the important role of GPR30 in breast cancer and encourage functional studies on the molecular mechanisms underlying the association of GPR30 polymorphisms with PR status and tumor growth.     

Nonrandom RNAseq gene expression associated with RNAlater and flash freezing storage methods

RNA sequencing is a popular next‐generation sequencing technique for assaying genome‐wide gene expression profiles. Nonetheless, it is susceptible to biases that are introduced by sample handling prior gene expression measurements. Two of the most common methods for preserving samples in both field‐based and laboratory conditions are submersion in RNAlater and flash freezing in liquid nitrogen. Flash freezing in liquid nitrogen can be impractical, particularly for field collections. RNAlater is a solution for stabilizing tissue for longer‐term storage as it rapidly permeates tissue to protect cellular RNA. In this study, we assessed genome‐wide expression patterns in 30‐day‐old fry collected from the same brood at the same time point that were flash‐frozen in liquid nitrogen and stored at ?80°C or submerged and stored in RNAlater at room temperature, simulating conditions of fieldwork. We show that sample storage is a significant factor influencing observed differential gene expression. In particular, genes with elevated GC content exhibit higher observed expression levels in liquid nitrogen flash‐freezing relative to RNAlater storage. Further, genes with higher expression in RNAlater relative to liquid nitrogen experience disproportionate enrichment for functional categories, many of which are involved in RNA processing. This suggests that RNAlater may elicit a physiological response that has the potential to bias biological interpretations of expression studies. The biases introduced to observed gene expression arising from mimicking many field‐based studies are substantial and should not be ignored.     

Transcriptome analysis of differentially expressed genes and pathways associated with mitoxantrone treatment prostate cancer

The global physiological function of specifically expressed genes of mitoxantrone (MTX)‐resistant prostate cancer (PCa) is unclear. In this study, gene expression pattern from microarray data was investigated for identifying differentially expressed genes (DEGs) in MTX‐resistant PCa xenografts. Human PCa cell lines DU145 and PC3 were cultured in vitro and xenografted into severe combined immunodeficiency (SCID) mice, treated with MTX intragastrically, three times a week until all mice relapsed. Gene expression profiles of the xenografts from castrated mice were performed with Affymetrix human whole genomic oligonucleotide microarray. The Cytoscape software was used to investigate the relationship between proteins and the signalling transduction network. A total of 355 overlapping genes were differentially expressed in MTX‐resistant DU145R and PC3R xenografts. Of these, 16 genes were selected to be validated by quantitative real‐time PCR (qRT‐PCR) in these xenografts, and further tested in a set of formalin‐fixed, paraffin‐embedded and optimal cutting temperature (OCT) clinical tumour samples. Functional and pathway enrichment analyses revealed that these DEGs were closely related to cellular activity, androgen synthesis, DNA damage and repair, also involved in the ERK/MAPK, PI3K/serine‐threonine protein kinase, also known as protein kinase B, PKB (AKT) and apoptosis signalling pathways. This exploratory analysis provides information about potential candidate genes and may bring new insights into the molecular cascade involvement in MTX‐resistant PCa.     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Multi-class cancer classification via partial least squares with gene expression profiles

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu     

Use of serological proteomic methods to find biomarkers associated with breast cancer

New technologies for the detection and therapy of early stage breast cancer are urgently needed. Pathological changes in breast might be reflected in proteomic patterns in serum. A proteomic tool was used to identify proteomic patterns in serum that distinguishes neoplastic from non-neoplastic disease within the breast. Preliminary results derived from the serum analysis from 54 unaffected women and 76 patients with breast cancer were analyzed by two-dimensional (2-D) electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry, HSP27 was found up-regulated while 14-3-3 sigma was down-regulated in the serum of breast cancer patients. The two protein biomarkers were then used to classify an independent set of 104 masked serum samples. The results showed that the protein pattern on 2-D gels can completely segregate the serum of breast cancer from non-cancer. The discriminatory pattern correctly identified all 69 breast cancer cases in the masked set. Of the 35 cases of non-malignant disease, 34 were recognized as non-cancer. These findings justify a prospective population-based assessment of proteomic technology as a screening or diagnostic tool for breast cancer in high-risk and general populations. These two protein biomarkers could also be used as targets for further study in drug design and breast cancer therapy.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.