Activating mutations in PTPN3 promote cholangiocarcinoma cell proliferation and migration and are associated with tumor recurrence in patients.

BACKGROUND & AIMS: The pathogenesis of intrahepatic cholangiocarcinoma (ICC), the second most common hepatic cancer, is poorly understood, and the incidence of ICC is increasing worldwide. We searched for mutations in human ICC tumor samples and investigated how they affect ICC cell function. METHODS: We performed whole exome sequencing of 7 pairs of ICC tumors and their surrounding nontumor tissues to detect somatic alterations. We then screened 124 pairs of ICC and nontumor samples for these mutations, including 7 exomes. We compared mutations in PTPN3 with tumor recurrence in 124 patients and PTPN3 expression levels with recurrence in 322 patients (the combination of both in 86 patients). The functional effects of PTPN3 variations were determined by RNA interference and transgenic expression in cholangiocarcinoma cell lines (RBE, HCCC-9810, and Huh28). RESULTS: Based on exome sequencing, pathways that regulate protein phosphorylation were among the most frequently altered in ICC samples and genes encoding protein tyrosine phosphatases (PTPs) were among the most frequently mutated. We identified mutations in 9 genes encoding PTPs in 4 of 7 ICC exomes. In the prevalence screen of 124 paired samples, 51.6% of ICCs contained somatic mutations in at least 1 of 9 PTP genes; 41.1% had mutations in PTPN3. Transgenic expression of PTPN3 in cell lines increased cell proliferation, colony formation, and migration. PTPN3(L232R) and PTPN3(L384H), which were frequently detected in ICC samples, were found to be gain-of-function mutations; their expression in cell lines further increased cell proliferation, colony formation, and migration. ICC-associated variants of PTPN3 altered phosphatase activity. Patients whose tumors contained activating mutations or higher levels of PTPN3 protein than nontumor tissues had higher rates of disease recurrence than patients whose tumors did not have these characteristics. CONCLUSIONS: Using whole exome sequencing of ICC samples from patients, we found that more than 40% contain somatic mutations in PTPN3. Activating mutations in and high expression levels of PTPN3 were associated with tumor recurrence.

Exome sequencing of hepatoblastoma reveals novel mutations and cancer genes in the Wnt pathway and ubiquitin ligase complex.

UNLABELLED: Hepatoblastoma (HB) is the most common primary liver tumor in children. Mutations in the ß-catenin gene that lead to constitutive activation of the Wnt pathway have been detected in a large proportion of HB tumors. To identify novel mutations in HB, we performed whole-exome sequencing of six paired HB tumors and their corresponding lymphocytes. This identified 24 somatic nonsynonymous mutations in 21 genes, many of which were novel, including three novel mutations targeting the CTNNB1 (G512V) and CAPRIN2 (R968H/S969C) genes in the Wnt pathway, and genes previously shown to be involved in the ubiquitin ligase complex (SPOP, KLHL22, TRPC4AP, and RNF169). Functionally, both the CTNNB1 (G512V) and CAPRIN2 (R968H/S969C) were observed to be gain-of-functional mutations, and the CAPRIN2 (R968H/S969C) was also shown to activate the Wnt pathway in HB cells. These findings suggested the activation of the Wnt pathway in HB, which was confirmed by immunohistochemical staining of the ß-catenin in 42 HB tumors. We further used short hairpin RNA (shRNA)-mediated interference to assess the effect of 21 mutated genes on HB cell survival. The results suggested that one novel oncogene (CAPRIN2) and three tumor suppressors (SPOP, OR5I1, and CDC20B) influence HB cell growth. Moreover, we found that SPOP S119N is a loss-of-function mutation in HB cells. We finally demonstrated that one of the mechanisms by which SPOP inhibits HB cell proliferation is through regulating CDKN2B expression. CONCLUSION: These results extend the landscape of genetic alterations in HB and highlight the dysregulation of Wnt and ubiquitin pathways in HB tumorigenesis.

Application of microRNA and mRNA expression profiling on prognostic biomarker discovery for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most highly malignant and lethal cancers of the world. Its pathogenesis has been reported to be multi-factorial, and the molecular carcinogenesis of HCC can not be attributed to just a few individual genes. Based on the microRNA and mRNA expression profiling of normal liver tissues, pericancerous hepatocellular tissues and hepatocellular carcinoma tissues, we attempted to find prognosis related gene sets for HCC patients. RESULTS: We identified differentially expressed genes (DEG) from three comparisons: Cancer/Normal, Cancer/Pericancerous and Pericancerous/Normal. GSEA (gene set enrichment analysis) were performed. Based on the enriched gene sets of GO terms, pathways and transcription factor targets, it was found that the genome instability and cell proliferation increased while the metabolism and differentiation decreased in HCC tissues. The expression profile of DEGs in each enriched gene set was used to correlate to the postoperative survival time of HCC patients. Nine gene sets were found to prognostic correlation. Furthermore, after substituting DEG-targeting-microRNA for DEG members of each gene set, two gene sets with the microRNA expression profiles were obtained that had prognostic potential. CONCLUSIONS: The malignancy of HCC could be represented by gene sets, and pericancerous liver exhibits important characteristics of liver cancer. The expression level of gene sets not only in HCC but also in the pericancerous liver showed potential for prognosis implying an option for HCC prognosis at an early stage. Additionally, the gene-targeting-microRNA expression profiles also showed prognostic potential, demonstrating that the multi-factorial molecular pathogenesis of HCC is contributed by various genes and microRNAs.

A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation.

Current predictors for estrogen receptor-positive (ER-positive) breast cancer patients receiving tamoxifen are often invalid in inter-laboratory validation. We aim to develop a robust predictor based on the relative ordering of expression measurement (ROE) in gene pairs. Using a large integrated dataset of 420 normal controls and 1,129 ER-positive breast tumor samples, we identified the gene pairs with stable ROEs in normal control and significantly reversed ROEs in ER-positive tumor. Using these gene pairs, we characterized each sample of a cohort of 292 ER-positive patients who received tamoxifen monotherapy for 5 years and then identified relapse risk-associated gene pairs. We extracted a gene pair subset that resulted in the largest positive and negative predictive values for predicting 10-year relapse-free survival (RFS) using a genetic algorithm. A predictor was developed based on the gene pair subset and was validated in 2 large multi-laboratory cohorts (N = 250 and 248, respectively) of ER-positive patients who received 5-year tamoxifen alone. In the first validation cohort, the patients predicted to be tamoxifen sensitive had a 10-year RFS of 91 % (95 % confidence interval [CI] 85-97 %) with an absolute risk reduction of 34 % (95 % CI 17-51 %). The patients predicted to be tamoxifen insensitive had a significantly higher relapse risk than the patients predicted to be tamoxifen sensitive (hazard ratio = 4.99, 95 % CI 2.45-10.17, P = 9.13 × 10(-7)). Similar performance was achieved for the second validation cohort. The predictor performed well in both node-negative and node-positive subsets and added significant predictive power to the clinical parameters. In contrast, 2 previously proposed predictors did not achieve significantly better performances than the baselines of the validation cohorts. In summary, the proposed predictor can accurately and robustly predict tamoxifen sensitivity of ER-positive breast cancer patients and identified patients with a high probability of 10-year RFS following tamoxifen monotherapy.

Analysis of pathway mutation profiles highlights collaboration between cancer-associated superpathways.

The biological interpretation of the complexity of cancer somatic mutation profiles is a major challenge in current cancer research. It has been suggested that mutations in multiple genes that participate in different pathways are collaborative in conferring growth advantage to tumor cells. Here, we propose a powerful pathway-based approach to study the functional collaboration of gene mutations in carcinogenesis. We successfully identify many pairs of significantly comutated pathways for a large-scale somatic mutation profile of lung adenocarcinoma. We find that the coordinated pathway pairs detected by comutations are also likely to be coaltered by other molecular changes, such as alterations in multifunctional genes in cancer. Then, we cluster comutated pathways into comutated superpathways and show that the derived superpathways also tend to be significantly coaltered by DNA copy number alterations. Our results support the hypothesis that comprehensive cooperation among a few basic functions is required for inducing cancer. The results also suggest biologically plausible models for understanding the heterogeneous mechanisms of cancers. Finally, we suggest an approach to identify candidate cancer genes from the derived comutated pathways. Together, our results provide guidelines to distill the pathway collaboration in carcinogenesis from the complexity of cancer somatic mutation profiles.

Extracting consistent knowledge from highly inconsistent cancer gene data sources.

BACKGROUND: Hundreds of genes that are causally implicated in oncogenesis have been found and collected in various databases. For efficient application of these abundant but diverse data sources, it is of fundamental importance to evaluate their consistency. RESULTS: First, we showed that the lists of cancer genes from some major data sources were highly inconsistent in terms of overlapping genes. In particular, most cancer genes accumulated in previous small-scale studies could not be rediscovered in current high-throughput genome screening studies. Then, based on a metric proposed in this study, we showed that most cancer gene lists from different data sources were highly functionally consistent. Finally, we extracted functionally consistent cancer genes from various data sources and collected them in our database F-Census. CONCLUSIONS: Although they have very low gene overlapping, most cancer gene data sources are highly consistent at the functional level, which indicates that they can separately capture partial genes in a few key pathways associated with cancer. Our results suggest that the sample sizes currently used for cancer studies might be inadequate for consistently capturing individual cancer genes, but could be sufficient for finding a number of cancer genes that could represent functionally most cancer genes. The F-Census database provides biologists with a useful tool for browsing and extracting functionally consistent cancer genes from various data sources.

MicroRNA-135b, a HSF1 target, promotes tumor invasion and metastasis by regulating RECK and EVI5 in hepatocellular carcinoma.

MicroRNAs (miRNAs) often localize to chromosomal fragile sites and are associated with cancer. In this study, we screened for the aberrant and functional miRNAs in the regions of copy number alterations (CNAs) in hepatocellular carcinoma (HCC), and found that miR-135b was frequently amplified and upregulated in HCC tissues. The expression level of miR-135b was inversely correlated with the occurrence of tumor capsules. In addition, miR-135b promoted HCC cell migration and invasion in vitro and metastasis in vivo. The reversion-inducing-cysteine-rich protein with kazal motifs (RECK) and ecotropic viral integration site 5 (EVI5) were identified as the direct and functional targets of miR-135b in HCC. Furthermore, we observed that heat shock transcription factor 1 (HSF1) directly activated miR-135b expression, consequently enhancing HCC cell motility and invasiveness. The newly identified HSF1/miR-135b/RECK&EVI5 axis provides novel insight into the mechanisms of HCC metastasis, which may facilitate the development of new therapeutics against HCC.

NF-κB signaling relieves negative regulation by miR-194 in hepatocellular carcinoma by suppressing the transcription factor HNF-1α.

Constitutive activation of the proinflammatory transcription factor nuclear factor κB (NF-κB) plays an important role in progression of hepatocellular carcinoma (HCC). Emerging modulators of NF-κB signaling are noncoding RNAs, especially microRNAs (miRNAs). We previously identified miRNAs that reduced the induction of NF-κB activity upon addition of tumor necrosis factor-α (TNFα) to HCC cells. We found that among these miRNAs, the abundance of liver-enriched miR-194 was decreased in HCC tissue and that low abundance of miR-194 correlated with a high occurrence of vascular invasion. Overexpressing miR-194 suppressed HCC cell migration and invasiveness in culture and metastatic seeding in mice. Transcripts encoding tripartite motif containing 23 (TRIM23), a ubiquitin ligase involved in NF-κB activation, and chromosome 21 open reading frame 91 (C21ORF91), a protein of unknown function, were identified as direct targets of miR-194 in HCC cells; knocking down either protein decreased the activity of a luciferase NF-κB reporter. Furthermore, the NF-κB pathway activator TNFα, an inflammatory cytokine, inhibited the transcription of miR-194 by decreasing the abundance of hepatocyte nuclear factor-1α (HNF-1α). The abundance of miR-194 positively correlated with that of HNF-1α and inversely correlated with that of TNFα in human HCC tissue. Thus, we identified a pathway in which TNFα-NF-κB signaling switches off negative regulation by suppressing HNF-1α-mediated expression of miR-194, revealing insight into the mechanisms linking inflammatory pathways, miRNA, and HCC metastasis.

Integrated analysis of mutation data from various sources identifies key genes and signaling pathways in hepatocellular carcinoma.

BACKGROUND: Recently, a number of studies have performed genome or exome sequencing of hepatocellular carcinoma (HCC) and identified hundreds or even thousands of mutations in protein-coding genes. However, these studies have only focused on a limited number of candidate genes, and many important mutation resources remain to be explored. PRINCIPAL FINDINGS: In this study, we integrated mutation data obtained from various sources and performed pathway and network analysis. We identified 113 pathways that were significantly mutated in HCC samples and found that the mutated genes included in these pathways contained high percentages of known cancer genes, and damaging genes and also demonstrated high conservation scores, indicating their important roles in liver tumorigenesis. Five classes of pathways that were mutated most frequently included (a) proliferation and apoptosis related pathways, (b) tumor microenvironment related pathways, (c) neural signaling related pathways, (d) metabolic related pathways, and (e) circadian related pathways. Network analysis further revealed that the mutated genes with the highest betweenness coefficients, such as the well-known cancer genes TP53, CTNNB1 and recently identified novel mutated genes GNAL and the ADCY family, may play key roles in these significantly mutated pathways. Finally, we highlight several key genes (e.g., RPS6KA3 and PCLO) and pathways (e.g., axon guidance) in which the mutations were associated with clinical features. CONCLUSIONS: Our workflow illustrates the increased statistical power of integrating multiple studies of the same subject, which can provide biological insights that would otherwise be masked under individual sample sets. This type of bioinformatics approach is consistent with the necessity of making the best use of the ever increasing data provided in valuable databases, such as TCGA, to enhance the speed of deciphering human cancers.

Integrative analysis of transcriptional regulatory network and copy number variation in intrahepatic cholangiocarcinoma.

BACKGROUND: Transcriptional regulatory network (TRN) is used to study conditional regulatory relationships between transcriptional factors and genes. However few studies have tried to integrate genomic variation information such as copy number variation (CNV) with TRN to find causal disturbances in a network. Intrahepatic cholangiocarcinoma (ICC) is the second most common hepatic carcinoma with high malignancy and poor prognosis. Research about ICC is relatively limited comparing to hepatocellular carcinoma, and there are no approved gene therapeutic targets yet. METHOD: We first constructed TRN of ICC (ICC-TRN) using forward-and-reverse combined engineering method, and then integrated copy number variation information with ICC-TRN to select CNV-related modules and constructed CNV-ICC-TRN. We also integrated CNV-ICC-TRN with KEGG signaling pathways to investigate how CNV genes disturb signaling pathways. At last, unsupervised clustering method was applied to classify samples into distinct classes. RESULT: We obtained CNV-ICC-TRN containing 33 modules which were enriched in ICC-related signaling pathways. Integrated analysis of the regulatory network and signaling pathways illustrated that CNV might interrupt signaling through locating on either genomic sites of nodes or regulators of nodes in a signaling pathway. In the end, expression profiles of nodes in CNV-ICC-TRN were used to cluster the ICC patients into two robust groups with distinct biological function features. CONCLUSION: Our work represents a primary effort to construct TRN in ICC, also a primary effort to try to identify key transcriptional modules based on their involvement of genetic variations shown by gene copy number variations (CNV). This kind of approach may bring the traditional studies of TRN based only on expression data one step further to genetic disturbance. Such kind of approach can easily be extended to other disease samples with appropriate data.

Pitfalls in experimental designs for characterizing the transcriptional, methylational and copy number changes of oncogenes and tumor suppressor genes.

BACKGROUND: It is a common practice that researchers collect a set of samples without discriminating the mutants and their wild-type counterparts to characterize the transcriptional, methylational and/or copy number changes of pre-defined candidate oncogenes or tumor suppressor genes (TSGs), although some examples are known that carcinogenic mutants may express and function completely differently from their wild-type counterparts. PRINCIPAL FINDINGS: Based on various high-throughput data without mutation information for typical cancer types, we surprisingly found that about half of known oncogenes (or TSGs) pre-defined by mutations were down-regulated (or up-regulated) and hypermethylated (or hypomethylated) in their corresponding cancer types. Therefore, the overall expression and/or methylation changes of genes detected in a set of samples without discriminating the mutants and their wild-type counterparts cannot indicate the carcinogenic roles of the mutants. We also found that about half of known oncogenes were located in deletion regions, whereas all known TSGs were located in deletion regions. Thus, both oncogenes and TSGs may be located in deletion regions and thus deletions can indicate TSGs only if the gene is found to be deleted as a whole. In contrast, amplifications are restricted to oncogenes and thus can be used to support either the dysregulated wild-type gene or its mutant as an oncogene. CONCLUSIONS: We demonstrated that using the transcriptional, methylational and/or copy number changes without mutation information to characterize oncogenes and TSGs, which is a currently still widely adopted strategy, will most often produce misleading results. Our analysis highlights the importance of evaluating expression, methylation and copy number changes together with gene mutation data in the same set of samples in order to determine the distinct roles of the mutants and their wild-type counterparts.

Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma.

The heterogeneity of genetic alterations in human cancer genomes presents a major challenge to advancing our understanding of cancer mechanisms and identifying cancer driver genes. To tackle this heterogeneity problem, many approaches have been proposed to investigate genetic alterations and predict driver genes at the individual pathway level. However, most of these approaches ignore the correlation of alteration events between pathways and miss many genes with rare alterations collectively contributing to carcinogenesis. Here, we devise a network-based approach to capture the cooperative functional modules hidden in genome-wide somatic mutation and copy number alteration profiles of glioblastoma (GBM) from The Cancer Genome Atlas (TCGA), where a module is a set of altered genes with dense interactions in the protein interaction network. We identify 7 pairs of significantly co-altered modules that involve the main pathways known to be altered in GBM (TP53, RB and RTK signaling pathways) and highlight the striking co-occurring alterations among these GBM pathways. By taking into account the non-random correlation of gene alterations, the property of co-alteration could distinguish oncogenic modules that contain driver genes involved in the progression of GBM. The collaboration among cancer pathways suggests that the redundant models and aggravating models could shed new light on the potential mechanisms during carcinogenesis and provide new indications for the design of cancer therapeutic strategies.

Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer.

In microarray-based case-control studies of a disease, people often attempt to identify a few diagnostic or prognostic markers amongst the most significant differentially expressed (DE) genes. However, the reproducibility of DE genes identified in different studies for a disease is typically very low. To tackle the problem, we could evaluate the reproducibility of DE genes across studies and define robust markers for disease diagnosis using disease-associated protein-protein interaction (PPI) subnetwork. Using datasets for four cancer types, we found that the most significant DE genes in cancer exhibit consistent up- or down-regulation in different datasets. For each cancer type, the 5 (or 10) most significant DE genes separately extracted from different datasets tend to be significantly coexpressed and closely connected in the PPI subnetwork, thereby indicating that they are highly reproducible at the PPI level. Consequently, we were able to build robust subnetwork-based classifiers for cancer diagnosis.

Genes dysregulated to different extent or oppositely in estrogen receptor-positive and estrogen receptor-negative breast cancers.

BACKGROUND: Directly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER- cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes. PRINCIPAL FINDINGS: Using microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER- cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER- cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER- cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER- cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER- cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER- cancers through rewiring different subpathways. CONCLUSIONS: GPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER- cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER- cancers through rewiring different subpathways.

Extensive up-regulation of gene expression in cancer: the normalised use of microarray data.

Based on the assumption that only a few genes are differentially expressed in a disease and have balanced upward and downward expression level changes, researchers usually normalise microarray data by forcing all of the arrays to have the same probe intensity distributions to remove technical variations in the data. However, accumulated evidence suggests that gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalisation assumption. Here, we show that the medians of the original probe intensities increase in most of the ten cancer types analyzed in this paper, indicating that genes may be widely up-regulated in many cancer types. Thus, at least for cancer study, normalising all arrays to have the same distribution of probe intensities regardless of the state (diseased vs. normal) tends to falsely produce many down-regulated differentially expressed (DE) genes while missing many truly up-regulated DE genes. We also show that the DE genes solely detected in the non-normalised data for cancers are highly reproducible across different datasets for the same cancers, indicating that effective biological signals naturally exist in the non-normalised data. Because the powers of current statistical analyses using the non-normalised data tend to be low, we suggest selecting DE genes in both normalised and non-normalised data and then filter out the false DE genes extracted from the normalised data that show opposite deregulation directions in the non-normalised data.

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome.

Nowadays, some researchers normalized DNA methylation arrays data in order to remove the technical artifacts introduced by experimental differences in sample preparation, array processing and other factors. However, other researchers analyzed DNA methylation arrays without performing data normalization considering that current normalizations for methylation data may distort real differences between normal and cancer samples because cancer genomes may be extensively subject to hypomethylation and the total amount of CpG methylation might differ substantially among samples. In this study, using eight datasets by Infinium HumanMethylation27 assay, we systemically analyzed the global distribution of DNA methylation changes in cancer compared to normal control and its effect on data normalization for selecting differentially methylated (DM) genes. We showed more differentially methylated (DM) genes could be found in the Quantile/Lowess-normalized data than in the non-normalized data. We found the DM genes additionally selected in the Quantile/Lowess-normalized data showed significantly consistent methylation states in another independent dataset for the same cancer, indicating these extra DM genes were effective biological signals related to the disease. These results suggested normalization can increase the power of detecting DM genes in the context of diagnostic markers which were usually characterized by relatively large effect sizes. Besides, we evaluated the reproducibility of DM discoveries for a particular cancer type, and we found most of the DM genes additionally detected in one dataset showed the same methylation directions in the other dataset for the same cancer type, indicating that these DM genes were effective biological signals in the other dataset. Furthermore, we showed that some DM genes detected from different studies for a particular cancer type were significantly reproducible at the functional level.

Extensive increase of microarray signals in cancers calls for novel normalization assumptions.

When using microarray data for studying a complex disease such as cancer, it is a common practice to normalize data to force all arrays to have the same distribution of probe intensities regardless of the biological groups of samples. The assumption underlying such normalization is that in a disease the majority of genes are not differentially expressed genes (DE genes) and the numbers of up- and down-regulated genes are roughly equal. However, accumulated evidences suggest gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalization assumption. Here, we analyzed 7 large Affymetrix datasets of pair-matched normal and cancer samples for cancers collected in the NCBI GEO database. We showed that in 6 of these 7 datasets, the medians of perfect match (PM) probe intensities increased in cancer state and the increases were significant in three datasets, suggesting the assumption that all arrays have the same median probe intensities regardless of the biological groups of samples might be misleading. Then, we evaluated the effects of three currently most widely used normalization algorithms (RMA, MAS5.0 and dChip) on the selection of DE genes by comparing them with LVS which relies less on the above-mentioned assumption. The results showed using RMA, MAS5.0 and dChip may produce lots of false results of down-regulated DE genes while missing many up-regulated DE genes. At least for cancer study, normalizing all arrays to have the same distribution of probe intensities regardless of the biological groups of samples might be misleading. Thus, most current normalizations based on unreliable assumptions may distort biological differences between normal and cancer samples. The LVS algorithm might perform relatively well due to that it relies less on the above-mentioned assumption. Also, our results indicate that genes may be widely up-regulated in most human cancer.

Evaluating the consistency of differential expression of microRNA detected in human cancers.

Differential expression of microRNA (miRNA) is involved in many human diseases and could potentially be used as a biomarker for disease diagnosis, prognosis, and therapy. However, inconsistency has often been found among differentially expressed miRNAs identified in various studies when using miRNA arrays for a particular disease such as a cancer. Before broadly applying miRNA arrays in a clinical setting, it is critical to evaluate inconsistent discoveries in a rational way. Thus, using data sets from 2 types of cancers, our study shows that the differentially expressed miRNAs detected from multiple experiments for each cancer exhibit stable regulation direction. This result also indicates that miRNA arrays could be used to reliably capture the signals of the regulation direction of differentially expressed miRNAs in cancer. We then assumed that 2 differentially expressed miRNAs with the same regulation direction in a particular cancer play similar functional roles if they regulate the same set of cancer-associated genes. On the basis of this hypothesis, we proposed a score to assess the functional consistency between differentially expressed miRNAs separately extracted from multiple studies for a particular cancer. We showed although lists of differentially expressed miRNAs identified from different studies for each cancer were highly variable, they were rather consistent at the level of function. Thus, the detection of differentially expressed miRNAs in various experiments for a certain disease tends to be functionally reproducible and capture functionally related differential expression of miRNAs in the disease.

Systematic interpretation of comutated genes in large-scale cancer mutation profiles.

By high-throughput screens of somatic mutations of genes in cancer genomes, hundreds of cancer genes are being rapidly identified, providing us abundant information for systematically deciphering the genetic changes underlying cancer mechanism. However, the functional collaboration of mutated genes is often neglected in current studies. Here, using four genome-wide somatic mutation data sets and pathways defined in various databases, we showed that gene pairs significantly comutated in cancer samples tend to distribute between pathways rather than within pathways. At the basic functional level of motifs in the human protein-protein interaction network, we also found that comutated gene pairs were overrepresented between motifs but extremely depleted within motifs. Specifically, we showed that based on Gene Ontology that describes gene functions at various specific levels, we could tackle the pathway definition problem to some degree and study the functional collaboration of gene mutations in cancer genomes more efficiently. Then, by defining pairs of pathways frequently linked by comutated gene pairs as the between-pathway models, we showed they are also likely to be codisrupted by mutations of the interpathway hubs of the coupled pathways, suggesting new hints for understanding the heterogeneous mechanisms of cancers. Finally, we showed some between-pathway models consisting of important pathways such as cell cycle checkpoint and cell proliferation were codisrupted in most cancer samples under this study, suggesting that their codisruptions might be functionally essential in inducing these cancers. All together, our results would provide a channel to detangle the complex collaboration of the molecular processes underlying cancer mechanism.

Evaluation of cDNA microarray data by multiple clones mapping to the same transcript.

Although novel technologies are rapidly emerging, the cDNA microarray data accumulated is still and will be an important source for bioinformatics and biological studies. Thus, the reliability and applicability of the cDNA microarray data warrants further evaluation. In cDNA microarrays, multiple clones are measured for a transcript, which can be exploited to evaluate the consistency of microarray data. We show that even for pairs of RCs, the average Pearson correlation coefficient of their measurements is not high. However, this low consistency could largely be explained by random noise signals for a fraction of unexpressed genes and/or low signal-to-noise ratios for low abundance transcripts. Encouragingly, a large fraction of inconsistent data will be filtered out in the procedure of selecting differentially expressed genes (DEGs). Therefore, although cDNA microarray data are of low consistency, applications based on DEGs selections could still reach correct biological results, especially at the functional modules level.