Lixin Cheng

Extracting consistent knowledge from highly inconsistent cancer gene data sources

BackgroundHundreds 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.ResultsFirst, 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.ConclusionsAlthough 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.

Mammalian ncRNA-disease repository: a global view of ncRNA-mediated disease network

Dear Editor  Recently, substantial studies have begun to explore the functional diversity and mechanistic roles of ncRNAs in mammals.1 Now, it has become increasingly apparent that ncRNAs are involved in multiple major biological processes, such as developmental timing, fat metabolism and cell death.2 Furthermore, the epigenetic and genetic defects in ncRNAs and their processing machinery have been implicated in the etiology of many forms of diseases.3 Several databases that documented the relevance of the microRNAs(miRNAs) to diseases have been constructed and provided useful results.4, 5 However, miRNAs are just the tip of the iceberg, other ncRNAs such as long non-coding RNAs (lncRNAs), PIWI-interacting RNAs (piRNAs) and small nucleolar RNAs (snoRNAs) have also been demonstrated to contribute to diseases.3, 6 Accumulated evidence suggest the diverse non-coding RNAs (ncRNAs) involved in a wide variety of diseases progression.3, 6, 7 It is a key challenge for understanding the precise behavior of diverse ncRNAs in mammalian diseases and deciphering the cross-regulations among disease-associated ncRNAs. Because there was no repository focused on diverse ncRNA-disease relationships in mammals, we have developed a manually curated diverse ncRNA-disease repository (MNDR, www.rna-society.org/mndr/) by integrating evidence in three mammals. Totally, 807 lncRNA-associated, 229 miRNA-associated, 13 piRNA-associated and 100 snoRNA-associated entries for 1149 curated entries were documented for three mammals (866 Homo sapiens-associated, 251 Mus musculus-associated and 32 Rattus norvegicus-associated entries) (Table 1). Table 1 The statistics of the ncRNA-disease entries in MNDR database Recent investigations indicated there are complex regulations among diverse ncRNAs and protein-coding genes. Such as, PTEN gene and the PTEN pseudogenes (ptenp1, one of lncRNAs) share a high degree of sequence homology, changes in ptenp1 expression levels indirectly affect PTEN expression by sequestering PTEN-targeting miRNAs.8 Thus understanding the mutual regulating pattern among diverse ncRNAs and protein-coding genes, particularly in disease conditions, is a key challenge. Thus, MNDR is not only a knowledge depository but providing us a good opportunity to view the ncRNA-mediated disease network globally (in visualization page: www.rna-society.org/mndr/visualization.html). Diverse ncRNAs and interaction genes were represented as nodes and the regulations were denoted as edges. Based on such a simplified ncRNA-mediated disease network, interesting observations have been achieved. The result showed that snoRNA htr, as a hub node, has intensively linked to 21 interaction genes in the network. More important, through BCL2, BCL2L1 and BAX, the snoRNA htr can communicate with the lncRNA malat1 (Supplementary Figure 1). Another example is snoRNA htr and lncRNA h19 are linked by E2F1 and MYC. When combined with human disease-associated miRNA evidence from mir2disease database, lncRNAs, miRNAs and snoRNAs, together with their interaction/target genes, can be integrated into bigger expanding ncRNA-mediated disease network. The biggest sub-network has 129 nodes and 149 edges, involving 33 lncRNAs, 1 snoRNA, 19 miRNAs and 76 interaction protein-coding genes(Supplementary Figure 2). In this network, more regulations among diverse ncRNAs directly or indirectly via intermediate genes, lncRNA dgcr5, har1a and har1b, were connected with hsa-mir-21 via intermediate gene REST. Interestingly, hsa-mir-21 and snoRNA htr were linked by key anti-apoptosis gene BCL2. Similar results were observed that lncRNA dgcr5, har1a and har1b can also communicate with snoRNA htr through alternative route NFKB1-hsa-mir-9-REST. Hence, according to current data, the two pivot protein-coding genes (BCL2 and NFKB1) and several ncRNAs (lncRNA malat1, snoRNA htr and miRNA hsa-mir-21, has-mir-9) collectively play an important role in the ncRNA-mediated disease network (Supplementary Figure 2). Importantly, the crosstalk between lncRNA malat1 and miRNA hsa-mir-21 can be found conserved in mouse ncRNA-mediated disease network. Above observations indicated diverse ncRNAs could communicate with each other in disease state through some disease-associated genes in mammals, highlighting the complexity, conservative and plasticity of the regulatory relationships between diverse ncRNAs and protein-coding genes in diseases.

ViRBase: a resource for virus-host ncRNA-associated interactions.

Increasing evidence reveals that diverse non-coding RNAs (ncRNAs) play critically important roles in viral infection. Viruses can use diverse ncRNAs to manipulate both cellular and viral gene expression to establish a host environment conducive to the completion of the viral life cycle. Many host cellular ncRNAs can also directly or indirectly influence viral replication and even target virus genomes. ViRBase (http://www.rna-society.org/virbase) aims to provide the scientific community with a resource for efficient browsing and visualization of virus-host ncRNA-associated interactions and interaction networks in viral infection. The current version of ViRBase documents more than 12 000 viral and cellular ncRNA-associated virus–virus, virus–host, host–virus and host–host interactions involving more than 460 non-redundant ncRNAs and 4400 protein-coding genes from between more than 60 viruses and 20 hosts. Users can query, browse and manipulate these virus–host ncRNA-associated interactions. ViRBase will be of help in uncovering the generic organizing principles of cellular virus–host ncRNA-associated interaction networks in viral infection.

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.

CrossNorm: a novel normalization strategy for microarray data in cancers.

Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.

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.

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. Mol Cancer Ther; 10(5); 752–60. ©2011 AACR.

Full Characterization of Localization Diversity in the Human Protein Interactome

Spatial-temporal regulation among proteins forms dynamic networks in cells. Coexistence in common cell compartments can improve biological reliability of the protein-protein interactions. However, this is usually overlooked by most proteomic studies and leads to unrealistic discoveries. In this paper, we systematically characterize the interaction localization diversity in the human protein interactome using the localization coefficient, a novel metric proposed for assessing how diversely the interactions localize among cell compartments. Our analysis reveals the following: (1) the subcellular networks of the nucleus, cytosol, and mitochondrion are dense but the interactions tend to localize in specific cell compartments, whereas the subnetworks of the secretory-pathway, membrane, and extracellular region are sparse but the interactions are diversely localized; (2) the housekeeping proteins tend to appear in multiple compartments, while the tissue-specific proteins present a relatively flat profile of localization breadth; (3) the autophagy proteins tend to diversely localize in multiple compartments, especially those with high connectivity, compared with the apoptosis proteins; (4) the proteins targeted by small-molecule drugs show no preference for compartments, whereas the proteins directed by antibody-based drugs tend to belong to transmembrane regions with a strong diversity. In summary, our analysis provides a comprehensive view of the subcellular localization for interacting proteins, demonstrates that localization diversity is an important feature of protein interactions, and shows its ability to highlight meaningful biological functions.

Quantification of non-coding RNA target localization diversity and its application in cancers

Subcellular localization is pivotal for RNAs and proteins to implement biological functions. The localization diversity of protein interactions has been studied as a crucial feature of proteins, considering that the protein-protein interactions take place in various subcellular locations. Nevertheless, the localization diversity of non-coding RNA (ncRNA) target proteins has not been systematically studied, especially its characteristics in cancers. In this study, we provide a new algorithm, non-coding RNA target localization coefficient (ncTALENT), to quantify the target localization diversity of ncRNAs based on the ncRNA-protein interaction and protein subcellular localization data. ncTALENT can be used to calculate the target localization coefficient of ncRNAs and measure how diversely their targets are distributed among the subcellular locations in various scenarios. We focus our study on long non-coding RNAs (lncRNAs), and our observations reveal that the target localization diversity is a primary characteristic of lncRNAs in different biotypes. Moreover, we found that lncRNAs in multiple cancers, differentially expressed cancer lncRNAs, and lncRNAs with multiple cancer target proteins are prone to have high target localization diversity. Furthermore, the analysis of gastric cancer helps us to obtain a better understanding that the target localization diversity of lncRNAs is an important feature closely related to clinical prognosis. Overall, we systematically studied the target localization diversity of the lncRNAs and uncovered its association with cancer.

SMILE: A Novel Procedure for Subcellular Module Identification with Localization Expansion

We propose a novel procedure, Subcellular Module Identification with Localization Expansion (SMILE), to identify super modules that consist of several subcellular modules performing specific biological functions among cell compartments. These super modules identified by SMILE are more functionally diverse and have been verified to be more associated with known protein complexes and biological pathways compared with the modules identified from the global protein interaction networks in both the ComPPI and InWeb_InBioMap protein interaction datasets. Our results reveal that subcellular localization is a principal feature of functional modules and offers important guidance in detecting biologically meaningful results.