Xiaopei Shen

Separate enrichment analysis of pathways for up- and downregulated genes

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.

Reproducibility and Concordance of Differential DNA Methylation and Gene Expression in Cancer

Background Hundreds of genes with differential DNA methylation of promoters have been identified for various cancers. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. Methodology/Principal Findings Using array data for seven types of cancers, we first evaluated the effects of experimental batches on differential DNA methylation detection. Second, we compared the directions of DNA methylation changes detected from different datasets for the same cancer. Third, we evaluated the concordance between methylation and gene expression changes. Finally, we compared DNA methylation changes in different cancers. For a given cancer, the directions of methylation and expression changes detected from different datasets, excluding potential batch effects, were highly consistent. In different cancers, DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression, whereas hypomethylation was only weakly correlated with the up-regulation of genes. Finally, we found that genes commonly hypomethylated in different cancers primarily performed functions associated with chronic inflammation, such as ‘keratinization’, ‘chemotaxis’ and ‘immune response’. Conclusions Batch effects could greatly affect the discovery of DNA methylation biomarkers. For a particular cancer, both differential DNA methylation and gene expression can be reproducibly detected from different studies with no batch effects. While DNA hypermethylation is significantly linked to gene down-regulation, hypomethylation is only weakly correlated with gene up-regulation and is likely to be linked to chronic inflammation.

Distinct functional patterns of gene promoter hypomethylation and hypermethylation in cancer genomes.

Background Aberrant DNA methylation plays important roles in carcinogenesis. However, the functional significance of genome-wide hypermethylation and hypomethylation of gene promoters in carcinogenesis currently remain unclear. Principal Findings Based on genome-wide methylation data for five cancer types, we showed that genes with promoter hypermethylation were highly consistent in function across different cancer types, and so were genes with promoter hypomethylation. Functions related to “developmental processes” and “regulation of biology processes” were significantly enriched with hypermethylated genes but were depleted of hypomethylated genes. In contrast, functions related to “cell killing” and “response to stimulus”, including immune and inflammatory response, were associated with an enrichment of hypomethylated genes and depletion of hypermethylated genes. We also observed that some families of cytokines secreted by immune cells, such as IL10 family cytokines and chemokines, tended to be hypomethylated in various cancer types. These results provide new hints for understanding the distinct functional roles of genome-wide hypermethylation and hypomethylation of gene promoters in carcinogenesis. Conclusions Genes with promoter hypermethylation and hypomethylation are highly consistent in function across different cancer types, respectively, but these two groups of genes tend to be enriched in different functions associated with cancer. Especially, we speculate that hypomethylation of gene promoters may play roles in inducing immunity and inflammation disorders in precancerous conditions, which may provide hints for improving epigenetic therapy and immunotherapy of cancer.

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”. Results Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer. Conclusion The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.

Viewing cancer genes from co-evolving gene modules

MOTIVATION Studying the evolutionary conservation of cancer genes can improve our understanding of the genetic basis of human cancers. Functionally related proteins encoded by genes tend to interact with each other in a modular fashion, which may affect both the mode and tempo of their evolution. RESULTS In the human PPI network, we searched for subnetworks within each of which all proteins have evolved at similar rates since the human and mouse split. Identified at a given co-evolving level, the subnetworks with non-randomly large sizes were defined as co-evolving modules. We showed that proteins within modules tend to be conserved, evolutionarily old and enriched with housekeeping genes, while proteins outside modules tend to be less-conserved, evolutionarily younger and enriched with genes expressed in specific tissues. Viewing cancer genes from co-evolving modules showed that the overall conservation of cancer genes should be mainly attributed to the cancer proteins enriched in the conserved modules. Functional analysis further suggested that cancer proteins within and outside modules might play different roles in carcinogenesis, providing a new hint for studying the mechanism of cancer.

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.

Identifying Candidate Cancer Genes Based on Their Somatic Mutations Co-Occurring with Cancer Genes in Cancer Genome Profiling

After decades of searching, only 10-20% of all cancer genes are known. Identifying candidate cancer genes are still a central aim of cancer research. In current high throughput studies of screening somatic mutations in cancer genome, only genes mutated more frequently than what would be expected by chance are determined as candidate cancer genes. However, mutations of multiple genes participating in different pathways are suggested to be synergistic in conferring selection advantages to the tumor. Here, considering the cooperation of multiple cancer genes during tumorigenesis, we identified 23 candidate cancer genes co-mutated with known cancer genes in the same cancer samples significantly more frequently than expected by random chance. The potential roles of these candidate cancer genes in tumorigenesis are strongly supported by literatures.

[Identifying candidate cancer genes based on co-evolving gene modules].

Data of somatic mutation screening of cancer genomes have provided us huge amounts of information for identifying new cancer genes. Current methods for identifying candidate cancer genes based on gene mutation frequencies tend to find cancer genes with high mutation frequencies. However, many genes with low mutation frequencies might also play important roles during tumorigenesis. Based on the assumption that genes with similar phylogenetic profiles and protein-protein interactions might have similar functions and their disruptions might lead to similar disease phenotypes, we proposed a new approach to find candidate cancer genes. First, we searched for protein-protein interaction subnetworks within which proteins have similar phylogenetic profiles, termed as co-evolving gene modules. Then, we identified genes that have at least one non-synonymous mutation in cancer genomes and directly interact with known cancer genes in the same co-evolving gene modules and predicted them as candidate cancer genes. In this way, we found 15 candidate cancer genes, among which only two genes had been identified previously as candidate cancer genes using the methods based on gene mutation frequencies. Thus, the candidate cancer genes with low mutation frequencies can be found by our method.