Identifying functions and prognostic biomarkers of network motifs marked by diverse chromatin states in human cell lines.

Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression patterns of targets. Notably, diverse chromatin state compositions could help C1- or I1-FFL to control a large number of distinct biological functions in human cell lines, such as four different types of chromatin state compositions cooperating with K562-associated C1-FFLs controlling "regulation of cytokinesis," "G1/S transition of mitotic cell cycle," "DNA recombination," and "telomere maintenance," respectively. Remarkably, we identified six chromatin state-marked C1-FFL instances (HCFC1-NFYA-ABL1, THAP1-USF1-BRCA2, ZNF263-USF1-UBA52, MYC-ATF1-UBA52, ELK1-EGR1-CCT4, and YY1-EGR1-INO80C) could act as prognostic biomarkers of acute myelogenous leukemia though influencing cancer-related biological functions, such as cell proliferation, telomere maintenance, and DNA recombination. Our results will provide novel insight for better understanding of chromatin state-mediated gene regulation and facilitate the identification of novel diagnostic and therapeutic biomarkers of human cancers.

Identification of Dysregulated Competitive Endogenous RNA Networks Driven by Copy Number Variations in Malignant Gliomas.

Gliomas represent 80% of malignant brain tumors. Because of the high heterogeneity, the oncogenic mechanisms in gliomas are still unclear. In this study, we developed a new approach to identify dysregulated competitive endogenous RNA (ceRNA) interactions driven by copy number variation (CNV) in both lower-grade glioma (LGG) and glioblastoma multiforme (GBM). By analyzing genome and transcriptome data from The Cancer Genome Atlas (TCGA), we first found out the protein coding genes and long non-coding RNAs (lncRNAs) significantly affected by CNVs and further determined CNV-driven dysregulated ceRNA interactions by a customized pipeline. We obtained 13,776 CNV-driven dysregulated ceRNA pairs (including 3,954 mRNAs and 306 lncRNAs) in LGG and 262 pairs (including 221 mRNAs and 11 lncRNAs) in GBM, respectively. Our results showed that most of the ceRNA interactions were weakened by CNVs in both LGG and GBM, and many CNV-driven genes shared the same ceRNAs in the dysregulated ceRNA networks. Functional analysis indicated that the CNV-driven ceRNA network involved in some important mechanisms of tumorigenesis, such as cell cycle, p53 signaling pathway and TGF-beta signaling pathway. Further investigation of the ceRNA pairs in the communities from the dysregulated ceRNA network revealed more detailed biological functions related to the oncogenesis of malignant gliomas. Moreover, by exploring the association of CNV-driven ceRNAs with prognosis and histological subtype, we found that the copy number status of MTAP, KLHL9, and ELAVL2 related to the overall survival in LGG and showed high correlation with histological subtype. In conclusion, this study provided new insight into the molecular mechanisms and clinical biomarkers in gliomas.

Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability.

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

Systematic Identification of Non-coding RNAs.

Non-coding RNAs (ncRNAs) are biologically significant in variable ways. They modulate gene expression at the levels of transcription and post-transcription. MiRNAs and lncRNAs are two major classes of non-coding RNAs and have been extensively characterized. They are implicated in various biological processes and diseases. Thus, identification of miRNAs and lncRNAs are fundamental to further understand their roles and dissect their mechanisms. Here, we overviewed pipelines of identifying miRNAs and lncRNAs based on next-generation sequencing technologies. We applied the pipelines to identify miRNAs in multiple cell lines and perform expression quantification of mature, precursor and primary miRNAs. In addition, we provided an alternative way to re-annotate lncRNAs from microarray data. We summarized multiple resources and databases for lncRNA annotation and compared their annotation processes and specific parameters. Finally, we utilized RNA-seq and miRNA-seq data to construct a comprehensive transcriptome containing miRNAs, lncRNAs and protein-coding genes in heart failure.

Aberrant Epigenetic Modifications of Non-coding RNAs in Human Disease.

Non-coding RNAs, especially lncRNAs, have emerged as key components in histone modification. The alterations in the epigenetic modifications of lncRNAs underlie some human disorders ranging from neurodegeneration to cancer. To characterize the epigenetic modifications of lncRNAs, we first constructed the histone modification maps of various epigenetic markers across different cell lines. Then, we developed a method to identify epigenetically regulated lncRNAs and their response genes by integrating large scale epigenetic and transcriptional profiles. Our results showed that epigenetic alterations at the promoters of lncRNAs can influence their expression and the negative response genes of most epigenetically regulated lncRNAs were enriched for PRC2-binding genes. At last, we inferred some lncRNAs with aberrant epigenetic modifications in glioblastoma and Alzheimer's disease, and proved that theses lncRNAs may contribute to the initiation of human diseases.

SEECancer: a resource for somatic events in evolution of cancer genome.

Cancer cells progressively evolve from a premalignant to a malignant state, which is driven by accumulating somatic alterations that confer normal cells a fitness advantage. Improvements in high-throughput sequencing techniques have led to an increase in construction of tumor phylogenetics and identification of somatic driver events that specifically occurred in different tumor progression stages. Here, we developed the SEECancer database (http://biocc.hrbmu.edu.cn/SEECancer), which aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders. By manually curating over 10 000 published articles, 1231 evolutionary stage-specific genomic events and 5772 temporal orders involving 82 human cancers and 23 tissue origins were collected and deposited in the SEECancer database. Each entry contains the somatic event, evolutionary stage, cancer type, detection approach and relevant evidence. SEECancer provides a user-friendly interface for browsing, searching and downloading evolutionary stage-specific somatic events and temporal relationships in various cancers. With increasing attention on cancer genome evolution, the necessary information in SEECancer will facilitate understanding of cancer etiology and development of evolutionary therapeutics, and help clinicians to discover biomarkers for monitoring tumor progression.

Optimizing prognosis-related key miRNA-target interactions responsible for cancer metastasis.

Increasing evidence suggests that the abnormality of microRNAs (miRNAs) and their downstream targets is frequently implicated in the pathogenesis of human cancers, however, the clinical benefit of causal miRNA-target interactions has been seldom studied. Here, we proposed a computational method to optimize prognosis-related key miRNA-target interactions by combining transcriptome and clinical data from thousands of TCGA tumors across 16 cancer types. We obtained a total of 1,956 prognosis-related key miRNA-target interactions between 112 miRNAs and 1,443 their targets. Interestingly, these key target genes are specifically involved in tumor progression-related functions, such as 'cell adhesion' and 'cell migration'. Furthermore, they are most significantly correlated with 'tissue invasion and metastasis', a hallmark of metastasis, in ten distinct types of cancer through the hallmark analysis. These results implicated that the prognosis-related key miRNA-target interactions were highly associated with cancer metastasis. Finally, we observed that the combination of these key miRNA-target interactions allowed to distinguish patients with good prognosis from those with poor prognosis both in most TCGA cancer types and independent validation sets, highlighting their roles in cancer metastasis. We provided a user-friendly database named miRNATarget (freely available at http://biocc.hrbmu.edu.cn/miRNATar/), which provides an overview of the prognosis-related key miRNA-target interactions across 16 cancer types.