One of the most fundamental questions in biology is what types of cells form different tissues and organs in a functionally coordinated fashion. Larger-scale single-cell sequencing and biology experiment studies are now rapidly opening up new ways to track this question by revealing substantial cell markers for distinguishing different cell types in tissues. Here, we developed the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/ or http://bio-bigdata.hrbmu.edu.cn/CellMarker/), aiming to provide a comprehensive and accurate resource of cell markers for various cell types in tissues of human and mouse. By manually curating over 100 000 published papers, 4124 entries including the cell marker information, tissue type, cell type, cancer information and source, were recorded. At last, 13 605 cell markers of 467 cell types in 158 human tissues/sub-tissues and 9148 cell makers of 389 cell types in 81 mouse tissues/sub-tissues were collected and deposited in CellMarker. CellMarker provides a user-friendly interface for browsing, searching and downloading markers of diverse cell types of different tissues. Furthermore, a summarized marker prevalence in each cell type is graphically and intuitively presented through a vivid statistical graph. We believe that CellMarker is a comprehensive and valuable resource for cell researches in precisely identifying and characterizing cells, especially at the single-cell level.

BackgroundFew long noncoding RNAs (lncRNAs) that act as oncogenic genes in breast cancer have been identified.MethodsOncogenic lncRNAs associated with tumourigenesis and worse survival outcomes were examined and validated in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), respectively. Then, the potential biological functions and expression regulation of these lncRNAs were studied via bioinformatics and genome data analysis. Moreover, progressive breast cancer subtype-specific lncRNAs were investigated via high-throughput sequencing in our cohort and TCGA validation. To elucidate the mechanisms of the regulation of these lncRNAs, genomic alterations from the TCGA, Broad, Sanger and BCCRC data, as well as epigenetic modifications from GEO data, were then applied and examined to meet this objective. Finally, cell proliferation assays, flow cytometry analyses and TUNEL assays were applied to validate the oncogenic roles of these lncRNAs in vitro.ResultsA cluster of oncogenic lncRNAs that was upregulated in breast cancer tissue and was associated with worse survival outcomes was identified. These oncogenic lncRNAs are involved in regulating immune system activation and the TGF-beta and Jak-STAT signalling pathways. Moreover, TINCR, LINC00511, and PPP1R26-AS1 were identified as subtype-specific lncRNAs associated with HER-2, triple-negative and luminal B subtypes of breast cancer, respectively. The up-regulation of these oncogenic lncRNAs is mainly caused by gene amplification in the genome in breast cancer and other solid tumours. Finally, the knockdown of TINCR, DSCAM-AS1 or HOTAIR inhibited breast cancer cell proliferation, increased apoptosis and inhibited cell cycle progression in vitro.ConclusionsThese findings enhance the landscape of known oncogenic lncRNAs in breast cancer and provide insights into their roles. This understanding may potentially aid in the comprehensive management of breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-017-0696-6) contains supplementary material, which is available to authorized users.

Long noncoding RNAs (lncRNAs) are emerging as a class of important regulators participating in various biological functions and disease processes. With the widespread application of next-generation sequencing technologies, large numbers of lncRNAs have been identified, producing plenty of lncRNA annotation resources in different contexts. However, at present, we lack a comprehensive overview of these lncRNA annotation resources. In this study, we reviewed 24 currently available lncRNA annotation resources referring to > 205 000 lncRNAs in over 50 tissues and cell lines. We characterized these annotation resources from different aspects, including exon structure, expression, histone modification and function. We found many distinct properties among these annotation resources. Especially, these resources showed diverse chromatin signatures, remarkable tissue and cell type dependence and functional specificity. Our results suggested the incompleteness and complementarity of current lncRNA annotations and the necessity of integration of multiple resources to comprehensively characterize lncRNAs. Finally, we developed 'LNCat' (lncRNA atlas, freely available at http://biocc.hrbmu.edu.cn/LNCat/), a user-friendly database that provides a genome browser of lncRNA structures, visualization of different resources from multiple angles and download of different combinations of lncRNA annotations, and supports rapid exploration, comparison and integration of lncRNA annotation resources. Overall, our study provides a comprehensive comparison of numerous lncRNA annotations, and can facilitate understanding of lncRNAs in human disease.

Extensive changes in DNA methylation have been observed in schizophrenia (SC) and bipolar disorder (BP), and may contribute to the pathogenesis of these disorders. Here, we performed genome-scale DNA methylation profiling using methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) on two brain regions (including frontal cortex and anterior cingulate) in 5 SC, 7 BP and 6 normal subjects. Comparing with normal controls, we identified substantial differentially methylated regions (DMRs) in these two brain regions of SC and BP. To our surprise, different brain regions show completely distinct distributions of DMRs across the genomes. In frontal cortex of both SC and BP subjects, we observed widespread hypomethylation as compared to normal controls, preferentially targeting the terminal ends of the chromosomes. In contrast, in anterior cingulate, both SC and BP subjects displayed extensive gain of methylation. Notably, in these two brain regions of SC and BP, only a few DMRs overlapped with promoters, whereas a greater proportion occurs in introns and intergenic regions. Functional enrichment analysis indicated that important psychiatric disorder-related biological processes such as neuron development, differentiation and projection may be altered by epigenetic changes located in the intronic regions. Transcriptome analysis revealed consistent dysfunctional processes with those determined by DMRs. Furthermore, DMRs in the same brain regions from SC and BP could successfully distinguish BP and/or SC from normal controls while differentially expressed genes could not. Overall, our results support a major role for brain-region-dependent aberrant DNA methylation in the pathogenesis of these two disorders.

MicroRNAs (miRNAs) have been validated to show widespread disruption of function in many cancers. However, despite concerted efforts to develop prioritization approaches based on a priori knowledge of disease-associated miRNAs, uncovering oncogene or tumor-suppressor miRNAs remains a challenge. Here, based on the assumption that diverse diseases with phenotype associations show similar molecular mechanisms, we present an approach for the systematic prioritization of disease-specific miRNAs by using known disease genes and context-dependent miRNA-target interactions derived from matched miRNA and mRNA expression data, independent of known disease miRNAs. After collecting matched miRNA and mRNA expression data for 11 cancer types, we applied this approach to systematically prioritize miRNAs involved in these cancers. Our approach yielded an average area under the ROC curve (AUC) of 75.84% according to known disease miRNAs from the miR2Disease database, with the highest AUC (80.93%) for pancreatic cancer. Moreover, we assessed the sensitivity and specificity as well as the integrative importance of this approach. Comparative analyses also showed that our method is comparable to previous methods. In summary, we provide a novel method for prioritization of disease-related miRNAs that can help researchers better understand the important roles of miRNAs in human disease.