Landscape and significance of human super enhancer-driven core transcription regulatory circuitry.

A core transcription regulatory circuitry (CRC) is an interconnected self-regulatory circuitry that is formed by a group of core transcription factors (TFs). These core TFs collectively regulate gene expression by binding not only to their own super enhancers (SEs) but also to the SEs of one another. For most human tissue/cell types, a global view of CRCs and core TFs has not been generated. Here, we identified numerous CRCs using two identification methods and detailed the landscape of the CRCs driven by SEs in large cell/tissue samples. The comprehensive biological analyses, including sequence conservation, CRC activity and genome binding affinity were conducted for common TFs, moderate TFs, and specific TFs, which exhibit different biological features. The local module located from the common CRC network highlighted the essential functions and prognostic performance. The tissue-specific CRC network was highly related to cell identity. Core TFs in tissue-specific CRC networks exhibited disease markers, and had regulatory potential for cancer immunotherapy. Moreover, a user-friendly resource named CRCdb (http://www.licpathway.net/crcdb/index.html) was developed, which contained the detailed information of CRCs and core TFs used in this study, as well as other interesting results, such as the most representative CRC, frequency of TFs, and indegree/outdegree of TFs.

CanMethdb: a database for genome-wide DNA methylation annotation in cancers.

MOTIVATION: DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation. RESULTS: We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/chunquanlipathway/CanMethdb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.

Cancer CRC: A Comprehensive Cancer Core Transcriptional Regulatory Circuit Resource and Analysis Platform.

A core transcriptional regulatory circuit (CRC) is a group of interconnected auto-regulating transcription factors (TFs) that form loops and can be identified by super-enhancers (SEs). Studies have indicated that CRCs play an important role in defining cellular identity and determining cellular fate. Additionally, core TFs in CRCs are regulators of cell-type-specific transcriptional regulation. However, a global view of CRC properties across various cancer types has not been generated. Thus, we integrated paired cancer ATAC-seq and H3K27ac ChIP-seq data for specific cell lines to develop the Cancer CRC (http://bio.liclab.net/Cancer_crc/index.html). This platform documented 94,108 cancer CRCs, including 325 core TFs. The cancer CRC also provided the "SE active core TFs analysis" and "TF enrichment analysis" tools to identify potentially key TFs in cancer. In addition, we performed a comprehensive analysis of core TFs in various cancer types to reveal conserved and cancer-specific TFs.

Characterization of super-enhancer-associated functional lncRNAs acting as ceRNAs in ESCC.

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well-characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA-mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce-lncRNAs). We found that most of them significantly regulated the expression of cancer-related hallmark genes. These ce-lncRNAs were significantly regulated by enhancers, especially super-enhancers (SEs). Landscape analyses for lncRNAs further identified SE-associated functional ce-lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE-associated ce-lncRNAs. We further demonstrate that a SE-associated ce-lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE-associated ce-lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.

Macrophage-Enriched lncRNA RAPIA: A Novel Therapeutic Target for Atherosclerosis.

OBJECTIVE: Despite the current antiatherosclerotic and antithrombotic therapies, the incidence of advanced atherosclerosis-associated clinical events remains high. Whether long noncoding RNAs (lncRNAs) affect the progression of atherosclerosis and whether they are potential targets for the treatment of advanced atherosclerosis are poorly understood. Approach and Results: The progression of atherosclerotic lesions was accompanied by dynamic alterations in lncRNA expression, as revealed by RNA sequencing and quantitative polymerase chain reaction. Among the dynamically changing lncRNAs, we identified a novel lncRNA, lncRNA Associated with the Progression and Intervention of Atherosclerosis (RAPIA), that was highly expressed in advanced atherosclerotic lesions and in macrophages. Inhibition of RAPIA in vivo not only repressed the progression of atherosclerosis but also exerted atheroprotective effects similar to those of atorvastatin on advanced atherosclerotic plaques that had already formed. In vitro assays demonstrated that RAPIA promoted proliferation and reduced apoptosis of macrophages. A molecular sponge interaction between RAPIA and microRNA-183-5p was demonstrated by dual-luciferase reporter and RNA immunoprecipitation assays. Rescue assays indicated that RAPIA functioned at least in part by targeting the microRNA-183-5p/ITGB1 (integrin ß1) pathway in macrophages. In addition, the transcription factor FoxO1 (forkhead box O1) could bind to the RAPIA promoter region and facilitate the expression of RAPIA. CONCLUSIONS: The progression of atherosclerotic lesions was accompanied by dynamic changes in the expression of lncRNAs. Inhibition of the pivotal lncRNA RAPIA may be a novel preventive and therapeutic strategy for advanced atherosclerosis, especially in patients resistant or intolerant to statins.

Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs.

Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23-52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures.

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

ReCirc: prediction of circRNA expression and function through probe reannotation of non-circRNA microarrays.

Growing evidence shows that circular RNAs (circRNAs) play important roles in physiological and pathological processes, but our knowledge about the function of circRNAs in diseases is still limited. CircRNA functions are closely related to their expression levels. We developed a probe reannotating program named ReCirc, which is based on sequence alignment between microarray probes and circRNAs, to reannotate circRNAs from non-circRNA microarrays (any microarray that was not designed to profile circRNAs) with microarray probe sequences that were aligned to the body and back-spliced junction sequences of circRNAs to identify circRNAs. Through ReCirc, we obtained 39 818 reannotated probe set-circRNA pairs, which involved 5388 circRNAs, from an Affymetrix human exon array. We evaluated our method by comparing circRNAs obtained by us with golden standard RNase R-resistant (RNase R+) circRNAs, predicted by an RNA-seq-based method find_circ, in the HeLa cell line. The results showed that ReCirc circRNAs, especially those with higher expression level, were partially present in RNase R+ data. In addition to RNA-seq, a circRNA microarray, such as the Agilent-069978 Arraystar Human CircRNA microarray, was also applied to predict and profile circRNAs. Thus, we compared the circRNA profile obtained from ReCirc with that from the circRNA microarray. The results showed that circRNA expression is similar between ReCirc and circRNA microarray in samples from the same tissue. We also evaluated ReCirc, by comparing ReCirc with the find_circ program, in their abilities to compute circRNA expression variation in multiple cell lines and performed molecular verification in the HeLaS3 cell line for those circRNAs that got good performance. As a result, 5 of the 9 randomly selected circRNAs were successfully verified. Functional analysis of identified circRNAs in 4 different cancers indicated that circRNAs may be crucial biomarkers for cancer diagnosis and prognosis. Thus, ReCirc allows us to identify circRNAs from any non-circRNA microarray, and to back-annotate old microarray data from public data sets, which would facilitate re-utilization of the wealth of microarray data sets, to enable the characterization of circRNAs in tissues and cell lines. Here we state that our method is designed only for microarrays and cannot be used for RNAseq data.