TRIM33 drives prostate tumor growth by stabilizing androgen receptor from Skp2-mediated degradation.

Androgen receptor (AR) is a master transcription factor that drives prostate cancer (PCa) development and progression. Alterations in the expression or activity of AR coregulators significantly impact the outcome of the disease. Using a proteomics approach, we identified the tripartite motif-containing 33 (TRIM33) as a novel transcriptional coactivator of AR. We demonstrate that TRIM33 facilitates AR chromatin binding to directly regulate a transcription program that promotes PCa progression. TRIM33 further stabilizes AR by protecting it from Skp2-mediated ubiquitination and proteasomal degradation. We also show that TRIM33 is essential for PCa tumor growth by avoiding cell-cycle arrest and apoptosis, and TRIM33 knockdown sensitizes PCa cells to AR antagonists. In clinical analyses, we find TRIM33 upregulated in multiple PCa patient cohorts. Finally, we uncover an AR-TRIM33-coactivated gene signature highly expressed in PCa tumors and predict disease recurrence. Overall, our results reveal that TRIM33 is an oncogenic AR coactivator in PCa and a potential therapeutic target for PCa treatment.

Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression.

Androgen receptor (AR) plays a central role in driving prostate cancer (PCa) progression. How AR promotes this process is still not completely clear. Herein, we used single-cell transcriptome analysis to reconstruct the transcriptional network of AR in PCa. Our work shows AR directly regulates a set of signature genes in the ER-to-Golgi protein vesicle-mediated transport pathway. The expression of these genes is required for maximum androgen-dependent ER-to-Golgi trafficking, cell growth, and survival. Our analyses also reveal the signature genes are associated with PCa progression and prognosis. Moreover, we find inhibition of the ER-to-Golgi transport process with a small molecule enhanced antiandrogen-mediated tumor suppression of hormone-sensitive and insensitive PCa. Finally, we demonstrate AR collaborates with CREB3L2 in mediating ER-to-Golgi trafficking in PCa. In summary, our findings uncover a critical role for dysregulation of ER-to-Golgi trafficking expression and function in PCa progression, provide detailed mechanistic insights for how AR tightly controls this process, and highlight the prospect of targeting the ER-to-Golgi pathway as a therapeutic strategy for advanced PCa.

Single-Cell Transcriptome Analysis Reveals Estrogen Signaling Coordinately Augments One-Carbon, Polyamine, and Purine Synthesis in Breast Cancer.

Estrogen drives breast cancer (BCa) progression by directly activating estrogen receptor α (ERα). However, because of the stochastic nature of gene transcription, it is important to study the estrogen signaling pathway at the single-cell level to fully understand how ERα regulates transcription. Here, we performed single-cell transcriptome analysis on ERα-positive BCa cells following 17ß-estradiol stimulation and reconstructed the dynamic estrogen-responsive transcriptional network from discrete time points into a pseudotemporal continuum. Notably, differentially expressed genes show an estrogen-stimulated metabolic switch that favors biosynthesis but reduces estrogen degradation. Moreover, folate-mediated one-carbon metabolism is reprogrammed through the mitochondrial folate pathway and polyamine and purine synthesis are upregulated coordinately. Finally, we show AZIN1 and PPAT are direct ERα targets that are essential for BCa cell survival and growth. In summary, our study highlights the dynamic transcriptional heterogeneity in ERα-positive BCa cells upon estrogen stimulation and uncovers a mechanism of estrogen-mediated metabolic switch.

KM-express: an integrated online patient survival and gene expression analysis tool for the identification and functional characterization of prognostic markers in breast and prostate cancers.

The identification and functional characterization of novel biomarkers in cancer requires survival analysis and gene expression analysis of both patient samples and cell line models. To help facilitate this process, we have developed KM-Express. KM-Express holds an extensive manually curated transcriptomic profile of 45 different datasets for prostate and breast cancer with phenotype and pathoclinical information, spanning from clinical samples to cell lines. KM-Express also contains The Cancer Genome Atlas datasets for 30 other cancer types with matching cell line expression data for 23 of them. We present KM-Express as a hypothesis generation tool for researchers to identify potential new prognostic RNA biomarkers as well as targets for further downstream functional cell-based studies. Specifically, KM-Express allows users to compare the expression level of genes in different groups of patients based on molecular, genetic, clinical and pathological status. Moreover, KM-Express aids the design of biological experiments based on the expression profile of the genes in different cell lines. Thus, KM-Express provides a one-stop analysis from bench work to clinical prospects. We have used this tool to successfully evaluate the prognostic potential of previously published biomarkers for prostate cancer and breast cancer. We believe KM-Express will accelerate the translation of biomedical research from bench to bed.Database URL: http://ec2-52-201-246-161.compute-1.amazonaws.com/kmexpress/index.php.

Identification of lncRNA-associated competing triplets reveals global patterns and prognostic markers for cancer.

Recent studies have suggested that long non-coding RNAs (lncRNAs) can interact with microRNAs (miRNAs) and indirectly regulate miRNA targets though competing interactions. However, the molecular mechanisms underlying these interactions are still largely unknown. In this study, these lncRNA-miRNA-gene interactions were defined as lncRNA-associated competing triplets (LncACTs), and an integrated pipeline was developed to identify lncACTs that are active in cancer. Competing lncRNAs had sponge features distinct from non-competing lncRNAs. In the lncACT cross-talk network, disease-associated lncRNAs, miRNAs and coding-genes showed specific topological patterns indicative of their competence and control of communication within the network. The construction of global competing activity profiles revealed that lncACTs had high activity specific to cancers. Analyses of clustered lncACTs revealed that they were enriched in various cancer-related biological processes. Based on the global cross-talk network and cluster analyses, nine cancer-specific sub-networks were constructed. H19- and BRCA1/2-associated lncACTs were able to discriminate between two groups of patients with different clinical outcomes. Disease-associated lncACTs also showed variable competing patterns across normal and cancer patient samples. In summary, this study uncovered and systematically characterized global properties of human lncACTs that may have prognostic value for predicting clinical outcome in cancer patients.

SNP@lincTFBS: an integrated database of polymorphisms in human LincRNA transcription factor binding sites.

Large intergenic non-coding RNAs (lincRNAs) are a new class of functional transcripts, and aberrant expression of lincRNAs was associated with several human diseases. The genetic variants in lincRNA transcription factor binding sites (TFBSs) can change lincRNA expression, thereby affecting the susceptibility to human diseases. To identify and annotate these functional candidates, we have developed a database SNP@lincTFBS, which is devoted to the exploration and annotation of single nucleotide polymorphisms (SNPs) in potential TFBSs of human lincRNAs. We identified 6,665 SNPs in 6,614 conserved TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. We also performed comprehensive annotation for these SNPs using 1000 Genomes Project datasets across 11 populations. Moreover, one of the distinctive features of SNP@lincTFBS is the collection of disease-associated SNPs in the lincRNA TFBSs and SNPs in the TFBSs of disease-associated lincRNAs. The web interface enables both flexible data searches and downloads. Quick search can be query of lincRNA name, SNP identifier, or transcription factor name. SNP@lincTFBS provides significant advances in identification of disease-associated lincRNA variants and improved convenience to interpret the discrepant expression of lincRNAs. The SNP@lincTFBS database is available at http://bioinfo.hrbmu.edu.cn/SNP_lincTFBS.

LincSNP: a database of linking disease-associated SNPs to human large intergenic non-coding RNAs.

BACKGROUND: Genome-wide association studies (GWAS) have successfully identified a large number of single nucleotide polymorphisms (SNPs) that are associated with a wide range of human diseases. However, many of these disease-associated SNPs are located in non-coding regions and have remained largely unexplained. Recent findings indicate that disease-associated SNPs in human large intergenic non-coding RNA (lincRNA) may lead to susceptibility to diseases through their effects on lincRNA expression. There is, therefore, a need to specifically record these SNPs and annotate them as potential candidates for disease. DESCRIPTION: We have built LincSNP, an integrated database, to identify and annotate disease-associated SNPs in human lincRNAs. The current release of LincSNP contains approximately 140,000 disease-associated SNPs (or linkage disequilibrium SNPs), which can be mapped to around 5,000 human lincRNAs, together with their comprehensive functional annotations. The database also contains annotated, experimentally supported SNP-lincRNA-disease associations and disease-associated lincRNAs. It provides flexible search options for data extraction and searches can be performed by disease/phenotype name, SNP ID, lincRNA name and chromosome region. In addition, we provide users with a link to download all the data from LincSNP and have developed a web interface for the submission of novel identified SNP-lincRNA-disease associations. CONCLUSIONS: The LincSNP database aims to integrate disease-associated SNPs and human lincRNAs, which will be an important resource for the investigation of the functions and mechanisms of lincRNAs in human disease. The database is available at http://bioinfo.hrbmu.edu.cn/LincSNP.

A global map for dissecting phenotypic variants in human lincRNAs.

Large intergenic noncoding RNAs (lincRNAs) are emerging as key factors of multiple cellular processes. Cumulative evidence has linked lincRNA polymorphisms to diverse diseases. However, the global properties of lincRNA polymorphisms and their implications for human disease remain largely unknown. Here we performed a systematic analysis of naturally occurring variants in human lincRNAs, with a particular focus on lincRNA polymorphism as novel risk factor of disease etiology. We found that lincRNAs exhibited a relatively low level of polymorphisms, and low single-nucleotide polymorphism (SNP) density lincRNAs might have a broad range of functions. We also found that some polymorphisms in evolutionarily conserved regions of lincRNAs had significant effects on predicted RNA secondary structures, indicating their potential contribution to diseases. We mapped currently available phenotype-associated SNPs to lincRNAs and found that lincRNAs were associated with a wide range of human diseases. Some lincRNAs could be responsible for particular diseases. Our results provided not only a global perspective on genetic variants in human lincRNAs but also novel insights into the function and etiology of lincRNA. All the data in this study can be accessed and retrieved freely via a web server at http://bioinfo.hrbmu.edu.cn/lincPoly.

mirTarPri: improved prioritization of microRNA targets through incorporation of functional genomics data.

MicroRNAs (miRNAs) are a class of small (19-25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3'untranslated regions (3'UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.