Stable bulged G-quadruplexes in the human genome: identification, experimental validation and functionalization.

DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478 263 pG4-BS regions that do not overlap 'canonical' G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content in R-loops and their flanks, longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability in vitro with 91.5% accuracy. Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B.

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons.

The stochastic dynamics and regulatory mechanisms that govern differentiation of individual human neural precursor cells (NPC) into mature neurons are currently not fully understood. Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC subtypes and critical developmental stages of alternative lineage specifications. This study comprises an unsupervised, high-resolution strategy for identifying cell developmental bifurcations, tracking the stochastic transcript kinetics of the subpopulations, elucidating regulatory networks, and finding key regulators. Our data revealed the bifurcation and developmental tracks of the two NPC subpopulations, and we captured an early (24 h) transition phase that leads to alternative neuronal specifications. The consequent up-regulation and down-regulation of stage- and subpopulation-specific gene groups during the course of maturation revealed biological insights with regard to key regulatory transcription factors and lincRNAs that control cellular programs in the identified neuronal subpopulations.

Toward predictive R-loop computational biology: genome-scale prediction of R-loops reveals their association with complex promoter structures, G-quadruplexes and transcriptionally active enhancers.

R-loops are three-stranded RNA:DNA hybrid structures essential for many normal and pathobiological processes. Previously, we generated a quantitative R-loop forming sequence (RLFS) model, quantitative model of R-loop-forming sequences (QmRLFS) and predicted ∼660 000 RLFSs; most of them located in genes and gene-flanking regions, G-rich regions and disease-associated genomic loci in the human genome. Here, we conducted a comprehensive comparative analysis of these RLFSs using experimental data and demonstrated the high performance of QmRLFS predictions on the nucleotide and genome scales. The preferential co-localization of RLFS with promoters, U1 splice sites, gene ends, enhancers and non-B DNA structures, such as G-quadruplexes, provides evidence for the mechanical linkage between DNA tertiary structures, transcription initiation and R-loops in critical regulatory genome regions. We introduced and characterized an abundant class of reverse-forward RLFS clusters highly enriched in non-B DNA structures, which localized to promoters, gene ends and enhancers. The RLFS co-localization with promoters and transcriptionally active enhancers suggested new models for in cis and in trans regulation by RNA:DNA hybrids of transcription initiation and formation of 3D-chromatin loops. Overall, this study provides a rationale for the discovery and characterization of the non-B DNA regulatory structures involved in the formation of the RNA:DNA interactome as the basis for an emerging quantitative R-loop biology and pathobiology.

Defining hypogonadism in male partners of couples with unexplained infertility.

INTRODUCTION: Men with unexplained infertility (UI) should undergo an initial hormonal evaluation including serum FSH and total testosterone (TT). Unfortunately, there is no consensus regarding which TT cut point should be used to define hypogonadism in such men. To determine the best definition for hypogonadism, three different, literature-based TT cut points were used to assess associations between TT and semen parameters. The hypothesis was that the lowest TT cut point would associate with poorest sperm parameters. MATERIALS AND METHODS: We performed an IRB-approved retrospective chart review of 247 consecutive males presenting for evaluation of male factor infertility. After exclusions, basic statistics and correlation analysis of semen analysis parameters, TT, age, and body mass index (BMI) were evaluated on 128 men (age 34+/-33.5) categorized by three different TT cut points: 65 males were hypogonadal according to a TT cutoff of < 264; 16 with a cutoff of 264-300; 44 with a cutoff of 301-400; and 42 with a TT over 400 ng/dL. Basic statistics, one-way ANOVA and Levene comparative analysis were performed. Besides a negative correlation between TT and BMI, there was no significant association between the three TT literature-based cut points and the other studied parameters. These findings were further supported by multiple comparison analyses. RESULTS: For men with UI, regardless of how hypogonadism was defined, no relationship between semen parameters and TT was found. CONCLUSION: Conventional, TT-based definitions of male hypogonadism in the setting of UI need to be clarified. Clinically relevant, accurate and reproducible multivariable biomarkers need to be investigated to further advance best practices for treating men with UI.

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification.

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated ß-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.

Abi1 loss drives prostate tumorigenesis through activation of EMT and non-canonical WNT signaling.

BACKGROUND: Prostate cancer development involves various mechanisms, which are poorly understood but pointing to epithelial mesenchymal transition (EMT) as the key mechanism in progression to metastatic disease. ABI1, a member of WAVE complex and actin cytoskeleton regulator and adaptor protein, acts as tumor suppressor in prostate cancer but the role of ABI1 in EMT is not clear. METHODS: To investigate the molecular mechanism by which loss of ABI1 contributes to tumor progression, we disrupted the ABI1 gene in the benign prostate epithelial RWPE-1 cell line and determined its phenotype. Levels of ABI1 expression in prostate organoid tumor cell lines was evaluated by Western blotting and RNA sequencing. ABI1 expression and its association with prostate tumor grade was evaluated in a TMA cohort of 505 patients and metastatic cell lines. RESULTS: Low ABI1 expression is associated with biochemical recurrence, metastasis and death (p = 0.038). Moreover, ABI1 expression was significantly decreased in Gleason pattern 5 vs. pattern 4 (p = 0.0025) and 3 (p = 0.0012), indicating an association between low ABI1 expression and highly invasive prostate tumors. Disruption of ABI1 gene in RWPE-1 cell line resulted in gain of an invasive phenotype, which was characterized by a loss of cell-cell adhesion markers and increased migratory ability of RWPE-1 spheroids. Through RNA sequencing and protein expression analysis, we discovered that ABI1 loss leads to activation of non-canonical WNT signaling and EMT pathways, which are rescued by re-expression of ABI1. Furthermore, an increase in STAT3 phosphorylation upon ABI1 inactivation and the evidence of a high-affinity interaction between the FYN SH2 domain and ABI1 pY421 support a model in which ABI1 acts as a gatekeeper of non-canonical WNT-EMT pathway activation downstream of the FZD2 receptor. CONCLUSIONS: ABI1 controls prostate tumor progression and epithelial plasticity through regulation of EMT-WNT pathway. Here we discovered that ABI1 inhibits EMT through suppressing FYN-STAT3 activation downstream from non-canonical WNT signaling thus providing a novel mechanism of prostate tumor suppression.

R-loopDB: a database for R-loop forming sequences (RLFS) and R-loops.

R-loopDB (http://rloop.bii.a-star.edu.sg) was originally constructed as a collection of computationally predicted R-loop forming sequences (RLFSs) in the human genic regions. The renewed R-loopDB provides updates, improvements and new options, including access to recent experimental data. It includes genome-scale prediction of RLFSs for humans, six other animals and yeast. Using the extended quantitative model of RLFSs (QmRLFS), we significantly increased the number of RLFSs predicted in the human genes and identified RLFSs in other organism genomes. R-loopDB allows searching of RLFSs in the genes and in the 2 kb upstream and downstream flanking sequences of any gene. R-loopDB exploits the Ensembl gene annotation system, providing users with chromosome coordinates, sequences, gene and genomic data of the 1 565 795 RLFSs distributed in 121 056 genic or proximal gene regions of the covered organisms. It provides a comprehensive annotation of Ensembl RLFS-positive genes including 93 454 protein coding genes, 12 480 long non-coding RNA and 7 568 small non-coding RNA genes and 7 554 pseudogenes. Using new interface and genome viewers of R-loopDB, users can search the gene(s) in multiple species with keywords in a single query. R-loopDB provides tools to carry out comparative evolution and genome-scale analyses in R-loop biology.

Gene expression profile analysis of aortic vascular smooth muscle cells reveals upregulation of cadherin genes in myocardial infarction patients.

Myocardial infarction (MI) induced by acute coronary arterial occlusion is usually secondary to atherosclerotic plaque rupture. Dysregulated response of vascular smooth muscle cells (VSMCs) in atherosclerotic plaques may promote plaque rupture. Cadherins (CDHs) form adherens junctions and are known stabilizers of atherosclerotic plaques. To date, the expression patterns of cadherin have not been well investigated in MI aortic VSMCs. We aimed to investigate the expression of cadherin genes in the aortic wall of patients with and without MI. Laser capture microdissected VSMCs were obtained from aortic tissue samples of patients undergoing coronary artery bypass graft surgery. Integrative bioinformatic analysis of the microarray profiles of the VSMCs revealed that MI is discriminated at the whole transcriptome level by hundreds of differentially expressed genes, including genes involved in cell adhesion, of which the cadherin superfamily genes were among the top structural category. Eleven significantly deregulated candidates of the cadherin superfamily were chosen and formed a new classifier that collectively discriminated MI vs. non-MI with ~95% accuracy. Significance validation was performed with an independent cohort by quantitative RT-quantitative PCR, confirming overexpression of CDH2, CDH12, PCDH17, and PCDH18 in MI VSMCs. The dysregulation of these cadherin superfamily genes might be related to an MI-induced remote effect on aortic wall VSMCs and to imbalances in signaling pathways and myocardial repair mechanisms. Although pathophysiological significance of our findings requires functional studies, mRNA upregulation of the identified cadherin superfamily members in VSMCs might be associated with the progression of atherosclerosis and angiogenesis activation in MI.

Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets.

BACKGROUND: High-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease aggressiveness and clinical outcomes. However, the functional relationships between microRNAs, the common pathways controlled by the microRNAs and their prognostic and therapeutic significance remain poorly understood. METHODS: We investigated the gene expression patterns of microRNAs in the tumors of 582 HG-SOC patients to identify prognosis signatures and pathways controlled by tumor miRNAs. We developed a variable selection and prognostic method, which performs a robust selection of small-sized subsets of the predictive features (e.g., expressed microRNAs) that collectively serves as the biomarkers of cancer risk and progression stratification system, interconnecting these features with common cancer-related pathways. RESULTS: Across different cohorts, our meta-analysis revealed two robust and unbiased miRNA-based prognostic classifiers. Each classifier reproducibly discriminates HG-SOC patients into high-confidence low-, intermediate- or high-prognostic risk subgroups with essentially different 5-year overall survival rates of 51.6-85%, 20-38.1%, and 0-10%, respectively. Significant correlations of the risk subgroup's stratification with chemotherapy treatment response were observed. We predicted specific target genes involved in nine cancer-related and two oocyte maturation pathways (neurotrophin and progesterone-mediated oocyte maturation), where each gene can be controlled by more than one miRNA species of the distinct miRNA HG-SOC prognostic classifiers. CONCLUSIONS: We identified robust and reproducible miRNA-based prognostic subsets of the of HG-SOC classifiers. The miRNAs of these classifiers could control nine oncogenic and two developmental pathways, highlighting common underlying pathologic mechanisms and perspective targets for the further development of a personalized prognosis assay(s) and the development of miRNA-interconnected pathway-centric and multi-agent therapeutic intervention.

QmRLFS-finder: a model, web server and stand-alone tool for prediction and analysis of R-loop forming sequences.

The possible formation of three-stranded RNA and DNA hybrid structures (R-loops) in thousands of functionally important guanine-rich genic and inter-genic regions could suggest their involvement in transcriptional regulation and even development of diseases. Here, we introduce the first freely available R-loop prediction program called Quantitative Model of R-loop Forming Sequence (RLFS) finder (QmRLFS-finder), which predicts RLFSs in nucleic acid sequences based on experimentally supported structural models of RLFSs. QmRLFS-finder operates via a web server or a stand-alone command line tool. This tool identifies and visualizes RLFS coordinates from any natural or artificial DNA or RNA input sequences and creates standards-compliant output files for further annotation and analysis. QmRLFS-finder demonstrates highly accurate predictions of the detected RLFSs, proposing new perspective to further discoveries in R-loop biology, biotechnology and molecular therapy. QmRLFS-finder is freely available at http://rloop.bii.a-star.edu.sg/?pg=qmrlfs-finder.

The TTSMI database: a catalog of triplex target DNA sites associated with genes and regulatory elements in the human genome.

A triplex target DNA site (TTS), a stretch of DNA that is composed of polypurines, is able to form a triple-helix (triplex) structure with triplex-forming oligonucleotides (TFOs) and is able to influence the site-specific modulation of gene expression and/or the modification of genomic DNA. The co-localization of a genomic TTS with gene regulatory signals and functional genome structures suggests that TFOs could potentially be exploited in antigene strategies for the therapy of cancers and other genetic diseases. Here, we present the TTS Mapping and Integration (TTSMI; http://ttsmi.bii.a-star.edu.sg) database, which provides a catalog of unique TTS locations in the human genome and tools for analyzing the co-localization of TTSs with genomic regulatory sequences and signals that were identified using next-generation sequencing techniques and/or predicted by computational models. TTSMI was designed as a user-friendly tool that facilitates (i) fast searching/filtering of TTSs using several search terms and criteria associated with sequence stability and specificity, (ii) interactive filtering of TTSs that co-localize with gene regulatory signals and non-B DNA structures, (iii) exploration of dynamic combinations of the biological signals of specific TTSs and (iv) visualization of a TTS simultaneously with diverse annotation tracks via the UCSC genome browser.

Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers.

BACKGROUND: The lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction. METHODS: We define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS. RESULTS: We identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs. CONCLUSIONS: Our method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development.

Expression and clinical role of chemoresponse-associated genes in ovarian serous carcinoma.

OBJECTIVE: To validate our earlier observation that 11 chemoresistance-associated mRNAs are molecular markers of poor overall survival in ovarian serous carcinoma. METHODS: Ovarian serous carcinomas (n=112) and solid metastases (n=63; total=175) were analyzed for mRNA expression of APC, BAG3, EGFR, S100A10, ITGAE, MAPK3, TAP1, BNIP3, MMP9, FASLG and GPX3 using quantitative real-time PCR. mRNA expression was studied for association with clinicopathologic parameters and survival. Tumor heterogeneity was assessed in 20 cases with >1 specimen per patient. APC, BAG3, S100A10 and ERK1 protein expression by immunohistochemistry was analyzed in 58 specimens (38 primary carcinomas, 20 metastases). RESULTS: BAG3 (p=0.013), TAP1 (p=0.014), BNIP3 (p<0.001) and MMP9 (p=0.036) were overexpressed in primary tumors, whereas S100A10 (p=0.027) and FASLG (p=0.006) were overexpressed in metastases. Analysis of patient-matched primary carcinomas and metastases showed overexpression of APC (p=0.022), MAPK3 (p=0.002) and BNIP3 (p=0.004) in the former. In primary carcinomas, higher APC (p=0.003) and MAPK3 (p=0.005) levels were related to less favorable chemoresponse. Higher S100A10 (p=0.029) and MAPK3 (p=0.041) levels were related to primary chemoresistance. Higher BAG3 (p=0.026) and APC (p=0.046) levels in primary carcinomas were significantly related to poor overall survival in univariate, though not in multivariate survival analysis. S100A10 protein expression was related to poor chemoresponse (p=0.002) and shorter overall (p=0.005) and progression-free (p<0.001) survival, the latter finding retained in multivariate analysis (p=0.035). CONCLUSIONS: Our data provide evidence of heterogeneity in ovarian serous carcinoma and identify APC, MAPK3, BAG3 and S100A10 as potential biomarkers of poor chemotherapy response and/or poor outcome in this cancer.

Ecotopic viral integration site 1 (EVI1) regulates multiple cellular processes important for cancer and is a synergistic partner for FOS protein in invasive tumors.

Ecotropic viral integration site 1 (EVI1) is an oncogenic dual domain zinc finger transcription factor that plays an essential role in the regulation of hematopoietic stem cell renewal, and its overexpression in myeloid leukemia and epithelial cancers is associated with poor patient survival. Despite the discovery of EVI1 in 1988 and its emerging role as a dominant oncogene in various types of cancer, few EVI1 target genes are known. This lack of knowledge has precluded a clear understanding of exactly how EVI1 contributes to cancer. Using a combination of ChIP-Seq and microarray studies in human ovarian carcinoma cells, we show that the two zinc finger domains of EVI1 bind to DNA independently and regulate different sets of target genes. Strikingly, an enriched fraction of EVI1 target genes are cancer genes or genes associated with cancer. We also show that more than 25% of EVI1-occupied genes contain linked EVI1 and activator protein (AP)1 DNA binding sites, and this finding provides evidence for a synergistic cooperative interaction between EVI1 and the AP1 family member FOS in the regulation of cell adhesion, proliferation, and colony formation. An increased number of dual EVI1/AP1 target genes are also differentially regulated in late-stage ovarian carcinomas, further confirming the importance of the functional cooperation between EVI1 and FOS. Collectively, our data indicate that EVI1 is a multipurpose transcription factor that synergizes with FOS in invasive tumors.

The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells.

Oct4 and Nanog are transcription factors required to maintain the pluripotency and self-renewal of embryonic stem (ES) cells. Using the chromatin immunoprecipitation paired-end ditags method, we mapped the binding sites of these factors in the mouse ES cell genome. We identified 1,083 and 3,006 high-confidence binding sites for Oct4 and Nanog, respectively. Comparative location analyses indicated that Oct4 and Nanog overlap substantially in their targets, and they are bound to genes in different configurations. Using de novo motif discovery algorithms, we defined the cis-acting elements mediating their respective binding to genomic sites. By integrating RNA interference-mediated depletion of Oct4 and Nanog with microarray expression profiling, we demonstrated that these factors can activate or suppress transcription. We further showed that common core downstream targets are important to keep ES cells from differentiating. The emerging picture is one in which Oct4 and Nanog control a cascade of pathways that are intricately connected to govern pluripotency, self-renewal, genome surveillance and cell fate determination.

HOXD-AS1 is a novel lncRNA encoded in HOXD cluster and a marker of neuroblastoma progression revealed via integrative analysis of noncoding transcriptome.

BACKGROUND: Long noncoding RNAs (lncRNAs) constitute a major, but poorly characterized part of human transcriptome. Recent evidence indicates that many lncRNAs are involved in cancer and can be used as predictive and prognostic biomarkers. Significant fraction of lncRNAs is represented on widely used microarray platforms, however they have usually been ignored in cancer studies. RESULTS: We developed a computational pipeline to annotate lncRNAs on popular Affymetrix U133 microarrays, creating a resource allowing measurement of expression of 1581 lncRNAs. This resource can be utilized to interrogate existing microarray datasets for various lncRNA studies. We found that these lncRNAs fall into three distinct classes according to their statistical distribution by length. Remarkably, these three classes of lncRNAs were co-localized with protein coding genes exhibiting distinct gene ontology groups. This annotation was applied to microarray analysis which identified a 159 lncRNA signature that discriminates between localized and metastatic stages of neuroblastoma. Analysis of an independent patient cohort revealed that this signature differentiates also relapsing from non-relapsing primary tumors. This is the first example of the signature developed via the analysis of expression of lncRNAs solely. One of these lncRNAs, termed HOXD-AS1, is encoded in HOXD cluster. HOXD-AS1 is evolutionary conserved among hominids and has all bona fide features of a gene. Studying retinoid acid (RA) response of SH-SY5Y cell line, a model of human metastatic neuroblastoma, we found that HOXD-AS1 is a subject to morphogenic regulation, is activated by PI3K/Akt pathway and itself is involved in control of RA-induced cell differentiation. Knock-down experiments revealed that HOXD-AS1 controls expression levels of clinically significant protein-coding genes involved in angiogenesis and inflammation, the hallmarks of metastatic cancer. CONCLUSIONS: Our findings greatly extend the number of noncoding RNAs functionally implicated in tumor development and patient treatment and highlight their role as potential prognostic biomarkers of neuroblastomas.

Meta-analysis of transcriptome reveals let-7b as an unfavorable prognostic biomarker and predicts molecular and clinical subclasses in high-grade serous ovarian carcinoma.

High-grade serous ovarian carcinoma (HG-SOC) is a heterogeneous, poorly classified, lethal disease that frequently exhibits altered expressions of microRNAs. Let-7 family members are often reported as tumor suppressors; nonetheless, clinicopathological functions and prognostic values of individual let-7 family members have not been addressed in HG-SOC. In our work, we performed an integrative study to investigate the potential roles, clinicopathological functions and prognostic values of let-7 miRNA family in HG-SOC. Using microarray and clinical data of 1,170 HG-SOC patients, we developed novel survival prediction and system biology methods to analyze prognostic values and functional associations of let-7 miRNAs with global transcriptome and clinicopathological factors. We demonstrated that individual let-7 members exhibit diverse evolutionary history and distinct regulatory characteristics. Statistical tests and network analysis suggest that let-7b could act as a global synergistic interactor and master regulator controlling hundreds of protein-coding genes. The elevated expression of let-7b is associated with poor survival rates, which suggests an unfavorable role of let-7b in treatment response for HG-SOC patients. A novel let-7b-defined 36-gene prognostic survival signature outperforms many clinicopathological parameters, and stratifies HG-SOC patients into three high-confidence, reproducible, clinical subclasses: low-, intermediate- and high-risk, with 5-year overall survival rates of 56-71%, 12-29% and 0-10%, respectively. Furthermore, the high-risk and low-risk subclasses exhibit strong mesenchymal and proliferative tumor phenotypes concordant with resistance and sensitivity to primary chemotherapy. Our results have led to identification of promising prognostic markers of HG-SOC, which could provide a rationale for genetic-based stratification of patients and optimization of treatment regimes.

In the pursuit of complexity: systems medicine in cancer biology.

Adler et al., in a paper appearing in Nature Genetics, exploited the intersect of genetic information from expression profiles with that from array comparative genomic hybridization in human breast cancers to identify genes that may induce the transcription of the prognostic "wound response" expression signature. The amplification of two genes, MYC and CSN5, appeared to be correlated with the wound response cassette. In vitro validation showed that the wound signature could be induced in MCF10A cells only when MYC and CSN5 were coexpressed. This work shows that the intersect analysis of gene amplification and transcriptional expression on a genome-wide scale can uncover complex conditional interactions embedded in the systems map of transcriptional regulation.

p53-Independent regulation of p21Waf1/Cip1 expression and senescence by PRMT6.

p21 is a potent cyclin-dependent kinase inhibitor that plays a role in promoting G1 cell cycle arrest and cellular senescence. Consistent with this role, p21 is a downstream target of several tumour suppressors and oncogenes, and it is downregulated in the majority of tumours, including breast cancer. Here, we report that protein arginine methyltransferase 6 (PRMT6), a type I PRMT known to act as a transcriptional cofactor, directly represses the p21 promoter. PRMT6 knock-down (KD) results in a p21 derepression in breast cancer cells, which is p53-independent, and leads to cell cycle arrest, cellular senescence and reduced growth in soft agar assays and in severe combined immunodeficiency (SCID) mice for all the cancer lines examined. We finally show that bypassing the p21-mediated arrest rescues PRMT6 KD cells from senescence, and it restores their ability to grow on soft agar. We conclude that PRMT6 acts as an oncogene in breast cancer cells, promoting growth and preventing senescence, making it an attractive target for cancer therapy.