Key nodes of a microRNA network associated with the integrated mesenchymal subtype of high-grade serous ovarian cancer

Metastasis is the main cause of cancer mortality. One of the initiating events of cancer metastasis of epithelial tumors is epithelial-to-mesenchymal transition (EMT), during which cells dedifferentiate from a relatively rigid cell structure/morphology to a flexible and changeable structure/morphology often associated with mesenchymal cells. The presence of EMT in human epithelial tumors is reflected by the increased expression of genes and levels of proteins that are preferentially present in mesenchymal cells. The combined presence of these genes forms the basis of mesenchymal gene signatures, which are the foundation for classifying a mesenchymal subtype of tumors. Indeed, tumor classification schemes that use clustering analysis of large genomic characterizations, like The Cancer Genome Atlas (TCGA), have defined mesenchymal subtype in a number of cancer types, such as high-grade serous ovarian cancer and glioblastoma. However, recent analyses have shown that gene expression-based classifications of mesenchymal subtypes often do not associate with poor survival. This “paradox” can be ameliorated using integrated analysis that combines multiple data types. We recently found that integrating mRNA and microRNA (miRNA) data revealed an integrated mesenchymal subtype that is consistently associated with poor survival in multiple cohorts of patients with serous ovarian cancer. This network consists of 8 major miRNAs and 214 mRNAs. Among the 8 miRNAs, 4 are known to be regulators of EMT. This review provides a summary of these 8 miRNAs, which were associated with the integrated mesenchymal subtype of serous ovarian cancer

On the Reproducibility of TCGA Ovarian Cancer MicroRNA Profiles

Dysregulated microRNA (miRNA) expression is a well-established feature of human cancer. However, the role of specific miRNAs in determining cancer outcomes remains unclear. Using Level 3 expression data from the Cancer Genome Atlas (TCGA), we identified 61 miRNAs that are associated with overall survival in 469 ovarian cancers profiled by microarray (p<0.01). We also identified 12 miRNAs that are associated with survival when miRNAs were profiled in the same specimens using Next Generation Sequencing (miRNA-Seq) (p<0.01). Surprisingly, only 1 miRNA transcript is associated with ovarian cancer survival in both datasets. Our analyses indicate that this discrepancy is due to the fact that miRNA levels reported by the two platforms correlate poorly, even after correcting for potential issues inherent to signal detection algorithms. Further investigation is warranted

Network-based stratification of tumor mutations.

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence

MicroRNA and transcription factor co-regulatory networks and subtype classification of seminoma and non-seminoma in testicular germ cell tumors

Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes

The Challenges and Opportunities of lncRNAs in Ovarian Cancer Research and Clinical Use

[Abstract] Ovarian cancer is one of the most lethal gynecological malignancies worldwide because it tends to be detected late, when the disease has already spread, and prognosis is poor. In this review we aim to highlight the importance of long non-coding RNAs (lncRNAs) in diagnosis, prognosis and treatment choice, to make progress towards increasingly personalized medicine in this malignancy. We review the effects of lncRNAs associated with ovarian cancer in the context of cancer hallmarks. We also discuss the molecular mechanisms by which lncRNAs become involved in cellular physiology; the onset, development and progression of ovarian cancer; and lncRNAs’ regulatory mechanisms at the transcriptional, post-transcriptional and post-translational stages of gene expression. Finally, we compile a series of online resources useful for the study of lncRNAs, especially in the context of ovarian cancer. Future work required in the field is also discussed along with some concluding remarks.This work was funded by Plan Estatal I + D + I by the Instituto de Salud Carlos III (ISCIII, Spain) under grant agreement AES number PI18/01714, cofounded by Fondo Europeo de Desarrollo Regional-FEDER (The European Regional Development Fund-ERDF) “A way of Making Europe,” and by Xunta de Galicia (Consolidación Grupos Referencia Competitiva contract number ED431C 2016-012). M.S.M. was funded by a predoctoral fellowship from FPU-2018 (Spain)Xunta de Galicia; ED431C 2016-01

Genome-Wide Survey of MicroRNA - Transcription Factor Feed-Forward Regulatory Circuits in Human

In this work, we describe a computational framework for the genome-wide identification and characterization of mixed transcriptional/post-transcriptional regulatory circuits in humans. We concentrated in particular on feed-forward loops (FFL), in which a master transcription factor regulates a microRNA, and together with it, a set of joint target protein coding genes. The circuits were assembled with a two step procedure. We first constructed separately the transcriptional and post-transcriptional components of the human regulatory network by looking for conserved over-represented motifs in human and mouse promoters, and 3'-UTRs. Then, we combined the two subnetworks looking for mixed feed-forward regulatory interactions, finding a total of 638 putative (merged) FFLs. In order to investigate their biological relevance, we filtered these circuits using three selection criteria: (I) GeneOntology enrichment among the joint targets of the FFL, (II) independent computational evidence for the regulatory interactions of the FFL, extracted from external databases, and (III) relevance of the FFL in cancer. Most of the selected FFLs seem to be involved in various aspects of organism development and differentiation. We finally discuss a few of the most interesting cases in detail.Comment: 51 pages, 5 figures, 4 tables. Supporting information included. Accepted for publication in Molecular BioSystem

Coordinated actions of microRNAs with other epigenetic factors regulate skeletal muscle development and adaptation

Epigenetics plays a pivotal role in regulating gene expression in development, in response to cellular stress or in disease states, in virtually all cell types. MicroRNAs (miRNAs) are short, non-coding RNA molecules that mediate RNA silencing and regulate gene expression. miRNAs were discovered in 1993 and have been extensively studied ever since. They can be expressed in a tissue-specific manner and play a crucial role in tissue development and many biological processes. miRNAs are responsible for changes in the cell epigenome because of their ability to modulate gene expression post-transcriptionally. Recently, numerous studies have shown that miRNAs and other epigenetic factors can regulate each other or cooperate in regulating several biological processes. On the one hand, the expression of some miRNAs is silenced by DNA methylation, and histone modifications have been demonstrated to modulate miRNA expression in many cell types or disease states. On the other hand, miRNAs can directly target epigenetic factors, such as DNA methyltransferases or histone deacetylases, thus regulating chromatin structure. Moreover, several studies have reported coordinated actions between miRNAs and other epigenetic mechanisms to reinforce the regulation of gene expression. This paper reviews multiple interactions between miRNAs and epigenetic factors in skeletal muscle development and in response to stimuli or disease

Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles

Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients