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

Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women.

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants

Molecular signatures identified by integrating gene expression and methylation in non-seminoma and seminoma of testicular germ cell tumours

Testicular germ cell tumours (TGCTs) are the most common cancer in young male adults (aged 15 to 40). Unlike most other cancer types, identification of molecular signatures in TGCT has rarely reported. In this study, we developed a novel integrative analysis framework to identify co-methylated and co-expressed genes [mRNAs and microRNAs (miRNAs)] modules in two TGCT subtypes: non-seminoma (NSE) and seminoma (SE). We first integrated DNA methylation and mRNA/miRNA expression data and then used a statistical method, CoMEx (Combined score of DNA Methylation and Expression), to assess differentially expressed and methylated (DEM) genes/miRNAs. Next, we identified co-methylation and co-expression modules by applying WGCNA (Weighted Gene Correlation Network Analysis) tool to these DEM genes/miRNAs. The module with the highest average Pearson’s Correlation Coefficient (PCC) after considering all pair-wise molecules (genes/miRNAs) included 91 molecules. By integrating both transcription factor and miRNA regulations, we constructed subtype-specific regulatory networks for NSE and SE. We identified four hub miRNAs (miR-182-5p, miR-520b, miR-520c-3p, and miR-7-5p), two hub TFs (MYC and SP1), and two genes (RECK and TERT) in the NSE-specific regulatory network, and two hub miRNAs (miR-182-5p and miR-338-3p), five hub TFs (ETS1, HIF1A, HNF1A, MYC, and SP1), and three hub genes (CDH1, CXCR4, and SNAI1) in the SE-specific regulatory network. miRNA (miR-182-5p) and two TFs (MYC and SP1) were common hubs of NSE and SE. We further examined pathways enriched in these subtype-specific networks. Our study provides a comprehensive view of the molecular signatures and co-regulation in two TGCT subtypes

Direct incorporating small amount of Ce (III) in Cu-SAPO-18 catalysts for enhanced low-temperature NH3-SCR activity: Influence on Cu distribution and Si coordination

International audienc

Network-based identification of critical regulators as putative drivers of human cleft lip

Abstract Background Cleft lip (CL) is one of the most common congenital birth defects with complex etiology. While genome-wide association studies (GWAS) have made significant advances in our understanding of mutations and their related genes with potential involvement in the etiology of CL, it remains unknown how these genes are functionally regulated and interact with each other in lip development. Currently, identifying the disease-causing genes in human CL is urgently needed. So far, the causative CL genes have been largely undiscovered, making it challenging to design experiments to validate the functional influence of the mutations identified from large genomic studies such as CL GWAS. Results Transcription factors (TFs) and microRNAs (miRNAs) are two important regulators in cellular system. In this study, we aimed to investigate the genetic interactions among TFs, miRNAs and the CL genes curated from the previous studies. We constructed miRNA-TF co-regulatory networks, from which the critical regulators as putative drivers in CL were examined. Based on the constructed networks, we identified ten critical hub genes with prior evidence in CL. Furthermore, the analysis of partitioned regulatory modules highlighted a number of biological processes involved in the pathology of CL, including a novel pathway “Signaling pathway regulating pluripotency of stem cells”. Our subnetwork analysis pinpointed two candidate miRNAs, hsa-mir-27b and hsa-mir-497, activating the Wnt pathway that was associated with CL. Our results were supported by an independent gene expression dataset in CL. Conclusions This study represents the first regulatory network analysis of CL genes. Our work presents a global view of the CL regulatory network and a novel approach on investigating critical miRNAs, TFs and genes via combinatory regulatory networks in craniofacial development. The top genes and miRNAs will be important candidates for future experimental validation of their functions in CL