Nutritional restriction during the peri-conceptional period alters the myometrial transcriptome during the peri-implantation period

This study hypothesized that female peri-conceptional undernutrition evokes transcriptomic alterations in the pig myometrium during the peri-implantation period. Myometrium was collected on days 15–16 of pregnancy from pigs fed a normal- (n = 4) or restricted-diet (n = 4) from conception until day 9th of pregnancy, and the transcriptomic profiles of the tissue were compared using Porcine (V2) Expression Microarrays 4 × 44 K. In restricted diet-fed pigs, 1021 differentially expressed genes (DEGs) with fold change ≥ 1.5, P ≤ 0.05 were revealed, and 708 of them were up-regulated. Based on the count score, the top within GOs was GO cellular components “extracellular exosome”, and the top KEGG pathway was the metabolic pathway. Ten selected DEGs, i.e. hydroxysteroid (17β) dehydrogenase 8, cyclooxygenase 2, prostaglandin F receptor, progesterone receptor membrane component 1, progesterone receptor membrane component 2, annexin A2, homeobox A10, S-phase cyclin A-associated protein in the ER, SRC proto-oncogene, non-receptor tyrosine kinase, and proliferating cell nuclear antigen were conducted through qPCR to validate microarray data. In conclusion, dietary restriction during the peri-conceptional period causes alterations in the expression of genes encoding proteins involved i.a. in the endocrine activity of the myometrium, embryo-maternal interactions, and mechanisms regulating cell cycle and proliferation.

The Identification of MiRNA and MRNA Expression Profiles Associated with Pediatric Atypical Teratoid/rhabdoid Tumor

Background: Atypical teratoid/rhabdoid tumor (AT/RT) is a malignant pediatric tumor of the central nervous system (CNS) with high recurrence and low survival rates that is often misdiagnosed. MicroRNAs (miRNAs) are involved in the tumorigenesis of numerous pediatric cancers, but their roles in AT/RT remain unclear.Methods: In this study, we used miRNA sequencing and gene expression microarrays from patient tissue to study both the miRNAome and transcriptome traits of AT/RT.Results: Our findings demonstrate that 5 miRNAs were up-regulated, 16 miRNAs were down-regulated, 179 mRNAs were up-regulated and 402 mRNAs were down-regulated in AT/RT. The expressions of hsa-miR-17-5p and MAP7 mRNA showed the most significant differences in AT/RT tissues as assayed by qPCR, and analyses using the miRTarBase database identified MAP7 mRNA as a target gene of hsa-miR-17-5p. Conclusions: Our findings suggest that the dysregulation of hsa-miR-17-5p may be a pivotal event in AT/RT and MAP7 miRNAs that may represent potential therapeutic targets and diagnostic biomarkers.

The Molecular Landscape Influencing Prognoses of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the major increasing lethal malignancies of the gynecological tract, mostly due to delayed diagnosis and chemoresistance, as well as its very heterogeneous genetic makeup. Application of high-throughput molecular technologies, gene expression microarrays, and powerful preclinical models has provided a deeper understanding of the molecular characteristics of EOC. Therefore, molecular markers have become a potent tool in EOC management, including prediction of aggressiveness, prognosis, and recurrence, and identification of novel therapeutic targets. In addition, biomarkers derived from genomic/epigenomic alterations (e.g., gene mutations, copy number aberrations, and DNA methylation) enable targeted treatment of affected signaling pathways in advanced EOC, thereby improving the effectiveness of traditional treatments. This review outlines the molecular landscape and discusses the impacts of biomarkers on the detection, diagnosis, surveillance, and therapeutic targets of EOC. These findings focus on the necessity to translate these potential biomarkers into clinical practice.

The Construction and Comprehensive Analysis of Inflammation-Related ceRNA Networks and Tissue-Infiltrating Immune Cells in Ulcerative Progression

Ulcerative colitis (UC) is a common disease with great variability in severity, with a high recurrence rate and heavy disease burden. In recent years, the different biological functions of competing endogenous RNA (ceRNA) networks of long noncoding RNAs (lncRNAs) and microRNAs (miRs) have aroused wide concerns, the ceRNA network of ulcerative colitis (UC) may have potential research value, and these expressed noncoding RNAs may be involved in the molecular basis of inflammation recurrence and progression. This study analyzed 490 colon samples associated with UC from 4 gene expression microarrays from the GEO database and identified gene modules by weighted correlation network analysis (WGCNA). CIBERSORT detected tissue-infiltrating leukocyte profiling by deconvolution of microarray data. LncBase and multiMIR were used to identify lncRNA-miRNA-mRNA interaction. We constructed a ceRNA network which includes 4 lncRNAs (SH3BP5-AS1, MIR4435-2HG, ENTPD1-AS1, and AC007750.1), 5 miRNAs (miR-141-3p, miR-191-5p, miR-192-5p, miR-194-5p, and miR196-5p), and 52 mRNAs. Those genes are involved in interleukin family signals, neutrophil degranulation, adaptive immunity, and cell adhesion pathways. lncRNA MIR4435-2HG is a variable in the decision tree for moderate-to-severe UC diagnostic prediction. Our work identifies potential regulated inflammation-related lncRNA-miRNA-mRNA regulatory axes. The regulatory axes are dysregulated during the deterioration of UC, suggesting that it is a risk factor for UC progression.

CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. Results We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. Availability CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock Supplementary information Supplementary data are available at Bioinformatics online.

Synergistic action of estradiol and PGE2 on endometrial transcriptome in vivo resembles pregnancy effects better than estradiol alone†

Successful pregnancy establishment in mammals depends on numerous interactions between embryos and the maternal organism. Estradiol-17β (E2) is the primary embryonic signal in the pig, and its importance has been questioned recently. However, E2 is not the only molecule of embryonic origin. In pigs, prostaglandin E2 (PGE2) is abundantly synthesized and secreted by conceptuses and endometrium. The present study aimed to determine the role of PGE2 and its simultaneous action with E2 in changes in porcine endometrial transcriptome during pregnancy establishment. The effects of PGE2 and PGE2 acting with E2 were studied using an in vivo model of intrauterine hormone infusions, and were compared to the effects of E2 alone and conceptuses’ presence on day 12 of pregnancy. The endometrial transcriptome was profiled using gene expression microarrays followed by statistical analyses. Downstream analyses were performed using bioinformatics tools. Differential expression of selected genes was verified by quantitative PCR. Microarray analysis revealed 2413 differentially expressed genes (DEGs) in the endometrium treated simultaneously with PGE2 and E2 (P < 0.01). No significant effect of PGE2 administered alone on endometrial transcriptome was detected. Gene ontology annotations enriched for DEGs were related to multiple processes such as: focal adhesion, vascularization, cell migration and proliferation, glucose metabolism, tissue remodeling, and activation of immune response. Simultaneous administration of E2 and PGE2 induced more changes within endometrial transcriptome characteristic to pregnancy than infusion of E2 alone. The present findings suggest that synergistic action of estradiol-17β and PGE2 resembles the effects of pregnancy on endometrial transcriptome better than E2 alone.

CuBlock: A cross-platform normalization method for gene-expression microarrays

MotivationCross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.ResultsWe present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct data sets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these data sets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.AvailabilityCuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/[email protected], [email protected] informationSupplementary data are available at bioRxiv online.

Identification of genes in hepatocellular carcinoma induced by non-alcoholic fatty liver disease

BACKGROUND: Hepatocellular carcinoma (HCC) is the leading cause of mortality worldwide. In recent years, the incidence of HCC induced by NAFLD is growing rapidly. OBJECTIVE: To screen for new pathogenic genes and related pathways both in NAFLD and HCC, and to explore the pathogenesis of progression from NAFLD to HCC. METHODS: Gene expression microarrays (GSE74656, GSE62232) were used for identifying differentially expressed genes (DEGs). Functional enrichment and pathway enrichment analyses indicated that these DEGs were related to cell cycle and extracellular exosome, which were closely related to NAFLD and HCC development. We then used the Search Tool for the Retrieval of Interacting Genes (STRING) to establish the protein-protein interaction (PPI) network and visualized them in Cytoscape. And the overall survival (OS) analysis and gene expression validation in TCGA of hub genes was performed. RESULTS: Seven hub genes, including CDK1, HSP90AA1, MAD2L1, PRKCD, ITGB3BP, CEP192, and RHOB were identified. Finally, we verified the expression level of ITGB3BP and CEP192 by quantitative real-time PCR in vitro. CONCLUSIONS: The present study implied possible DEGs, especially the new gene CEP192, in the progression of NAFLD developing to HCC. Further rigorous experiments are required to verify the above results.

Molecular Markers Guiding Thyroid Cancer Management

The incidence of thyroid cancer is rapidly increasing, mostly due to the overdiagnosis and overtreatment of differentiated thyroid cancer (TC). The increasing use of potent preclinical models, high throughput molecular technologies, and gene expression microarrays have provided a deeper understanding of molecular characteristics in cancer. Hence, molecular markers have become a potent tool also in TC management to distinguish benign from malignant lesions, predict aggressive biology, prognosis, recurrence, as well as for identification of novel therapeutic targets. In differentiated TC, molecular markers are mainly used as an adjunct to guide management of indeterminate nodules on fine needle aspiration biopsies. In contrast, in advanced thyroid cancer, molecular markers enable targeted treatments of affected signalling pathways. Identification of the driver mutation of targetable kinases in advanced TC can select treatment with mutation targeted tyrosine kinase inhibitors (TKI) to slow growth and reverse adverse effects of the mutations, when traditional treatments fail. This review will outline the molecular landscape and discuss the impact of molecular markers on diagnosis, surveillance and treatment of differentiated, poorly differentiated and anaplastic follicular TC.