Similar Datasets
Project description:The human thymus suffers a transient neonatal involution, recovers and then starts a process of decline between the 1st and 2nd years of life. Age-related morphological changes in thymus were extensively investigated, but the genomic mechanisms underlying this process remain largely unknown. Through Weighted Gene Co-expression Network Analysis (WGCNA) and TF-miRNA-mRNA integrative analysis we studied the transcriptome of neonate and infant thymic tissues grouped by age: 0-30 days (A); 31days-6 months (B); 7-12 months (C); 13-18 months (D); 19-31months (E). Age-related transcriptional modules, hubs and high gene significance (HGS) genes were identified, as well as TF-miRNA-hub/HGS co-expression correlations. Three transcriptional modules were correlated with A and/or E groups. Hubs were mostly related to cellular/metabolic processes; few were differentially expressed (DE) or related to T-cell development. Inversely, HGS genes in groups A and E were mostly DE. In A (neonate) one third of the hyper-expressed HGS genes were related to T-cell development, against one-twentieth in E, what may correlate with the early neonatal depletion and recovery of thymic T-cell populations. This genomic mechanism is tightly regulated by TF-miRNA-hub/HGS interactions that differentially govern cellular and molecular processes involved in the functioning of the neonate thymus and in the beginning of thymic decline.
| S-EPMC7159188 | biostudies-literature
Project description:The human thymus shows a transient involution along the neonatal period and starts a continuous process of decline between the 1st and 2nd years. The immune alterations associated to thymic aging in humans have been extensively investigated, but the genomic mechanisms underlying this process remain largely unknown. Here we conducted a temporal gene co-expression network analysis in the newborn and infant thymic tissue aiming to identify transcriptional modules associated to age.
2019-12-27 | GSE131241 | GEO
Project description:The human thymus shows a transient involution along the neonatal period and starts a continuous process of decline between the 1st and 2nd years. The immune alterations associated to thymic aging in humans have been extensively investigated, but the genomic mechanisms underlying this process remain largely unknown. Here we conducted a temporal gene co-expression network analysis in the newborn and infant thymic tissue aiming to identify transcriptional modules associated to age.
2019-12-27 | GSE131191 | GEO
Project description:Age-related transcriptional modules and TF-miRNA-mRNA interactions in neonatal and infant human thymus [miRNA]
| PRJNA543034 | ENA
Project description:Age-related transcriptional modules and TF-miRNA-mRNA interactions in neonatal and infant human thymus [mRNA]
| PRJNA542875 | ENA
Project description:Age-related transcriptional modules and TF-miRNA-mRNA interactions in neonatal and infant human thymus
| PRJNA543032 | ENA
Project description:Nitidine chloride (NC) has reported tumor suppressive activities for various human cancers, including hepatocellular carcinoma (HCC). Nevertheless, the pharmacological mechanism of NC on HCC has not previously been elucidated. SMMC7721 HCC cell lines, before and after the treatment of NC, were injected into nude mice for a subcutaneous tumor xenograft model. MiRNA and mRNA sequencing were performed for both control and treated xenograft tissues to further analyze differential expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs). The ten most significant DEmiRNAs were selected for prediction of transcription factors (TFs) and target genes. We constructed an interconnected network composed of TFs the ten most significant DEmiRNAs, the 100 most significant DEmRNAs, and selected target genes from online programs. Hub genes chosen from a protein-to-protein interaction network of hub genes were validated by correlation analysis, expression analysis, and Kaplan-Meier survival analysis. The five most up-regulated miRNAs (hsa-miR-628-5p, hsa-miR-767-5p, hsa-miR-767-3p, hsa-miR-1257, and hsa-miR-33b-3p) and the five most down-regulated miRNAs (hsa-miR-378d, hsa-miR-136-5p, hsa-miR-451a, hsa-miR-144-5p, and hsa-miR-378b) were singled out from the DEmiRNAs. Functional annotations indicated that potential target genes of the top five up-regulated miRNAs were mainly clustered in molecular processes concerning epithelial-to-mesenchymal transition. Hub genes, such as ITGA6 and ITGB4, were validated as up-regulated in HCC; both IFIT2 and IFIT3 were revealed by Kaplan-Meier survival curves as good prognostic factors for HCC. In summary, the regulating axes of NC-DEmiRNAs-DEmRNAs and TFs-DEmiRNAs-DEmRNAs in HCC that were discovered in this study may shed light on the possible molecular mechanism of NC in HCC.
| S-EPMC6943467 | biostudies-literature
Project description:Gastric cancer (GC), as an epidemic cancer worldwide, has more than 1 million new cases and an estimated 769,000 deaths worldwide in 2020, ranking fifth and fourth in global morbidity and mortality. In mammals, both miRNAs and transcription factors (TFs) play a partial role in gene expression regulation. The mRNA expression profile and miRNA expression profile of GEO database were screened by GEO2R for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Then, DAVID annotated the functions of DEGs to understand the functions played in biological processes. The prediction of potential target genes of miRNA and key TFs of mRNA was performed by mipathDB V2.0 and CHEA3, respectively, and the gene list comparison was performed to look for overlapping genes coregulated by key TFs and DEMs. Finally, the obtained miRNAs, TF, and overlapping genes were used to construct the miRNA-mRNA-TF regulatory network, which was verified by RT-qPCR. 76 upregulated DEGs, 199 downregulated DEGs, and 3 upregulated miRNAs (miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p) were identified from the expression profiles of mRNA (GSE26899, GSE29998, GSE51575, and GSE13911) and miRNA (GSE93415), respectively. Through database prediction and gene list comparison, it was found that among the 199 downregulated DEGs, 61, 71, and 69 genes were the potential targets of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p, respectively. 199 downregulated DEGs were used as the gene list for the prediction of key TFs, and the results showed that RFX6 ranked the highest. The potential target overlap genes of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p were 4 genes (SH3GL2, ATP4B, CTSE, and SORBS2), 7 genes (SLC7A8, RNASE4, ESRRG, PGC, MUC6, Fam3B, and FMO5), and 6 genes (CHGA, PDK4, TMPRSS2, CLIC6, GPX3, and PSCA), respectively. Finally, we constructed a miRNA-mRNA-TF regulatory network based on the above 17 mRNAs, 3 miRNAs, and 1 TF and verified by RT-qPCR and western blot results that the expression of RFX6 was downregulated in GC tissues. These identified miRNAs, mRNAs, and TF have a certain reference value for further exploration of the regulatory mechanism of GC.
| S-EPMC8616677 | biostudies-literature
Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.
| S-EPMC4818025 | biostudies-literature
Project description:Uterine fibroids (UF) are the most common benign gynecologic tumors and lead to heavy menstrual bleeding, severe anemia, abdominal pain, and infertility, which seriously harm a women's health. Unfortunately, the regulatory mechanisms of UF have not been elucidated. Recent studies have demonstrated that miRNAs play a vital role in the development of uterine fibroids. As a high-throughput technology, microarray is utilized to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between UF and myometrium. We identified 373 candidate DEGs and the top 100 DEMs. Function enrichment analysis showed that candidate DEGs were mainly enriched in biological adhesion, locomotion and cell migration, and collagen-containing extracellular matrix. Subsequently, protein-protein interaction (PPI) networks are constructed to analyze the functional interaction between DEGs and screen hub DEGs. Subsequently, the expression levels of hub DEGs were validated by real-time PCR of clinical UF samples. The DGIdb database was used to select candidate drugs for hub DEGs. Molecular docking was applied to test the affinity between proteins and drugs. Furthermore, target genes for 100 candidate DEMs were predicted by miRwalk3.0. After overlapping with 373 candidate DEGs, 28 differentially expressed target genes (DEGTs) were obtained. A miRNA-mRNA network was constructed to investigate the interactions between miRNA and mRNA. Additionally, two miRNAs (hsa-miR-381-3p and hsa-miR-181b-5p) were identified as hub DEMs and validated through RT-PCR. In order to better elucidate the pathogenesis of UF and the synergistic effect between miRNA and transcription factor (TF), we constructed a miRNA-TF-mRNA regulatory network. Meanwhile, in vitro results suggested that dysregulated hub DEMs were associated with the proliferation, migration, and apoptosis of UF cells. Our findings provided a novel horizon to reveal the internal mechanism and novel targets for the diagnosis and treatment of UF.
| S-EPMC8981149 | biostudies-literature