Identification of key regulators in parathyroid adenoma using an integrative gene network analysis.
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ABSTRACT:
SUBMITTER: Imam N
PROVIDER: S-EPMC8573468 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.MethodsCIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.ResultsIn comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT-PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.ConclusionsThree novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.
| S-EPMC9363882 | biostudies-literature
Project description:BackgroundCarotenoids are a class of terpenoid pigments that contribute to the color and nutritional value of many fruits. Their biosynthetic pathways have been well established in a number of plant species; however, many details of the regulatory mechanism controlling carotenoid metabolism remain to be elucidated. Apricot is one of the most carotenoid-rich fruits, making it a valuable system for investigating carotenoid metabolism. The purpose of this study was to identify key genes and regulators associated with carotenoid metabolism in apricot fruit based on transcriptome sequencing.ResultsDuring fruit ripening in the apricot cultivar 'Luntaixiaobaixing' (LT), the total carotenoid content of the fruit decreased significantly, as did the levels of the carotenoids β-carotene, lutein and violaxanthin (p < 0.01). RNA sequencing (RNA-Seq) analysis of the fruit resulted in the identification of 44,754 unigenes and 6916 differentially expressed genes (DEGs) during ripening. Among these genes, 33,498 unigenes were annotated using public protein databases. Weighted gene coexpression network analysis (WGCNA) showed that two of the 13 identified modules ('blue' and 'turquoise') were highly correlated with carotenoid metabolism, and 33 structural genes from the carotenoid biosynthetic pathway were identified. Network visualization revealed 35 intramodular hub genes that putatively control carotenoid metabolism. The expression levels of these candidate genes were determined by quantitative real-time PCR analysis, which showed ripening-associated carotenoid accumulation. This analysis revealed that a range of genes (NCED1, CCD1/4, PIF3/4, HY5, ERF003/5/12, RAP2-12, AP2, AP2-like, BZR1, MADS14, NAC2/25, MYB1R1/44, GLK1/2 and WRKY6/31/69) potentially affect apricot carotenoid metabolism during ripening. Based on deciphering the molecular mechanism involved in ripening, a network model of carotenoid metabolism in apricot fruit was proposed.ConclusionsOverall, our work provides new insights into the carotenoid metabolism of apricot and other species, which will facilitate future apricot functional studies and quality breeding through molecular design.
| S-EPMC6865023 | biostudies-literature
Project description:Parathyroid adenoma is the main cause of primary hyperparathyroidism, which is characterized by enlarged parathyroid glands and excessive parathyroid hormone secretion. Here, we performed transcriptome analysis, comparing parathyroid adenomas with normal parathyroid gland tissue. RNA extracted from ten parathyroid adenoma and five normal parathyroid samples was sequenced, and differentially expressed genes (DEGs) were identified using strict cut-off criteria. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using DEGs as the input, and protein-protein interaction (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and visualized in Cytoscape. Among DEGs identified in parathyroid adenomas (n = 247; 45 up-regulated, 202 down-regulated), the top five GO terms for up-regulated genes were nucleoplasm, nucleus, transcription DNA-template, regulation of mRNA processing, and nucleic acid binding, while those for down-regulated genes were extracellular exosome, membrane endoplasmic reticulum (ER), membrane, ER, and melanosome. KEGG enrichment analysis revealed significant enrichment of five pathways: protein processing in ER, protein export, RNA transport, glycosylphosphatidylinositol-anchor biosynthesis, and pyrimidine metabolism. Further, PPI network analysis identified a densely connected sub-module, comprising eight hub molecules: SPCS2, RPL23, RPL26, RPN1, SEC11C, SEC11A, RPS25, and SEC61G. These findings may be helpful in further analysis of the mechanisms underlying parathyroid adenoma development.
| S-EPMC6463127 | biostudies-literature
Project description:Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.
| S-EPMC7585874 | biostudies-literature
Project description:BackgroundHistologically similar tumors even from the same anatomical position may still show high variability at molecular level hindering analysis of genome-wide data. Leveling the analysis to a gene regulatory network instead of focusing on single genes has been suggested to overcome the heterogeneity issue although the majority of the network methods require large datasets. Network methods that are able to function at a single sample level are needed to overcome the heterogeneity and sample size issues.MethodsWe present a novel network method, Differentially Expressed Regulation Analysis (DERA) that integrates expression data to biological network information at a single sample level. The sample-specific networks are subsequently used to discover samples with similar molecular functions by identification of regulations that are shared between samples or are specific for a subgroup.ResultsWe applied DERA to identify key regulations in triple negative breast cancer (TNBC), which is characterized by lack of estrogen receptor, progesterone receptor and HER2 expression and has poorer prognosis than the other breast cancer subtypes. DERA identified 110 core regulations consisting of 28 disconnected subnetworks for TNBC. These subnetworks are related to oncogenic activity, proliferation, cancer survival, invasiveness and metastasis. Our analysis further revealed 31 regulations specific for TNBC as compared to the other breast cancer subtypes and thus form a basis for understanding TNBC. We also applied DERA to high-grade serous ovarian cancer (HGS-OvCa) data and identified several common regulations between HGS-OvCa and TNBC. The performance of DERA was compared to two pathway analysis methods GSEA and SPIA and our results shows better reproducibility and higher sensitivity in a small sample set.ConclusionsWe present a novel method called DERA to identify subnetworks that are similarly active for a group of samples. DERA was applied to breast cancer and ovarian cancer data showing our method is able to identify reliable and potentially important regulations with high reproducibility. R package is available at http://csbi.ltdk.helsinki.fi/pub/czliu/DERA/.
| S-EPMC4424448 | biostudies-literature
Project description:The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds.
| S-EPMC9256319 | biostudies-literature
Project description:N6-methyladenosine (m6A) is a reversible and dynamic RNA modification in eukaryotes. However, how cells establish cell-specific m6A methylomes is still poorly understood. Here, we developed a computational framework to systematically identify cell-specific trans regulators of m6A through integrating gene expressions, binding targets and binding motifs of large number of RNA binding proteins (RBPs) with a co-methylation network constructed using large-scale m6A methylomes across diverse cell states. We applied the framework and successfully identified 32 high-confidence m6A regulators that modulated the variable m6A sites away from stop codons in a cell-specific manner. To validate them, we knocked down three regulators respectively and found two of them (TRA2A and CAPRIN1) selectively promoted the methylations of the m6A sites co-localized with their binding targets on RNAs through physical interactions with the m6A writers. Knockdown of TRA2A increased the stabilities of the RNAs with TRA2A bound near the m6A sites and decreased the viability of cells. The successful identification of m6A regulators demonstrates a powerful and widely applicable strategy to elucidate the cell-specific m6A regulators. Additionally, our discovery of pervasive trans-acting regulating of m6A provides novel insights into the mechanisms by which spatial and temporal dynamics of m6A methylomes are established.
| S-EPMC7038928 | biostudies-literature
Project description:Glycyrrhizic acid (GA) is primarily used as an anti‑inflammatory agent in cases of chronic hepatitis. However, its underlying mechanisms in diverse biological processes and its reported benefits are yet to be fully elucidated. In the current study, an analytical method based on pharmacogenomics was established to mine disease‑modulatory activities mediated by GA. Five primary protein targets and 138 functional partners were identified for GA by querying open‑source databases, including Drugbank and STRING. Subsequently, GA‑associated primary and secondary protein targets were integrated into Cytoscape to construct a protein‑protein interaction network to establish connectivity. GA‑associated target genes were then clustered based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The tumor necrosis factor axis was revealed to be a primary module regulated by GA‑associated targets. Furthermore, 12 hub genes were queried to assess their potential anti‑cancer effects using cBioPortal. The results indicated that pharmacogenomics‑based analysis improved understanding of the underlying drug‑target events of GA and provided predictive and definitive leads for future studies.
| S-EPMC6691250 | biostudies-literature
Project description: Not available
| S-EPMC10167439 | biostudies-literature
Project description:BackgroundInvasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes.MethodsRNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA.ResultsIn this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein-protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA.ConclusionFZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.
| S-EPMC8734245 | biostudies-literature