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Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis.

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The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed.

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Animals plastically adjust their physiological and behavioural phenotypes to conform to their social environment-social niche conformance. The degree of sexual competition is a critical part of the social environment to which animals adjust their phenotypes, but the underlying genetic mechanisms are poorly understood. We conducted a study to investigate how differences in sperm competition risk affect the gene expression profiles of the testes and two brain areas (posterior pallium and optic tectum) in breeding male zebra finches (Taeniopygia castanotis). In this pre-registered study, we investigated a large sample of 59 individual transcriptomes. We compared two experimental groups: males held in single breeding pairs (low sexual competition) versus those held in two pairs (elevated sexual competition) per breeding cage. Using weighted gene co-expression network analysis (WGCNA), we observed significant effects of the social treatment in all three tissues. However, only the treatment effects found in the pallium were confirmed by an additional randomisation test for statistical robustness. Likewise, the differential gene expression analysis revealed treatment effects only in the posterior pallium (ten genes) and optic tectum (six genes). No treatment effects were found in the testis at the single gene level. Thus, our experiments do not provide strong evidence for transcriptomic adjustment specific to manipulated sperm competition risk. However, we did observe transcriptomic adjustments to the manipulated social environment in the posterior pallium. These effects were polygenic rather than based on few individual genes with strong effects. Our findings are discussed in relation to an accompanying paper using the same animals, which reports behavioural results consistent with the results presented here.

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AIMS: This study aimed to address inconsistencies in results between the H9C2 myocardial hypoxia (MH) cell line and myocardial infarction (MI) rat models used in MI research. We identified differentially expressed genes (DEGs) and underlying molecular mechanisms using RNA sequencing technology. METHODS: RNA sequencing was used to analyse DEGs in MI rat tissues and H9C2 cells exposed to hypoxia for 24 h. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify key biological processes and pathways. Weighted correlation network analysis [weighted gene co-expression network analysis (WGCNA)] was used to construct gene co-expression networks, and hub genes were compared with published MI datasets [Gene Expression Omnibus (GEO)] for target identification. RESULTS: GO analysis revealed enrichment of immune inflammation and mitochondrial respiration processes among 5139 DEGs in MI tissues and 2531 in H9C2 cells. KEGG analysis identified 537 overlapping genes associated with metabolism and oxidative stress pathways. Cross-analyses using the published GSE35088 and GSE47495 datasets identified 40 and 16 overlapping genes, respectively, with nine genes overlapping across all datasets and our models. WGCNA identified a key module in the MI model enriched for mRNA processing and protein binding. GO analysis revealed enrichment of mRNA processing, protein binding and mitochondrial respiratory chain complex I assembly in MI and H9C2 MH models. Five relevant hub genes were identified via a cross-analysis between the 92 hub genes that showed a common expression trend in both models. CONCLUSIONS: This study reveals both shared and distinct transcriptomic responses in the MI and H9C2 models, highlighting the importance of model selection for studying myocardial ischaemia and hypoxia.

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Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level. The existing methods cluster genes based on only one type of omics data, which ignores the information from other types. A large sample size is required to achieve an accurate clustering structure for thousands of genes, which can be challenging due to the cost of multi-omics data. Meta-analysis has been used to aggregate the data from multiple studies and improve the analysis results. We propose a computationally efficient meta-analytic gene-clustering algorithm that combines multi-omics datasets from multiple studies, using the fixed effects linear models and a modified weighted correlation network analysis framework. The simulation study shows that the proposed method outperforms existing single omic-based clustering approaches when multi-omics data and/or multiple studies are available. A real data example demonstrates that our meta-analytic method outperforms single-study based methods.

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Consumer partiality for food products is related to purchase and consumption behavior, and are influenced by sensory preferences. The sensory and chemical drivers behind consumer preference in the infant formula (IF) were analyzed. A total of 31 aroma-active compounds were identified, playing an important role in the production of off-flavors (especially fishy). Combined with the correlation analysis, the key aroma substances affecting the sensory attributes of IF were initially identified. A21, A22, B9 represented the key substances responsible for producing milky and creamy, while A2, A5, A11, A12, B5, C15, H5 primarily produced fishy. In addition, the two sensory attributes namely milky and creamy, and the T-sweet were more strongly correlated with consumer preference. Therefore, it can be concluded that consumers are more interested in the main flavor of the product than the off-flavor. These findings will inform the quality control of IF and the maintenance of sensory quality.

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Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in pregnant women, posing significant risks to the life and health of both mothers and fetuses. With improving living standards, the incidence of GDM is increasing rapidly. Therefore, understanding the underlying mechanism of GDM is of paramount importance. We downloaded two datasets from the Gene Expression Omnibus (GEO) database, containing sequencing data specifically related to "gestational diabetes" and "placenta". By merging these two datasets, a mRNA expression dataset was obtained and subjected to bioinformatics analyses. To screen out corresponding genes, differential analysis and weighted correlation network analysis (WGCNA) were carried out. Lasso, support vector machine and random forest analyses were subsequently performed for identifying key genes from the differentially expressed genes (DEGs) jointly screened out through differential analysis and WGCNA. Afterwards, immunoinfiltration and correlation analysis were performed to screen immune cells that play a role in disease progression and explore the correlation between the screened key genes and immune cells, after which Western Blot, quantitative real-time polymerase chain reaction, Immunohistochemistry, methyl thiazolyl tetrazolium, flow cytometry, scratch and Transwell assays were, respectively, performed for verification. For further verification, we found that the expression levels of MAP6D1 and SCUBE1 in embryonic tissues of GDM patients was higher compared to those of healthy pregnant women, which was consistent with the results of bioinformatics analysis. Consequently, SCUBE1 was selected for follow-up experiment. In order to explore the role of SCUBE1 in the development of GDM, we treated the trophoblastic cells HTR-8/SVneo with high glucose, and on this basis downregulated the expression of SCUBE1. Through further analysis, we observed that SCUBE1 had a role in reducing cell activity, migration and invasion, and promoting cell apoptosis. In summary, SCUBE1 promotes the development of GDM by increasing cell apoptosis and reducing cell activity, migration, and invasion.

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Background and aims: Problematic smartphone use (PSU) has gained attention, but its definition remains debated. This study aimed to develop and validate a new scale measuring PSU-the Smartphone Use Problems Identification Questionnaire (SUPIQ). Methods: Using two separate samples, a university community sample (N = 292) and a general population sample (N = 397), we investigated: (1) the construct validity of the SUPIQ through exploratory and confirmatory factor analyses; (2) the convergent validity of the SUPIQ with correlation analyses and the visualized partial correlation network analyses; (3) the psychometric equivalence of the SUPIQ across two samples through multigroup confirmatory factor analyses; (4) the explanatory power of the SUPIQ over the Short Version of Smartphone Addiction Scale (SAS-SV) with hierarchical multiple regressions. Results: The results showed that the SUPIQ included 26 items and 7 factors (i.e., Craving, Coping, Habitual Use, Social Conflicts, Risky Use, Withdrawal, and Tolerance), with good construct and convergent validity. The configural measurement invariance across samples was established. The SUPIQ also explained more variances in mental health problems than the SAS-SV. Discussion and conclusions: The findings suggest that the SUPIQ shows promise as a tool for assessing PSU. Further research is needed to enhance and refine the SUPIQ as well as to investigate its clinical utility.

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Dapagliflozin (DAPA) are clinically effective in improving diabetic nephropathy (DN). However, whether and how chromatin accessibility changed by DN responds to DAPA treatment is unclear. Therefore, we performed ATAC-seq, RNA-seq, and weighted gene correlation network analysis to identify the chromatin accessibility, the messenger RNA (mRNA) expression, and the correlation between clinical phenotypes and mRNA expression using kidney from three mouse groups: db/m mice (Controls), db/db mice (case group), and those treated with DAPA (treatment group). RNA-Seq and ATAC-seq conjoint analysis revealed many overlapping pathways and networks suggesting that the transcriptional changes of DN and DAPA intervention largely occured dependently on chromatin remodeling. Specifically, the results showed that some key signal transduction pathways, such as immune dysfunction, glucolipid metabolism, oxidative stress and xenobiotic and endobiotic metabolism, were repeatedly enriched in the analysis of the RNA-seq data alone, as well as combined analysis with ATAC-seq data. Furthermore, we identified some candidate genes (UDP glucuronosyltransferase 1 family, Dock2, Tbc1d10c, etc.) and transcriptional regulators (KLF6 and GFI1) that might be associated with DN and DAPA restoration. These reversed genes and regulators confirmed that pathways related to immune response and metabolism pathways were critically involved in DN progression.

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Stress response is a fundamental mechanism for cell survival, providing protection under unfavorable conditions. Mitochondrial stress, in particular, can trigger mitophagy, a process that restores cellular health. Exhaustive exercise (EE) is a form of acute mitochondrial stress. The objective of this current study is to investigate the impact of EE on tau pathology in pR5 mice, as well as the potential underlying mechanisms. To evaluate this, we examined the levels of total and phosphorylated tau in the hippocampus of pR5 mice, both with and without EE treatment. Furthermore, the application of weighted correlation network analysis (WGCNA) was employed to identify protein modules associated with the phenotype following the proteomic experiment. The findings of our study demonstrated a significant decrease in tau phosphorylation levels upon EE treatment, in comparison to the pR5 group. Moreover, the proteomic analysis provided additional insights, revealing that the mitigation of tau pathology was primarily attributed to the modulation of various pathways, such as translation factors and oxidative phosphorylation. Additionally, the analysis of heatmaps revealed a significant impact of EE treatment on the translation process and electron transport chain in pR5 mice. Furthermore, biochemical analysis provided further confirmation that EE treatment effectively modulated the ATP level in pR5 mice. In conclusion, our study suggests that the observed decrease in tau phosphorylation resulting from EE treatment may primarily be attributed to its regulation of the translation process and enhancement of mitochondrial function.

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Flavor profiles of various Pyrus spp. cultivars exhibit significant variations, yet the underlying flavor-contributing factors remain elusive. In this investigation, a comprehensive approach encompassing metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis was employed to elucidate the flavor disparities among Nanguoli, Korla fragrant pear, and Qiuyueli cultivars and uncover potential flavor contributor. The study comprehensively characterized the categories and concentrations of nonvolatile and volatile metabolites, and 925 metabolites were identified. Flavonoids and esters dominated the highest cumulative response, respectively. Utilizing weighted correlation network analysis (WGCNA), seven highly correlated modules were identified, yielding 407 pivotal metabolites. Further correlation analysis of the differential substances provided potential flavor constituents strongly associated with various sensory attributes; taste factors had a certain association with olfactory characteristics. Our findings demonstrated the manifestation of flavor was a result of the synergistic effect of various compounds; evaluation olfactory flavor necessitated a comprehensive consideration of taste substances.

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DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function.

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The objective of this study is to investigate the potential biomarkers and therapeutic target genes for Parkinson's disease (PD). We analyzed four datasets (GSE8397, GSE20292, GSE20163, GSE20164) from the Gene Expression Omnibus database. We employed weighted gene co-expression network analysis and differential expression analysis to select genes and perform functional analysis. We applied three algorithms, namely, random forest, support vector machine recursive feature elimination, and least absolute shrinkage and selection operator, to identify hub genes, perform functional analysis, and assess their clinical diagnostic potential using receiver operating characteristic (ROC) curve analysis. We employed the xCell website to evaluate differences in the composition patterns of immune cells in the GEO datasets. We also collected serum samples from PD patients and established PD cell model to validate the expression of hub genes using enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction. Our findings identified SV2C and DENR as two hub genes for PD and decreased in PD brain tissue compared with controls. ROC analysis showed effectively value of SV2C and DENR to diagnose PD, and they were downregulated in the serum of PD patients and cell model. Functional analysis revealed that dopamine vesicle transport and synaptic vesicle recycling are crucial pathways in PD. Besides, the differences in the composition of immune cells, especially basophils and T cells, were discovered between PD and controls. In summary, our study identifies SV2C and DENR as potential biomarkers for diagnosing PD and provides a new perspective for exploring the molecular mechanisms of PD.

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BACKGROUND: Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis. METHODS: First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them. RESULTS: Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set. CONCLUSION: Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.

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BACKGROUND: The sexual maturity of chickens is an important economic trait, and the breeding of precocious and delayed puberty roosters is an important selection strategy for broilers. The comb serves as an important secondary sexual characteristic of roosters and determines their sexual precocity. Moreover, comb development is closely associated with gonad development in roosters. However, the underlying molecular mechanism regulating the sexual maturity of roosters has not yet been fully explored. RESULTS: In order to identify the genes related to precocious puberty in Qingyuan partridge roosters, and based on the synchrony of testis and combs development, combined with histological observation and RNA-seq method, the developmental status and gene expression profile of combs and testis were obtained. The results showed that during the early growth and development period (77 days of age), the development of combs and testis was significant in the high comb (H) group versus the low comb (L) group (p < 0.05); however, the morphological characteristic of the comb and testicular tissues converged during the late growth and development period (112 days of age) in the H and L groups. Based on these results, RNA-sequencing analysis was performed on the comb and testis tissues of the 77 and 112 days old Qingyuan Partridge roosters with different comb height traits. GO and KEGG analysis enrichment analysis showed that the differentially expressed genes were primarily enriched in MAPK signaling, VEGF signaling, and retinol metabolism pathways. Moreover, weighted correlation network analysis and module co-expression network analysis identified WNT6, AMH, IHH, STT3A, PEX16, KPNA7, CATHL2, ROR2, PAMR1, WISP2, IL17REL, NDRG4, CYP26B1, and CRHBP as the key genes associated with the regulation of precocity and delayed puberty in Qingyuan Partridge roosters. CONCLUSIONS: In summary, we identified the key regulatory genes of sexual precocity in roosters, which provide a theoretical basis for understanding the developmental differences between precocious and delayed puberty in roosters.

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This study aimed to clarify the effect of long-term continuous cropping of pepper on soil fungal community structure, reveal the mechanism of continuous cropping obstacles, and provide a theoretical basis for the ecological safety and sustainable development of pepper industry. We took the pepper continuous cropping soil in the vegetable greenhouse planting base of Tongren City as the research object. The diversity and community structure of fungi in farmland soil were analyzed using Illumina MiSeq high-throughput sequencing, the responses of soil physio-chemical properties and fungal community characteristics to long-term continuous pepper cropping were discussed, and the relationships between the characteristics of fungal community structure and environmental factors were determined using CCA and correlation network analysis. The results showed that with the extension of pepper continuous cropping years, the soil pH value and organic matter (OM) content decreased, total phosphorus (TP) and available phosphorus (AP) contents increased, hydrolyzed nitrogen (AN) and available potassium (AK) contents decreased first and then increased, and total nitrogen (TN) and total potassium (TK) contents did not change significantly. Long-term continuous cropping decreased the Chao1 index and observed species index and decreased the Shannon index and Simpson index. The change in continuous cropping years had a significant effect on the relative abundance of soil fungal dominant flora. At the phylum level, the relative abundance of Mortierellomycota decreased with the extension of pepper continuous cropping years, the relative abundance of Ascomycota increased first and then decreased, and the relative abundance of Basidiomycota decreased first and then increased. At the genus level, with the increasing of pepper continuous cropping years, the relative abundance of Fusarium increased, and the relative abundance of Mortierella and Penicillium decreased. In addition, long-term continuous cropping simplified the soil fungal symbiosis network. CCA analysis indicated that pH, OM, TN, AN, AP, and AK were the driving factors of soil fungal community structure, and correlation network analysis showed that pH, OM, TN, TP, TK, AN, AP, and AK were the driving factors of soil fungal community structure, including Fusarium, Lophotrichus, Penicillium, Mortierella, Botryotrichum, Staphylotrichum, Plectosphaerella, and Acremonium. In conclusion, continuous cropping changed the soil physical and chemical properties, affected the diversity and community structure of the soil fungal community, changed the interaction between microorganisms, and destroyed the microecological balance of the soil, which might explain obstacles associated with continuous cropped pepper.

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Trichlorfon, one of the most widely used organophosphate insecticides, is commonly employed in aquaculture and agriculture to combat parasitic infestations. However, its inherent instability leads to rapid decomposition into dichlorvos (DDVP), increasing its toxicity by eightfold. Therefore, the environmental effects of trichlorfon in real-world scenarios involve the combined effects of trichlorfon and its degradation product, DDVP. In this study, we systematically investigated the degradation of trichlorfon in tap water over time using HPLC and LC-MS/MS analysis. Subsequently, an experiment was conducted to assess the acute toxicity of trichlorfon and DDVP on goldfish (Carassius auratus), employing a 1H NMR-based metabolic approach in conjunction with serum biochemistry, histopathological inspection, and correlation network analysis. Exposure to trichlorfon and its degradation product DDVP leads to increased lipid peroxidation, reduced antioxidant activity, and severe hepatotoxicity and nephrotoxicity in goldfish. Based on the observed pathological changes and metabolite alterations, short-term exposure to trichlorfon significantly affected the liver and kidney functions of goldfish, while exerting minimal influence on the brain, potentially due to the presence of the blood-brain barrier. The changes in the metabolic profile indicated that trichlorfon and DDVP influenced several pathways, including oxidative stress, protein synthesis, energy metabolism, and nucleic acid metabolism. This study demonstrated the applicability and potential of 1H NMR-based metabonomics in pesticide environmental risk assessment, providing a feasible method for the comprehensive study of pesticide toxicity in water environments.

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BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood. MATERIAL/METHODS: Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers. RESULTS: WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells. CONCLUSION: Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.

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OBJECTIVES: Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored. METHODS: GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi-array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver-operating characteristic curve analysis was used to verify the validity and agility of the hub genes-based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real-time polymerase chain reaction and western blotting. RESULTS: Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue. CONCLUSION: We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis.

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The sea cucumber Apostichopus japonicus is an economically important marine species in China, and understanding the mechanisms underlying its gonad development is crucial for successful reproduction and breeding. In this study, we performed transcriptome comparisons and analyses of A. japonicus gonadal and non-gonadal tissues to identify genes and molecular pathways associated with gonadal development. We also supplemented the annotation of the A. japonicus genome. Collectively, results revealed a total of 941 ovary-specific genes and 2499 testis-specific genes through different expression analysis and WGCNA analysis. The most enriched pathways in ovary and testis were "DNA replication" and "purine metabolism", respectively. Additionally, we identified key candidate gene modules that control gonad development and germ cell maturation, with CDT1 and DYNC2LI1 serving as hub genes. Our findings provide important insights into the gonadal development system of A. japonicus and offer valuable references for further research on reproductive biology in this marine invertebrate species.

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