Allergic rhinitis is a prevalent inflammatory condition that impacts individuals of all age groups. Despite reports indicating the potential of berberine in alleviating allergic rhinitis symptoms, the specific molecular mechanisms and therapeutic targets of berberine remain unclear. This research aims to explore the pharmacological mechanism of berberine in the treatment of allergic rhinitis through bioinformatic analyses and experimental validation. The research utilized public databases to identify potential targets of berberine. Furthermore, differentially expressed genes (DEGs) related to allergic rhinitis were pinpointed from the GSE52804 dataset. Through bioinformatics techniques, the primary targets were discovered and key KEGG and GO-BP pathways were established. To confirm the therapeutic mechanisms of berberine on allergic rhinitis, an OVA-induced allergic rhinitis model was developed using guinea pigs. We identified 32 key genes responsible for the effectiveness of berberine in treating allergic rhinitis. In addition, five central genes (Alb, Il6, Tlr4, Ptas2, and Il1b) were pinpointed. Further examination using KEGG and GO-BP pathways revealed that the main targets were primarily involved in pathways such as NF-kappa B, IL-17, TNF, and inflammatory response. Molecular docking analysis demonstrated that berberine exhibited strong affinity towards these five key targets. Furthermore, the expression levels of IL-6, TLR4, PTGS2, and IL-1β were significantly upregulated in the model group but downregulated following berberine treatment. This research has revealed the mechanism through which berberine combats allergic rhinitis and has identified its potential to regulate pathways linked to inflammation. These discoveries provide valuable insights for the development of novel medications for the treatment of allergic rhinitis.

UNASSIGNED: Both the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group.
UNASSIGNED: The NN group\'s placental weight differed significantly from that of the other groups. The structure of NN group\'s placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn\'t be discounted.
UNASSIGNED: In diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn\'t be ignored because of their tight relationships.

BACKGROUND: Helicobacter pylori (H. pylori) infection is related to various extragastric diseases including type 2 diabetes mellitus (T2DM). However, the possible mechanisms connecting H. pylori infection and T2DM remain unknown.
OBJECTIVE: To explore potential molecular connections between H. pylori infection and T2DM.
METHODS: We extracted gene expression arrays from three online datasets (GSE60427, GSE27411 and GSE115601). Differentially expressed genes (DEGs) commonly present in patients with H. pylori infection and T2DM were identified. Hub genes were validated using human gastric biopsy samples. Correlations between hub genes and immune cell infiltration, miRNAs, and transcription factors (TFs) were further analyzed.
RESULTS: A total of 67 DEGs were commonly presented in patients with H. pylori infection and T2DM. Five significantly upregulated hub genes, including TLR4, ITGAM, C5AR1, FCER1G, and FCGR2A, were finally identified, all of which are closely related to immune cell infiltration. The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links. TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs, the largest number of TFs among the 5 hub genes.
CONCLUSIONS: We identified five hub genes that may have molecular connections between H. pylori infection and T2DM. This study provides new insights into the pathogenesis of H. pylori-induced onset of T2DM.

Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals and/or angioedema for >6 weeks. The pathogenesis involves skin mast cells, but the complex causes of their activation remain to be characterized in detail.
To explore disease-driving genes and biological pathways in CSU.
Two microarray data sets, e.g., GSE57178 and GSE72540, with mRNA information of skin from CSU patients, were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction (PPI) network analysis, co-expression and drug prediction analysis, and immune and stromal cells deconvolution analyses were applied to identify hub genes and key drivers of CSU pathogenesis.
In total, we identified 92 up-regulated and 7 down-regulated genes in CSU lesions. These were significantly enriched in CSU-related pathways such as TNF, NF-κB, and JAK-STAT signaling. Based on PPI network modeling, four genes, i.e., IL-6, TLR-4, ICAM-1, and PTGS-2, were computationally identified as key pathogenic players in CSU. Immune infiltration analyses indicated that dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and M1 macrophages were increased in lesional CSU skin.
Our results offer new insights on the pathogenesis of CSU and suggest that TNF, NF-κB, JAK-STAT, IL-6, TLR-4, ICAM-1, and PTGS-2 may be candidate targets for novel CSU treatments.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis.
METHODS: We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses.
RESULTS: The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the \"Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis\" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells.
CONCLUSIONS: Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

OBJECTIVE: To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification.
METHODS: In the Gene Expression Omnibus (GEO) database, the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes (DEGs) by the GEO2R tool. Through bioinformatics analysis, 9 hub genes were identified. Receiver operating characteristic (ROC) curves and principal component analysis (PCA) were performed to verify whether the hub gene can distinguish glaucoma from normal eyes. The mouse model of glaucoma was constructed, and the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was performed to detect the expression levels of hub genes in glaucoma.
RESULTS: There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets, mainly involved in intracellular signalling, cell adhesion molecules and the Ras signalling pathway. A total of 9 hub genes were screened out, including GNAL, BGN, ETS2, FCGP4, MAPK10, MMP15, STAT1, TSPAN8, and VCAM1. The area under the curve (AUC) values of 9 hub genes were greater than 0.8. The PC1 axle could provide a 70.5% interpretation rate to distinguish glaucoma from normal eyes. In the ocular tissues of glaucoma in the mice model, the expression of BGN, ETS2, FCGR4, STAT1, TSPAN8, and VCAM1 was increased, while the expression of GNAL, MAPK10, and MMP15 was decreased.
CONCLUSIONS: Nine hub genes in glaucoma are identified, which may provide new biomarkers and therapeutic targets for glaucoma.

Although intrauterine adhesion (IUA) has been well recognized as a critical factor in infertility, little information is available regarding the molecular mechanisms. We performed a high-throughput RNA sequencing in the endometrium of three IUA patients and three normal controls. And another two gene expression profiles (PMID34968168 and GSE160365) were analyzed together. A total of 252 DEGs were identified. Cell cycle, E2F target, G2M checkpoint, integrin3 pathway and H1F1 signaling were aberrantly regulated in the IUA endometrium. 10 hub genes (CCL2, TFRC, THY1, IGF1, CTGF, SELL, SERPINE1, HBB, HBA1, LYZ) were exhibited in PPI analysis. FOXM1, IKBKB and MYC were three common transcription factors of DEGs. Five chemicals (MK-1775, PAC-1, TW-37, BIX-01294, 3-matida) were identified as putative therapeutic agents for IUA. Collectively, a series of DEGs associated with IUA were disclosed. Five chemicals and ten hub genes may be further explored as potential drugs and targets for IUA treatment.

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.

UNASSIGNED: Neuropathic pain as a complex chronic disease that occurs after neurological injury, however the underlying mechanisms are not clarified in detail, hence therapeutic options are limited. The purpose of this study was to explore potential hub genes for neuropathic pain and evaluate the clinical application of these genes in predicting neuropathic pain.
UNASSIGNED: Differentially expressed analysis and weighted gene co-expression network analysis (WGCNA) was used to explore new neuropathic pain susceptibility modules and hub genes. KEGG and GO analyses was utilized to explore the potential role of these hub genes. Nomogram model and ROC curves was established to evaluate the diagnostic efficacy of hub genes. Additionally, the correlation of IL-2 with immune infiltration was explored. Finally, a Mendelian randomization study was conducted to determine the causal effect of IL-2 on neuropathic pain based on genome-wide association studies.
UNASSIGNED: WGCNA was performed to establish the networks of gene co-expression, screen for the most relevant module, and screen for 440 overlapping WGCNA-derived key genes. GO and KEGG pathway enrichment analyses demonstrated that the key genes were correlated with cytokine receptor binding, chemokine receptor binding, positive regulation of JAK-STAT cascade, chemokine-mediated signaling pathway, PI3K-AKT pathway and chemokine pathway. Through Cytoscape software, top ten up-regulated genes with high scores were IL2, SMELL, CCL4, CCR3, CXCL1, CCR1, HGF, CXCL2, GATA3, and CRP. In addition, nomogram model performed well in predicting neuropathic pain risk, and with the ROC curve, the model was showed to be effective in diagnosis. Finally, IL2 was selected and we observed that IL2 was causally associated with immune cell infiltrates in trigeminal neuralgia. In inverse variance weighting, we found that IL2 was associated with the risk of trigeminal neuralgia with an OR of 1.203 (95% CI = 1.004-1.443, p = 0.045).
UNASSIGNED: We constructed a WGCNA-based co-expression network and identified neuropathic pain-related hub genes, which may offer further insight into pre-symptomatic diagnostic approaches and may be useful for the study of molecular mechanisms for understanding neuropathic pain risk genes.

Background: Adrenocortical carcinoma (ACC) is an orphan tumor which has poor prognoses. Therefore, it is of urgent need for us to find candidate prognostic biomarkers and provide clinicians with an accurate method for survival prediction of ACC via bioinformatics and machine learning methods. Methods: Eight different methods including differentially expressed gene (DEG) analysis, weighted correlation network analysis (WGCNA), protein-protein interaction (PPI) network construction, survival analysis, expression level comparison, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were used to identify potential prognostic biomarkers for ACC via seven independent datasets. Linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and time-dependent ROC were performed to further identify meaningful prognostic biomarkers (MPBs). Cox regression analyses were performed to screen factors for nomogram construction. Results: We identified nine hub genes correlated to prognosis of patients with ACC. Furthermore, four MPBs (ASPM, BIRC5, CCNB2, and CDK1) with high accuracy of survival prediction were screened out, which were enriched in the cell cycle. We also found that mutations and copy number variants of these MPBs were associated with overall survival (OS) of ACC patients. Moreover, MPB expressions were associated with immune infiltration level. Two nomograms [OS-nomogram and disease-free survival (DFS)-nomogram] were established, which could provide clinicians with an accurate, quick, and visualized method for survival prediction. Conclusion: Four novel MPBs were identified and two nomograms were constructed, which might constitute a breakthrough in treatment and prognosis prediction of patients with ACC.