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The exact mechanisms associated with secondary brain damage following traumatic brain injury (TBI) remain unclear; therefore, identifying the critical molecular mechanisms involved in TBI is essential. The mRNA expression microarray GSE2871 was downloaded from the Gene Expression Omnibus (GEO) repository. GSE2871 comprises a total of 31 cerebral cortex samples, including two post-TBI time points. The microarray features eight control and seven TBI samples, from 4 hours post-TBI, and eight control and eight TBI samples from 24 hours post-TBI. In this bioinformatics-based study, 109 and 66 differentially expressed genes (DEGs) were identified in a Sprague-Dawley (SD) rat TBI model, 4 and 24 hours post-TBI, respectively. Functional enrichment analysis showed that the identified DEGs were significantly enriched in several terms, such as positive regulation of nuclear factor-κB transcription factor activity, mitogen-activated protein kinase signaling pathway, negative regulation of apoptotic process, and tumor necrosis factor signaling pathway. Moreover, the hub genes with high connectivity degrees were primarily related to inflammatory mediators. To validate the top five hub genes, a rat model of TBI was established using the weight-drop method, and real-time quantitative polymerase chain reaction analysis of the cerebral cortex was performed. The results showed that compared with control rats, Tnf-α, c-Myc, Spp1, Cxcl10, Ptprc, Egf, Mmp9, and Lcn2 were upregulated, and Fn1 was downregulated in TBI rats. Among these hub genes, Fn1, c-Myc, and Ptprc may represent novel biomarkers or therapeutic targets for TBI. These identified pathways and key genes may provide insights into the molecular mechanisms of TBI and provide potential treatment targets for patients with TBI. This study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Nanchang University, China (approval No. 003) in January 2016.

Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease. Objective: This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC. Materials and Methods: In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened. Results: Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. NCAPG, KIF11, TTK, PBK, MELK, ASPM, TPX2, BUB1, TOP2A, and KIF2C are the key genes, of which 5 genes (KIF11, TTK, PBK, MELK, and TOP2A) were druggable. Conclusion: This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments. Key words: Protein interactions, Cervical cancer, Hub genes, Gene expression, DEGs.

Hereditary gingival fibromatosis (HGF) is a rare genetic disorder characterized by excessive gum growth, often presenting in childhood or adolescence. Symptoms include difficulties in speech, eating, oral hygiene, and psychological distress. Understanding the molecular mechanisms behind HGF is crucial for identifying potential therapeutic targets. This study aimed to predict interactomic hub genes in HGF using neural networks. We analyzed the GEO dataset GSE4250 using the geor2 tool to identify differentially expressed genes. Cytoscape and the CytoHubba plugin were employed to construct the interactome, ranking hub genes based on centrality scores. A neural network model with an 80:20 train-test split was used to predict hub and non-hub genes, achieving an AUC of 0.853, classification accuracy of 0.720, F1 score of 0.720, precision of 0.720, and recall of 0.720. The resulting network consisted of 147 nodes and 1092 edges, demonstrating moderate heterogeneity and connectivity. Ten key hub genes were identified, offering insights into the molecular basis of HGF. While the neural network model shows promising predictive capacity, further validation in larger cohorts is required. Adding predictive features and functional validation experiments could deepen understanding of HGF's biological mechanisms. La fibromatosis gingival hereditaria (FGH) es un trastorno genético raro caracterizado por un crecimiento excesivo de las encías, que suele manifestarse en la infancia o adolescencia. Los síntomas incluyen dificultades en el habla, la alimentación, la higiene oral y angustia psicológica. Comprender los mecanismos moleculares subyacentes a la FGH es crucial para identificar posibles dianas terapéuticas. Este estudio tuvo como objetivo predecir genes hub en el interactoma de la FGH utilizando redes neuronales. Analizamos el conjunto de datos GEO GSE4250 mediante la herramienta geor2 para identificar genes diferencialmente expresados. Se emplearon Cytoscape y el complemento CytoHubba para construir el interactoma, clasificando los genes hub según puntuaciones de centralidad. Se utilizó un modelo de red neuronal con una división 80:20 entrenamiento-prueba para predecir genes hub y no hub, logrando un AUC de 0.853, una precisión de clasificación de 0.720, un F1-score de 0.720, una precisión de 0.720 y una exhaustividad de 0.720. La red resultante constó de 147 nodos y 1092 aristas, mostrando una heterogeneidad y conectividad moderadas. Se identificaron diez genes hub clave, lo que aporta información sobre las bases moleculares de la FGH. Aunque el modelo de red neuronal muestra una capacidad predictiva prometedora, se requiere validación en cohortes más grandes. La incorporación de características predictivas adicionales y experimentos de validación funcional podría profundizar en la comprensión de los mecanismos biológicos de la FGH.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer in the world, accounting for more than 90% of head and neck malignant tumors. However, its molecular mechanism is largely unknown. To help elucidate the potential mechanism of HNSCC tumorigenesis, we investigated the gene interaction patterns associated with tumorigenesis. Weighted gene co-expression network analysis (WGCNA) can help us to predict the intrinsic relationship or correlation between gene expression. Additionally, we further explored the combination of clinical information and module construction. Sixteen modules were constructed, among which the key module most closely associated with clinical information was identified. By analyzing the genes in this module, we found that the latter may be related to the immune response, inflammatory response and formation of the tumor microenvironment. Sixteen hub genes were identified- , and . We further validated these genes at the transcriptional and translation levels. The innovative use of a weighted network to analyze HNSCC samples provides new insights into the molecular mechanism and prognosis of HNSCC. Additionally, the hub genes we identified can be used as biomarkers and therapeutic targets of HNSCC, laying the foundation for the accurate diagnosis and treatment of HNSCC in clinical and research in the future.

Non-alcoholic fatty liver disease (NAFLD), also known as Metabolic dysfunction-associated fatty liver disease (MASLD), has become one of the most common chronic liver diseases worldwide, approximately 30% of adults and 70%~80% of patients with obesity and diabetes suffer from NAFLD. We attempted to find a potential hub gene in NAFLD hepatocyte cell model induced by palmitic acid and oil acid (PAOA), and investigated the function of the hub-gene. We searched and downloaded the GSE122660 dataset from GEO-DataSets, and differentially expressed genes (DEGs) were analyzed using R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify the significantly activated signaling pathways in steatohepatitis. A protein-protein interaction (PPI) network was constructed to identify hub genes among the DEGs. qRT-PCR, Western blotting, and Oil Red O staining were used to explore the function of hub genes in PAOA-induced hepatocytes in vitro. A total of 255 DEGs were identified in the GSE122660 dataset and were primarily associated with inflammation-and lipid metabolism-related pathways. The tribbles pseudokinase 3 (TRIB3) was highlighted as a hub gene. We found that TRIB3 was upregulated in CDHFDinduced NAFLD mouse liver tissue and hepatocyte cell models. Furthermore, TRIB3 aggravated PAOA-induced lipid accumulation and inflammation in hepatocytes in vitro. The present study identified TRIB3 as a hub gene for steatohepatitis and aggravated lipid accumulation and inflammation in vitro. Therefore, targeting TRIB3 could be a potential pharmacological strategy for NAFLD treatment.

Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data. Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients' survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database. A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN. In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.

Breast cancer is the second most common cause of mortality in women; therefore, the identification of novel putative markers is required to improve its diagnosis and prognosis. Selenium is known to protect mammary epithelial cells from oxidative DNA damage, and to inhibit the initiation phase of carcinogenesis by stimulating DNA repair and apoptosis regulation. Consequently, the present study has focused attention on the selenoprotein family and their involvement in breast cancer. The present study performed a global analysis of the seleno-transcriptome expression in human breast cancer MCF-7 and MDA-MB231 cell lines compared with healthy breast MCF-10A cells using reverse transcription-quantitative polymerase chain reaction. The present data revealed the presence of differently expressed genes in MCF-7 and MDA-MB231 cells compared with MCF-10A cells: Four downregulated [glutathione peroxidase (GPX)1, GPX4, GPX5 and GPX7] and three upregulated (deiodinase iodothyronine, type II, GPX2 and GPX3) genes. Additionally, interactomic investigation were performed by the present study to evaluate the association between the downregulated and upregulated genes, and to identify putative HUB nodes, which represent the centers of association between the genes that are capable of direct control over the gene networks. Network analysis revealed that all differentially regulated genes, with the exception of selenoprotein T, are implicated in the same network that presents three HUB nodes interconnected to the selenoprotein mRNAs, including TP53, estrogen receptor 1 and catenin-β1 (CTNNB1). Overall, these data demonstrated for the first time, a profile of seleno-mRNAs specific for human breast cells, indicating that these genes alter their expression on the basis of the ER-positivity or negativity of breast cancer cells.

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

BackgroundAlthough more and more evidence has supported psoriasis is prone to atherosclerosis, the common mechanism of its occurrence is still not fully elucidated. The purpose of this study is to further explore the molecular mechanism of the occurrence of this complication.MethodsThe gene expression profiles of psoriasis (GSE30999) and atherosclerosis (GSE28829) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis.ResultsA total of 94 common DEGs (24 downregulated genes and 70 upregulated genes) was selected for subsequent analyses. Functional analysis emphasizes the important role of chemokines and cytokines in these two diseases. In addition, lipopolysaccharide-mediated signaling pathway is closely related to both. Finally, 16 important hub genes were identified using cytoHubba, including LYN, CSF2RB, IL1RN, RAC2, CCL5, IRF8, C1QB, MMP9, PLEK, PTPRC, FYB, BCL2A1, LCP2, CD53, NCF2 and TLR2.ConclusionsOur study reveals the common pathogenesis of psoriasis and atherosclerosis. These common pathways and hub genes may provide new ideas for further mechanism research.