BACKGROUND: Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI.
METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape.
RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted.
CONCLUSIONS: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.

Diabetic cardiomyopathy (DCM) is a common cardiovascular complication of diabetes, which may threaten the quality of life and shorten life expectancy in the diabetic population. However, the molecular mechanisms underlying the diabetes cardiomyopathy are not fully elucidated. We analyzed two datasets from Gene Expression Omnibus (GEO). Differentially expressed and weighted gene correlation network analysis (WGCNA) was used to screen key genes and molecules. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were constructed to identify hub genes. The diagnostic value of the hub gene was evaluated using the receiver operating characteristic (ROC). Quantitative real-time PCR (RT-qPCR) was used to validate the hub genes. A total of 13 differentially co-expressed modules were selected by WGCNA and differential expression analysis. KEGG and GO analysis showed these DEGs were mainly enriched in lipid metabolism and myocardial hypertrophy pathway, cytomembrane, and mitochondrion. As a result, six genes were identified as hub genes. Finally, five genes (Pdk4, Lipe, Serpine1, Igf1r, and Bcl2l1) were found significantly changed in both the validation dataset and experimental mice with DCM. In conclusion, the present study identified five genes that may help provide novel targets for diagnosing and treating DCM.

UNASSIGNED: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research.
UNASSIGNED: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes.
UNASSIGNED: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-κB signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene.
UNASSIGNED: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.

Bone non-union is a common fracture complication that can severely impact patient outcomes, yet its mechanism is not fully understood. This study used differential analysis and weighted co-expression network analysis (WGCNA) to identify susceptibility modules and hub genes associated with fracture healing. Two datasets, GSE125289 and GSE213891, were downloaded from the GEO website, and differentially expressed miRNAs and genes were analysed and used to construct the WGCNA network. Gene ontology (GO) analysis of the differentially expressed genes showed enrichment in cytokine and inflammatory factor secretion, phagocytosis, and trans-Golgi network regulation pathways. Using bioinformatic site prediction and crossover gene search, miR-29b-3p was identified as a regulator of LIN7A expression that may negatively affect fracture healing. Potential miRNA-mRNA interactions in the bone non-union mechanism were explored, and miRNA-29-3p and LIN7A were identified as biomarkers of skeletal non-union. The expression of miRNA-29b-3p and LIN7A was verified in blood samples from patients with fracture non-union using qRT-PCR and ELISA. Overall, this study identified characteristic modules and key genes associated with fracture non-union and provided insight into its molecular mechanisms. Downregulated miRNA-29b-3p was found to downregulate LIN7A protein expression, which may affect the healing process after fracture in patients with bone non-union. These findings may serve as a prognostic biomarker and potential therapeutic target for bone non-union.

BACKGROUND: Gastric cancer is a leading cause of cancer-related deaths influenced by both genetic and environmental factors. Triphenyl phosphate (TPP) is a prevalent flame retardant, but its health implications remain to be thoroughly understood.
OBJECTIVE: To explore the link between TPP exposure and gastric cancer by examining gene expression patterns and developing a predictive model.
METHODS: Gene expression data were sourced from The Cancer Genome Atlas (TCGA) and the Comparative Toxicogenomics Database (CTD). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were employed for analysis. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to obtain phosphate flame retardant-related scores. A predictive model was constructed through differential analysis, univariate COX regression, and LASSO regression. Molecular docking was performed to assess protein interactions with TPP.
RESULTS: ssGSEA identified scores related to phosphate flame retardants in gastric cancer, which had a strong association with immune-related traits. Several genes associated with TPP were identified and used to develop a prognostic model that has clinical significance. Molecular docking showed a high binding affinity of TPP with MTTP, a gene related to lipid metabolism. Pathway analysis indicated that TPP exposure contributes to gastric cancer through lipid metabolic processes.
CONCLUSIONS: The study establishes a potential correlation between TPP exposure and gastric cancer onset, pinpointing key genes and pathways involved. This underscores the significance of environmental factors in gastric cancer research and presents a potential diagnostic tool for clinical application.

This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model\'s precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

BACKGROUND: Wilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys.
METHODS: Differential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes\' functions in WiT-49 cells.
RESULTS: The enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients\' prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells.
CONCLUSIONS: EMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

BACKGROUND: Variations exist in the response of patients with Crohn\'s disease (CD) to ustekinumab (UST) treatment, but the underlying cause remains unknown. Our objective was to investigate the involvement of immune cells and identify potential biomarkers that could predict the response to interleukin (IL) 12/23 inhibitors in patients with CD.
METHODS: The GSE207022 dataset, which consisted of 54 non-responders and 9 responders to UST in a CD cohort, was analyzed. Differentially expressed genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Least absolute shrinkage and selection operator (LASSO) regression was used to screen the most powerful hub genes. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performances of these genes. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to estimate the proportions of immune cell types. These significantly altered genes were subjected to cluster analysis into immune cell-related infiltration. To validate the reliability of the candidates, patients prescribed UST as a first-line biologic in a prospective cohort were included as an independent validation dataset.
RESULTS: A total of 99 DEGs were identified in the integrated dataset. GO and KEGG analyses revealed significant enrichment of immune response pathways in patients with CD. Thirteen genes (SOCS3, CD55, KDM5D, IGFBP5, LCN2, SLC15A1, XPNPEP2, HLA-DQA2, HMGCS2, DDX3Y, ITGB2, CDKN2B and HLA-DQA1), which were primarily associated with the response versus nonresponse patients, were identified and included in the LASSO analysis. These genes accurately predicted treatment response, with an area under the curve (AUC) of 0.938. T helper cell type 1 (Th1) cell polarization was comparatively strong in nonresponse individuals. Positive connections were observed between Th1 cells and the LCN2 and KDM5D genes. Furthermore, we employed an independent validation dataset and early experimental verification to validate the LCN2 and KDM5D genes as effective predictive markers.
CONCLUSIONS: Th1 cell polarization is an important cause of nonresponse to UST therapy in patients with CD. LCN2 and KDM5D can be used as predictive markers to effectively identify nonresponse patients.
BACKGROUND: Trial registration number: NCT05542459; Date of registration: 2022-09-14; URL: https://www.
RESULTS: gov .

Gegensan (GGS) has been reported for the treatment of alcoholic liver disease (ALD), but its therapeutic mechanism is still unclear. This paper aims to determine the therapeutic mechanism and targets of action of GGS on alcoholic liver disease utilizing network pharmacology and bioinformatics. The active ingredients in GGS were screened in the literature and databases, and common targets of ALD were then obtained from public databases to construct the network diagram of traditional Chinese medicine-active ingredient targets. Based on the common targets, Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to find target enrichment pathways, and the core targets were screened out by combining differential analysis and protein-protein interaction network analysis. Molecular docking was performed to verify the binding effect between the core targets and the corresponding active ingredients. ALD and GGS have 84 common targets, corresponding to 91 active ingredients. After subsequent differential analysis and protein-protein interaction network analysis, 10 core targets were identified. Gene Ontology and KEGG enrichment analyses showed that the main BPs corresponding to the common targets included the response to lipopolysaccharide, inflammatory response, etc. The KEGG pathways involved in the regulation of the common targets included the lipid-atherosclerosis pathway and the alcoholic liver disease pathway, etc. Further molecular docking showed that the core targets CYP1A1, CYP1A2, CXCL8, ADH1C, MMP1, SERPINE1, COL1A1, APOB, MMP1, and their corresponding 4 active ingredients, Naringenin, Kaempferol, Quercetin, and Stigmasterol, have a greater docking potential. The above results suggest that GGS can regulate lipid metabolism and inflammatory response in the ALD process, and alleviate the lipid accumulation and oxidative stress caused by ethanol. This study analyzed the core targets and mechanisms of action of GGS on ALD, which provides certain theoretical support for the further development of GGS in the treatment of ALD, and provides a reference for the subsequent research on the treatment of ALD.

OBJECTIVE: Keratoconus (KC) is a condition characterized by progressive corneal steepening and thinning. However, its pathophysiological mechanism remains vague. We mainly performed literature mining to extract bioinformatic and related data on KC at the RNA level. The objective of this study was to explore the potential pathological mechanisms of KC by identifying hub genes and key molecular pathways at the RNA level.
METHODS: We performed an exhaustive search of the PubMed database and identified studies that pertained to gene transcripts derived from diverse corneal layers in patients with KC. The identified differentially expressed genes were intersected, and overlapping genes were extracted for further analyses. Significantly enriched genes were screened using \"Gene Ontology\" (GO) and \"Kyoto Encyclopedia of Genes and Genomes\" (KEGG) analysis with the \"Database for Annotation, Visualization, and Integrated Discovery\" (DAVID) database. A protein-protein interaction (PPI) network was constructed for the significantly enriched genes using the STRING database. The PPI network was visualized using the Cytoscape software, and hub genes were screened via betweenness centrality values. Pathways that play a critical role in the pathophysiology of KC were discovered using the GO and KEGG analyses of the hub genes.
RESULTS: 68 overlapping genes were obtained. Fifty genes were significantly enriched in 67 biological processes, and 16 genes were identified in 7 KEGG pathways. Moreover, 14 nodes and 32 edges were identified via the PPI network constructed using the STRING database. Multiple analyses identified 4 hub genes, 12 enriched biological processes, and 6 KEGG pathways. GO enrichment analysis showed that the hub genes are mainly involved in the positive regulation of apoptotic process, and KEGG analysis showed that the hub genes are primarily associated with the interleukin-17 (IL-17) and tumor necrosis factor (TNF) pathways. Overall, the matrix metalloproteinase 9, IL-6, estrogen receptor 1, and prostaglandin-endoperoxide synthase 2 were the potential important genes associated with KC.
CONCLUSIONS: Four genes, matrix metalloproteinase 9, IL-6, estrogen receptor 1, and prostaglandin endoperoxide synthase 2, as well as IL-17 and TNF pathways, are critical in the development of KC. Inflammation and apoptosis may contribute to the pathogenesis of KC.