BACKGROUND: The pathophysiology and molecular mechanisms of schizophrenia (SCZ) remain unclear, and the effective treatment resources are still limited. The goal of this study is to identify the expression of AQP4 in SCZ patients and explore whether AQP4 inhibition could ameliorate schizophrenia-like behaviors and its mechanisms.
METHODS: Microarray datasets of PFC compared with healthy control were searched in the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were analyzed with the GEO2R online tool. The Venny online tool and metascape online software were used to identify common abnormally expressed genes and conduct cell type signature enrichment analysis. SCZ mouse models were induced with MK-801, an NMDA receptor antagonist (intraperitoneal injection, 0.1 mg/kg/day for 7 days), and C6 cell models were treated with 100 μM MK-801. RT-qPCR, Western Blotting, and immunofluorescence were employed to determine the expression of AQP4, proinflammatory cytokines, and GFAP. Open field tests and social interaction tests were performed to evaluate the schizophrenia-like behaviors.
RESULTS: Bioinformatics analysis identified upregulation of AQP4 in the PFC of SCZ patients compared with healthy controls. Cell type signature enrichment analysis showed that all three DEGs lists were strongly enriched in the FAN EMBRYONIC CTX ASTROCYTE 2 category. Upregulation of AQP4 was also observed in MK-801-treated C6 cells and the PFC of MK-801-induced SCZ mouse model. Moreover, AQP4 inhibition with TGN-020 (an inhibitor of AQP4) improved anxiety-like behavior and social novelty preference defects in MK-801-treated mice. AQP4 inhibition also reduced the expression of IL-1β, IL-6, and TNF-α in MK-801-treated C6 cells and mouse model.
CONCLUSIONS: AQP4 is upregulated in the PFC of SCZ patients compared with healthy controls. AQP4 inhibition could alleviate the anxiety-like behavior and social novelty defects in MK-801-treated mice, this may be due to the role of AQP4 in the regulation of the expression of proinflammatory cytokines.

Crohn\'s disease (CD) presents with diverse clinical phenotypes due to persistent inflammation of the gastrointestinal tract. Its global incidence is on the rise. Neutrophil extracellular traps (NETs) are networks released by neutrophils that capture microbicidal proteins and oxidases targeting pathogens. Research has shown that NETs are implicated in the pathogenesis of several immune-mediated diseases such as rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. The goal of this study was to identify a panel of NET-related genes to construct a diagnostic and therapeutic model for CD. Through analysis of the GEO database, we identified 1950 differentially expressed genes (DEGs) associated with CD. Gene enrichment and immune cell infiltration analyses indicate that neutrophil infiltrates and chemokine-related pathways are predominantly involved in CD, with other immune cells such as CD4 and M1 macrophages also playing a role in disease progression. Utilizing weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks, we identified six hub genes (SPP1, SOCS3, TIMP1, IRF1, CXCL2 and CD274). To validate the accuracy of our model, we performed external validation with statistical differences(p < 0.05). Additionally, immunohistochemical experiments demonstrated higher protein expression of the hub genes in colonic tissues from CD patients compared to healthy subjects (p < 0.05). In summary, we identified six effective hub genes associated with NETs as potential diagnostic markers for CD. These markers not only offer targets for future research but also hold promise for the development of novel therapeutic interventions for CD.

OBJECTIVE: Heart failure (HF) and non-alcoholic fatty liver disease (NAFLD) are significant global health issues with a complex interrelationship. This study investigates their shared biomarkers and causal relationships using bioinformatics and Mendelian randomization (MR) approaches.
METHODS: We analysed NAFLD and HF datasets from the Gene Expression Omnibus (GEO). The GSE126848 dataset included 57 liver biopsy samples [14 healthy individuals, 12 obese subjects, 15 NAFL patients and 16 non-alcoholic steatohepatitis (NASH) patients]. The GSE24807 dataset comprised 12 NASH samples and 5 healthy controls. The GSE57338 dataset included 313 cardiac muscle samples [177 HF patients (95 ischaemic heart disease patients and 82 idiopathic dilated cardiomyopathy patients) and 136 healthy controls]. The GSE84796 dataset consisted of 10 end-stage HF patients and 7 healthy hearts procured from organ donors. We identified differentially expressed genes (DEGs) and constructed a protein-protein interaction (PPI) network. Functional pathways were elucidated through enrichment analyses using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneMANIA annotation. Single nucleotide polymorphism (SNP) data for HF and NAFLD were sourced from genome-wide association studies (GWAS). The HF dataset included 486 160 samples (14 262 experimental and 471 898 control), and the NAFLD dataset comprised 377 988 samples (4761 experimental and 373 227 control). MR analysis investigates the causal interrelations.
RESULTS: Our analysis revealed 4032 DEGs from GSE126848 and 286 DEGs from GSE57338. The top 10 hub genes (CD163, VSIG4, CXCL10, FCER1G, FPR1, C1QB, CCR1, C1orf162, MRC1 and CD38) were significantly enriched in immune response, calcium ion concentration regulation and positive regulation of monocyte chemotaxis. CIBERSORT analysis indicated associations between these hub genes and natural killer (NK) cells and macrophages. Transcription factor (TF) target prediction for CD38, CXCL10 and CCR1 highlighted related TFs. A two-sample MR analysis confirmed a bidirectional causal relationship between NAFLD and HF. The main method [inverse variance weighted (IVW)] demonstrated a significant positive causal relationship between NAFLD and HF [P = 0.037; odds ratio (OR) = 1.024; 95% confidence interval (CI): 1.001 to 1.048]. Similarly, HF was associated with an increase in the risk of NAFLD (P < 0.001; OR = 1.117; 95% CI: 1.053 to 1.185).
CONCLUSIONS: Our findings reveal novel molecular signatures common to NAFLD and HF and confirm their bidirectional causality, highlighting the potential for targeted therapeutic interventions and prompting further investigation into their intricate relationship.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive disease of vascular remodeling characterized by persistent pulmonary arterial pressure elevation, which can lead to right heart failure and premature death. Given the complex pathogenesis and poor prognosis of PAH, the identification and investigation of biomarkers become increasingly critical for advancing further understanding of the disease.
METHODS: PAH-related datasets, GSE49114, GSE180169 and GSE154959, were downloaded from the publicly available GEO database. By performing WGCNA on the GSE49114 dataset, a total of 906 PAH-related key module genes were screened out. By carrying out differential analysis on the GSE180169 dataset, a total of 576 differentially expressed genes were identified. Additionally, the GSE154959 single-cell sequencing dataset was also subjected to differential analysis, leading to the identification of 34 DEGs within endothelial cells. By taking intersection of the above three groups of DEGs, five PAH-related hub genes were screened out, namely Plvap, Cyp4b1, Foxf1, H2-Ab1, and H2-Eb1, among which H2-Ab1 was selected for subsequent experiments.
RESULTS: A SuHx mouse model was prepared using the SU5416/hypoxia method, and the successful construction of the model was evaluated through Hematoxylin-Eosin staining, hemodynamic detection, fulton index, and Western Blot (WB). The results of WB and qRT-PCR demonstrated a significant upregulation of H2-Ab1 expression in SuHx mice. Consistent with the results of bioinformatics analysis, a time-dependent increase was observed in H2-Ab1 expression in hypoxia-treated mouse pulmonary artery endothelial cells (PAECs). To investigate whether H2-Ab1 affects the development and progression of PAH, we knocked down H2-Ab1 expression in PAECs, and found that its knockdown inhibited the viability, adhesion, migration, and angiogenesis, while concurrently promoted the apoptosis of PAECs.
CONCLUSIONS: H2-Ab1 could regulate the proliferation, apoptosis, adhesion, migration, and angiogenesis of PAECs.

Sepsis is a highly lethal disease that poses a serious threat to human health. Increasing evidence indicates that neutrophil extracellular traps (NETs) are key factors in the pathological progression of sepsis. This study aims to screen potential biomarkers for sepsis and delve into their regulatory function in the pathogenesis. We downloaded 6 microarray datasets from the Gene Expression Omnibus (GEO) database, with 4 as the training sets and 2 as the validation sets. NETs-related genes (NRGs) were obtained from relevant literature. Differential expression analysis was performed on four training sets separately. We intersected differentially expressed genes (DEGs) from the four training sets and NRGs, finally resulting in 19 NETs-related sepsis genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) unearthed that NETs-related sepsis genes were majorly abundant in functions and pathways such as defense response to bacterium and Neutrophil extracellular trap formation. Using the PPI network, the MCC algorithm, and the MCODE algorithm in the CytoHubba plugin, 7 sepsis hub genes (ELANE, TLR4, MPO, PADI4, CTSG, MMP9, S100A12) were identified. ROC curve for each Hub gene in the training and validation sets were plotted, which revealed that the Area Under Curve (AUC) values are all greater than 0.6, indicating good classification ability. A total of 349 miRNAs targeting Hub genes were predicted in the mirDIP database, and 620 lncRNAs targeting miRNAs were predicted in the ENCORI database. The ceRNA regulatory network was constructed using Cytoscape software. Finally, we employed the cMAP database to predict small molecular complexes as potentially effective drugs for the treatment of sepsis, such as chloroquine, harpagoside, and PD-123319. In conclusion, this project successfully identified 7 core genes, which may serve as promising candidates for novel sepsis biomarkers. Meanwhile, we constructed a related ceRNA network and predicted potential targeted drugs, providing potential therapeutic targets and treatment strategies for sepsis patients.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan-Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

UNASSIGNED: This study focuses on evaluating the prognostic value of the NDC80 kinetochore complex in ovarian cancer (OC) using the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database and reveals the relationship between the NDC80 complex and immune infiltrates in OC.
UNASSIGNED: We collected data on NDC80 complex expression levels in both OC tissues and non-OC ovarian tissues from the University of California Santa Cruz Xena and GEO databases. The clinicopathological characteristics correlated with overall survival were analyzed using Cox regression and the Kaplan-Meier method. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis and CIBERSORT were performed using data from TCGA database. Immunohistochemical staining was used to verify the higher expression level of NUF2 protein in OC in vitro. Meanwhile, we utilized the Tumor Immune Estimation Resource to analyze the correlation between the NDC80 complex and immunocyte infiltration.
UNASSIGNED: The NDC80 complex expression level was prominently higher in OC tissues than in non-OC ovarian tissues and correlated with advanced histologic grade characteristics. Gene expression profiling interactive analysis and the Kaplan-Meier survival curve uncovered a close relationship between high expression of the NDC80 complex and poor overall survival in OC patients. The univariate Cox regression hazard model produced age, pathologic stage, tumor status, primary therapy outcome, SPC24 expression level, and Karnofsky performance score as prognostic factors for OC patients. NDC80 complex expression levels were highly associated with immune cell infiltration, showing NK CD56 bright cells and NK cells with a negative correlation and T helper 2 cells with a positive correlation (P<0.05).
UNASSIGNED: These findings provide evidence that an increased expression level of the NDC80 complex is closely associated with the progression of OC and could also serve as a novel target of immunotherapy in OC.

Aim: This study aims to explore the molecular mechanisms of cystic fibrosis (CF) complicated with nontuberculous mycobacteria (NTM) infection. Materials & methods: Expression profiles of CF with NTM-infected patients were downloaded from GEO database. Intersection analysis yielded 78 genes associated with CF with NTM infection. The protein-protein interaction (PPI) network and the functions of hub genes were investigated. Results: Five hub genes (PIK3R1, IL1A, CXCR4, ACTN1, PFN1) were identified, which were primarily enriched in actin-related biological processes and pathways. Transcription factors RELA, JUN, NFKB1 and FOS that regulated hub genes modulated IL1A expression, while 21 other transcription factors regulated CXCR4 expression. Conclusion: In summary, this study may provide new insights into the mechanisms of CF with NTM infection.
[Box: see text].

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 - / - p73 + / + and p53 - / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients\' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.