RESUMEN
BACKGROUND: Steroid-induced osteonecrosis of femoral head (SONFH) is a severe health risk, and this study aims to identify immune-related biomarkers and pathways associated with the disease through bioinformatics analysis and animal experiments. METHOD: Using SONFH-related datasets obtained from the GEO database, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to extract SONFH-related genes. A protein-protein interaction (PPI) network was then constructed, and core sub-network genes were identified. Immune cell infiltration and clustering analysis of SONFH samples were performed to assess differences in immune cell populations. WGCNA analysis was used to identify module genes associated with immune cells, and hub genes were identified using machine learning. Internal and external validation along with animal experiments were conducted to confirm the differential expression of hub genes and infiltration of immune cells in SONFH. RESULTS: Differential expression analysis revealed 502 DEGs. WGCNA analysis identified a blue module closely related to SONFH, containing 1928 module genes. Intersection analysis between DEGs and blue module genes resulted in 453 intersecting genes. The PPI network and MCODE module identified 15 key targets enriched in various signaling pathways. Analysis of immune cell infiltration showed statistically significant differences in CD8 + t cells, monocytes, macrophages M2 and neutrophils between SONFH and control samples. Unsupervised clustering classified SONFH samples into two clusters (C1 and C2), which also exhibited significant differences in immune cell infiltration. The hub genes (ICAM1, NR3C1, and IKBKB) were further identified using WGCNA and machine learning analysis. Based on these hub genes, a clinical prediction model was constructed and validated internally and externally. Animal experiments confirmed the upregulation of hub genes in SONFH, with an associated increase in immune cell infiltration. CONCLUSION: This study identified ICAM1, NR3C1, and IKBKB as potential immune-related biomarkers involved in immune cell infiltration of CD8 + t cells, monocytes, macrophages M2, neutrophils and other immune cells in the pathogenesis of SONFH. These biomarkers act through modulation of the chemokine signaling pathway, Toll-like receptor signaling pathway, and other pathways. These findings provide valuable insights into the disease mechanism of SONFH and may aid in future drug development efforts.
Asunto(s)
Necrosis de la Cabeza Femoral , Mapas de Interacción de Proteínas , Animales , Necrosis de la Cabeza Femoral/inducido químicamente , Necrosis de la Cabeza Femoral/genética , Necrosis de la Cabeza Femoral/inmunología , Humanos , Biomarcadores/metabolismo , Perfilación de la Expresión Génica , Modelos Animales de Enfermedad , Biología Computacional , Redes Reguladoras de Genes , Ratones , Masculino , Esteroides , Aprendizaje Automático , Transducción de Señal/genéticaRESUMEN
Chronic hepatitis B virus infection (HBV) infection appears to be associated with extrahepatic cancers. This study aims to evaluate the causality and evolutionary mechanism of chronic HBV infection and gastric cancer through Mendelian randomization (MR) analysis and bioinformatics analysis. We conducted 2-sample MR to investigate the causal relationship between chronic HBV infection and gastric cancer. We identified 5 independent genetic variants closely associated with exposure (chronic HBV infection) as instrumental variables in a sample of 1371 cases and 2938 controls of East Asian descent in Korea. The genome wide association study (GWAS) data for the outcome variable came from the Japanese Biobank. Bioinformatics analysis was used to explore the evolutionary mechanism of chronic HBV infection and gastric cancer. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify key targets that are commonly associated with both diseases, and their biological functions were investigated. Multiple machine-learning models were employed to select hub genes. The MR analysis showed a positive causal relationship between chronic HBV infection and gastric cancer (IVW: ORâ =â 1.165, 95% CIâ =â 1.085-1.250, Pâ <â .001), and the result was robust in sensitivity analysis. According to the bioinformatics analysis, the 5 key targets were mainly enriched in Toll-like receptor signaling and PI3K-Akt signaling. Two hub genes, CXCL9 and COL6A2, were identified, and a high-performing predictive model was constructed. Chronic HBV infection is positively associated with gastric cancer, and the evolutionary mechanism may be related to Toll-like receptor signaling. Prospective studies are still needed to confirm these findings.
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Hepatitis B Crónica , Hepatitis B , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/genética , Hepatitis B Crónica/complicaciones , Hepatitis B Crónica/genética , Estudio de Asociación del Genoma Completo , Análisis de la Aleatorización Mendeliana , Fosfatidilinositol 3-Quinasas , Biología Computacional , Receptores Toll-LikeRESUMEN
Type 2 diabetes mellitus (T2DM) is a progressive disease. We utilized bioinformatics analysis and experimental research to identify biomarkers indicative of the progression of T2DM, aiming for early detection of the disease and timely clinical intervention. Integrating Mfuzz analysis with differential expression analysis, we identified 76 genes associated with the progression of T2DM, which were primarily enriched in signaling pathways such as apoptosis, p53 signaling, and necroptosis. Subsequently, using various analytical methods, including machine learning, we further narrowed down the hub genes to STK17A and CCT5. Based on the hub genes, we calculated the risk score for samples and interestingly found that the score correlated with multiple programmed cell death (PCD) pathways. Animal experiments revealed that the diabetes model exhibited higher levels of MDA and LDH, with lower expression of SOD, accompanied by islet cell apoptosis. In conclusion, our study suggests that during the progression of diabetes, STK17A and CCT5 may contribute to the advancement of the disease by regulating oxidative stress, programmed cell death pathways, and critical signaling pathways such as p53 and MAPK, thereby promoting the death of islet cells. This provides substantial evidence in support of further disease prevention and treatment strategies.
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Diabetes Mellitus Tipo 2 , Intolerancia a la Glucosa , Animales , Diabetes Mellitus Tipo 2/metabolismo , Intolerancia a la Glucosa/metabolismo , Proteína p53 Supresora de Tumor/genética , Biomarcadores , Biología ComputacionalRESUMEN
Myocardial infarction (MI) is a major cause of death and disability worldwide, but current treatments are limited by their invasiveness, side effects, and lack of efficacy. Novel drug targets for MI prevention are urgently needed. In this study, we used Mendelian randomization to identify potential therapeutic targets for MI using plasma protein quantitative trait loci as exposure variables and MI as the outcome variable. We further validated our findings using reverse causation analysis, Bayesian co-localization analysis, and external datasets. We also constructed a protein-protein interaction network to explore the relationships between the identified proteins and known MI targets. Our analysis revealed 2 proteins, LPA and APOA5, as potential drug targets for MI, with causal effects on MI risk confirmed by multiple lines of evidence. LPA and APOA5 are involved in lipid metabolism and interact with target proteins of current MI medications. We also found 4 other proteins, IL1RN, FN1, NT5C, and SEMA3C, that may have potential as drug targets but require further confirmation. Our study demonstrates the utility of Mendelian randomization and protein quantitative trait loci in discovering novel drug targets for complex diseases such as MI. It provides insights into the underlying mechanisms of MI pathology and treatment.
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Análisis de la Aleatorización Mendeliana , Infarto del Miocardio , Humanos , Teorema de Bayes , Infarto del Miocardio/tratamiento farmacológico , Infarto del Miocardio/genética , Mapas de Interacción de Proteínas , Estudio de Asociación del Genoma Completo , Polimorfismo de Nucleótido SimpleRESUMEN
Acute myocardial infarction (AMI) is a global health threat, and programmed cell death (PCD) plays a crucial role in its occurrence and development. In this study, integrated bioinformatics tools were used to explore new biomarkers and therapeutic targets in AMI. Thirteen types of PCD-related genes were identified through literature review, KEGG, and GSEA pathways. Gene expression matrices and clinical data from AMI patients and healthy controls were obtained from the GEO database. Statistical analysis in R identified 377 differentially expressed genes in AMI patients. Intersection analysis between the differentially expressed genes and PCD-related genes revealed 24 genes positively correlated with immune cells such as Neutrophils and Monocytes, while negatively correlated with T cells CD4 memory resting and Plasma cells. Unsupervised clustering analysis divided patients into two groups (C1 and C2) based on the expression levels of these 24 genes. GSVA analysis showed that C2 patients were more active in pathways related to maintaining normal cell morphology and promoting phagocytosis, suggesting a lower programmed cell death rate and a higher tendency to maintain cell survival. Two hub genes, TNFAIP3 and TP53INP2, were identified through LASSO regression analysis and SVM-RFE, and were validated using an external dataset and RT-qPCRãWestern blot and ELISA analysis. These hub genes showed significantly higher expression and protein secretion levels in AMI patients compared to healthy individuals. Overall, regulating and controlling PCD, particularly through the identified hub genes, TNFAIP3 and TP53INP2, may provide new therapeutic strategies for improving the prognosis of AMI patients and preventing heart failure.