Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study.

OBJECTIVE: Acute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence. METHODS: Gene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI. RESULTS: 285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF-kappa B signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911-0.984, p = 0.005). CONCLUSIONS: The result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart.

Lipid traits and lipid-lowering drug target genes and risk of melanoma: a mendelian randomization study.

Our study aimed to investigate the role of lipids in melanoma risk and the effect of lipid-lowering drug targets on melanoma. Using Mendelian Randomization analysis, we examined the genetic agents of nine lipid-lowering drugs and their association with melanoma risk. We found that genetically proxied inhibition of HMGCR, ABCG5/ABCG8, and ANGPTL3 was associated with a reduced risk of melanoma. On the other hand, inhibition of LPL and Apo-B100 was significantly associated with an increased risk of melanoma. Sensitivity analyses did not reveal any statistical evidence of bias from pleiotropy or genetic confounding. We did not find a robust association between lipid traits NPC1L1, PCSK9, APOC3 inhibition, and melanoma risk. These findings were validated using two independent lipid datasets. Our analysis also revealed that HMGCR, ANGPTL3, and ABCG5/ABCG8 inhibitors reduced melanoma risk independent of their effects on lipids. This suggests that these targets may have potential for melanoma prevention or treatment. In conclusion, our study provides evidence for a causal role of lipids in melanoma risk and highlights specific lipid-lowering drug targets that may be effective in reducing the risk of melanoma. These findings contribute to the understanding of the underlying mechanisms of melanoma development and provide potential avenues for further research and therapeutic interventions.

Dietary factors and the risk of atopic dermatitis: a Mendelian randomisation study.

Previous studies have revealed an association between dietary factors and atopic dermatitis (AD). To explore whether there was a causal relationship between diet and AD, we performed Mendelian randomisation (MR) analysis. The dataset of twenty-one dietary factors was obtained from UK Biobank. The dataset for AD was obtained from the publicly available FinnGen consortium. The main research method was the inverse-variance weighting method, which was supplemented by MR‒Egger, weighted median and weighted mode. In addition, sensitivity analysis was performed to ensure the accuracy of the results. The study revealed that beef intake (OR = 0·351; 95 % CI 0·145, 0·847; P = 0·020) and white bread intake (OR = 0·141; 95 % CI 0·030, 0·656; P = 0·012) may be protective factors against AD. There were no causal relationships between AD and any other dietary intake factors. Sensitivity analysis showed that our results were reliable, and no heterogeneity or pleiotropy was found. Therefore, we believe that beef intake may be associated with a reduced risk of AD. Although white bread was significant in the IVW analysis, there was large uncertainty in the results given the wide 95 % CI. Other factors were not associated with AD in this study.

Identification of platelet-related subtypes and diagnostic markers in pediatric Crohn's disease based on WGCNA and machine learning.

Background: The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD. Methods: Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences. Results: Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes. Conclusion: In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.

Identification of cuproptosis-associated subtypes and signature genes for diagnosis and risk prediction of Ulcerative colitis based on machine learning.

Background: Ulcerative colitis (UC) is a chronic and debilitating inflammatory bowel disease that impairs quality of life. Cuproptosis, a recently discovered form of cell death, has been linked to many inflammatory diseases, including UC. This study aimed to examine the biological and clinical significance of cuproptosis-related genes in UC. Methods: Three gene expression profiles of UC were obtained from the Gene Expression Omnibus (GEO) database to form the combined dataset. Differential analysis was performed based on the combined dataset to identify differentially expressed genes, which were intersected with cuproptosis-related genes to obtain differentially expressed cuproptosis-related genes (DECRGs). Machine learning was conducted based on DECRGs to identify signature genes. The prediction model of UC was established using signature genes, and the molecular subtypes related to cuproptosis of UC were identified. Functional enrichment analysis and immune infiltration analysis were used to evaluate the biological characteristics and immune infiltration landscape of signature genes and molecular subtypes. Results: Seven signature genes (ABCB1, AQP1, BACE1, CA3, COX5A, DAPK2, and LDHD) were identified through the machine learning algorithms, and the nomogram built from these genes had excellent predictive performance. The 298 UC samples were divided into two subtypes through consensus cluster analysis. The results of the functional enrichment analysis and immune infiltration analysis revealed significant differences in gene expression patterns, biological functions, and enrichment pathways between the cuproptosis-related molecular subtypes of UC. The immune infiltration analysis also showed that the immune cell infiltration in cluster A was significantly higher than that of cluster B, and six of the characteristic genes (excluding BACE1) had higher expression levels in subtype B than in subtype A. Conclusions: This study identified several promising signature genes and developed a nomogram with strong predictive capabilities. The identification of distinct subtypes of UC enhances our current understanding of UC's underlying pathogenesis and provides a foundation for personalized diagnosis and treatment in the future.

Development and Validation of Nomogram-Based Prognosis Tools for Patients with Extremity Osteosarcoma: A SEER Population Study.

Objective: Osteosarcoma, usually occurring in the extremities, is the most common malignant bone tumour. The purpose of this study is to develop and validate nomogram-based prognosis tools for survival (OS) and cancer special survival (CSS) of patients with osteosarcoma of the extremities via the application of survival analysis. Materials and Methods: A total of 1427 patients diagnosed with osteosarcoma of the extremities during 2004-2015 were selected from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results- (SEER-) Medicare database. The samples were randomly assigned to either the training set (n = 856) or the validation cohort (n = 571). Kaplan-Meier (K-M) survival analysis was conducted to calculate patients' 1-, 3-, and 5-year OS and CSS rates. Cox proportional hazard ratio (HR) regression models were employed to identify and examine the factors that have a significant impact on OS and CSS with data from the training cohort. Results: The results of univariate and multivariate analyses performed in the training cohort indicated that older age, increased tumour size, higher grade, distant tumour extension, amputation, or no surgery (all HR > 1, P < 0.05) were risk predictors of poor OS and CSS. Subsequently, the independent prognosis signatures were utilised to construct nomograms. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were simultaneously used to validate the nomograms. The internally validated C-index values of the OS and CSS prediction models for the training set were 0.752 (95% confidence interval [CI]: 0.738-0.765) and 0.754 [95% CI: 0.740-0.768], respectively. Then, the models were validated in the validation cohort population, which also demonstrated the models had good reliability for prognostication. Conclusions: The SEER cohort of patients with osteosarcoma of the extremities can be employed to produce effective tools that can assist in prognosis modelling.

Diagnostic Significance of Serum Levels of Nerve Growth Factor and Brain Derived Neurotrophic Factor in Diabetic Peripheral Neuropathy.

BACKGROUND Our study aimed to explore the levels of nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) in healthy participants, type 2 diabetes mellitus (T2DM) patients, and diabetic peripheral neuropathy (DPN) patients in order to find their effects on DPN. MATERIAL AND METHODS The clinical data of 110 healthy participants (age: 57.3±8.2 year, height: 165.4±5.5 cm, weight: 64.1±7.5 kg), 83 T2DM patients (age: 56.5±7.9 year, height: 164.8±6.2 cm, and weight: 63.6±6.6 kg), and 65 DPN patients (age: 58.2±7.3 year, height: 166.7±6.7 cm, weight: 63.1±5.8 kg) were observed. ELISA was applied to detect serum NGF and BDNF levels. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic value of serum NGF and BDNF levels in DPN. Logistic regression analysis was performed to analyze risk factors for DPN. RESULTS Serum NGF and BDNF levels decreased most in DPN patients. Subsequently, we determined that serum NGF and BDNF levels were correlated with: the course of disease for patients, fasting C-peptide (FCP), 2-hour postprandial C-peptide level (2-h PCP), glycosylated hemoglobin level (HbAlc), and 24-hour urinary microalbumin excretion (24-h UME). ROC curve analysis identified high sensitivity, specificity, and accuracy of NGF and BDNF levels on DPN. Serum levels of NGF and BDNF, course of disease, 2-h PCP level, and postprandial blood glucose level were determined to be risk factors for DPN. CONCLUSIONS Our study highlights that serum levels of NGF and BDNF might be associated with the occurrence and development of DPN.