Xinling Li

Background: Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Accumulating evidence suggests that short-chain fatty acids (SCFAs) can regulate the central nervous system, thereby affecting cognitive and behavior function. Objective: This study aimed to investigate the association between the AD development and SCFA metabolism via bioinformatic analysis. Methods: Gene expression profiles were obtained from the GEO database. 1243 genes related to SCFA were screened from Genecards database. Through weighted gene co-expression network analysis (WGCNA) and differential analysis, 10 SCFA hub genes were screened. Machine learning algorithms, including support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression models, were used to identify candidate biomarkers. The CIBERSORT algorithm was utilized to evaluate the infiltration of immune cells and its relationship with the potential biomarkers. The candidate biomarker chemicals were identified in the Comparative Toxicogenomics Database as underlying targeted drugs for treating AD. Results: Five genes—EZR, SNCA, GFAP, NFKBIA, and SST—were identified as potential biomarkers for AD through LASSO and SVM-RFE analyses. These genes can also be used to predict the risk of AD and have good diagnostic effects. The candidate biomarkers are associated with plasma cells, activated dendritic cells, M1 macrophages and resting natural killer cells. Notably, valproic acid and tretinoin were found to target these candidate genes, suggesting a new treatment approach for AD. Conclusions: This study identified EZR, SNCA, GFAP, NFKBIA, and SST as potential key SCFA-related genes associated with the progression of AD, providing new insights into the prevention and treatment of AD.

Recent studies suggest that the human microbiome influence tumor development. Endometrial carcinoma (EC) is the sixth most common malignancy in women. Recent research has demonstrated the microbes play a critical role in the development and metastasis of EC. However, it remains unclear whether intratumoral microbes are associated with tumor microenvironment (TME) and prognosis of EC. In this study, we collected the EC microbiome data from cBioPortal and constructed a prognostic model based on Resident Microbiome of Endometrium (RME). We then examined the relationship between the RME score, immune cell infiltration, immunotherapy-related signature, and prognosis. The findings demonstrated the independent prognostic value of the RME score for EC. The group with low RME scores had higher enrichment of immune cells. Drug sensitivity analysis revealed that the RME score may serve as a potential predictor of chemotherapy efficacy. In conclusion, our research offers new perspectives on the relationships between tumor immunity and microbes.