RGS1 mediates renal interstitial fibrosis through activation of the inflammatory response

Research dynamics and drug treatment of renal fibrosis from a mitochondrial perspective: a historical text data analysis based on bibliometrics

Renal fibrosis (RF) represents a significant public health challenge, necessitating the urgent identification of effective and safe therapeutic agents. Mitochondrial-targeted strategies have demonstrated considerable promise in restoring renal function and mitigating fibrosis. This study aims to examine the evolution of research and therapeutic interventions for RF from a mitochondrial perspective through bibliometric analysis. Literature retrieval was primarily conducted using the Web of Science Core Collection. Visual analysis was performed utilizing the Bibliometrix package (R- 4.4.2), CiteSpace 6.3.R1, and VOSviewer 1.6.19. A total of 819 documents were included for analysis. Significant contributions were made by researchers from China and the USA, with Nanjing Medical University leading in publication volume. Zhang Aihua and Huang Songming emerge as key scholars in the field, while the International Journal of Molecular Sciences is the journal with the highest publication output. Key research themes include oxidative stress, expression, injury, activation, mechanisms, and mitochondrial dysfunction. Mitochondrial-targeted approaches for treating RF can be categorized into six main strategies: mitochondrial biogenesis regulators, mitochondrial dynamics modulators, mitophagy inducers, oxidative stress regulators, NLRP3 inhibitors, and other mitochondrial-targeted therapeutic approaches. This study comprehensively examines the current state of RF research from a mitochondrial standpoint, summarizing key drugs and potential mechanisms of mitochondrial regulation. The findings aim to enhance scholarly understanding of the ongoing research trends and provide valuable insights for the development of targeted therapeutic agents.

Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning

Background

Chronic kidney disease (CKD) involves complex immune dysregulation and altered gene expression profiles. This study investigates immune cell infiltration, differential gene expression, and pathway enrichment in CKD patients to identify key diagnostic biomarkers through machine learning methods.

Methods

We assessed immune cell infiltration and immune microenvironment scores using the xCell algorithm. Differentially expressed genes (DEGs) were identified using the limma package. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to evaluate pathway enrichment. Machine learning techniques (LASSO and Random Forest) pinpointed diagnostic genes. A nomogram model was constructed and validated for diagnostic prediction. Spearman correlation explored associations between key genes and immune cell recruitment.

Results

The CKD group exhibited significantly altered immune cell infiltration and increased immune microenvironment scores compared to the normal group. We identified 2335 DEGs, including 124 differentially expressed immune-related genes. GSEA highlighted significant enrichment of inflammatory and immune pathways in the high immune score (HIS) subgroup, while GSVA indicated upregulation of immune responses and metabolic processes in HIS. Machine learning identified four key diagnostic genes: RGS1, IL4I1, NR4A3, and SOCS3. Validation in an independent dataset (GSE96804) and clinical samples confirmed their diagnostic potential. The nomogram model integrating these genes demonstrated high predictive accuracy. Spearman correlation revealed positive associations between the key genes and various immune cells, indicating their roles in immune modulation and CKD pathogenesis.

Conclusion

This study provides a comprehensive analysis of immune alterations and gene expression profiles in CKD. The identified diagnostic genes and the constructed nomogram model offer potent tools for CKD diagnosis. The immunomodulatory roles of RGS1, IL4I1, NR4A3, and SOCS3 warrant further investigation as potential therapeutic targets in CKD.

Revealing the molecular landscape of calcium oxalate renal calculi utilizing a tree shrew model: a transcriptomic analysis of the kidney

Our comprehensive genomic investigation employing tree shrew calcium oxalate stone models unveils intricate links between kidney stone formation and diverse physiological systems. We identify a constellation of genes whose expression patterns point to multifaceted interactions among cardiovascular health, renal fibrosis, and bone homeostasis in the pathogenesis of renal calculi. Key players include CHIT1, TNFRSF18, CLEC4E, RGS1, DCSTAMP, and SLC37A2, which emerge as pivotal actors in arteriosclerosis, renal fibrosis, and osteoclastogenesis respectively, showcasing the complexity of stone disease. The downregulation of ADRA1D, LVRN, and ABCG8 underscores roles in urodynamics, epithelial-mesenchymal transition, and vitamin D metabolism, linking these to nephrolithiasis. Comparative genomics across tree shrew, human (Randall's plaque), rat, and mouse identifies shared KEGG pathways including Calcium signaling, Actin cytoskeleton regulation, Neuroactive ligand-receptor interactions, Complement and coagulation cascades, TRP channel regulation by inflammatory mediators, p53 signaling, and Fc gamma R-mediated phagocytosis. These pathways underscore the interconnectedness of immune, inflammatory, and metabolic processes in stone development. Our findings suggest novel targets for future therapeutics and prevention strategies against nephrolithiasis, highlighting the need for a holistic view of the disease encompassing multiple pathogenic factors.

Investigating the impact of inflammatory response-related genes on renal fibrosis diagnosis: a machine learning-based study with experimental validation

Renal fibrosis plays a crucial role in the progression of renal diseases, yet the lack of effective diagnostic markers poses challenges in scientific and clinical practices. In this study, we employed machine learning techniques to identify potential biomarkers for renal fibrosis. Utilizing two datasets from the GEO database, we applied LASSO, SVM-RFE and RF algorithms to screen for differentially expressed genes related to inflammatory responses between the renal fibrosis group and the control group. As a result, we identified four genes (CCL5, IFITM1, RIPK2, and TNFAIP6) as promising diagnostic indicators for renal fibrosis. These genes were further validated through in vivo experiments and immunohistochemistry, demonstrating their utility as reliable markers for assessing renal fibrosis. Additionally, we conducted a comprehensive analysis to explore the relationship between these candidate biomarkers, immunity, and drug sensitivity. Integrating these findings, we developed a nomogram with a high discriminative ability, achieving a concordance index of 0.933, enabling the prediction of disease risk in patients with renal fibrosis. Overall, our study presents a predictive model for renal fibrosis and highlights the significance of four potential biomarkers, facilitating clinical diagnosis and personalized treatment. This finding presents valuable insights for advancing precision medicine approaches in the management of renal fibrosis.