Immune repertoire and evolutionary trajectory analysis in the development of diabetic nephropathy

Static magnetic fields alleviate diabetic nephropathy by reducing renal cell inflammation and promoting M2 macrophage polarization

Diabetic nephropathy (DN) is one of the most severe diabetic complications, which can easily progress into irreversible and detrimental end-stage renal disease if not properly controlled. However, the effective prevention of DN progression has always remained a huge challenge. Moderate intensity static magnetic fields (SMFs), which have the advantages of non-invasive and high penetration, have shown beneficial effects in reducing blood glucose in type 2 diabetes mice in recent years. In this study, by using both db/db severe diabetic mice and high-fat diet and streptozotocin-induced moderate diabetic mice, we found that SMFs have significant effects on reducing DN compared to blood glucose control. Further analyzing the db/db mice with severe diabetes, we found that kidney inflammation, vascular abnormalities, and fibrosis were all greatly reduced. Moreover, SMFs can promote macrophages polarized into M2. In vitro cellular experiments also demonstrate the positive effects of SMFs in reducing kidney cell inflammation, as well as increasing M2 macrophage polarization by promoting F-actin assembly. Therefore, our results show that moderate intensity SMFs have great potential to be developed as a new physical modality to be used in the treatment of DN, and possibly other types of chronic kidney diseases.

Comprehensive analysis reveals the tumor suppressor role of macrophage signature gene FCER1G in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) progression is closely linked to the role of macrophages. This study utilized single-cell RNA sequencing and genomic analysis to explore the characteristic genes of macrophages in HCC and their impact on patient prognosis. We obtained single-cell se-quencing data from seven HCC samples in the GEO database. Through principal component analysis and t-SNE dimensionality reduction, we identified 2,000 highly variable genes and per-formed clustering and annotation of 17 cell clusters, revealing 482 macrophage-related feature genes. A LASSO regression model based on these genes was developed to predict the prognosis of HCC patients, with validation in the TCGA-LIHC cohort demonstrating model accuracy (AUC = 0.78, 0.72, 0.71 for 1-, 3-, and 5-year survival rates, respectively). Additionally, patients in the high-risk group exhibited elevated tumor stemness scores, although no significant differences were observed in microsatellite instability (MSI) and tumor mutational burden (TMB) scores. Immune-related analyses revealed that FCER1G expression was downregulated in HCC and was associated with key pathways such as apoptosis and ferroptosis. Reduced FCER1G expression significantly affected HCC cell proliferation and migration. Our prognostic model provides new insights into precision and immunotherapy for HCC and holds significant implications for future clinical applications.

Developing an advanced diagnostic model for hepatocellular carcinoma through multi-omics integration leveraging diverse cell-death patterns

Introduction

Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data.

Methods

Six categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments.

Results

Collectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy.

Conclusions

The seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

The Role of Immune Cells in DKD: Mechanisms and Targeted Therapies

Diabetic kidney disease (DKD), is a common microvascular complication and a major cause of death in patients with diabetes. Disorders of immune cells and immune cytokines can accelerate DKD development of in a number of ways. As the kidney is composed of complex and highly differentiated cells, the interactions among different cell types and immune cells play important regulatory roles in disease development. Here, we summarize the latest research into the molecular mechanisms underlying the interactions among various immune and renal cells in DKD. In addition, we discuss the most recent studies related to single cell technology and bioinformatics analysis in the field of DKD. The aims of our review were to explore immune cells as potential therapeutic targets in DKD and provide some guidance for future clinical treatments.

Evidence of a pan-tissue decline in stemness during human aging

Despite their biological importance, the role of stem cells in human aging remains to be elucidated. In this work, we applied a machine learning methodology to GTEx transcriptome data and assigned stemness scores to 17,382 healthy samples from 30 human tissues aged between 20 and 79 years. We found that ~60% of the studied tissues exhibit a significant negative correlation between the subject's age and stemness score. The only significant exception was the uterus, where we observed an increased stemness with age. Moreover, we observed that stemness is positively correlated with cell proliferation and negatively correlated with cellular senescence. Finally, we also observed a trend that hematopoietic stem cells derived from older individuals might have higher stemness scores. In conclusion, we assigned stemness scores to human samples and show evidence of a pan-tissue loss of stemness during human aging, which adds weight to the idea that stem cell deterioration may contribute to human aging.