Background: Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.Methods: Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.Results: Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.Conclusion: By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.

Background: There is still limited research on the association between immune cells and the risk of prostate cancer. Further investigations are warranted to comprehend the intricate associations at play.Methods: We used a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between immune cell phenotypes and prostate cancer. The summary data for immune cell phenotypes was derived from a study cohort, including 3,757 individuals from Sardinia with data on 731 immune cell phenotypes. The summary data for prostate cancer were obtained from the UK Biobank database. Sensitivity analyses were conducted, and the combination of MR-Egger and MR-Presso was used to assess horizontal pleiotropy. Cochran’s Q test was employed to evaluate heterogeneity, and the results were subjected to FDR correction.Results: Our study identified two immune cell phenotypes significantly associated with the risk of prostate cancer, namely CD25 on naive-mature B cells (OR = 0.998, 95% CI, 0.997-0.999, P = 2.33E-05, FDR = 0.017) and HLA DR on CD14- CD16- cells (OR = 1.001, 95% CI, 1.000-1.002, P = 8.01E-05, FDR = 0.03). When adjusting FDR to 0.2, we additionally found six immune cell phenotypes influencing the incidence of prostate cancer. These include FSC-A on B cells (OR = 1.002, 95% CI, 1.001-1.002, P = 7.77E-04, FDR = 0.133), HLA DR on plasmacytoid dendritic cells (OR = 1.001, 95% CI, 1.000-1.001, P = 0.001, FDR = 0.133), CD14+ CD16- monocyte % monocytes (OR = 1.002, 95% CI, 1.001-1.003, P = 0.001, FDR = 0.133), and HVEM on effector memory CD4+ T cells (OR = 1.001, 95% CI, 1.000-1.002, P = 0.002, FDR = 0.169), which are positively correlated with the risk of prostate cancer. Conversely, CD25 on IgD+ B cells (OR = 0.998, 95% CI, 0.997-0.999, P = 0.002, FDR = 0.169) and Monocytic Myeloid-Derived Suppressor Cells AC (OR = 0.999, 95% CI, 0.999-1.000, P = 0.002, FDR = 0.17) are negatively correlated with the risk of prostate cancer.Conclusion: This study has revealed causal relationships between immune cell phenotypes and prostate cancer, supplying novel insights that might aid in identifying potential therapeutic targets of prostate cancer.

Introduction: Semen quality is decreasing worldwide, leading to increased male infertility. This study analyzed the microbiota of the gut, semen, and urine in individuals with semen abnormalities to identify potential probiotics and pathogenic bacteria that affect semen parameters and help develop new methods for the diagnosis and treatment of patients with semen abnormalities.Methods: We recruited 12 individuals with normal semen parameters (control group), 12 with asthenospermia but no semen hyperviscosity (Group_1), 6 with oligospermia (Group_2), 9 with severe oligospermia or azoospermia (Group_3), and 14 with semen hyperviscosity only (Group_4). The semen, gut, and urine microbiota were examined by analyzing the 16S ribosomal RNA gene sequence using next-generation sequencing.Results: The gut microbes were clustered into the highest number of operational taxonomic units, followed by urine and semen. Furthermore, the α-diversity of gut microbes was highest and significantly different from that of urine and semen microbiota. The microbiota of the gut, urine, and semen were all significantly different from each other in terms of β-diversity. The gut abundance of Collinsella was significantly reduced in groups 1, 3, and 4. Furthermore, the gut abundance of Bifidobacterium and Blautia was significantly decreased in Group_1, while that of Bacteroides was significantly increased in Group_3. The abundance of Staphylococcus was significantly increased in the semen of groups 1 and 4. Finally, Lactobacillus abundance was significantly reduced in the urine of groups 2 and 4.Discussion: This study comprehensively describes the differences in intestinal and genitourinary tract microbiota between healthy individuals and those with abnormal semen parameters. Furthermore, our study identified Collinsella, Bifidobacterium, Blautia, and Lactobacillus as potential probiotics. Finally, the study identified Bacteroides in the gut and Staphylococcus in semen as potential pathogenic bacteria. Our study lays the foundation of a new approach to the diagnosis and treatment of male infertility.

Bladder urothelial carcinoma (BC) has been identified as one of the most common malignant neoplasm worldwide. High-grade bladder urothelial carcinoma (HGBC) is aggressive with a high risk of recurrence, progression, metastasis, and poor prognosis. Therefore, HGBC clinical management is still a challenge. We performed the present study to seek new urine biomarkers for HGBC and investigate how they promote HGBC progression and thus affect the prognosis based on large-scale sequencing data. We identified the overlapped differentially expressed genes (DEGs) by combining GSE68020 and The Cancer Genome Atlas (TCGA) datasets. Subsequent receiver operating characteristic (ROC) curves, Kaplan-Meier (KM) curves, and Cox regression were conducted to test the diagnostic and prognostic role of the hub genes. Chi-square test and logistic regression were carried out to analyze the associations between clinicopathologic characteristics and the hub genes. Ultimately, we performed gene set enrichment analysis (GSEA), protein-protein interaction (PPI) networks, and Bayesian networks (BNs) to explore the underlying mechanisms by which ECM1, CRYAB, CGNL1, and GPX3 are involved in tumor progression. Immunohistochemistry based on The Human Protein Atlas and quantitative real-time polymerase chain reaction based on urine samples confirmed the downregulation and diagnostic values of the hub genes in HGBC. In conclusion, our study indicated that CRYAB, CGNL1, ECM1, and GPX3 are potential urine biomarkers of HGBC. These four novel urine biomarkers will have attractive applications to provide new diagnostic methods, prognostic predictors and treatment targets for HGBC, which could improve the prognosis of HGBC patients, if validated by further experiments and larger prospective clinical trials.

Wang S, et al. World J Mens Health. 2022 Oct;40:e65. https://doi.org/10.5534/wjmh.220091