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Abstract Background Cuproptosis is a newly discovered programmed cell death dependent on mitochondrial respiratory disorder induced by copper overload. Pyruvate dehydrogenase E1 subunit beta (PDHB) is one of the cuproptosis genesand is a nuclear-encoded pyruvate dehydrogenase, which catalyzes the conversion of pyruvate to acetyl coenzyme A. However, the mechanism of PDHB in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods We used data from TCGA and GEO to assess the expression of PDHB in normal and tumor tissues. We further analyzed the relationship between PDHB and somatic mutations and immune infiltration. Finally, we preliminarily explored the impact of PDHB on ccRCC. Results The expression level of PDHB was lower in tumor tissue compared with normal tissue. Meanwhile, the expression level of PDHB was also lower in high-grade tumors than low-grade tumors. PDHB is positively correlated with prognosis in ccRCC. Furthermore, PDHB may be associated with decreased risk of VHL, PBRM1 and KDM5C mutations. In 786-O cells, copper chloride could promote the expression of cuproptosis genes (DLAT, PDHB and FDX1) and inhibit cell growth. Last but not least, we found that PDHB could inhibit the proliferation and migration of ccRCC cells. Conclusion Our results demonstrated that PDHB could inhibit the proliferation, migration and invasion in ccRCC cells, which might be a prognostic predictor of ccRCC. Targeting this molecular might provide a new therapeutic strategy for patients with advanced ccRCC.

Sertoli cell -only syndrome (SCOS) is a type of testicular pathological failure that causes male infertility and no effective treatment strategy, is available for this condition. Moreover, the molecular mechanism underlying its development remains unknown. We identified DExD/H-Box helicase 58 (DDX58) as a key gene in SCOS based on four datasets of testicular tissue samples obtained from the Gene Expression Synthesis database. DDX58 was significantly upregulated in SCOS testicular Sertoli cells. Moreover, high expression of DDX58 was positively correlated with the expression of several testicular inflammatory factors, such as IL -1β, IL-18, and IL-6. Interestingly, DDX58 could be induced in the D-galactose (D-gal)-stimulated TM4 cell injury model. Whereas silencing of DDX58 inhibited D-gal -mediated p65 expression, inflammatory cytokine release, and growth arrest. Mechanistically, we found that DDX58 acts as an RNA-binding protein, which enhances p65 expression by promoting mRNA stability. Furthermore, p65 gene silencing decreased the expression of inflammatory cytokines and inhibition of cell growth in D-gal-induced cells. In conclusion, our findings demonstrate that DDX58 promotes inflammatory responses and growth arrest in SCOS Sertoli cells by stabilizing p65 mRNA. Accordingly, the DDX58/p65 regulatory axis might be a therapeutic target for SCOS.

Introduction: Prediction models are increasingly being used to predict outcomes after surgery, and such a model would be a precious tool for patients with clear cell renal cell carcinoma (ccRCC) after surgery. Aim: To develop a comprehensive model for predicting disease-free survival (DFS) in patients with localized ccRCC. Material and methods: In a retrospective analysis of 612 patients, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to identify significant predictors, and then risk factors were used to construct a prognostic model. Harrell's concordance index (C-index) was used to assess the accuracy of the model. Results: The lymphocyte-to-monocyte ratio (LMR), Mayo Clinic stage, size, grade, necrosis score (SSIGN), and Mayo adhesive probability score (MAPS) were the significant risk factors screened by LASSO Cox regression and reconfirmed by multivariate Cox regression analysis in 44 variables. Then a model was constructed by combining the LMR, SSIGN, and MAPS. The C-index of the LMR-SSIGN-MAPS model was greater than the SSIGN score alone. Kaplan-Meier survival analysis demonstrated a significant association between higher LMR-SSIGN-MAPS score and poorer DFS. Conclusions: The LMR-SSIGN-MAPS model, which consists of preoperative inflammation biomarkers, a perinephric adipose tissue image-based scoring system, and pathological features, showed the strengths of easy-to-use and high predictability and might also be used as a promising prognosis model in predicting DFS for patients with localized ccRCC. [ABSTRACT FROM AUTHOR]