Diabetic kidney disease (DKD) is a leading cause of end-stage kidney disease and chronic kidney disease. Oxidative stress, a key driver of renal fibrosis and a hallmark of DKD pathological changes, has been extensively studied for its role in DKD progression. However, its specific mechanisms remain unclear. Here, we show that homocysteine (Hcy) accumulation in proximal tubular epithelial cells (PTECs) is a significant contributor to mitochondrial oxidative stress in DKD. Through single-cell RNA sequencing (scRNA-seq) screening, we identify lncPTEC, a DKD-associated long non-coding RNA (lncRNA) from the PTEC cluster. Notably, we find that upregulated lncPTEC correlates with elevated albuminuria in DKD patients and exacerbates mitochondrial oxidative stress, epithelial-mesenchymal transition (EMT) and renal tubular fibrosis both in vitro and in vivo. Mechanistically, lncPTEC is transcriptionally upregulated by the transcription factor specificity protein 1 (SP1) under hyperglycemic conditions. Furthermore, lncPTEC directly interacts with the established key factor of Hcy metabolism, methylenetetrahydrofolate dehydrogenase 1 (MTHFD1), promoting its ubiquitination and degradation via the ubiquitination-related protein UBQLN1. This process leads to Hcy accumulation, mitochondrial oxidative stress, and subsequent DKD progression. Hence, our findings elucidate the role of the lncPTEC/MTHFD1 axis in Hcy-mediated mitochondrial oxidative stress, offering potential diagnostic biomarkers and therapeutic targets for DKD.
Qi-Jia Wang, Tianhui Wu, Peilin Li et al.· Cell Death and Disease· 0 citations
Background Breast cancer-related lymphedema (BCRL) is a common and disabling complication after breast cancer surgery, with substantial effects on limb function and quality of life. Liposuction is an established option for selected patients with chronic BCRL. However, postoperative response is heterogeneous. This study aimed to develop a machine learning model to predict liposuction efficacy in patients with unilateral BCRL. Methods We analyzed 623 unilateral BCRL cases undergoing liposuction at Beijing Shijitan Hospital, randomly splitting them 7:3 into training (n=437) and validation (n=186) cohorts. Least absolute shrinkage and selection operator (LASSO) regression with cross-validation guided feature selection. Seven algorithms—logistic regression (LR), support vector machines (SVM), decision trees (DT), artificial neural networks (ANN), LightGBM, XGBoost, and random forests (RF)—were trained and benchmarked. Performance via AUC, calibration, DCA, and Brier score identified SVM as optimal. SHAP interpretation facilitated deployment of a web-based calculator. Results The overall rate of favorable outcomes following liposuction was 64.7%. Cross-validated Lasso analysis, factoring in clinical validity and predictive importance, yielded four key predictors: history of erysipelas, preoperative affected-to-unaffected limb volume difference (Preoperative_difference), extracellular water ratio of the affected limb (r-ECW%), and body fat percentage. Among the seven models evaluated, the SVM exhibited the most balanced overall performance, achieving an accuracy of 77.42%, precision of 75.00%, specificity of 90.00%, F1-score of 0.632, Brier score of 0.163, and an AUC of 0.818 (95% CI: 0.757–0.876). Although not possessing the highest AUC, the SVM demonstrated exceptional resistance to overfitting, evidenced by a minimal AUC decrement of merely 0.0298 from the training to the validation set. Both calibration and decision curve analyses corroborated its robust generalizability, underscoring its tangible clinical utility. Conclusions An SVM model predicting surgical outcomes in BCRL was created and integrated into a user-friendly online tool. This calculator guides surgical choices based on predictive outputs, offering a valuable reference for refining patient management and treatment plans.
Shuai Pang, Hao Dong, Zhetan Ren et al.· Frontiers in Oncology· 0 citations
