Histone ubiquitination regulates sperm formation and is important for nucleosome removal during spermatogenesis. RNF8 is an E3 ubiquitin ligase, and RAD6B is an E2 ubiquitin-conjugating enzyme. Both proteins participate in DNA damage repair processes via histone ubiquitination. Loss of RNF8 or RAD6B can lead to sterility in male mice. However, the specific mechanisms regulating these ubiquitin-mediated processes are unclear. In this study, we found that RNF8 knockout mice were either subfertile or sterile based on the numbers of offspring they produced. We explored the mechanism by which RAD6B and RNF8 knockouts cause infertility in male mice and compared the effects of their loss on spermatogenesis. Our results demonstrate that RAD6B can polyubiquitinate histones H2 A and H2B. In addition, RNF8 was shown to monoubiquitinate histones H2 A and H2B. Furthermore, we observed that absence of histone ubiquitination was not the only reason for infertility. Senescence played a role in intensifying male sterility by affecting the number of germ cells during spermatogenesis. In summary, both histone ubiquitination and senescence play important roles in spermatogenesis.

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation.