Qiang Wei

Purpose In urological practice, the decision to resect or preserve a kidney with obstruction, tumor or atrophy is based on split renal function, although SPECT renal dynamic scintigraphy as the reference standard is resource-intensive. We developed and externally validated a non-contrast CT radiomics model to identify nonfunctioning kidneys and support urological decision-making in this setting. Methods In this three-center retrospective study (TRIPOD+AI), adults underwent non-contrast CT and SPECT renal dynamic scintigraphy within 72 h. IBSI-compliant radiomics features were extracted with PyRadiomics, and between-kidney asymmetry features were constructed. After three-stage selection (Spearman, recursive feature elimination, and LASSO), 12 machine learning models were developed and externally validated in 2 independent cohorts. A Rad-score and clinical nomogram were also derived. Results Among 1029 patients, the internal cohort included 418 patients. Twenty-three of features were retained. The random forest model yielded an internal test AUC of 0.9426, sensitivity of 85.5%, specificity of 88.3%, and negative predictive value of 95.6%. Its external AUCs were 0.875 and 0.913. The nomogram attained external AUCs of 0.920 and 0.881. At the thresholds fixed in the training data, the random forest achieved external sensitivity of 66.0 and 76.4% with negative predictive values of 94.8 and 93.3%, and the nomogram 92.5 and 93.7% with 98.3 and 97.2%. Both radiomics models outperformed logistic-regression baselines built from clinical variables and simple CT size and asymmetry measurements, although the margin over the combined baseline was not statistically significant in one external cohort. The Rad-score was correlated with single-kidney GFR (ρ = −0.738, P < 0.001) and facilitated risk stratification. Conclusion Non-contrast CT radiomics identified nonfunctioning kidneys without contrast agents or radiotracers. Because non-contrast CT is already part of routine urological workup, this externally validated rule-out tool may reduce unnecessary SPECT examinations and support decisions between nephrectomy and nephron-sparing management.

Objectives: Accurate pre-biopsy or preoperative diagnosis and risk stratification of prostate cancer remain challenging. This study aimed to develop an automated framework for prostate and suspicious lesion segmentation, feature extraction, and multiclass classification of prostate cancer. Materials and methods: This study enrolled 2543 patients with suspected clinically significant prostate cancer (csPCa). An nnUNet was used for prostate segmentation, followed by suspected lesion identification and feature extraction. Unimodal and multimodal deep learning models were trained to classify patients into non-csPCa (benign/ISUP 1), mid-risk csPCa (ISUP 2–3), and high-risk csPCa (ISUP 4–5). Subsequently, we extracted prostate radiomics, lesion radiomics, and learned features from the unimodal DL model. Radiomics features, learned features from the unimodal deep learning model, and clinical variables were integrated to develop machine-learning classifiers. The best-performing model was compared with conventional clinical variables and evaluated across subgroups defined by PSA levels and PI-RADS 4. Results: The nnUNet achieved scores of 0.971 in internal validation and 0.934 in external testing. The optimal machine learning model, which integrated all features, achieved a macro-AUC of 0.87 [0.84–0.90] across both internal and external evaluations. Further comparisons indicate it outperforms traditional metrics such as total PSA, PSAD, and PI-RADS. Additionally, it exhibits strong robustness in most subgroup analyses, consistently surpassing conventional variables. Conclusion: We developed an automated bpMRI-based framework that segments the prostate and suspicious lesions and enables multiclass risk stratification of prostate cancer by integrating imaging and clinical features. The proposed approach demonstrated strong performance and robustness, outperforming conventional clinical variables. Key Points: Question accurate pre-biopsy stratification of prostate cancer is essential for clinical decision-making. Current studies predominantly focus on binary classification of csPCa, and multi-class classification remains challenging. Findings the comprehensive framework achieved an area under the curve of 0.87 in both internal and external tests, exceeding clinical variables and demonstrating robustness. Critical relevance statement this study presents a robust multi-class classification framework based on biparametric MRI and machine learning to improve pre-biopsy prostate cancer stratification, outperform conventional metrics, and provide a valuable tool for risk stratification and clinical decision-making.

Prostate cancer (PCa) progression, particularly to castration-resistant prostate cancer (CRPC), is driven by androgen receptor (AR) reactivation and epigenetic alterations. Here, we identify lysine methyltransferase 2D (KMT2D) as a critical epigenetic oncogene in PCa. KMT2D expression is elevated in PCa and correlates with poor prognosis. Mechanistically, KMT2D facilitates AR signaling by recruiting the pioneer factor FOXA1 to AR-specific enhancers, promoting chromatin accessibility and activating AR target genes. FOXA1 mutations impair this regulation, demonstrating their functional interplay. Furthermore, KMT2D-FOXA1-AR axis modulates ketone body metabolism via transcriptional control of HMGCS2, supporting tumor growth. Pharmacological inhibition of UTX, a COMPASS complex demethylase essential for KMT2D function, disrupts H3K4me1 deposition and suppresses AR signaling and tumor proliferation. Altogether, we characterize KMT2D as a key driver of AR-dependent PCa progression and propose UTX inhibition as a promising therapeutic strategy.

Enzalutamide, as a second-generation anti-androgen agent, has been used to treat castration-resistant prostate cancer (CRPC) or metastatic castration-sensitive prostate cancer (mCSPC). However, enzalutamide resistance inevitably developed for most treated CRPC/mCSPC, and limited effective therapies are currently available for these enzalutamide-resistant prostate cancers. In this study, we utilize our established enzalutamide-resistant prostate cancer cell lines to reveal a vulnerability of these cancer cells to GPX4-targeted ferroptosis. Interestingly, the established enzalutamide-resistant prostate cancer cells are mixed populations that predominantly exhibit stem cell-like (SCL) and neuroendocrine-like (NEL) phenotypes and may reflect cellular heterogeneity during the development of enzalutamide resistance in prostate cancer. We further demonstrated that ACSL4, a long-chain fatty acid-CoA ligase, was upregulated by the JAK/STAT pathway in enzalutamide-resistant SCL/NEL cells, thereby facilitating tumor proliferation and metastasis while increasing sensitivity to ferroptosis. To antagonize the ACSL4-conferred ferroptosis risk, SCL/NEL cells upregulated GPX4 through AP-1 transcription complex to suppress ferroptosis and thus promoted the malignant progression of SCL/NEL cells. Notably, we characterized Auranofin, an anti-rheumatoid arthritis drug, as a ferroptosis inducer for these SCL/NEL cells in vitro and in vivo by targeting AP-1 and decreasing GPX4 expression, suggesting a new application for Auranofin in treating enzalutamide-resistant stem cell-like AP-1 ^High CRPC.

Enzalutamide resistance is a dynamic process often culminating in the aggressive progression to neuroendocrine prostate cancer (NEPC). This lineage plasticity is hypothesized to be driven by underlying epigenetic alterations, yet the core molecular drivers remain unclear. Elucidating these factors is of significant clinical importance for overcoming resistance. To model this transition, we established a dynamic gradient-resistant cell model simulating the clinical response to enzalutamide, and found robust upregulation of the chromatin remodeling factor SMARCA4 in resistant cells. Both in vitro and in vivo experimental results demonstrated that inhibiting SMARCA4 effectively suppresses tumor progression and reverses neuroendocrine transformation. Mechanistically, integrated multi-omics analysis, correlation studies, and protein interaction experiments revealed the transcription factor PROX1 as a crucial downstream target of SMARCA4, where its inhibition alone was sufficient to reverse the aggressive malignancy and neuroendocrine characteristics of resistant cells. We further demonstrated that SMARCA4 enhances H3K27ac levels and chromatin accessibility at the PROX1 locus to regulate its expression. Importantly, the tumor-suppressive effect of SMARCA4 knockdown could be rescued by histone deacetylase inhibitors (HDACi), achieving a level of recovery comparable to PROX1 overexpression. In summary, this study defines a core epigenetic pathway, showing that increased SMARCA4 activity promotes luminal-to-neuroendocrine transformation by enhancing histone acetylation and chromatin accessibility at the PROX1 locus. Targeting the SMARCA4-PROX1 axis provides a valuable therapeutic strategy for combating enzalutamide resistance and NEPC progression.

Lineage plasticity has emerged as an important mechanism of treatment resistance in prostate cancer, increasingly associated with loss of androgen receptor (AR) signaling, and in many cases induction of stemness phenotypes and neuroendocrine features. However, targeted therapies for this stage of the disease are currently lacking. In this study, we demonstrated the critical role of the epigenetic regulator UHRF1 in the enzalutamide resistance development of prostate cancer. We have shown that UHRF1 is highly expressed in enzalutamide-resistant prostate cancer cells and its expression correlates with the loss of AR-dependent glandular features. Knocking down UHRF1 led to increased AR expression and enhanced the activity of canonical AR signaling pathway in prostate cancer cells. The combination of UHRF1 knockdown with enzalutamide treatment demonstrated synergistic tumor inhibitory effects both in vitro and in vivo. Mechanistically, UHRF1 was found to bind to AR and promote its ubiquitination and degradation. Furthermore, inhibition of UHRF1 restored AR pathway activity and re-sensitized resistant prostate cancer cells to enzalutamide. Therefore, our findings elucidate an intracellular molecular mechanism that promotes prostate cancer lineage plasticity and suggest that UHRF1 may serve as a potential therapeutic target for overcoming resistance to AR-targeted therapies.

Introduction: Urological cancers, including prostate, renal, and bladder cancers, present significant challenges to human health, particularly for patients in advanced stages due to limited targeted therapy options, high drug resistance, and poor prognosis. This study seeks to systematically identify new therapeutic targets by integrating multi-omics data and elucidating their molecular mechanisms. Methods: Our methodology involved a multi-phase integrative approach to identify therapeutic targets in urological cancers. We conducted a transcriptome-wide Mendelian randomization (TWMR) analysis using the druggable genome. We used cis-expression quantitative trait loci (cis-eQTL) data from the eQTLGen consortium (N = 31,684) as exposures and GWAS summary statistics from FinnGen R11 and Open GWAS as outcomes. Significant genes were identified through Bonferroni correction, followed by colocalization analysis to assess shared genetic effects between risk SNPs and gene expression. Summary-data-based MR (SMR) analysis was carried out using eQTLGen data. Validation of potential targets included drug prediction using DSigDB and molecular docking with AutoDock Vina. Furthermore, we integrated DNA methylation quantitative trait loci (mQTL) data from GoDMC to explore methylation-mediated regulatory mechanisms through mediation MR analysis. Results: Our transcriptome-wide MR analysis identified 11 high-confidence therapeutic targets in urological cancers: 4 in prostate cancer (BNIP2, KAT5, MMP24, SIK2), 2 in renal cancer (GRB10, GPR17), and 5 in bladder cancer (CELSR1, GPS1, PROC, GSTM1, KLC3). Molecular docking confirmed favorable interactions between these targets and existing drugs. Epigenetic analyses showed DNA methylation-mediated regulation of key targets: PROC (cg09747827) and CELSR1 (cg00784671) in bladder cancer, and SIK2 (cg11344533) in prostate cancer. Conclusions: This study integrates multi-omics data to identify 11 high-confidence therapeutic targets in urological cancers and is the first to elucidate DNA methylation-mediated regulatory mechanisms for targets like PROC and SIK2. The findings introduce a “DNA methylation-gene expression” axis-based strategy for combined targeted-epigenetic therapy, providing crucial evidence for optimizing precision treatment in urological oncology.