As an important member of T cytotoxic pathway-related genes, CD8a molecule (CD8A) may be a useful biomarker of immunotherapeutic response and immune cell infiltration. We aimed to investigate the clinical predictive value of CD8A in prognosis and tumor microenvironment (TME) and preoperatively predict the expression of CD8A using radiogenomics in bladder cancer (BCa). Among 12 T cytotoxic pathway-related genes, CD8A was a novel protective gene and had the highest correlations with T cells and Macrophages M1 in BCa. In advanced cancer patients treated with immunotherapy, low CD8A expression was associated with immunotherapeutic failure and poor survival outcomes. CD8A expression was highly related to tumor mutation burden, critical immune checkpoint genes and several types of tumor-infiltrating immune cells, predicting effective response to immunotherapy. The preoperative MRI radiomics features and RNA-sequence data of 111 BCa samples were used to develop a radiomics signature that achieved good performance in the prediction of CD8A expression in both the training (area under curve (AUC): 0.857) and validation sets (AUC: 0.844). CD8A is a novel indicator for predicting the prognosis and immunotherapeutic response in BCa. A radiomics signature has the potential to preoperatively predict the expression of CD8A in BCa patients.

Docetaxel was used to treat metastatic CRPC patients. However, Doc resistance in prostate cancer (PCa) hinders its clinical application. To understand the underlying mechanisms by which Doc resistance is developed and to find novel therapeutic target to cure Doc resistant PCa has clinical importance. We established Doc resistant cell lines and explored the role of Ezh2 in the development of Doc resistance by overexpressing its cDNA or using its inhibitor. We found that Ezh2 was induced in our established Doc resistant (DocR) cells, which was attributable to the silenced expression of miR-101-3p and miR-138-5p. Blockage of Ezh2 activity by either inhibitor or miRNA mimics could overcome Doc resistance by suppressing Doc-induced cancer stem cells populations. Mechanistically, Ezh2 activity was required for the induced expression of Nanog, Sox2 and CD44 upon Doc treatment. Targeting Ezh2 could overcome Doc resistance.

Chemotherapy-related cognitive impairment (CRCI) or “chemo brain” is a devastating neurotoxic sequela of cancer-related treatments, especially for the elderly individuals. Here we show that PTPRO, a tyrosine phosphatase, is highly enriched in the hippocampus, and its level is tightly associated with neurocognitive function but declined significantly during aging. To understand the protective role of PTPRO in CRCI, a mouse model was generated by treating Ptpro ^–/– female mice with doxorubicin (DOX) because Ptpro ^–/– female mice are more vulnerable to DOX, showing cognitive impairments and neurodegeneration. By analyzing PTPRO substrates that are neurocognition-associated tyrosine kinases, we found that SRC and EPHA4 are highly phosphorylated/activated in the hippocampi of Ptpro ^–/– female mice, with increased sensitivity to DOX-induced CRCI. On the other hand, restoration of PTPRO in the hippocampal CA3 region significantly ameliorate CRCI in Ptpro ^–/– female mice. In addition, we found that the plant alkaloid berberine (BBR) is capable of ameliorating CRCI in aged female mice by upregulating hippocampal PTPRO. Mechanistically, BBR upregulates PTPRO by downregulating miR-25-3p, which directly targeted PTPRO. These findings collectively demonstrate the protective role of hippocampal PTPRO against CRCI.

Clear cell renal cell carcinoma (ccRCC) presents challenges in early diagnosis and effective treatment. In this study, we aimed to establish a prognostic model based on G2M checkpoint-related genes and identify associated clusters in ccRCC through clinical bioinformatic analysis and experimental validation. Utilizing a single-cell RNA dataset (GSE159115) and bulk-sequencing data from The Cancer Genome Atlas (TCGA) database, we analyzed the G2M checkpoint pathway in ccRCC. Differential expression analysis identified 45 genes associated with the G2M checkpoint, leading to the construction of a predictive model with four key genes (E2F2, GTSE1, RAD54L, and UBE2C). The model demonstrated reliable predictive ability for 1-, 3-, and 5-year overall survival, with AUC values of 0.794, 0.790, and 0.794, respectively. Patients in the high-risk group exhibited a worse prognosis, accompanied by significant differences in immune cell infiltration, immune function, TIDE and IPS scores, and drug sensitivities. Two clusters of ccRCC were identified using the "ConsensusClusterPlus" package, cluster 1 exhibited a worse survival rate and was resistant to chemotherapeutic drugs of Axitinib, Erlotinib, Pazopanib, Sunitinib, and Temsirolimus, but not Sorafenib. Targeted experiments on RAD54L, a gene involved in DNA repair processes, revealed its crucial role in inhibiting proliferation, invasion, and migration in 786-O cells. In conclusion, our study offers valuable insights into the molecular mechanisms underlying ccRCC, identifying potential prognostic genes and molecular subtypes associated with the G2M checkpoint. These findings hold promise for guiding personalized treatment strategies in the management of ccRCC.

Background: The field of urological tumor histopathology has long relied on subjective pathologist expertise, leading to diagnostic variability. Recent advances in digital pathology and artificial intelligence (AI) offer transformative potential by standardizing diagnoses, improving accuracy, and bridging healthcare disparities. This study conducted a 20-year bibliometric analysis to map global research trends and innovations in AI-driven urological pathology. Methods: For this bibliometric analysis, literature from 2004 to 2024 was retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and Microsoft Excel were used to visualize coauthorship, cocitation, and co-occurrence analyses of countries/regions, institutions, authors, references, and keywords in the field of AI for urological tumor histopathology. Results: A total of 199 papers were included. Research on AI-driven urological tumor pathology has steadily increased since 2005, with a significant surge between 2020 and 2023. The United States made the largest contribution in terms of publications (131), citations (4725), and collaborations. The most productive institution was the University of Southern California, while Patel et al. and Epstein et al. were identified as the most active and most cocited authors, respectively. European Urology led in both publication volume and impact. Keyword analysis identified “machine learning,” “prostate cancer,” “deep learning,” and “diagnosis” as major research foci. Conclusions: The integration of AI into urological tumor pathology demonstrates transformative potential, significantly enhancing diagnostic accuracy and efficiency through automated analysis of whole-slide imaging and Gleason grading, comparable to pathologist-level performance. However, clinical translation encounters critical challenges, including data bias, model interpretability (“black-box” limitations), and regulatory-ethical complexities. Future advancements hinge on developing explainable AI frameworks, multimodal systems integrating histopathology, radiomics, and genomics and establishing global collaborative networks to address resource disparities. Prioritizing standardized data protocols, fairness-aware algorithms, and dynamic regulatory guidelines will be essential to ensure equitable, reliable, and clinically actionable AI solutions, ultimately advancing precision oncology in urological malignancies.

Background: The integration of multi-omics approaches provides a powerful strategy for understanding cancer. By combining these methods, researchers gain insights into tumor diversity, gene activity, and the tumor microenvironment, which are essential for advancing cancer biology, improving early detection, refining prognostic tools, and developing targeted treatments. This study aims to explore key biomarkers in muscle-invasive bladder cancer (MIBC) and to develop a predictive model for better understanding disease progression and therapeutic responses Methods: Single-cell analysis of MIBC samples from public datasets identified basal-related genes. Using Cox analysis of The Cancer Genome Atlas (TCGA)-bladder urothelial carcinoma (BLCA) clinical data and the expression matrix, and combining it with weighted gene co-expression network analysis (WGCNA) of another MIBC dataset, a disintegrin and metalloprotease protein 17 (ADAM17) was identified as a key target gene. We collected BLCA patient samples from TCGA, Shanghai Tenth People’s Hospital (STPH), and Guangdong Second Provincial General Hospital (GD2H) to develop the pathomics model for predicting ADAM17 expression. Single-cell validation of ADAM17 expression was performed using GD2H MIBC samples. Results: ADAM17 was highly expressed in MIBC and associated with poor prognosis. Its expression correlated with clinical features such as non-papillary subtype, advanced pathological stage, and higher tissue grade. ADAM17 overexpression was linked to immune reprogramming and drug resistance. The pathomics model effectively predicted ADAM17 expression in BLCA samples. Single-cell analysis of GD2H MIBC samples confirmed ADAM17 overexpression in epithelial cells and identified key pathways in cell communication, matrix remodeling, tumor invasion, and immune regulation. Conclusions: Multi-omics approaches effectively identify biomarkers for MIBC, with ADAM17 emerging as a key biomarker. Further research is needed to clarify its role in MIBC progression.