Chemoresistance remains the major barrier to cancer treatment. Metabolic and epigenetic reprogramming are involved in this process; however, the precise roles and mechanisms are largely unknown. Here, we report that lactate-induced lactylation promotes chemoresistance to anthracyclines by regulating homologous recombination (HR) repair. Using the global lactylome, we revealed the landscape of differentially lactylated sites and proteins in cancer cells isolated from resistant and nonresistant tumors. Specifically, BLM, a crucial helicase in the HR repair process, is highly lactylated at Lys24 by AARS1 in response to chemotherapy. Mechanistically, hyperlactylation of BLM improves its stability by inhibiting MIB1-mediated ubiquitination and increasing its interaction with DNA repair factors, promoting DNA end resection and HR repair. Delactylation of BLM via the Lys24 mutation impairs HR repair and increases anthracycline chemosensitivity. Irinotecan shows synergistic effects and safety for alleviating anthracycline resistance by targeting BLM lactylation and suppressing HR repair in pancancer PDX models. A single-arm, phase I study (identifier NCT06766266) initiated by us suggested that the combination of irinotecan liposomes plus EPI is a feasible and safe treatment strategy for patients with anthracycline-resistant bladder cancer who experience recurrence. These findings exemplify how glycolytic reprogramming regulates HR repair through promoting protein lactylation and highlight the promising therapeutic potential of irinotecan for reversing anthracycline chemoresistance by suppressing BLM lactylation.

Risk factors, follow-up, and treatment of urethral recurrence following radical cystectomy and urinary diversion for bladder cancer: a meta-analysis of 9498 patients Risk factors, follow-up, and treatment of urethral recurrence following radical cystectomy and urinary diversion for bladder cancer: a meta-analysis of 9498 patients

Dysregulation of ferroptosis is linked to chemoresistance, and reprogramming of glucose metabolism is involved in this progress. However, the underlying mechanisms remain obscure. Herein, using metabolic profiling, we find that hexosamine biosynthetic pathway (HBP) metabolism and the byproduct, UDP-GlcNAc, are substantially up-regulated in chemoresistant tumor tissues and cells. UDP-GlcNAc-derived O-GlcNAcylation levels increase with the decreased ferroptosis and chemosensitivity in cancer cells. Knockout of the rate-limiting enzyme GFPT1 in HBP metabolism inhibits O-GlcNAcylation, induces ferroptosis, and mitigates chemoresistance of orthotopic bladder cancer in Gfpt1^−/− mice. The global O-GlcNAcylation omics mapped the O-GlcNAcylated sites and proteins in resistant and nonresistant tumor cells, showing that NR3C1 is highly O-GlcNAcylated at Thr²⁹⁹ in response to chemotherapy. The chromatin immunoprecipitation sequencing delineates that NR3C1 O-GlcNAcylation at Thr²⁹⁹ prominently enhances transcriptional activity of GPX4 by facilitating the binding of NR3C1 on GPX4 promoter, inhibiting ferroptosis. Higher O-GlcNAcylation of NR3C1 improves protein stability and reduces proteasome-dependent degradation by suppressing ubiquitination. Inhibition of NR3C1 O-GlcNAcylation via Thr²⁹⁹ mutant or knockout of NR3C1 facilitates ferroptosis and improves chemosensitivity of resistant cancer cells in vitro and in vivo. In addition, we propose a novel predicting model for chemoresistance based on the GFPT1 and NR3C1 levels in pre-chemotherapy biopsy tissues through a training set and a validation set. These findings exemplify how metabolic and epigenetic reprogramming regulates ferroptosis via the GFPT1/NR3C1/GPX4 axis, and implicate NR3C1 O-GlcNAcylation as a potential target for reversing chemoresistance.

Backgrounds: Bladder cancer (BLCA) is one of the most prevalent cancers of the genitourinary system, the clinical outcomes of patients with BLCA are bad, and the morbidity rate is high. One of the key components of the tumor microenvironment (TME) is cancer-associated fibroblasts (CAFs) which are critically involved in BLCA tumorigenesis. Previous studies have shown the involvement of CAFs in tumor growth, cancer progression, immune evasion, angiogenesis, and chemoresistance in several cancers such as breast, colon, pancreatic, ovarian, and prostate cancers. However, only a few studies have shown the role of CAFs in the occurrence and development of BLCA.Methods: We have retrieved and merged the data on RNA-sequencing of patients with BLCA from databases including “the Cancer Genome Atlas” and “Gene Expression Omnibus.” Next, we compared the differences in CAFs-related genes (CRGs) expression between normal and BLCA tissues. Based on CRGs expression, we randomly divided patients into two groups. Next, we determined the correlation between CAFs subtypes and differentially expressed CRGs (DECRGs) between the two subtypes. Furthermore, the “Gene Ontology” and “Kyoto Encyclopedia of Genes and Genomes pathway” enrichment analyses were conducted to determine the functional characteristics between the DECRGs and clinicopathology.Results: We identified five genes (POF1B, ARMCX1, ALDOC, C19orf33, and KRT13) using multivariate COX regression and “Least Absolute Shrinkage and Selection Operator (LASSO) COX regression analysis” for developing a prognostic model and calculating the CRGs-risk score. The TME, mutation, CSC index, and drug sensitivity were also analyzed.Conclusion: We constructed a novel five- CRGs prognostic model, which sheds light on the roles of CAFs in BLCA.

Objectives: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). Methods: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model’s performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). Results: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48–65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52–69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53–69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). Conclusion: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. Critical relevance statement: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. Key Points: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility. Graphical