Wenlong Zhong

Intratumoral microbiota‐related metabolites are emerging regulators of tumor immunity, yet their therapeutic potential remains largely unexplored. Here, indole‐3‐pyruvic acid (I3P), a Lactobacillus‐associated tryptophan metabolite, is identified as a molecule associated with immunotherapy response in bladder cancer. Mechanistically, I3P suppresses macrophages ferroptosis and sustains CD8 ⁺ T cell activity through an AHR–NF‐κB–SLC7A11 signaling axis that maintains macrophages redox homeostasis. Disruption of AHR or NF‐κB signaling abolishes these effects. Notably, liposomal delivery of I3P facilitates efficient targeting of tumor‐associated macrophages and enhances immunotherapy response without apparent toxicity. Together, these findings identify I3P as an immunoregulatory metabolite that potentiates anti‐tumor immunity and support nanoparticle‐mediated delivery as a promising strategy for immunotherapy sensitization in bladder cancer.

Ferroptotic waves aggravate kidney ischemia-reperfusion injury and drive delayed graft function (DGF). We demonstrate that elevated glycolysis and lactate production in graft kidney correlate with ferroptosis and functional impairment. A signaling axis composed of the long non-coding RNA IGIP-5, microRNA 670-3p, and lactate dehydrogenase A promotes lactate secretion from injured tubular cells, inducing lactylation and ferroptosis in neighboring cells and triggering ferroptotic waves. Lactylome profiling identifies that nucleophosmin 1 (NPM1), an epigenetic regulator, is lactylated at lysine 257 by the lactyltransferase AARS1. Suppressing NPM1 lactylation—via K257 mutation, AARS1 knockout, or taurochenodeoxycholic acid—upregulates SLC7A11 and inhibits ferroptosis. Mechanistically, lactylation stabilizes NPM1 by reducing MDM2-mediated ubiquitination and strengthens SLC7A11 repression, disrupting cystine metabolism. In mouse allografts, blocking lactate shuttle-mediated NPM1 lactylation prevents ferroptotic propagation and ameliorates graft function. Additionally, we develop an early prediction model for DGF using postoperative urinary lactate concentrations. These findings reveal a metabolic-epigenetic axis driving ferroptotic propagation and propose NPM1 lactylation as a therapeutic target for DGF.

Background Resistance to immune checkpoint inhibitors remains a significant challenge in the treatment of cancer. Emerging evidence suggests that metabolic reprogramming plays a crucial role in tumor metabolism and progression. Our study strived to investigate the role and underlying mechanisms of the glutamate transporter SLC1A6 in resistance to immunotherapy of cancer. Methods Single-cell RNA sequencing was performed on bladder cancer patients receiving neoadjuvant immunotherapy to identify the expression of SLC1A6 in treatment-resistant cases. The clinical prognostic value of SLC1A6 in cancer was validated using publicly available lung cancer single-cell datasets, as well as transcriptomic data from both bladder and lung cancer cohorts. Flow cytometry was employed to assess the impact of SLC1A6 knockdown on the effector function of CD8⁺ T cell. In vivo tumor models were used to evaluate the role of SLC1A6 in immunotherapy resistance, with immunofluorescence staining performed to examine GZMB⁺ CD8⁺ T cell infiltration. Results SLC1A6 was highly expressed in bladder cancer patients resistant to neoadjuvant immunotherapy, and its expression was associated with disease progression, poor prognosis, and low immune infiltration. Knockdown of SLC1A6 in tumor cells enhanced CD8⁺ T cell effector function. SLC1A6 knockdown also improved the efficacy of immunotherapy and increased the infiltration of GZMB⁺ CD8⁺ T cells within the tumor microenvironment. Conclusions SLC1A6 plays a critical role in resistance to immunotherapy in cancer. Targeting SLC1A6 may provide a promising therapeutic strategy for improving responses to neoadjuvant immunotherapy and advancing combination treatment approaches.

The clinical benefits of neoadjuvant chemoimmunotherapy (NACI) are demonstrated in patients with bladder cancer (BCa); however, more than half fail to achieve a pathological complete response (pCR). This study utilizes multi‐center cohorts of 2322 patients with pathologically diagnosed BCa, collected between January 1, 2014, and December 31, 2023, to explore the correlation between tumor budding (TB) status and NACI response and disease prognosis. A deep learning model is developed to noninvasively evaluate TB status based on CT images. The deep learning model accurately predicts the TB status, with area under the curve values of 0.932 (95% confidence interval: 0.898–0.965) in the training cohort, 0.944 (0.897–0.991) in the internal validation cohort, 0.882 (0.832–0.933) in external validation cohort 1, 0.944 (0.908–0.981) in the external validation cohort 2, and 0.854 (0.739–0.970) in the NACI validation cohort. Patients predicted to have a high TB status exhibit a worse prognosis ( p < 0.05) and a lower pCR rate of 25.9% (7/20) than those predicted to have a low TB status (pCR rate: 73.9% [17/23]; p < 0.001). Hence, this model may be a reliable, noninvasive tool for predicting TB status, aiding clinicians in prognosis assessment and NACI strategy formulation.

Modifiable factors affect cancer’s survival but literature did not differentiate prior to versus after cancer diagnosis. It is essential to provide references for the intervention prioritized at different stages. In this prospective cohort study, we analyzed national data from the UK Biobank, including 121,399 participants, to assess the association of modifiable factors with cancer-specific survival (CSS) in two independent cohorts: a pre-cancer cohort (n = 78,027) and a post-cancer cohort (n = 43,372). Additionally, a weighted standardized score was derived to evaluate the joint effects across different domains. Interactions between the six domains and age at diagnosis, sex, and cancer site were evaluated using likelihood ratio tests. Subgroup analyses were then performed for factors showing significant effect modification. Population-attributable fractions (PAF) of different domains on 5-year cancer-specific death were calculated. Our study comprehensively presented the differential patterns of modifiable factors’ impact on CSS among pre-cancer and post-cancer cohorts, sexes and different cancer sites. In the pre-cancer cohort, CSS were predominantly attributable to smoking/alcohol consumption (PAF 9·2%) and daily activity (PAF 10·6%). Men exhibited a higher risk than women for dietary habits (HR:1·25 versus 1·18), daily activity (HR:1·50 versus 1·29) and living environment (HR:1·13 versus 1·03). The impact of modifiable factors, including daily activity, smoking/alcohol consumption, and physical measures, on CSS varied across different cancer sites. In the post-cancer cohort, 18·6% of 5-year cancer-specific deaths were attributable to unfavourable mental health. In subgroup analysis, the risk of CSS in the domain of smoking/alcohol consumption was higher in men than that in women (HR: 1·58 versus 1·34). The impact of modifiable factors, including smoking/alcohol consumption, mental health and physical measures, on CSS varied across different cancer sites. Our findings suggested that targeted prevention and early intervention strategies should be implemented to reduce the risk of cancer-related deaths.

Background: Tumour-infiltrating lymphocytes (TILs) are strongly correlated with the prognosis and immunotherapy response in bladder cancer. The TIL status is typically assessed through microscopy as part of tissue pathology. Here, the authors developed Rad-TIL model, a novel radiomics model, to predict TIL status in patients with bladder cancer. Material and methods: The authors enrolled 1089 patients with bladder cancer and developed the Rad-TIL model by using a machine-learning method based on computed tomography (CT) images. The authors applied a radiogenomics cohort to reveal the key pathways underlying the Rad-TIL model. Finally, the authors used an independent treatment cohort to evaluate the predictive efficacy of the Rad-TIL model for Bacillus Calmette-Guérin (BCG) immunotherapy. Results: The authors developed the Rad-TIL model by integrating tumoral and peritumoral features on CT images and obtained areas under the receiver operating characteristic curves of 0.844 and 0.816 in the internal and external validation cohorts, respectively. Patients were stratified into two groups based on the predicted radiomics score of TILs (RS _TIL ). RS _TIL exhibited prognostic significance for both overall and cancer-specific survival in each cohort (hazard ratios: 2.27–3.15, all P TIL with immunoregulatory pathways and immune checkpoint molecules (all P TIL patients than in low-RS _TIL patients ( P =0.007). Conclusion: The Rad-TIL model, a noninvasive method for assessing TIL status, can predict clinical outcomes and BCG immunotherapy response in patients with bladder cancer.