Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy’s response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

Oxidative stress damage to renal epithelial cells is the main pathological factor of calcium oxalate calculi formation. The development of medicine that could alleviate oxidative damage has become the key to the prevention and treatment of urolithiasis. Herein, porous nanorods CeO2 nanoparticles (CNPs) were selected from CeO2 with different morphologies as an antioxidant reagent to suppress kidney calcium oxalate crystal depositions with excellent oxidation resistance due to its larger specific surface area. The reversible transformation from Ce3+ to Ce4+ could catalyze the decomposition of excess free radicals and act as a biological antioxidant enzyme basing on its strong ability to scavenge free radicals. The protection capability of CNPS against oxalate-induced damage and the effect of CNPS on calcium oxalate crystallization were studied. CNPS could effectively reduce reactive oxygen species production, restore mitochondrial membrane potential polarity, recover cell cycle progression, reduce cell death, and inhibit the formation of calcium oxalate crystals on the cell surface in vitro. The results of high-throughput sequencing of mRNA showed that CNPs could protect renal epithelial cells from oxidative stress damage caused by high oxalate by suppressing the expression gene of cell surface adhesion proteins. In addition, CNPS can significantly reduce the pathological damage of renal tubules and inhibit the deposition of calcium oxalate crystals in rat kidneys while having no significant side effect on other organs and physiological indicators in vivo. Our results provide a new strategy for CNPS as a potential for clinical prevention of crystalline kidney injury and crystal deposition.