BackgroundTo examine the microbial profiles in parenchyma tissues in bladder cancer.MethodsTissue samples of cancerous bladder mucosa were collected from patients diagnosed with bladder cancer (22 carcinoma tissues and 12 adjacent normal tissues). The V3‐V4 region of the bacterial 16S rRNA gene was PCR amplified, followed by sequencing on an Illumina MiSeq platform. Bioinformatics analysis for microbial classification and functional assessment was performed to assess bladder microbiome diversity and variations.ResultsThe predominant phylum in both tissues was Proteobacteria. The cancerous tissues exhibited lower species richness and diversity. Beta diversity significantly differed between the cancerous and normal tissues. Lower relative abundances of the microbial genera Lactobacillus, Prevotella_9, as well as Ruminococcaceae were observed, whereas those of Cupriavidus spp., an unknown genus of family Brucellaceae, and Acinetobacter, Anoxybacillus, Escherichia‐Shigella, Geobacillus, Pelomonas, Ralstonia, and Sphingomonas were higher in the cancerous tissues. These findings indicate that these genera may be potentially utilized as biomarkers for bladder cancer. PICRUSt analysis revealed that several pathways involved in the metabolism of harmful chemical compounds were enriched in the cancer tissues, thereby providing evidence that environmental factors are strongly associated with bladder cancer etiology.ConclusionThis is the first study that has described and analyzed the dysbiotic motifs of urinary microbiota in the parenchymatous tissues of bladder cancer via 16S rRNA gene sequencing. Our results suggest that changes in the bladder microbiome may serve as biomarkers for bladder cancer, possibly assisting in disease screening and monitoring.

Cisplatin-based chemotherapy is the first-line treatment for patients with advanced bladder cancer. However, as more than 50% of patients are ineligible for cisplatin-based chemotherapy, there is an urgent need to develop new drugs. Cuprous oxide nanoparticles (CONPs), as a new nano-therapeutic agent, have been proved to be effective in many kinds of tumors. In the present study, CONPs showed dose-dependent and time-dependent inhibitory effects on various bladder cancer cell lines (T24, J82, 5637, and UMUC3) and weak inhibitory effects on non-cancerous epithelial cells (SVHUCs). We found that CONPs induced cell cycle arrest and apoptosis in bladder cancer cells. We further demonstrated that the potential mechanisms of CONP-induced cytotoxicity were apoptosis, which was triggered by reactive oxygen species through activation of ERK signaling pathway, and autophagy. Moreover, the cytotoxic effect of CONPs on bladder cancer was confirmed both in orthotopic xenografts and subcutaneous nude mouse models, indicating that CONPs could significantly suppress the growth of bladder cancer in vivo. In further drug combination experiments, we showed that CONPs had a synergistic drug–drug interaction with cisplatin and gemcitabine in vitro, both of which are commonly used chemotherapy agents for bladder cancer. We further proved that CONPs potentiated the antitumor activity of gemcitabine in vivo without exacerbating the adverse effects, suggesting that CONPs and gemcitabine can be used for combination intravesical chemotherapy. In conclusion, our preclinical data demonstrate that CONPs are a promising nanomedicine against bladder cancer and provide good insights into the application of CONPs and gemcitabine in combination for intravesical bladder cancer treatment.

Background: Thoracic ossification of the ligamentum flavum (OLF) is a major cause of thoracic myelopathy, which is often accompanied by multiple segmental stenosis or other degenerative spinal diseases.However, in the above situations, it is difficult to determine the exact segment responsible. The objective of this study was to analyze three-dimensional (3D) radiological parameters in order to establish a novel diagnostic method for discriminating the responsible segment in OLF-induced thoracic myelopathy, and to evaluate its superiority compared to the conventional diagnostic methods.Methods: Eighty-one patients who underwent surgery for thoracic myelopathy caused by OLF from 2016 to 2020 were enrolled in this study as the myelopathy group, and 79 patients who had thoracic OLF but displayed no definite neurological signs from 2018 to 2020 were enrolled as the non-myelopathy group. We measured the one-dimensional (1D), two-dimensional (2D), and 3D radiological parameters, calculated their optimal cutoff values, and compared their diagnostic values.Results: Significant differences were observed in the 1D, 2D, and 3D radiological parameters between the myelopathy and non-myelopathy groups (P<0.01). As a 3D radiological parameter, the OLF volume (OLFV) ratio (OLFV ratio = OLFV/normal canal volume × 100%) was the most accurate parameter for diagnosing OLF-induced thoracic myelopathy, with a diagnostic coincidence rate of 88.1%. We also found that an OLFV ratio of 26.3% could be used as the optimal cutoff value, with a sensitivity of 87.7% and a specificity of 88.6%. Moreover, the OLFV ratio [area under the curve (AUC): 0.92, 95% confidence interval (CI): 0.86-0.95] showed a statistically higher diagnostic value than the 1D and 2D parameters (AUC: 0.75, 95% CI: 0.67-0.81; AUC: 0.84, 95% CI: 0.77-0.89, respectively) (P<0.05). Pearson correlation analysis illustrated that the OLFV ratio was significantly negatively correlated with preoperative modified Japanese Orthopedic Association (mJOA) score (r=-0.73, 95% CI: -0.81 to -0.60, P<0.01). Conclusions:Our results demonstrate the superiority of the OLFV ratio over the conventional 1D and 2D computed tomography (CT)-based radiological parameters for the diagnosis of OLF-induced thoracic myelopathy. The novel diagnostic method based on the OLFV ratio will help to determine the responsible segment in multi-segmental thoracic OLF or when thoracic OLF coexists with other degenerative spinal diseases. The OLFV ratio also accurately reflects the clinical state of symptomatic patients with thoracic OLF.

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