Purpose Aim of this study was to identify biomarkers between different grades of bladder cancer (BLCA) and its prognostic value. Methods mRNA expression data from GSE32549 and GSE71576 were extracted for further analysis. Differentially expressed genes (DEGs) were identified using GEO2R web tool. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein–protein interaction (PPI) network were conducted to explore the function and relationship of DEGs. The Cancer Genome Atlas (TCGA) database was used for external validation and Gene set enrichment analysis (GSEA) analysis was used to further identify FADS1 pathways. Bladder cancer cells and patient specimens were used to further demonstrate the function of FADS1. Results Datasets from GEO identified a panel of DEGs. Functional enrichment analysis highlighted that DEGs were associated with nuclear division, spindle, cell cycle and p53 signaling pathway. External validation from TCGA demonstrated that FADS1 was an independent prognostic marker in BLCA patients. In cell lines and tumor specimen analysis, FADS1 was overexpressed in the tumor specimen, compared with adjacent tissues, and positively correlated with tumor grade of BLCA. Moreover, FADS1 could enhance the proliferation ability and influence cell cycle of bladder cancer cells. Conclusion FADS1 was an independent prognostic biomarker for BLCA and could confer the bladder cancer cells increased proliferation ability.

Clear cell renal cell carcinoma (ccRCC) is one of the most commonly diagnosed kidney tumors and is often accompanied by immune cell infiltration. In this study, we attempted to identify microenvironment-associated genes and explore the correlation between CXCL13 and tumor-infiltrating immune cells (TIICs). Gene expression profiles and their corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm was used to calculate immune cell and stromal cell scores, according to which patients were divided into high- and low-score groups, allowing differentially expressed genes (DEGs) to be identified. Functional enrichment and PPI network analysis were used to identify the functions of the DEGs. CIBERSORT algorithm and TIMER analysis were used to evaluate the immune score. Oncomine and TCGA database were used to explore CXCL13 mRNA expression level in ccRCC. High ESTIMATE score was significantly associated with prognosis. Functional enrichment analysis clarified that DEGs were associated with T cell activation, immune response-regulating cell surface receptor signaling pathway, and positive regulation of cytokine production. PPI network was used to identify CXCL13 as a hub gene. And CIBERSORT algorithm and TIMER analysis showed that strong correlation between CXCL13 expression level and TIICs. Oncomine database was used to validate high CXCL13 expression level in ccRCC tissue, compared to normal tissues. In conclusion, we obtained a list of tumor microenvironment-related genes and identified CXCL13 as an immune response biomarker in patients with ccRCC, GSEA analysis, wound healing and transwell assays showed CXCL13 played a role in tumor migration.

IntroductionThe prognosis of bladder cancer (BLCA) and response to immune checkpoint inhibitors (ICIs) are determined by multiple factors. Existed biomarkers for predicting the effect of immunotherapy cannot accurately predict the response of BLCA patients to ICIs.MethodsTo further accurately stratify patients’ response to ICIs and identify potential novel predictive biomarkers, we used the known T cell exhaustion (TEX)-related specific pathways, including tumor necrosis factor (TNF), interleukin (IL)-2, interferon (IFN)-g, and T- cell cytotoxicpathways, combined with weighted correlation network analysis (WGCNA) to analyze the characteristics of TEX in BLCA in detail, constructed a TEX model.ResultsThis model including 28 genes can robustly predict the survival of BLCA and immunotherapeutic efficacy. This model could divide BLCA into two groups, TEXhigh and TEXlow, with significantly different prognoses, clinical features, and reactivity to ICIs. The critical characteristic genes, such as potential biomarkers Charged Multivesicular Body Protein 4C (CHMP4C), SH2 Domain Containing 2A (SH2D2A), Prickle Planar Cell Polarity Protein 3 (PRICKLE3) and Zinc Finger Protein 165 (ZNF165) were verified in BLCA clinical samples by real-time quantitative chain reaction (qPCR) and immunohistochemistry (IHC).DiscussionOur findings show that the TEX model can serve as biological markers for predicting the response to ICIs, and the involving molecules in the TEX model might provide new potential targets for immunotherapy in BLCA.

Background and Objective: With the development of endoscopic techniques, narrow-band imaging (NBI) has been widely used in the diagnosis of various types of diseases. NBI can detect mucosal lesions at an early stage and different classification strategies have been established to help clinicians in disease diagnosis. However, there is currently no consensus for the classification criteria. This report summarizes the current classifications of diseases using NBI, so as to provide a comprehensive understanding of the various manifestations of mucosal lesions under NBI, and to promote the development of more practical NBI classifications.Methods: The PubMed database was searched for English language articles published between January 1994 and November 2021 using the keywords 'narrow band imaging', 'NBI', and 'classification'.