Background: Stem cells characterized by self-renewal and therapeutic resistance play crucial roles in bladder cancer (BLCA). However, the genes modulating the maintenance and proliferation of BLCA stem cells are still unclear. In this study, we aimed to characterize the expression of stem cell-related genes in BLCA. Methods: The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Corrected mRNAsi were further analyzed with regard to muscle-invasive bladder cancer molecular subtypes, survival analysis, pathological staging characteristics, and outcomes after primary treatment. Next, weighted gene co-expression network analysis was used to find modules of interest and key genes. Functional enrichment analysis was performed to functionally annotate the modules and key genes. The expression levels of key genes in all cancers were validated using Oncomine and Gene Expression Omnibus (GEO) database containing molecular subtypes in BLCA. Protein interaction networks were used to identify upstream genes, and the relationships between genes were analyzed at the protein and transcription levels. Findings: mRNAsi was significantly upregulated in cancer tissues. Corrected mRNAsi in BLCA increased as tumor stage increased, with T3 having the highest stem cell characteristics. Lower corrected mRNAsi scores had better overall survival and treatment outcome. The modules of interest and key genes were determined based on topological overlap measurement clustering results and the inclusion criteria. For 13 key genes ( AURKA, BUB1B, CDCA5, CDCA8, KIF11, KIF18B, KIF2C, KIFC1, KPNA2, NCAPG, NEK2, NUSAP1 , and RACGAP1 ), enriched gene ontology terms related to cell proliferation (e.g., mitotic nuclear division, spindle, and microtubule binding) were determined. Their expression did not differ according to the pathological stages of BLCA, and these genes were clearly overexpressed in many types of cancers. In GEO database, the expression levels of 13 key genes were higher in basal subtype with the highest stem cell characteristics than in luminal and its subtypes. AURKB and PLK1 may be regulated upstream of other key genes, and the key genes were found to be strongly correlated with each other and with upstream genes. Interpretation: The 13 key genes identified in this study were found to play important roles in the maintenance of BLCA stem cells. Controlling the upstream genes AURKB and PLK1 may have applications in the treatment of BLCA. These genes may act as therapeutic targets for inhibiting the stemness characteristics of BLCA.

Background: Bladder urothelial cancer (BLCA) treatment using immune checkpoint inhibitors (IMCIs) can result in long-lasting clinical benefits. However, only a fraction of patients respond to such treatment. In this study, we aimed to identify the relationships between immune cell infiltration levels (ICILs) and IMCIs and identify markers for ICILs.Methods: ICILs were estimated based on single-sample gene set enrichment analysis. The response rates of different ICILs to IMCIs were calculated by combining the ICILs of molecular subtypes in BLCA with the response rates of different molecular subtypes of IMvigor 210 trials to a programmed cell death ligand-1 inhibitor. Weighted gene co-expression network analysis was used to identify modules of interest with ICILs. Functional enrichment analysis was performed to functionally annotate the modules. Screening of key genes and unsupervised clustering were used to identify candidate biomarkers. Tumor IMmune Estimation Resource was used to validate the relationships between the biomarkers and ICILs. Finally, we verified the expression of key genes in molecular subtypes of different response rates for IMCIs.Findings: The basal squamous subtype and luminal infiltrated subtype, which showed low response rates for IMCIs, had the highest levels of immune infiltration. The neuronal subtypes, which showed the highest response rates to IMCIs, had low ICILs. The modules of interest and key genes were determined based on topological overlap measurement, clustering results, and inclusion criteria. Modules highly correlated with ICILs were mainly enriched in immune responses and epithelial–mesenchymal transition. After screening the key genes in the modules, five candidate biomarkers (CD48, SEPT1, ACAP1, PPP1R16B, and IL16) were selected by unsupervised clustering. The key genes were inversely associated with tumor purity and were mostly expressed in the basal squamous subtype and luminal infiltrated subtypes.Interpretation: Patients with high ICILs may benefit the least from treatment with IMCIs. Five key genes could predict ICILs in BLCA, and their high expression suggested that the response rate to IMCIs may decrease.

The role of cancer-associated fibroblasts (CAFs) has been thoroughly investigated in tumour microenvironments but not in bladder urothelial carcinoma (BLCA). The cell fraction of CAFs gradually increased with BLCA progression. Weighted gene coexpression network analysis (WGCNA) revealed a specific gene expression module of CAFs that are relevant to cancer progression and survival status. Fifteen key genes of the module were consistent with a fibroblast signature in single-cell RNA sequencing, functionally related to the extracellular matrix, and significant in survival analysis and tumour staging. A comparison of the luminal-infiltrated versus luminal-papillary subtypes and fibroblast versus urothelial carcinoma cell lines and immunohistochemical data analysis demonstrated that the key genes were specifically expressed in CAFs. Moreover, these genes are highly correlated with previously reported CAF markers. In summary, CAFs play a major role in the progression of BLCA, and the 15 key genes act as BLCA-specific CAF markers and can predict CAF changes. WGCNA can, therefore, be used to sort CAF-specific gene set in cancer tissues. K E Y W O R D S bladder cancer, cancer-associated fibroblast, marker, tumour microenvironment, weighted gene co-expression network analysis 1 | INTRODUCTION Bladder urothelial carcinoma (BLCA) is one of the leading causes of cancer-related death worldwide, with a 5-year survival rate of only 5% in patients with metastases (Antoni et al., 2017). BLCA with local and distant metastases remains an urgent challenge for researchers. Most cancers exhibit epithelial abnormalities and mutations in transformed cells. Over the past 20 years, this scenario has evolved, and the matrix has been shown to act as a driver of the tumourigenic process and promote cancer progression (Gascard & Tlsty, 2016). Cancer-associated fibroblasts (CAFs) are the main components of the tumour microenvironment (TME), and they appear in the matrix surrounding cancer. Biochemical crosstalk between cancer cells and CAFs and the mechanical remodelling of the stromal extracellular matrix (ECM) by CAFs are important factors in tumour cell migration and invasion (Erdogan & Webb, 2017; Valkenburg, de Groot, & Pienta, 2018). However, the current understanding of the interplay between CAFs and TME in BLCA is limited. High-throughput data from a large number of patient samples reveal the pathogenesis of and mechanisms underlying various cancers. In combination with weighted gene co-expression network analysis (WGCNA), the search for highly co-expressed key genes or hub genes is also common in tumour progression (Giulietti et al.,

AIMTo assess the accuracy of Look-Locker on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for staging liver fibrosis in chronic hepatitis B/C (CHB/C).METHODSWe prospectively included 109 patients with CHB or CHC who underwent a 3.0-Tesla MRI examination, including T1-weighted and Look-Locker sequences for T1 mapping. Hepatocyte fractions (HeF) and relaxation time reduction rate (RE) were measured for staging liver fibrosis. A receiver operating characteristic analysis using the area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance in predicting liver fibrosis between HeF and RE.RESULTSA total of 73 patients had both pathological results and MRI information. The number of patients in each fibrosis stage was evaluated semiquantitatively according to the METAVIR scoring system: F0, n = 23 (31.5%); F1, n = 19 (26.0%); F2, n = 13 (17.8%); F3, n = 6 (8.2%), and F4, n = 12 (16.4%). HeF by EOB enhancement imaging was significantly correlated with fibrosis stage (r = -0.808, P < 0.05). AUC values for diagnosis of any (≥ F1), significant (≥ F2) or advanced (≥ F3) fibrosis, and cirrhosis (F4) using HeF were 0.837 (0.733-0.913), 0.890 (0.795-0.951), 0.957 (0.881-0.990), and 0.957 (0.882-0.991), respectively. HeF measurement was more accurate than use of RE in establishing liver fibrosis staging, suggesting that calculation of HeF is a superior noninvasive liver fibrosis staging method.CONCLUSIONA T1 mapping-based HeF method is an efficient diagnostic tool for the staging of liver fibrosis.

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