Background: Stromal components of the tumor microenvironment contribute to bladder cancer progression, and Cancer-Associated Fibroblasts (CAFs) were reported to play an important role. Accumulating pieces of evidence indicate that CAFs participate in the crosstalk with tumor cells and have a complex interaction network with immune components. Further studies on the role of CAFs in the bladder cancer microenvironment and searching for possible specific markers are important for a deeper understanding of CAFs in bladder cancer progression and immunomodulation. Methods: In the present study, we examined the abundance of CAFs in the TCGA and GEO datasets using the MCP-COUNTER algorithm. Additionally, the expression of genes related to CAFs was analyzed through the Weighted Gene Co-expression Network Analysis (WGCNA). The CIBERSORT and ESTIMATE algorithms were used to discuss the correlation of the key CAFs-related gene and the tumor microenvironment components. Immunohistochemistry analysis in clinical samples was used to validate the results of bioinformatics analysis. Results: The results showed that CAFs were closely associated with the progression and prognosis of bladder cancer. WGCNA also revealed that CALD1 was a key CAFs-related gene in bladder cancer. Moreover, further in-depth analysis showed that CALD1 significantly affected the progression and prognosis of bladder cancer. The CIBERSORT and ESTIMATE algorithms demonstrated significant correlations between CALD1 and the tumor microenvironment components, including CAFs, macrophages, T cells, and multiple immune checkpoint related genes. Finally, immunohistochemistry results validated the strong association of CALD1 with CAFs and macrophages. Conclusions: In the present study, we confirmed the cancer-promoting roles of CAFs in bladder cancer. Being a key gene associated with CAFs, CALD1 may promote bladder cancer progression by remodeling the tumor microenvironment. The bioinformatics methods, including the CIBERSORT, MCP-COUNTER and ESTIMATE algorithms, may provide important value for studying the tumor microenvironment.

The tumor microenvironment (TME) plays a critical regulatory role in bladder cancer (BLCA) progression and metastasis. Epithelial-mesenchymal transition (EMT) presents as an essential mechanism of tumor invasion and metastasis. Accumulating pieces of evidence indicated that several microenvironmental factors, including fibroblasts, endothelial, and immune cells, induced EMT in tumor cells. As a hallmark gene of the EMT process, calumenin (CALU) was previously reported to directly impact cancer metastasis. However, the functions and molecular mechanisms of CALU have been rarely reported in BLCA. By multi-omics bioinformatics analysis of 408 TCGA BLCA patients, we demonstrated that CALU was an independent risk factor for BLCA outcome. Subsequently, we verified the correlation of CALU with cancer-associated fibroblasts (CAFs) and tumor-infiltrating immune cells. The results suggested a positive correlation of CALU with CAFs, CD8+ T cells and macrophages. Also, CALU was significantly associated with multiple immune checkpoint-related genes, which ultimately influenced patients’ responsiveness to immunotherapy. Further, we found that the impact of CALU on BLCA prognosis might also be correlated with gene mutations and ferroptosis. Finally, we validated the roles of CALU by single-cell RNA sequencing, PCR and immunohistochemistry. In conclusion, we found that CALU affected BLCA prognosis associated with multiple mechanisms, including TME remodeling, gene mutation and ferroptosis. Further studies on CALU may provide new targets for BLCA immunotherapy and precision medicine.

Deep learning (DL) is a rapidly developing field in machine learning (ML). The concept of deep learning originates from research on artificial neural networks and is an upgrade of traditional neural networks. It has achieved great success in various domains and has shown potential in solving medical problems, particularly when using medical images. Bladder cancer (BCa) is the tenth most common cancer in the world. Imaging, as a safe, noninvasive, and relatively inexpensive technique, is a powerful tool to aid in the diagnosis and treatment of bladder cancer. In this review, we provide an overview of the latest progress in the application of deep learning to the imaging assessment of bladder cancer. First, we review the current deep learning approaches used for bladder segmentation. We then provide examples of how deep learning helps in the diagnosis, staging, and treatment management of bladder cancer using medical images. Finally, we summarize the current limitations of deep learning and provide suggestions for future improvements.

Bladder cancer (BLCA) has become one of the most common malignant tumors in the genitourinary system. BLCA is one of the tumors considered suitable for immunotherapy because of the large proportion of immune cells in TME. Epithelial to mesenchymal transition (EMT) is closely related to tumor immunity through its crosstalk with immune cells. A recent study validated that EMT-related genes were mainly expressed by stromal cells and could influence immunotherapy responsiveness. Stromal EMT-related gene signature was also demonstrated to affect the prognosis of multiple tumors, including BLCA. To further explore the prognostic roles of stromal components, we performed a comprehensive analysis of LncRNAs closely associated with stromal EMT-related genes in the TCGA BLCA cohort. We identified a signature including five stromal EMT gene-related LncRNAs that showed significant prognostic value for BLCA patients. By the CIBERSORT and MCP-COUNTER algorithm, we found the signature was markedly correlated with infiltrated immune cells and stromal components of the tumor microenvironment, which may further influence patient’s responsiveness to immune checkpoint blockade therapy. Through immunohistochemical analysis, we confirmed the correlation of the signature with macrophages M2 and CAFs. Meanwhile, key genes related to these LncRNAs, including VIM, MMP2, were also differentially expressed in the stromal components concerning the signature. Our research confirmed the prognostic and immune-associated role of stromal EMT-related LncRNAs. Meantime, we further confirmed that EMT-related genes were mainly expressed in stromal components. Targeting these LncRNAs as well as their related stromal EMT genes may provide potential therapeutic targets for BLCA immunotherapy and precision medicine.

Introduction: Lung metastasis occurs in parts of the bladder carcinoma (BC) patients but represents the highest severity and a poor outcome of the disease. The molecular mechanism underlying lung metastasis of BC is not fully understood. Fibroblast growth factor receptor 2 (FGFR2) signaling plays a substantial role in the BC cell growth and invasion. In this study, we assessed the regulation of the alternative splicing of FGFR2 by epithelial splicing regulatory proteins (ESRPs) in lung metastasis of BC.Methods: Gene profile of BC in comparison with adjacent non-tumor bladder tissue was obtained from GEO public database to analyze the levels of differentiated genes and pathways. Moreover, the association of ESRP1 or ESRP2 with lung metastasis of BC was analyzed on our own clinic samples. The effects of altered expression of ESRP1 or ESRP2 on alternative splicing of FGFR2 IIIb and IIIc, which represents epithelial and mesenchymal-like splicing, were analyzed on BC cell lines T24 and RT4. The in vivo effects of ESRP1 or ESRP2 on lung metastasis of BC were assessed in mice subcutaneously grafted with ESRP1/2-modified BC labeled with fluorescent and luciferase reporters.Results: We detected significant reduction of ESRP1 and ESRP2 in BC in public database of BC specimens. Moreover, analysis on our own specimens also showed strong downregulation of ESRP1 or ESRP2 in BC, and the latter was more pronounced in cases with lung metastasis. In vitro, altered levels of ESRP1 or ESRP2 caused a switch of FGFR2 splicing between FGFR2-IIIb and FGFR2-IIIc, resulting in changes in tumor cell growth and metastatic potential. In vivo, re-expression of ESRP1 or ESRP2 in BC cells not only inhibited the growth of the xenografted tumor formation in nude mice, but also reduced the occurrence of lung metastasis, partially through altering polarization of tumor-associated macrophages.Conclusion: Our data thus suggest that reduction in ESRP1 or ESRP2 promotes lung metastasis of BC through altering FGFR2 splicing and macrophage polarization.

Background: Improving the potency of immune response is paramount among issues concerning immunotherapy of muscle‐invasive bladder cancer (MIBC).Methods: On the basis of immune subtypes, we investigated possible molecular mechanisms involved in tumor immune escape in MIBC. According to the 312 immune‐related genes, three MIBC immune subtypes were clustered.Results: Cluster 2 subtype is characterized by FGFR3 mutations and has a better clinical prognosis. However, the expression levels of MHC‐I and immune checkpoints genes were the lowest, indicating that this subtype is subject to immune escape and has a low response rate to immunotherapy. Bioinformatics analysis and immunofluorescence staining of clinical samples revealed that the FGFR3 is involved in the immune escape in MIBC. Besides, after FGFR3 knockout with siRNA in RT112 and UMUC14 cells, the TLR3/NF‐kB pathway was significantly activated and was accompanied by upregulation of MHC‐I and PD‐L1 gene expression. Furthermore, the use of TLR3 agonists poly(I:C) can further improve the effect.Conclusion: Together, our results suggest that FGFR3 might involve in immunosuppression by inhibition of NF‐kB pathway in BC. Considering that TLR3 agonists are currently approved for clinical treatment as immunoadjuvants, our study might provide more insights for improving the efficacy of immunotherapy in MIBC.