Tumour-associated macrophages (TAMs) are associated with both the progression and poor prognosis of a variety of solid tumours. This study aimed to investigate and clarify the tumour-promoting role of CXCL8 secreted by TAMs in the urothelial carcinoma microenvironment of the bladder. Immunohistochemistry (n = 55) was used to detect Chemokine (C-X-C motif) ligand 8 (CXCL8), CD163 (a TAM marker), Matrixmetalloproteinase-9 (MMP-9), vascular endothelial growth factor (VEGF), and E-cadherin in cancerous and adjacent tissues of bladder cancer patients. TAMs-like PBM (peripheral blood mononuclear)-derived macrophages were developed using in vitro experiments. T24, 5637, and UM-UC-3 were treated with conditioned medium (CM) for the experimental intervention group, without CM for the blank control group, and with CM and an anti-CXCL8 neutralizing antibody for the experimental control group, respectively. The immunohistochemical study showed that the expression of CXCL8 was significantly upregulated as the number of infiltrating TAMs increased in the tumour tissues. A high expression of CXCL8 significantly correlated with an increase in the expression of MMP-9 and VEGF and a decrease in expression of E-cadherin in the microenvironment. This revealed that TAM-derived CXCL8 is highly associated with bladder cancer migration, invasion, and angiogenesis. The concentration of CXCL8 was significantly higher in CM collected from TAM-like PBM-derived macrophages than that from THP-1 cells. In subsequent in vitro experiments, we found that CM derived from TAM-like PBM-derived macrophages can also increase the migration rate, invasiveness, and pro-angiogenic properties of tumour cells. Additionally, the effect of CXCL8 was significantly diminished by the addition of an anti-CXCL8 neutralizing antibody to CM. The infiltration of TAMs in the tumour microenvironment leads to the elevation of CXCL8, which in turn promotes the secretion of MMP-9, VEGF, and E-cadherin by bladder cancer cells. This alters the migration, invasion, and pro-angiogenic capacity of bladder cancer cells and accelerates cancer progression.

Bladder cancer is the most common cancer of the urinary system and its treatment has scarcely progressed for nearly 30 years. Advances in checkpoint inhibitor research have seemingly provided a new approach for treatment. However, there have been issues predicting immunotherapeutic biomarkers and identifying new therapeutic targets. We downloaded the gene expression profile and clinical data of 408 cases bladder urinary cancer from the Cancer Genome Atlas (TCGA) portal, and the abundance ratio of immune cells for each sample was obtained via the “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm. Then, four survival-related immune cells were obtained via Kaplan-Meier survival analysis, and 933 immune-related genes were obtained via a variance analysis. Enrichment, protein-protein interaction, and co-expression analyses were performed for these genes. Lastly, 4 survival-related immune cells and 24 hub genes were identified, four of which were related to overall survival. More importantly, these immune cells and genes were closely related to the clinical features. These cells and genes may have research value and clinical application in bladder cancer immunotherapy. Our study not only provides cell and gene targets for bladder cancer immunotherapy, but also provides new ideas for researchers to explore the immunotherapy of various tumors.

Background: Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. Methods: The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. Results: SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. Conclusions: In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

Muscle‐invasive bladder cancer (MIBC) is one of the common malignant tumors. Patients with MIBC still have high tumor recurrence and progression rates after surgery. Bioinformatics analysis of stromal infiltration‐related genes in the tumor microenvironment (TME) of MIBC patients was performed in this study to determine the major stromal cells types and biomarkers for their progression and poor prognosis. The ESTIMATE algorithm was applied to evaluate the stromal score and immune score of samples from MIBC patients in The Cancer Genome Atlas (TCGA) and found that stromal score was closely related to the clinical characteristics of the patients. The Gene Set Enrichment Analysis (GSEA) further revealed that stromal cells were involved in biological processes such as activation of leukocytes and positive regulation of cell migration during MIBC progression, as well as PI3K‐Akt, MAPK, and Rap1 signaling pathways. Five hub genes related to prognosis, including ACTA2, COL5A1, DCN, LUM, and PRRX1 were identified by the Weighted Gene Co‐Expression Network Analysis (WGCNA), Protein‐Protein Interaction (PPI), survival analysis, and Oncomine, Gene Expression Omnibus (GEO) database validation. Besides, we identified five stromal cell types associated with overall survival time, among which chondrocytes and fibroblasts were identified as the major stromal cell types through correlation analysis. Finally, the Receiver Operating Characteristic (ROC) curve and immunohistochemistry were used to verify the diagnostic value and expression of hub genes in different invasive tumors. In summary, we investigated the biological behavior of stromal cells in the TME of MIBC to promote tumor progression obtained hub genes associated with progression and poor prognosis and identified the main stromal cells types in the TME.

Background: Pyroptosis is a form of programmed cell death accompanied by specific inflammatory and immune responses, and it is closely related to the occurrence and progression of various cancers. However, the roles of pyroptosis-related genes (PRGs) in the prognosis, treatment response, and tumor microenvironment (TME) of prostate cancer (PCa) remain to be investigated.Methods: The mRNA expression data and clinical information of PCa patients were obtained from the Cancer Genome Atlas database (TCGA) and the cBioPortal for Cancer Genomics website, and the 52 PRGs were obtained from the published papers. The univariate, multivariate, and LASSO Cox regression algorithms were used to obtain prognostic hub PRGs. Meanwhile, qRT-PCR was used to validate the expression of hub genes between PCa lines and normal prostate epithelial cell lines. We then constructed and validated a risk model associated with the patient’s disease-free survival (DFS). Finally, the relationships between risk score and clinicopathological characteristics, tumor immune microenvironment, and drug treatment response of PCa were systematically analyzed.Results: A prognostic risk model was constructed with 6 hub PRGs (CHMP4C, GSDMB, NOD2, PLCG1, CYCS, GPX4), and patients were divided into high and low-risk groups by median risk score. The risk score was confirmed to be an independent prognostic factor for PCa in both the training and external validation sets. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they had more increased somatic mutations, higher immune cell infiltration and higher expression of immune checkpoint-related genes. Moreover, they were more sensitive to cell cycle-related chemotherapeutic drugs and might be more responsive to immunotherapy.Conclusion: In our study, pyroptosis played a significant role in the management of the prognosis and tumor microenvironment of PCa. Meanwhile, the established model might help to develop more effective individual treatment strategies.

Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs—SERPINE2, DPYSL3, CTSE, and KRT16—was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes—SERPINE2, DPYSL3, CTSE, and KRT16—that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.