Chronic prostatitis and chronic pelvic pain syndrome (CP/CPPS) is an inflammatory immune disease characterized by intraprostatic leukocyte infiltration and pelvic or perineal pain. Macrophages play vital roles in the pathogenesis of CP/CPPS. However, the mechanisms controlling the activation and chemotaxis of macrophages in CP/CPPS remain unclear. This study aimed to investigate the roles of the CXCL10/CXCR3 pathway in the activation and chemotaxis of macrophages in CP/CPPS patients. The serums of CP/CPPS patients and healthy volunteers were collected and measured. Results showed that CXCL10 expression was significantly elevated and correlated with the severity of CP/CPPS patients. The experimental autoimmune prostatitis (EAP) model was generated, and adeno-associated virus and CXCR3 inhibitors were used to treat EAP mice. Immunofluorescence, flow cytometry, and Western blotting were used to analyze the functional phenotype and regulation mechanism of macrophages. Results showed that CXCL10 deficiency ameliorates EAP severity by inhibiting infiltration of macrophages to prostate. Moreover, CXCL10 could induce macrophage migrations and secretions of proinflammatory mediators via CXCR3, which consequently activated the downstream Erk1/2 and p38 MAPK signaling pathways. We also showed that prostatic stromal cell is a potential source of CXCL10. Our results indicated CXCL10 as an important mediator involved in inflammatory infiltration and pain symptoms of prostatitis by promoting the migration of macrophages and secretion of inflammatory mediators via CXCR3-mediated ERK and p38 MAPK activation.

Prostate cancer (PCa) is a common lethal malignancy in men. RNA binding proteins (RBPs) have been proven to regulate the biological processes of various tumors, but their roles in PCa remain less defined. In the present study, we used bioinformatics analysis to identify RBP genes with prognostic and diagnostic values. A total of 59 differentially expressed RBPs in PCa were obtained, comprising 28 upregulated and 31 downregulated RBP genes, which may play important roles in PCa. Functional enrichment analyses showed that these RBPs were mainly involved in mRNA processing, RNA splicing, and regulation of RNA splicing. Additionally, we identified nine RBP genes (EXO1, PABPC1L, REXO2, MBNL2, MSI1, CTU1, MAEL, YBX2, and ESRP2) and their prognostic values by a protein-protein interaction network and Cox regression analyses. The expression of these nine RBPs was validated using immunohistochemical staining between the tumor and normal samples. Further, the associations between the expression of these nine RBPs and pathological T staging, Gleason score, and lymph node metastasis were evaluated. Moreover, these nine RBP genes showed good diagnostic values and could categorize the PCa patients into two clusters with different malignant phenotypes. Finally, we constructed a prognostic model based on these nine RBP genes and validated them using three external datasets. The model showed good efficiency in predicting patient survival and was independent of other clinical factors. Therefore, our model could be used as a supplement for clinical factors to predict patient prognosis and thereby improve patient survival.

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in clear cell renal cell carcinoma (ccRCC) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in ccRCC and construct a prognostic signature to improve treatment decision-making and clinical outcomes. We performed the first comprehensive analysis of costimulatory molecules in patients with ccRCC and identified 13 costimulatory molecule genes with prognostic values and diagnostic values. Consensus clustering analysis based on these 13 costimulatory molecular genes showed different distribution patterns and prognostic differences for the two clusters identified. Then, a costimulatory molecule-related signature was constructed based on these 13 costimulatory molecular genes, and validated in an external dataset, showing good performance for predicting a patient’s prognosis. The signature was an independent risk factor for ccRCC patients and was significantly correlated with patients’ clinical factors, which could be used as a complement for clinical factors. In addition, the signature was associated with the tumor immune microenvironment and the response to immunotherapy. Patients identified as high-risk based on our signature exhibited a high mutation frequency, a high level of immune cell infiltration, and an immunosuppressive microenvironment. High-risk patients tended to have high cytolytic activity scores and immunophenoscore of CTLA4 and PD1/PD-L1/PD-L2 blocker than low-risk patients, suggesting these patients may be more suitable for immunotherapy. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for ccRCC patients.