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.

Immune status affects the initiation and progression of clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma. In this study, we identified an immune-related, five-gene signature that improves survival prediction in ccRCC. Patients were classified as high-and low-risk based on the signature risk score. Survival analysis showed differential prognosis, while principal component analysis revealed distinctly different immune phenotypes between the two risk groups. High-risk patients tended to have advanced stage, higher grade disease, and poorer prognoses. Functional enrichment analysis showed that the signature genes were mainly involved in the cytokine-cytokine receptor interaction pathway. Moreover, we found that tumors from high-risk patients had higher relative abundance of T follicular helper cells, regulatory T cells, and M0 macrophages, and higher expression of PD-1, CTLA-4, LAG3, and CD47 than low-risk patients. This suggests our gene signature may not only serve as an indicator of tumor immune status, but may be a promising tool to select high-risk patients who may benefit from immune checkpoint inhibitor therapy. Multivariate Cox regression analysis showed that the signature remained an independent prognostic factor after adjusting for clinicopathological variables, while prognostic accuracy was further improved after integrating clinical parameters into the analysis.

Both RNA N6-methyladenosine (m6A) modification of SARS-CoV-2 and immune characteristics of the human body have been reported to play an important role in COVID-19, but how the m6A methylation modification of leukocytes responds to the virus infection remains unknown. Based on the RNA-seq of 126 samples from the GEO database, we disclosed that there is a remarkably higher m6A modification level of blood leukocytes in patients with COVID-19 compared to patients without COVID-19, and this difference was related to CD4+ T cells. Two clusters were identified by unsupervised clustering, m6A cluster A characterized by T cell activation had a higher prognosis than m6A cluster B. Elevated metabolism level, blockage of the immune checkpoint, and lower level of m6A score were observed in m6A cluster B. A protective model was constructed based on nine selected genes and it exhibited an excellent predictive value in COVID-19. Further analysis revealed that the protective score was positively correlated to HFD45 and ventilator-free days, while negatively correlated to SOFA score, APACHE-II score, and crp. Our works systematically depicted a complicated correlation between m6A methylation modification and host lymphocytes in patients infected with SARS-CoV-2 and provided a well-performing model to predict the patients’ outcomes.

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.

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a very common urological disorder and has been gradually regarded as an immune-mediated disease. Multiple studies have indicated that the gut microflora plays a pivotal part in immune homeostasis and autoimmune disorder development. However, whether the gut microflora affects the CP/CPPS, and the underlying mechanism behind them remain unclear. Here, we built an experimental autoimmune prostatitis (EAP) mouse model by subcutaneous immunity and identified that its Th17/Treg frequency was imbalanced. Using fecal 16s rRNA sequencing and untargeted/targeted metabolomics, we discovered that the diversity and relative abundance of gut microflora and their metabolites were obviously different between the control and the EAP group. Propionic acid, a kind of short-chain fatty acid (SCFA), was decreased in EAP mice compared to that in controls, and supplementation with propionic acid reduced susceptibility to EAP and corrected the imbalance of Th17/Treg cell differentiation in vivo and in vitro. Furthermore, SCFA receptor G-protein-coupled receptor 43 and intracellular histone deacetylase 6 regulated by propionic acid in Th17 and Treg cells were also evaluated. Lastly, we observed that fecal transplantation from EAP mice induced the decrease of Treg cell frequency in recipient mice. Our data showed that gut dysbiosis contributed to a Th17/Treg differentiation imbalance in EAP via the decrease of metabolite propionic acid and provided valuable immunological groundwork for further intervention in immunologic derangement of CP/CPPS by targeting propionic acid.

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.

Costimulatory molecules have been proven to enhance antitumor immune responses, but their roles in prostate cancer (PCa) remain unexplored. In this study, we aimed to explore the gene expression profiles of costimulatory molecule genes in PCa and construct a prognostic signature to improve treatment decision making and clinical outcomes. Five prognosis-related costimulatory molecule genes (RELT, TNFRSF25, EDA2R, TNFSF18, and TNFSF10) were identified, and a prognostic signature was constructed based on these five genes. This signature was an independent prognostic factor according to multivariate Cox regression analysis; it could stratify PCa patients into two subgroups with different prognoses and was highly associated with clinical features. The prognostic significance of the signature was well validated in four different independent external datasets. Moreover, patients identified as high risk based on our prognostic signature exhibited a high mutation frequency, a high level of immune cell infiltration and an immunosuppressive microenvironment. Therefore, our signature could provide clinicians with prognosis predictions and help guide treatment for PCa patients.

Background: RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. Systematic exploration of the roles of RBPs in ccRCC may provide new insights for the treatments of ccRCC. Methods: Expression data of RBPs was obtained from The Cancer Genome Atlas database. The roles of RBPs in ccRCC were systematically investigated using consensus clustering methods. Differentially expressed RBPs between normal and tumor tissues were obtained. Protein-protein interaction (PPI) network was constructed using "STRING" software. The expression levels of hub genes were validated in The Human Protein Atlas (HPA) database and receiver operating characteristic (ROC) curves were used to evaluate diagnostic value. Univariate and Lasso Cox regression and Kaplan-Meier curves were used to screen the most useful prognostic genes. Multivariate Cox regression was performed to construct a risk score model. The e ciency of the model was evaluated using time-dependent ROC and Kaplan-Meier curves, and validated in E-MTAB-3267 set. Results: Two clusters were identi ed based on the expression similarity of RBPs, and the cluster 2 was closely correlated with the malignancy of ccRCC. Several oncogenic pathways, including epithelial mesenchymal transition, G2M checkpoint, KRAS signaling and IL6 JAK STAT3 signaling were enriched in cluster 2. In addition, we obtained 115 differently expressed RBPs in ccRCC, comprising 71 up-regulated and 44 down-regulated ones. Ten hub RBPs with good diagnostic value were obtained from PPI network and validated in HPA database. Ten RBPs were identi ed as survival-related genes and used to construct a risk score model. The model could be used to stratify patients with different prognosis. We found highrisk patients tended to be advanced stage, high grade, high pathological T staging and could be an independent risk factor for overall survival of ccRCC patients. Conclusions: We identi ed ten RBPs with diagnostic value, which might be the potential diagnostic biomarkers for ccRCC. A risk score model was established to stratify patients and could be used as a complementation for clinical factors to guide clinical practice in the future.