The relationship between inflammation and tumor development and progression has been recognized in recent decades. NLR is an easily reproducible and widely used inflammatory response marker. The prognostic value of NLR for urologic tumors has been reported in succession. Here, we perform a systematic review and meta-analysis to summarize the association between the NLR and prognosis of urologic tumors. We conducted a computerized search of PubMed, Embase, and ISI Web of Knowledge to identify clinical studies that had evaluated the association between the pretreatment NLR and prognosis in urologic tumors. Prognostic outcomes included overall survival (OS), cancer-specific survival (CSS), recurrence-free survival (RFS), progression-free survival (PFS), and metastasis-free survival (MFS). We extracted and synthesized corresponding hazard ratios (HRs) and confidence intervals (CIs) using Review Manager 5.3 and STATA 13. We identified 34 retrospective cohort studies and conducted the meta-analysis. The results showed that all OS, CSS, RFS, PFS, and MFS risks were significantly different between patients with an elevated NLR and those with a low NLR in various urologic tumors. A high NLR portended poor prognosis. However, no significance was observed for CSS in patients with renal cell carcinoma (HR = 1.38, 95% CI: 0.96–1.99). Our meta-analysis suggests that NLR could be a prognostic predictor for urologic tumors. Patients with a high NLR were deemed to have a poor prognosis.

Background: Pneumonia accounts for the majority of infection-related deaths after kidney transplantation. We aimed to build a predictive model based on machine learning for severe pneumonia in recipients of deceased-donor transplants within the perioperative period after surgery. Methods: We collected the features of kidney transplant recipients and used a tree-based ensemble classification algorithm (Random Forest or AdaBoost) and a nonensemble classifier (support vector machine, Naïve Bayes, or logistic regression) to build the predictive models. We used the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to evaluate the predictive performance via ten-fold cross validation. Results: Five hundred nineteen patients who underwent transplantation from January 2015 to December 2018 were included. Forty-three severe pneumonia episodes (8.3%) occurred during hospitalization after surgery. Significant differences in the recipients’ age, diabetes status, HBsAg level, operation time, reoperation, usage of anti-fungal drugs, preoperative albumin and immunoglobulin levels, preoperative pulmonary lesions, and delayed graft function, as well as donor age, were observed between patients with and without severe pneumonia (P<0.05). We screened eight important features correlated with severe pneumonia using the recursive feature elimination method and then constructed a predictive model based on these features. The top three features were preoperative pulmonary lesions, reoperation and recipient age (with importance scores of 0.194, 0.124 and 0.078, respectively). Among the machine learning algorithms described above, the Random Forest algorithm displayed better predictive performance, with a sensitivity of 0.67, specificity of 0.97, positive likelihood ratio of 22.33, negative likelihood ratio of 0.34, AUROC of 0.91, and AUPRC of 0.72. Conclusions: The Random Forest model is potentially useful for predicting severe pneumonia in kidney transplant recipients. Recipients with a potential preoperative potential pulmonary infection, who are of older age and who require reoperation should be monitored carefully to prevent the occurrence of severe pneumonia.

Background: The contributions of various types of cell populations in dialysis‐related peritoneal fibrosis are poorly understood. Single‐cell RNA sequencing brings single‐cell level resolution to the analysis of cellular transcriptomics, which provides a new way to further characterize the distinct roles and functional states of each cell population during peritoneal fibrosis.Methods: Single‐cell transcriptomics from normal peritoneal tissues of six patients, from effluent of patients with short‐term peritoneal dialysis (less than 2 weeks, n = 6), and from long‐term peritoneal dialysis patients (more than 6 years, n = 4) were analyzed.Results: We identified a distinct cell component between samples among different groups. Functional analysis of the differentially expressed genes identified cell type specific biological processes relevant to different fibrosis stages. Well‐known key molecular mechanisms participating in the pathophysiology of peritoneal fibrosis were vitrified, and some of them were found to be restricted to specific cell types. Gradually growing enrichment of PI3K/AKT/mTOR pathway and impairment of oxidative phosphorylation in mesothelial cells and fibroblasts were found from healthy control, short‐term dialysis, to long‐term dialysis, respectively. The fibroblasts’ population obtained from the patients, who received peritoneal dialysis, showed a functional characteristic of immune‐chemotaxis and immune response, which was characterized by broadly significant increase in the expression of interleukins, chemokines, cytokines, and human leukocyte antigens. Furthermore, we described the intercellular crosstalk networks based on receptor‐ligand interactions, and highlighted a central role of fibroblasts in regulating the key mechanisms of peritoneal fibrosis through crosstalk with other cells.Conclusions: In summary, despite describing information for fibrogenic molecular mechanisms in the resolution level of individual cell populations, this work identifies the significant functional evolution of fibroblasts during peritoneal fibrosis. This study also reveals the intercellular receptor‐ligand interactions in which the fibroblasts serve as a major node, eventually providing new insights into the role of fibroblasts during disease pathogenesis.

Ischemia reperfusion injury (IRI) is a major challenge for renal transplantation. This study was performed to explore the mechanisms and potential molecular targets involved in renal IRI. In this study, the gene datasets GSE43974 and GSE126805 from the Gene Expression Omnibus database, which include ischemic and reperfused renal specimens, were analyzed to determine differentially expressed genes (DEGs). Gene ontology annotations, Kyoto Encyclopedia of Genes and Genomes analysis, and gene set enrichment analysis were performed to determine the pathways that are significantly enriched during ischemia and reperfusion. We also determined the microenvironment cell types xCell and performed correlation analyses to reveal the relationship between the molecular pathways and microenvironment cell infiltration. We found 77 DEGs (76 up- and 1 downregulated) and 323 DEGs (312 up- and 11 downregulated) in the GSE43974 and GSE126805 datasets, respectively. Similar signaling pathway enrichment patterns were observed between the two datasets. The combined analyses demonstrate that the NOD-like receptor signaling pathway and its two downstream signaling pathways, MAPK and NF-kβ, are the major significantly enriched pathways. The xCell analysis identified immune cells that are significantly changed after reperfusion, including hematopoietic stem cells, M2 macrophages, monocytes, Treg cells, conventional dendritic cells, and pro B-cells. Enrichment scores of the NOD-like receptor signaling pathway and its downstream pathways during IRI was significantly correlated with the change levels in class-switched memory B-cell and hematopoietic stem cells in both datasets. These data reveal the important role of the NOD-like receptor signaling pathway during IRI, and the close relationship between this pathway and infiltration of specific immune cell types. Our data provide compelling insights into the pathogenesis and potential therapeutic targets for renal IRI.

Objective: To define the survival effect of surgery of primary adrenal malignant lesions in metastatic adrenocortical carcinoma (ACC) patients. Patients and methods: We used the Surveillance, Epidemiology and End Results (SEER) database (1973–2014) to identify metastatic ACC patients (stage IV by using European Network for the Study of Adrenal Tumors stage classification). Correlated variables, including age, sex, race, tumor laterality, treatment modality, lymph node dissection, surgery of metastatic site, tumor size, and tumor stage, were extracted. Univariate and multivariate Cox regression analyses were used to define the efficacy of surgery on survival outcomes, including overall survival and cancer-specific survival of ACC. Results: There were 290 metastatic ACC patients identified from the database. The overall median survival time was 7 (95% CI, 6–8) months. Among these patients, 118 patients received primary site surgery and 172 patients did not. In both univariate and multivariate analyses, primary site surgery significantly improved both overall (hazard ratio 0.413, 95% CI, 0.299–0.571, P<0.01) and cancer-specific survival (hazard ratio 0.408, 95% CI, 0.290–0.574, P<0.01) for metastatic ACC patients. Conclusion: Our study suggests that primary site surgery in metastatic ACC patients significantly improved overall and cancer-specific survival. Further multicenter prospective studies are still needed to validate these outcomes.

Renal ischemia-reperfusion injury (IRI) contributes to acute kidney injury (AKI), increases morbidity and mortality, and is a significant risk factor for chronic kidney disease (CKD). Macrophage infiltration is a common feature after renal IRI, and infiltrating macrophages can be polarized into the following two distinct types: M1 macrophages, i.e., classically activated macrophages, which can not only inhibit infection but also accelerate renal injury, and M2 macrophages, i.e., alternatively activated macrophages, which have a repair phenotype that can promote wound healing and subsequent fibrosis. The role of TSC1, which is a negative regulator of mTOR signaling that regulates macrophage polarization in inflammation-linked diseases, has been well documented, but whether TSC1 contributes to macrophage polarization in the process of IRI is still unknown. Here, by using a mouse model of renal ischemia-reperfusion, we found that myeloid cell-specific TSC1 knockout mice (termed Lyz-TSC1 cKO mice) had higher serum creatinine levels, more severe histological damage, and greater proinflammatory cytokine production than wild-type (WT) mice during the early phase after renal ischemia-reperfusion. Furthermore, the Lyz-TSC1 cKO mice showed attenuated renal fibrosis during the repair phase of IRI with decreased levels of M2 markers on macrophages in the operated kidneys, which was further confirmed in a cell model of hypoxia-reoxygenation (H/R) in vitro. Mechanistically, by using RNA sequencing of sorted renal macrophages, we found that the expression of most M1-related genes was upregulated in the Lyz-TSC1 cKO group (Supplemental Table 1) during the early phase. However, C/EBPβ and CD206 expression was decreased during the repair phase compared to in the WT group. Overall, our findings demonstrate that the expression of TSC1 in macrophages contributes to the whole process of IRI but serves as an inflammation suppressor during the early phase and a fibrosis promoter during the repair phase.