Background To help with the clinical practice of renal cancer patients, prognostic models are urgently warranted. We hunted and identified prognostic variables associated with recurrence-free survival (RFS) for renal cancer patients. Patients and Methods In this retrospective study, 187 renal cancer patients who had received curative radical/partial nephrectomy between November 2011 and January 2017 were enrolled in the current study. These patients were randomly split into the training (n = 95) and validation sets (n = 92) by the ratio of 1:1. Univariate and multivariable Cox regression analyses were used to establish the nomogram, which was then evaluated by receiver operating characteristic (ROC) and Kaplan-Meier (K-M) analyses. Results Patient characteristics and outcomes were well balanced between the training and validation sets; the median RFS values were 54.1 months and 58.9 months for the training and validation cohorts, respectively. The final nomogram included six independent prognostic variables (prothrombin time (%), prothrombin time (second), albumin/globulin ratio, platelets, sex and fibrinogen). The mean values of RFS for the low- and high-risk groups defined by a prognostic formula were 56.22 ± 18.50 months and 49.54 ± 23.57 months, respectively, in the training cohort and were 59.00 ± 19.50 months and 53.32 ± 19.95 months, respectively, in the validation cohort. The significance and stability of the model were tested by the time-dependent K-M model and ROC curves, respectively. Conclusion Our validated prognostic model incorporates variables routinely collected from renal cancer patients, identifying subsets of patients with different survival outcomes, which provides useful information for patient care and clinical trial design.

We aimed to investigate the potential mechanisms of progression and identify novel prognosis-related biomarkers for papillary renal cell carcinoma (PRCC) patients. The related data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by weighted gene coexpression network analysis (WGCNA). The correlation between each module and the clinical traits were analyzed by Pearson’s correlation analysis. Pathway analysis was conducted to reveal potential mechanisms. Hub genes within each module were screened by intramodule analysis, and visualized by Cytoscape software. Furthermore, important hub genes were validated in an external dataset and clinical samples. A total of 5,839 differentially expressed genes were identified. By using WGCNA, we identified 21 coregulatory gene clusters based on 289 PRCC samples. We found many modules were significantly associated with clinicopathological characteristics. The gray, pink, light yellow, and salmon modules served as prognosis indicators for PRCC patients. Pathway enrichment analyses found that the hub genes were significantly enriched in the cancer-related pathways. With the external Gene Expression Omnibus (GEO) validation dataset, we found that PCDH12, GPR4, and KIF18A in the pink and yellow modules were continually associated with the survival status of PRCC, and their expressions were positively correlated with pathological grade. Notably, we randomly chose PCDH12 for validation, and the results suggested that the PRCC patients with higher pathological grades (II + III) mostly had higher PCDH12 protein expression levels compared with those patients in grade I. These validated hub genes play critical roles in the prognosis prediction of PRCC and serve as potential biomarkers for future personalized treatment.

Piles of evidence have supported the relationship between miR-618 rs2682818 polymorphism and tumorigenesis, but the conclusion remains inconsistent. In the present study, we conducted a meta-analysis to sniff out the potential risk between miR-618 rs2682818 and overall cancers. Crude odds ratios (ORs) and 95% confidence intervals (CIs) analyzed by Z-test were employed to estimate the potential interrelation in five genetic models. We also prospected how the rs2682818 affects the second structure of miR-618. Finally, 10 independent studies meet the enrolled criteria, along with 4099 cancer cases and 5057 healthy controls. Overall, no exceeding interrelation was sniffed out in the pooled data among five inherited models, as well as stratified analyses. Whereas, the enhanced cancer risk of miR-618 rs2682818 variant stratified by breast cancer was revealed, in heterozygote genetic model (AC vs. CC: OR = 1.291, 95%CI = 1.012–1.648, P = 0.040) and dominant contrast model (AA + AC vs. CC: OR = 1.280, 95%CI = 1.009–1.623, P = 0.042). The second structure prediction result shown that the mutant A allele might change the first stem-loop of miR-618, and the free energy of it would turn from –39.1 to –35.1 kcal/mol. All in all, our meta-analysis had successfully chased down that miR-618 rs2682818 polymorphism is not linked with overall cancer risk, but in the dominant genotype of breast cancer.

Several studies were conducted to explore the association between haematological parameters and erectile dysfunction (ED), but the conclusions were contradictory with small sample size. The extensively search was conducted in PubMed, Cochrane Library and Web of science from inception to August 2021. Studies comparing the haematological parameter (at least NLR, PLR) between ED patients and healthy controls were eligible for the present meta-analysis. The differences in NLR and PLR between ED patients and healthy controls were assessed by calculating the standardised mean difference (SMD) and 95% confidence interval (95% CI). Eventually, 7 studies were remained for our meta-analysis, with a total of 929 ED patients and 737 healthy controls. For the methodological quality based on NOS, 5 studies were of high quality, scored 7, and 8. 2 studies were of moderate quality, scored 6. There were statistically significant differences in NLR values between ED patients and healthy controls, based on the pooled results (SMD: 0.53, 95% CI: 0.24-0.82). Pooled results from the 6 studies revealed that ED patients had higher PLR values than healthy controls (SMD: 0.70, 95%CI: 0.12-1.28). Our meta-analysis solidly confirmed the association between NLR, PLR and ED. Increased NLR and PLR should be independent risk factors for ED.

Recurrence is a major problem for prostate cancer patients, thus, identifying prognosis-related markers to evaluate clinical outcomes is essential. Here, we established a fifteen-miRNA-based recurrence-free survival (RFS) predicting signature based on the miRNA expression profile extracted from The Cancer Genome Atlas (TCGA) database by the LASSO Cox regression analysis. The median risk score generated by the signature in both the TCGA training and the external Memorial Sloan-Kettering Cancer Center (MSKCC) validation cohorts was employed and the patients were subclassified into low- and high-risk subgroups. The Kaplan-Meier plot and log-rank analyses showed significant survival differences between low- and high-risk subgroups of patients (TCGA, log-rank P < 0.001 & MSKCC, log-rank P = 0.045). In addition, the receiver operating characteristic curves of both the training and external validation cohorts indicated the good performance of our model. After predicting the downstream genes of these miRNAs, the miRNA-mRNA network was visualized by Cytoscape software. In addition, pathway analyses found that the differences between two groups were mainly enriched on tumor progression and drug resistance-related pathways. Multivariate analyses revealed that the miRNA signature is an independent indicator of RFS prognosis for prostate cancer patients with or without clinicopathological features. In summary, our novel fifteen-miRNA-based prediction signature is a reliable method to evaluate the prognosis of prostate cancer patients.

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ObjectiveThe purpose of the study was to investigate the relationship between neutrophil-to-lymphocyte ratio (NLR) and erectile dysfunction (ED) in adult American males using a large database.MethodsWe adopted a series of statistical analyses of the relationship between NLR indices and ED prevalence among participants in the 2001-2004 National Health and Nutrition Examination Survey (NHANES) database using the R software.ResultsThe study included a total of 3012 participants, of whom 570 (18.9%) presented with ED. NLR levels were 2.13 (95% CI: 2.08,2.17) in those without ED and 2.36 (95% CI: 2.27,2.45) in those with ED. After adjusting for confounding variables, NLR levels were higher in patients with ED, (β, 1.21, 95% CI, 1.09-1.34, P &lt; 0.001). In addition, a U-shaped relationship between NLR and ED was observed after controlling for all confounders. A more significant correlation (β, 1.35, 95% CI, 1.19 to 1.53, P &lt; 0.001) existed to the right of the inflection point (1.52).ConclusionThe results of the large cross-sectional study showed a statistically significant association between the occurrence of ED and NLR, a simple, inexpensive, and readily available parameter of inflammation, in US adults. Further studies are still needed in the future to validate and replicate our findings and to investigate the specific mechanisms involved.

Background:Prostate cancer (PCa) is one of the major causes of cancer-induced death among males. Here, we applied integrated bioinformatics analysis to identify key prognostic factors for PCa patients. Material/Methods:The gene expression data were obtained from the UCSC Xena website. We calculated the differentially expressed genes between PCa tissues and normal controls. Pathway enrichment analyses found cell cycle-related pathways might play crucial roles during PCa tumorigenesis. The genes were assigned into 22 modules established via weighted gene co-expression network analysis (WGCNA). Results:The results indicated that the purple and red modules were obviously linked to the Gleason score, pathological N, pathological T, recurrence, and recurrence-free survival (RFS). In addition, Kaplan-Meier curve analysis found 8 modules were markedly correlated with RFS, serving as prognostic markers for PCa patients. Then, the hub genes in the most 2 critical modules (purple and red) were visualized by Cytoscape software. Pathway enrichment analyses confirmed the above findings that cell cycle-related pathways might play vital roles during PCa initiation and progression. Lastly, we randomly chose the PILRb (also termed PILRB) in the red module for clinical validation. The immunohistochemistry (IHC) results showed that PILRb was significantly increased in the high-risk PCa population compared with low-/middle-risk patients. Conclusions:We used integrated bioinformatics approaches to identify hub genes that can serve as prognosis markers and potential treatment targets for PCa patients.