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.