In our recent study, we explored the efficacy of three-dimensional (3D) measurement of tumor volume in predicting the improvement of quality of life (QoL) in patients suffering from renal cell cancer (RCC), who were treated with axitinib and anti-PD-L1 antibodies. This study encompassed 18 RCC patients, including 10 men and 8 women, with an average age of 56.83 ± 9.94 years. By utilizing 3D Slicer software, we analyzed pre- and post-treatment CT scans to assess changes in tumor volume. Patients\' QoL was evaluated through the FKSI-DRS questionnaire. Our findings revealed that 3D models for all patients were successfully created, and there was a moderate agreement between treatment response classifications based on RECIST 1.1 criteria and volumetric analysis (kappa = 0.556, p = 0.001). Notably, nine patients reported a clinically meaningful improvement in QoL following the treatment. Interestingly, the change in tumor volume as indicated by the 3D model showed a higher area under the curve in predicting QoL improvement compared to the change in diameter measured by CT, although this difference was not statistically significant (z = 0.593, p = 0.553). Furthermore, a multivariable analysis identified the change in tumor volume based on the 3D model as an independent predictor of QoL improvement (odds ratio = 1.073, 95% CI 1.002-1.149, p = 0.045).In conclusion, our study suggests that the change in tumor volume measured by a 3D model may be a more effective predictor of symptom improvement in RCC patients than traditional CT-based diameter measurements. This offers a novel approach for assessing treatment response and patient well-being, presenting a significant advancement in the field of RCC treatment.

Renal cell carcinoma (RCC) is a type of tumor with high morbidity and recurrence rates. The application of circulating tumor cells (CTCs) in RCC remains controversial. Hence, we performed a meta-analysis to elucidate the diagnostic and prognostic value of CTCs in RCC. To obtain a precise conclusion, a systematic search was conducted in Pubmed, Cochrane Database, Embase and Web of Science up to Dec 01, 2022. We also further identified the references in relevant studies. The diagnostic accuracy variables (sensitivity, specificity) and odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were used to access precise of CTCs and relationship between CTCs and disease stages, respectively. Heterogeneity test, sensitivity analysis, meta-regression and publishing bias were also applied. A total of 12 studies involving 767 patients were considered to be included in the final meta-analysis. The results revealed that the overall sensitivity, specificity of CTC detection in RCC were 45% (95%CI, 32-60%) and 99% (95%CI, 97-100%), respectively. In subgroup analysis, diagnostic sensitivity of CTCs in clear cell renal cell carcinoma (ccRCC) (69%, 95%CI; 50-88%) was significantly higher than other RCC subtypes (34%, 95%CI; 21-48%) (p<0.05). Meanwhile, advanced diseases (stage III-IV) were more likely to find CTCs than localized diseases (stage I-II) (OR, 2.29; 95%CI, 1.37-3.83; p = 0.002). This systematic review and meta-analysis demonstrated that CTC detection could be considered as a promising auxiliary diagnostic and staging method for RCC, especially ccRCC subtype. Meanwhile, the presence of cytokeratin-positive CTCs is highly likely associated with increased risk of poor prognosis in RCC.

UNASSIGNED: Statin may confer anticancer efficacy, while the studies evaluating the influence of statin on survival of patients with renal cell cancer (RCC) yielded inconsistent results. A systematic review and meta-analysis was performed to investigate the association between statin use and survival of patients with RCC.
UNASSIGNED: Cohort studies were identified by search of PubMed, Embase, and Web of Science databases according to the objective of the meta-analysis. A random-effect model incorporating the possible between-study heterogeneity was used for meta-analysis. Subgroup analyses according to study characteristics were also performed.
UNASSIGNED: Seventeen cohort studies involving 42528 patients with RCC were available for the meta-analysis. Results showed that statin use was associated with a better overall survival (OS, hazard ratio [HR]: 0.73, 95% confidence interval [CI]: 0.65 to 0.84, p < 0.001; I2 = 40%), progression progression-free survival (PFS, HR: 0.82, 95% CI: 0.68 to 0.98, p = 0.03; I2 = 52%), and cancer-specific survival (CSS, HR: 0.76, 95% CI: 0.59 to 0.99, p = 0.04; I2 = 38%). Besides, for the outcome of OS and PFS, subgroup analyses showed similar results in patients with surgical and non-surgical anticancer treatments, and in patients with stage I-III and stage IV RCC (p values for subgroup difference all > 0.05).
UNASSIGNED: Statin use may be associated with improved survival outcomes in patients with RCC. Although prospective clinical studies should be considered to validate these results, these findings suggest that statins may be potential adjuvant therapy for patients with RCC.

暂无摘要。

Renal cell carcinoma accounts for 2-3% of all cancers. It is difficult to diagnose early. Recently, genome-wide studies have identified that histone methylation was one of the functional classes that is most frequently dysregulated in renal cell cancer. Mutation or mis-regulation of histone methylation, methyltransferases, demethylases are associated with gene expression and tumor progression in renal cell cancer. Herein, we summarize histone methylations, demethylases and their alterations and mechanisms in renal cell cancer.

Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.
Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.
Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.
The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.
The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

Capsaicin (CAP), extracted from Capsicum fruits, has been reported to exhibit antitumor effects in various lines of cancer cells. However, the mechanism underlying its antitumor efficiency is not fully understood. Autophagy is a fundamental self-degradation process of cells that maintains homeostasis and plays a controversial role in tumor initiation and progression. The EMT is defined as a system regulating cells transformed from an epithelial-like phenotype into a mesenchymal phenotype by several internal and external factors, following the metastatic performance of the cells developed. The present study aimed to investigate the potential role of autophagy in CAP-induced antitumor effects in renal cell carcinoma (RCC) 786-O and CAKI-1 cell lines. The results revealed that CAP remarkably inhibited the migration and invasion of RCC cells in vitro and metastasis in vivo. Moreover, we found that the CAP treatment increased the formation of autophagolysosome vacuoles and LC3 yellow and red fluorescent puncta in RCC cells and upregulated the expression of LC3, suggesting that autophagy was induced by CAP in 786-O and CAKI-1 cell lines. Our further results demonstrated that CAP-induced autophagy was mediated by the AMPK/mTOR pathway. In conclusion, our study provides new knowledge of the potential relationship between autophagy and metastasis inhibition induced by CAP, which might be a promising therapeutic strategy in RCC.

Long non-coding RNAs (lncRNAs) have been implicated in the progression and development of many types of cancer by interacting with RNA, DNA and proteins, including DLEU7-AS1. However, the function of DLEU7-AS1 in renal cell cancer (RCC) remains unclear. In this study, two in silico prediction algorithms were used to discover the potential target of miR-26a-5p, which was determined to be a tumor suppressor gene, possibly DLEU7-AS1, through the downregulation of coronin-3 in RCC. Thus, we hypothesized that DLEU7-AS1 promotes RCC by silencing the miR-26a-5p/coronin-3 axis. To test our hypothesis, we confirmed that DLEU7-AS1 directly targets miR-26a-5p using the pmirGLO dual-luciferase reporter assay. Next, we observed that DLEU7-AS1 expression was markedly upregulated in RCC samples and inversely correlated with clinical prognosis and miR-26a-5p levels. Knockdown of DLEU7-AS1 significantly suppressed the growth and metastasis of RCC cells in vitro and attenuated tumor growth in vivo. Interestingly, exogenous expression of coronin-3 or miR-26a-5p inhibitor treatment almost completely rescued the DLEU7-AS1 knockdown-induced inhibitory effects on cell proliferation, migration and invasion. In conclusion, our data demonstrate that DLEU7-AS1 is an oncogene in RCC capable of regulating the growth and metastasis of RCC by silencing the miR-26a-5p/coronin-3 axis, suggesting that DLEU7-AS1 can be employed as a potential therapeutic target and prognostic biomarker for RCC.

UNASSIGNED: Metabolic syndrome (MetS) has been related to increased risks of a variety of cancers. However, the association between MetS and the risk of renal cell cancer (RCC) remains not fully determined. This meta-analysis was conducted to investigate whether MetS is independently associated with the risk of RCC in adults.
UNASSIGNED: Relevant observational studies were obtained by searching PubMed, Embase, Cochrane\'s Library, and Web of Science databases. Study characteristics and outcome data were extracted independently by two authors. The random-effect model was used for meta-analysis considering the possible influence of between-study heterogeneity. Predefined subgroup analyses were used to evaluate the possible influences of study characteristics on the outcome.
UNASSIGNED: Eight studies involving 10,601,006 participants contributed to the meta-analysis. Results showed that MetS was independently associated with a higher risk of RCC in adult population (risk ratio [RR]: 1.62, 95% confidence interval [CI]: 1.41 to 1.87, p<0.001; I2 = 85%). Subgroup analyses showed consistent association in men (RR: 1.52, 95% CI: 1.23 to 1.89, p<0.001) and in women (RR: 1.71, 95% CI: 1.28 to 2.27, p<0.001), in Asians (RR: 1.51, 95% CI: 1.25 to 1.83, p<0.001) and in Caucasians (RR: 1.76, 95% CI: 1.46 to 2.12, p<0.001), and in community derived (RR: 1.56, 95% CI: 1.34 to 1.82, p<0.001) and non-community derived population (RR: 1.87, 95% CI: 1.71 to 2.04, p<0.001). Differences in study design or quality score also did not significantly affect the association (p for subgroup difference both >0.05).
UNASSIGNED: MetS may be independently associated with RCC in adult population.

UNASSIGNED: As the survival rates of patients with renal cell carcinoma (RCC) continue to increase, noncancer causes of death cannot be ignored. The cause-specific mortality in patients with RCC is not well understood.
UNASSIGNED: Our study aimed to explore the mortality patterns of contemporary RCC survivors.
UNASSIGNED: We performed a retrospective cohort study involving patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database. We used standardized mortality ratios (SMRs) to compare the death rates in patients with RCC with those in the general population.
UNASSIGNED: A total of 106,118 patients with RCC, including 39,630 who died (27%), were included in our study. Overall, compared with the general US population, noncancer SMRs were increased 1.25-fold (95% confidence intervals [CI], 1.22 to 1.27; observed, 11,235), 1.19-fold (95% CI, 1.14 to 1.24; observed, 2,014), and 2.24-fold (95% CI, 2.11 to 2.38; observed, 1,110) for stage I/II, III, and IV RCC, respectively. The proportion of noncancer causes of death increased with the extension of survival time. A total of 4,273 men with stage I/II disease (23.13%) died of RCC; however, patients who died from other causes were 3.2 times more likely to die from RCC (n = 14,203 [76.87%]). Heart disease was the most common noncancer cause of death (n = 3,718 [20.12%]; SMR, 1.23; 95% CI, 1.19-1.27). In patients with stage III disease, 3,912 (25.98%) died from RCC, and 2,014 (13.37%) died from noncancer causes. Most patients (94.99%) with stage IV RCC died within 5 years of initial diagnosis. Although RCC was the leading cause of death (n = 12,310 [84.65%]), patients with stage IV RCC also had a higher risk of noncancer death than the general population (2.24; 95% CI, 2.11-2.38).
UNASSIGNED: Non-RCC death causes account for more than 3/4 of RCC survivors among patients with stage I/II disease. Patients with stage IV are most likely to die of RCC; however, there is an increased risk of dying from septicemia, and suicide cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with RCC.