A web-based prediction model for overall survival of elderly patients with early renal cell carcinoma: a population-based study

Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis

Background

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies.

Methods

Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments.

Results

Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control.

Conclusion

This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis

Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.

Predicting the Survival Benefit of Radiotherapy in Elderly Breast Cancer Patients: A Population-Based Analysis

INTRODUCTION

This study aimed to establish two prediction tools predicting cancer-specific survival (CSS) and overall survival (OS) in elderly breast cancer patients with or without radiotherapy.

METHODS

Clinicopathological data of breast cancer patients aged more than 70 y from 2010 to 2018 were retrospectively collected from the Surveillance, Epidemiology, and End Results database. Patients were randomly divided into the training and validation cohorts at 7:3, and the Cox proportional risk model was used to construct the nomograms. The concordance index, the area under the receiver operating characteristic curve, and the calibration plot are used to evaluate the discrimination and accuracy of the nomograms.

RESULTS

One lakh twenty eight thousand two hundred twenty three elderly breast cancer patients were enrolled, including 57,915 who received radiotherapy. The Cox regression model was used to identify independent factors. These independent influencing factors are used to construct the prediction models. The calibration plots reflect the excellent consistency between the predicted and actual survival rates. The concordance index of nomograms for CSS and OS was more than 0.7 in both the radiotherapy group and the nonradiotherapy group, and similar results are also shown in area under the receiver operating characteristic curve. Decision curve analysis showed that the prognostication accuracy of the model was much higher than that of the traditional tumor, node, metastasis staging.

CONCLUSIONS

Radiotherapy can benefit elderly breast cancer patients significantly. The two prediction tools provide a personalized survival scale for evaluating the CSS and OS of elderly breast cancer patients, which can better provide clinicians with better-individualized management for these patients.

Prognostic factors of renal cell cancer in elderly patients: a population-based cohort study

Mortality in renal cell cancer (RCC) is high in the elderly population. Comorbidities have a greater impact on overall prognosis of RCC among elderly patients than in younger patients. All new RCC cases were collected in people over 74 years of age between 1995 and 2018 from the Finnish cancer registry. The comorbidities were identified from the Care Registry for Healthcare. Charlson Comorbidity Index (CCI) was used to evaluate the risk of death based on comorbidities. The overall risk of death was analyzed using the Cox regression model. The risk for RCC death was analyzed using Fine and Gray regression analysis. Individual prognostic role of CCI components was evaluated by adding each component separately into the multivariable Fine and Gray regression model. Using the most prognostic comorbidities we constructed a nomogram to predict RCC mortality. Statistically significant prognostic factors of RCC death were tumor morphology (clear cell, papillary and chromophobe), sex, operative treatment, age, primary tumor extent and CCI. The strongest prognostic factors for overall mortality were tumor extent, tumor morphology and operative treatment. Among the components of CCI, the most important comorbidities predicting mortality were dementia, heart failure and kidney disease. The limitation of this study is that the comorbidities have only been recorded at inpatient and outpatient hospital contacts, which is why the prevalence of comorbidities is probably underestimated. In addition, physical performance status was not available from registry data, but it significantly affects the treatment decisions. RCC mortality is high in the elderly population. Among comorbidities, dementia and heart failure have the greatest impact on the prognosis. Curative treatment in selected elderly patients is efficient and should be considered in patients who can tolerate it and have only limited comorbidities.

A web-based prediction model for long-term cancer-specific survival of middle-aged patients with early-stage gastric cancer: a multi-institutional retrospective study

BACKGROUND

This study constructed and validated a prognostic model to evaluate long-term cancer-specific survival (CSS) in middle-aged patients with early gastric cancer (EGC).

METHODS

We extracted clinicopathological data from relevant patients between 2004 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided the patients into a training group (N = 688) and a validation group (N = 292). In addition, 102 Chinese patients were enrolled for external validation. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict CSS. We used the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the predictive performance of the model.

RESULTS

Univariate and multivariate COX regression analyses showed that tumor location, differentiation grade, N stage, chemotherapy, and number of regional nodes examined were independent risk factors for prognosis, and these factors were used to construct the nomogram. The C-index of the model in the training cohort, internal validation cohort, and external validation cohort was 0.749 (95% CI 0.699-0.798), 0.744 (95% CI 0.671-0.818), and 0.807 (95% CI 0.721-0.893), respectively. The calibration curve showed that the model had an excellent fit. The DCA curve showed that the model had good predictive performance and practical clinical value.

CONCLUSION

This study developed and validated a new nomogram to predict CSS in middle-aged patients with EGC. The prediction model has unique and practical value and can help doctors carry out individualized treatment and judge prognosis.

Percutaneous radiofrequency ablation is an effective treatment option for small renal masses, comparable to partial nephrectomy

OBJECTIVES

The standard therapy for small renal masses (SRMs) remains partial nephrectomy (PN), which is associated with relatively high morbidity and complication rate. Therefore, percutaneous radiofrequency ablation (PRFA) emerges as an alternative therapy. This study aimed to compare the efficacy, safety, and oncological outcomes of PRFA versus PN.

METHODS

A multicenter non-inferiority study with retrospective analysis of 291 patients with SRMs (N0M0), who underwent PN or PRFA (2:1), recruited prospectively from two hospitals in the Andalusian Public Health System, Spain, between 2014 and 2021. Comparisons of treatment features were evaluated using the t test, Wilcoxon-Mann-Whitney U test, chi-square test, Fisher test, and Cochran-Armitage trend test. Kaplan-Meier curves depicted overall survival (OS), local recurrence-free survival (LRFS), and metastasis-free survival (MFS) rates in the overall study population.

RESULTS

A total of 291 consecutive patients were identified; 111 and 180 patients underwent PRFA and PN, respectively. Median follow-up time was 38 and 48 months, and mean hospitalization days were 1.04 and 3.57 days, respectively. The variables underpinned with high surgical risk were significantly increased in PRFA compared to those in PN (mean age was 64.56 and 57.47 years, the solitary kidney presence was 12.6% and 5.6%, ASA score ≥ 3 was 36% and 14.5%, respectively). The rest of oncological outcomes were comparable amongst PRFA and PN. Patients undergoing PRFA did not improve OS, LRFS, and MFS compared to those undergoing PN. Limitations comprise retrospective design and limited statistical power.

CONCLUSION

PRFA for SMRs in high-risk patients is non-inferior in terms of oncological outcomes and safety compared to PN.

CLINICAL RELEVANCE STATEMENT

Our study has a direct clinical application as it proves that radiofrequency ablation is an effective and uncomplicated therapeutic option for patients with small renal masses.

KEY POINTS

•There are non-inferiority results in overall survival, local recurrence-free survival, and metastasis-free survival between PRFA and PN. •Our two-center study showed that PRFA is non-inferior to PN in oncological outcomes. •Contrast-enhanced power ultrasound-guided PRFA provides an effective therapy for T1 renal tumors.

The association of marital status with kidney cancer surgery morbidity - a retrospective cohort study

Purpose

To better understand whether the marital status impacts 90-day postoperative outcomes following kidney cancer surgery.

Methods

We performed a retrospective cohort study of adult patients undergoing elective partial or radical nephrectomy to manage kidney masses from 2003 to 2017 using the Premier Hospital Database, a national hospital discharge dataset. Multinomial logistic regression models controlling for a wide range of clinicodemographic, surgical, and hospital characteristics were used to assess an association between marital status and postoperative complications. The primary outcome was 90-day complications, including minor complications (Clavien grades 1-2), non-fatal major complications (Clavien grades 3-4), and mortality (Clavien grade 5). Secondary outcomes included patient disposition and readmission rates.

Results

The study cohort comprised 106,752 patients, of which 61,188 (57.32%) were married. The overall incidence of minor complications, major complications, and death was 24.04%, 6.00%, and 0.71%, respectively. Marriage was associated with a significantly lower incidence of minor (RR 0.97; 95% CI: 0.94-0.99) complications following open or radical nephrectomy and major complications (RR 0.89; 95% CI: 0.84-0.95) for all surgical types and approaches. There was no association between marital status and mortality (RR 0.94; 95% CI: 0.81-1.10).

Conclusion

Marriage is associated with a significant reduction in major complications following kidney cancer surgery, likely because it is associated with greater social support, which is beneficial in the postoperative phase of care. Marital status and social support may play a role in the preoperative decision-making process and counseling for patients considering kidney cancer surgery.

Development and validation of competitive risk model for older women with metaplastic breast cancer

BACKGROUND

Metaplastic breast cancer (MpBC) is a rare histological subtype of breast cancer. This study aims to establish a competitive risk model for older women with MpBC to predict patients' survival accurately.

METHODS

Data on patients diagnosed with MpBC from 2010 to 2019 are from the Surveillance, Epidemiology and End Results (SEER) program in the United States. All patients were randomly assigned to the training set and validation set. The proportional sub-distribution risk model was used in the training set to analyze the risk factors affecting patient death. Based on the risk factors for cancer-specific mortality (CSM) in patients, we constructed a competitive risk model to predict patients' 1-, 3-, and 5-year cancer-specific survival. Then we used the concordance index (C-index), the calibration curve and the area under the receiver operating characteristic curve (AUC) to validate the discrimination and accuracy of the model.

RESULTS

One thousand, four hundred twelve older women with MpBC were included in this study. Age, T stage, N stage, M stage, tumor size, surgery and radiotherapy were risk factors for CSM. We established a competitive risk model to predict 1-, 3-, and 5-year cancer-specific survival in older women with MpBC. The C-index of the model was 0.792 in the training set and 0.744 in the validation set. The calibration curves in the training and validation sets showed that the model's predicted values were almost consistent with the actual observed values. The AUC results show that the prediction model has good accuracy.

CONCLUSION

We developed a competitive risk model based on these risk factors to predict cancer-specific survival in older women with MpBC. The validation results of the model show that it is a very effective and reliable prediction tool. This predictive tool allows doctors and patients to make individualized clinical decisions.

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Background

Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.

Method

We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.

Result

A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.

Conclusion

We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02544-y.

Tumor Size Is an Independent Prognostic Factor for Stage I Ovarian Clear Cell Carcinoma: A Large Retrospective Cohort Study of 1,000 Patients

Objective

The aim of this study was to investigate the prognostic value and stratification cutoff point for tumor size in stage I ovarian clear cell carcinoma (OCCC).

Methods

This was a retrospective cohort study using the Surveillance, Epidemiology, and End Results database (version: SEER 8.3.9). Patients diagnosed with stage I OCCC from 1988 to 2018 were included for further analysis. X-Tile software was used to identify the potential cutoff point for tumor size. Stratification analysis, propensity score matching, and inverse probability weighting analysis were used to balance the potential confounding factors.

Results

A total of 1,000 stage I OCCC patients were included. Of these 1,000 patients, median follow-up was 106 months (95% confidence interval [CI]: 89-112 months). Multivariate analysis showed that tumor size, age at diagnosis, and stage IC were significantly associated with stage I OCCC patients. Eight centimeters is a promising cutoff point that can divide stage I OCCC patients into a good or a poor prognosis group. After controlling potential confounding factors with propensity score matching and inverse probability weighting, we demonstrated that stage I OCCC patients with tumor size ≤ 8 cm enjoyed a significantly better 5-year overall survival (OS, 89.8% vs. 81%, p < 0.0001). Tumor size ≤ 8 cm was an independent prognostic factor of stage I OCCC patients (hazard ratio [HR] 0.5608, 95% CI: 0.4126-0.7622, p = 0.0002).

Conclusions

Tumor size is an independent prognostic factor for stage I OCCC, and 8 cm is a promising cutoff point for tumor size for risk stratification. However, using tumor size in the stratification management of stage I OCCC patients warrants further investigation.