Oncological and functional outcomes after radical nephrectomy for renal cell carcinoma: a comprehensive analysis of prognostic factors

Prediction of the Values of CRP, eGFR, and Hemoglobin in the Follow-Up of Renal Cell Carcinoma Patients after (Cryo)Surgery Using Machine Learning Algorithms

Background

Artificial intelligence can support clinical decisions by predictive modeling. Using patient-specific characteristics, models may predict the course of clinical parameters, thus guiding monitoring approaches for the individual patient. Here, we present prediction models for inflammation and for the course of renal function and hemoglobin (Hb) in renal cell carcinoma patients after (cryo)surgery.

Methods

Using random forest machine learning in a longitudinal value-based healthcare data set (n = 86) of renal cell carcinoma patients, prediction models were established and optimized using random and grid searches. Data were split into a training and test set in a 70:30 ratio. Inflammation was predicted for a single timepoint, whereas for renal function estimated glomerular filtration rate (eGFR) and Hb time course prediction was performed.

Results

Whereas the last Hb and eGFR values before (cryo)surgery were the main basis for the course of Hb and renal function, age and several time frame features also contributed significantly. For eGFR, the type of (cryo)surgery was also a main predicting feature, and for Hb, tumor location, and body mass index were important predictors. With regard to prediction of inflammation no feature was markedly prominent. Inflammation prediction was based on a combination of patient characteristics, physiological parameters, and time frame features.

Conclusions

This study provided interesting insights into factors influencing complications and recovery in individual renal cell carcinoma patients. The established prediction models provide the basis for development of clinical decision support tools for selection and timing of laboratory analyses after (cryo)surgery, thus contributing to quality and efficiency of care.

Antioxidant Gene Signature Impacts the Immune Infiltration and Predicts the Prognosis of Kidney Renal Clear Cell Carcinoma

Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC.

Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated.

Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC.

Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.

Identification of Key Genes of Prognostic Value in Clear Cell Renal Cell Carcinoma Microenvironment and a Risk Score Prognostic Model

Objective

We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model.

Materials and Methods

Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores.

Results

The median survival time of the low immune score group was longer than that of the high immune score group (p = 0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p < 0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237-1.0641, p < 0.0001).

Conclusions

Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

Development and Validation of a Nomogram Predicting the Prognosis of Renal Cell Carcinoma After Nephrectomy

Objective

To develop and validate a nomogram for predicting the overall survival (OS) of renal cell carcinoma (RCC) patients after nephrectomy.

Materials and Methods

In total, 488 patients with RCC who underwent nephrectomy at the Urology Department of the First Hospital of Shanxi Medical University between January 2013 and December 2018 were randomly divided into a development cohort (n = 344) and a validation cohort (n = 144). The development cohort was used to build a prediction model, and the validation cohort was used for validation. Single-factor and multifactor analyses were carried out with R software, and the nomogram, calibration chart, ROC curve and C index were constructed.

Results

The median follow-up time of the development and validation cohorts was 34 months. The total 3-year and 5-year survival rates of the development cohort were 93.3% and 91.6%, respectively; those of the validation cohort were 92.4% and 91.0%, respectively. Cox univariate analysis of the development cohort showed that age, type 2 diabetes mellitus (T2DM), smoking history, type of surgery, T stage, N stage, M stage and Fuhrman nuclear grade were prognostic factors for OS in RCC patients undergoing nephrectomy. Cox multivariate analysis showed that T2DM, smoking history and T stage were independent prognostic factors for OS in RCC patients undergoing nephrectomy (P < 0.05). According to the univariate and multivariate analyses, a nomogram was constructed. In the development cohort, the C index of predicted OS was 0.875 (95% CI, 0.820-0.930). The calibration curve of the 3-year and 5-year survival rates showed that the predicted value of the nomogram was consistent with the actual observed value. The area under the 3-year and 5-year survival ROC curves was 0.861 and 0.901, respectively. In the validation cohort, the C index was 0.880 (95% CI, 0.778-0.982). The calibration curve of the 3-year and 5-year survival rates showed that the predicted value of the nomogram was consistent with the actual observed value. The area under the 3-year and 5-year survival ROC curves was 0.813 and 0.799, respectively.

Conclusion

We developed and verified a new and accurate nomogram with available clinicopathological data that can effectively predict the OS of RCC patients after nephrectomy.

Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information

We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources information into the Super learner model, and then introduce the time-varying effect into the combined model using the landmark method for real-time dynamic prediction. The Super learner model has better prognostic performance since it can not only employ the preferable clinical prognostic model constructed by oneself or reported in the current literature, but also incorporate genome level information to strengthen effectiveness. Apart from this, four representative patients' mortality curves are drawn in the dynamically updated manner based on the Super learner model. It is found that effectively reducing the two prognostic indices value through suitable treatments might achieve the purpose of controlling the mortality of patients. Combining clinical and genetic information in the Super learner model would enhance the prognostic performance and yield more accurate results for dynamic predictions. Doctors could give patients more personalized treatment with dynamically updated monitoring of disease status, as well as some candidate prognostic factors for future research.

Radical versus partial nephrectomy, chronic kidney disease progression and mortality in US veterans

Background

Partial nephrectomy is considered the preferred care for localized kidney tumors and may yield better patient and kidney survival and similar oncological outcomes compared with radical nephrectomy. We sought to reexamine these hypotheses in a large nationally representative cohort of US veterans who underwent radical or partial nephrectomy.

Methods

We identified 7073 US veterans who had a partial or radical nephrectomy between 2004 and 2013. We collected data on estimated glomerular filtration rate (eGFR) prior to admission for nephrectomy surgery, immediately after surgery and 180 days postsurgery. We evaluated the association of nephrectomy type and eGFR at different time points with long-term mortality risk in adjusted survival models.

Results

Patients who underwent radical (compared to partial) nephrectomy had a 2-fold greater decline in eGFR (-21.8 ± 17.7 versus -10.3 ± 17.4 mL/min/1.73 m2) immediately after surgery. This larger drop in eGFR resulted in a larger proportion of radical nephrectomy patients having an eGFR <60 mL/min/1.73 m2 at ≥180 days postsurgery. Radical (compared to partial) nephrectomy patients also exhibited a 2.2-fold higher mortality [adjusted death hazard ratio 2.21 (95% confidence interval 1.91-2.55)]. Low eGFRs prior to surgery and 180 days postsurgery were associated with higher risk of postnephrectomy death.

Conclusions

Worse postnephrectomy kidney function and higher mortality were observed with radical nephrectomy, and a low presurgical eGFR and a greater decrease in eGFR postsurgery were associated with worse mortality irrespective of the type of nephrectomy. Additional studies are needed to examine predictors of postnephrectomy outcomes.

Identify clear cell renal cell carcinoma related genes by gene network

Clear cell renal cell carcinoma (ccRCC) is the most prominent type of kidney cancer in adults. The patients within metastatic ccRCC have a poor 5-year survival rate that is less than 10%. It is essential to identify ccRCC -related genes to help with the understanding of molecular mechanism of ccRCC. In this literature, we aim to identify genes related to ccRCC based on a gene network. We collected gene expression level data of ccRCC from the Cancer Genome Atlas (TCGA) for our analysis. We constructed a co-expression gene network as the first step of our study. Then, the network sparse boosting approach was performed to select the genes which are relevant to ccRCC. Results of our study show there are 15 genes selected from the all genes we collected. Among these genes, 7 of them have been demonstrated to play a key role in development and progression or in drug response of ccRCC. This finding offers clues of gene markers for the treatment of ccRCC.

Renal parenchymal histopathology predicts life-threatening chronic kidney disease as a result of radical nephrectomy

The preoperative prediction of post-radical nephrectomy renal insufficiency plays an important role in the decision-making process regarding renal surgery options. Furthermore, the prediction of both postoperative renal insufficiency and postoperative cardiovascular disease occurrence, which is suggested to be an adverse consequence caused by renal insufficiency, contributes to the preoperative policy decision as well as the precise informed consent for a renal cell carcinoma patient. Preoperative nomograms for the prediction of post-radical nephrectomy renal insufficiency, calculated using patient backgrounds, are advocated. The use of these nomograms together with other types of nomograms predicting oncological outcome is beneficial. Post-radical nephrectomy attending physicians can predict renal insufficiency based on the normal renal parenchymal pathology in addition to preoperative patient characteristics. It is suggested that a high level of global glomerulosclerosis in nephrectomized normal renal parenchyma is closely associated with severe renal insufficiency. Some studies showed that post-radical nephrectomy severe renal insufficiency might have an association with increased mortality as a result of cardiovascular disease. Therefore, such pathophysiology should be recognized as life-threatening, surgically-related chronic kidney disease. On the contrary, the investigation of the prediction of mild post-radical nephrectomy renal insufficiency, which is not related to adverse consequences in the postoperative long-term period, is also promising because the prediction of mild renal insufficiency might be the basis for the substitution of radical nephrectomy for nephron-sparing surgery in technically difficult or compromised cases. The deterioration of quality of life caused by post-radical nephrectomy renal insufficiency should be investigated in conjunction with life-threatening matters.