Use of the University of California Los Angeles Integrated Staging System (UISS) to predict survival in localized renal cell carcinoma in an Asian population

A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning

Background

Renal cell carcinoma (RCC) is a highly metastatic urological cancer. RCC with liver metastasis (LM) carries a dismal prognosis. The objective of this study is to develop a machine learning (ML) model that predicts the risk of RCC with LM, which is used to assist clinical treatment.

Methods

The retrospective study data of 42,547 patients with RCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. ML includes algorithmic methods and is a fast-rising field that has been widely used in the biomedical field. Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), random forest (RF), decision tree (DT), and naive Bayesian model [Naive Bayes Classifier (NBC)] were applied to develop prediction models to predict the risk of RCC with LM. The six models were 10-fold cross-validated, and the best-performing model was selected based on the area under the curve (AUC) value. A web online calculator was constructed based on the best ML model.

Results

Bone metastasis, lung metastasis, grade, T stage, N stage, and tumor size were independent risk factors for the development of RCC with LM by multivariate regression analysis. In addition, the correlation of the relative proportions of the six clinical variables was shown by a heat map. In the prediction models of RCC with LM, the mean AUC of the XGB model among the six ML algorithms was 0.947. Based on the XGB model, the web calculator (https://share.streamlit.io/liuwencai4/renal_liver/main/renal_liver.py) was developed to evaluate the risk of RCC with LM.

Conclusions

This XGB model has the best predictive effect on RCC with LM. The web calculator constructed based on the XGB model has great potential for clinicians to make clinical decisions and improve the prognosis of RCC patients with LM.

High mucin 5AC expression predicts adverse postoperative recurrence and survival of patients with clear-cell renal cell carcinoma

Background

Mucin 5AC (MUC5AC), as a member of secreted/gel-forming mucin family, was frequently found to be abnormally expressed in inflammation or malignant diseases. However, the clinic pathologic features and prognostic values of MUC5AC in clear-cell renal cell carcinoma (ccRCC) have not been reported up to now.

Methods

MUC5AC expression was analyzed by immunohistochemistry on tissue microarrays. Kaplan-Meier survival curves, Univariate and Multivariate Cox analysis and newly-established nomogram model were performed to evaluate the prognostic value.

Results

MUC5AC expression was firstly found to be up-regulated in patients with ccRCC, positively associated with tumor size, pN stage, lymphovascular invasion, Fuhrman grade, rahbdoid differentiation, sarcomatoid features, tumor necrosis, ECOG-PS and recurrence. Furthermore, MUC5AC expression might be contributed to risk stratification of ccRCC patients with TNM stage III+IV or Fuhrman grade 3 or 4 for overall survival (OS) and recurrence-free survival (RFS) analysis, and it was demonstrated to be negatively correlated with OS and RFS of ccRCC patients. What's more, MUC5AC was identified as a potential independent adverse prognostic factor; prediction accuracy of MUC5AC-based new nomogram model was drastically improved for OS and RFS of ccRCC patients.

Conclusion

MUC5AC is a promising independent adverse prognostic factor for ccRCC patients, it maybe conducive to postoperative risk stratification and guide treatment in the future.

High Expression of Stearoyl-CoA Desaturase 1 Predicts Poor Prognosis in Patients with Clear-Cell Renal Cell Carcinoma

Stearoyl-CoA desaturase 1 (SCD1), the rate-limiting enzymes in the biosynthesis of monounsaturated fatty acids from saturated fatty acids, have been gradually recognized as a potential therapeutic target for various malignancies, particularly in clear-cell renal cell carcinoma (ccRCC). However, the prognostic value of SCD1 in ccRCC is still unknown. The aim of this study is to evaluate the clinical significance of SCD1 expression in patients with ccRCC. SCD1 expression in tumor tissues obtained from 359 patients who underwent nephrectomy for ccRCC are retrospectively assessed. During a median follow-up of 63 months (range: 1–144month), 56 patients in total died before the last follow-up in this study. Survival curves were plotted with the Kaplan–Meier method and compared with the log-rank test. Meanwhile, univariate and multivariate Cox regression models were applied to evaluate the prognostic value of SCD1 expression in overall survival (OS) for ccRCC patients. Moreover, SCD1 was enrolled into a newly built nomogram with factors selected by multivariate analysis, and the calibration was built to evaluate the predictive accuracy of nomogram. High SCD1 expression occurred in 61.6% (221/359) of ccRCC patients, which was significantly associated with age (p = 0.030), TNM stage (p = 0.021), pN stage (p = 0.014), Fuhrman grade (p = 0.014) and tumor sizes (p = 0.040). In multivariate analysis, SCD1 expression was confirmed as an adverse independent prognostic factor for OS. The prognostic accuracy of TNM stage, Fuhrman grade and tumor sizes was significantly increased when SCD1 expression was added. The independent prognostic factors, pT stage, pN stage, Fuhrman grade and tumor sizes, as well as SCD1 expression were integrated to establish a predictive nomogram with high predictive accuracy. Calibration curves revealed optimal consistency between observations and prognosis. In conclusion, high SCD1 expression is an independent prognostic factor for OS in patients with ccRCC. Our data suggest that the expression of SCD1 might guide the clinical decisions for patients with ccRCC.

Breaking through a plateau in renal cell carcinoma therapeutics: development and incorporation of biomarkers

With the Food and Drug Administration approval of 6 novel targeted agents since December 2005 and limited comparative trials to discern relative efficacy, the treatment of metastatic renal cell carcinoma (RCC) has become immensely complex. The research community must look to novel ways in which to identify appropriate candidates for selected targeted therapies; one potential strategy is the use of clinical and molecular biomarkers. A growing body of knowledge-related von Hippel Lindau-driven pathways in this disease has highlighted the potential role of hypoxia-inducible factor subtypes in distinguishing RCC patients clinically. Techniques applied in other malignancies, such as gene expression and proteomic profiling, may also ultimately allow for clinical stratification. An emerging understanding of immunologic phenomena that may affect cancer progression (i.e., tumor infiltration by CD68 lymphocytes, memory T-cells, etc.) has unveiled a number of other potential biomarkers of response. Several vascular endothelial growth factor receptor-directed therapies classically thought to function as antiangiogenics may also have complex effects upon the tumor microenvironment including the associated immune cell milieu. As such, immunologic parameters could potentially predict response to current therapies. Finally, clinical biomarkers, such as hypertension, may predict the efficacy of several currently available targeted agents, although implementation of such biomarkers remains challenging. Herein, the clinical relevance of putative RCC biomarkers is examined in detail.