Prognostic Factors for Renal Cell Carcinoma Subtypes Diagnosed According to the 2016 WHO Renal Tumor Classification: a Study Involving 928 Patients

Validation of novel grading schemes and refinement of the Leibovich risk groups for chromophobe renal cell carcinoma

BACKGROUND

Traditional grading systems have proven inadequate in stratifying chRCC patients based on recurrence risk. Recently, several novel grading schemes, including three-tiered, two-tiered, and four-tiered systems, have been proposed, but their prognostic value remains controversial and lacks external validation.

MATERIALS AND METHODS

We included 528 patients with pathologically proven chRCC (chromophobe renal cell carcinoma) from multiple medical institutions and the Cancer Genome Atlas-Kidney Chromophobe cohort. Three experienced pathologists independently reassessed the slides based on the three novel grading schemes. Survival outcomes, including disease-specific survival (DSS), recurrence-free survival (RFS), were analyzed using Kaplan-Meier methods and Cox proportional hazards regression models. The prognostic value of the original and adjusted Leibovich risk groups was compared using Harrell's C-index.

RESULTS

All grading systems demonstrated significant survival differences among their respective groups (p < 0.001 for all). However, within the four-tiered system, no significant survival disparity was observed between grade 1 and grade 2 tumors (GTG2 without necrosis) (p = 0.619 for DSS). When patients with necrosis were excluded, no survival difference was detected between CTG1 and CTG2 tumors in the three-tiered system (p = 0.870 for DSS), challenging the prognostic utility of distinguishing between these two grades. The adjusted Leibovich risk stratification (C-index = 0.840 for DSS), incorporating necrosis and tumor thrombus, demonstrated superior prognostic value compared to the original model (C-index = 0.762 for DSS), with more pronounced survival distinctions and improved predictive performance.

CONCLUSION

Our study validates the prognostic significance of recently developed grading systems for chRCC. The observed survival difference between CTG1 and CTG2 in the three-tiered system may be attributed to varying percentages of coagulative necrosis. By integrating necrosis and tumor thrombus into the Leibovich risk groups, we enhanced the model's ability to distinguish between patients and improved its predictive performance.

Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma

Objective

To explore the effectiveness of a machine learning-based multiparametric MRI radiomics nomogram for predicting the WHO/ISUP nuclear grading of clear cell renal cell carcinoma (ccRCC) before surgery.

Methods

Data from 86 patients who underwent preoperative renal MRI scans (both plain and enhanced) and were confirmed to have ccRCC were retrospectively collected. Based on the 2016 WHO/ISUP grading standards, patients were divided into a low-grade group (Grade I and II) and a high-grade group (Grade III and IV), and randomly split into training and testing sets at a 7:3 ratio. Radiomics features were extracted from FS-T2WI, DWI, and CE-T1WI sequences. Optimal features were selected using the Mann-Whitney U test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Five machine learning classifiers-logistic regression (LR), naive bayes (NB), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and multilayer perceptron (MLP)-were used to build models to predict ccRCC WHO/ISUP nuclear grading. The model with the highest area under the curve (AUC) in the testing set was chosen as the best radiomics model. Independent clinical risk factors were identified using univariate and multivariate logistic regression to create a clinical model, which was combined with radiomics score (rad-score) to develop a nomogram. The model's effectiveness was assessed using the receiver operating characteristic (ROC) curve, its calibration was evaluated using a calibration curve, and its clinical utility was analyzed using decision curve analysis.

Results

Six radiomics features were ultimately selected. The MLP classifier showed the highest diagnostic performance in the testing set (AUC=0.933). Corticomedullary enhancement level (P=0.020) and renal vein invasion (P=0.011) were identified as independent risk factors for predicting the WHO/ISUP nuclear classification and were included in the nomogram with the rad-score. The ROC curves indicated that the nomogram model had strong diagnostic performance, with AUC values of 0.964 in the training set and 0.933 in the testing set.

Conclusion

The machine learning-based multiparametric MRI radiomics nomogram provides a highly predictive, non-invasive tool for preoperative prediction of WHO/ISUP nuclear grading in patients with ccRCC.

A Nomogram Including Sarcopenia for Predicting Progression-Free Survival in Patients with Localized Papillary Renal Cell Carcinoma: A Retrospective Cohort Study

BACKGROUND

Because of to the removal of subclassification of papillary renal cell carcinoma (pRCC), the survival prognostification of localized pRCC after surgical treatment became inadequate. Sarcopenia was widely evaluated and proved to be a predictive factor for prognosis in RCC patients. Therefore, we comprehensively investigated the survival prediction of the body composition parameters for localized pRCC.

METHODS

Patients pathologically diagnosed with pRCC between February 2012 and February 2022 in our center were enrolled. The body composition parameters, including skeletal muscle index (SMI), subcutaneous adipose tissue (SAT), and perirenal adipose tissue (PRAT), were measured by the images of preoperative computed tomography (CT). The primary outcome was set as progression-free survival (PFS), and the cutoff values of body composition parameters were calculated by using the Youden from receiver operating characteristic curve (ROC) curves. Univariate and multivariate Cox proportional regression analyses were performed to explore independent risk factors for survival prediction. Then, significant factors were used to construct a prognostic nomogram. The performance of the nomogram was evaluated by Harrell's C-index, calibration curves and time-dependent ROC curves.

RESULTS

A total of 105 patients were enrolled for analysis. With a median follow-up time of 30.48 months, 25 (23.81%) patients experienced cancer progression. The percentage of sarcopenia was 74.29%. Univariate Cox analysis identified that gender, PRAT, SAT, skeletal muscle (SM), sarcopenia, surgical technique, and tumor diameter were associated with progression. Further multivariate analysis showed that sarcopenia (hazard ratio [HR] 0.15, 95% confidence interval [CI] 0.03-0.66), SAT (HR 6.36, 95% CI 2.39-16.93), PRAT (HR 4.66, 95% CI 1.77-12.27), tumor diameter (HR 0.35, 95% CI 0.14-0.86), and surgical technique (HR 2.85, 95% CI 1.06-7.64) were independent risk factors for cancer progression. Then, a prognostic nomogram based on independent risk factors was constructed and the C-index for progression prediction was 0.831 (95% CI 0.761-0.901), representing a reasonable discrimination, the calibration curves, and the time-dependent ROC curves verified the good performance of the nomogram.

CONCLUSIONS

A prognostic nomogram, including sarcopenia, SAT, PRAT, tumor diameter, and surgical technique, was constructed to calculate the probability of progression for localized pRCC patients and needs further external validation for clinical use in the future.

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma

OBJECTIVES

This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.

RESULTS

The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).

CONCLUSION

The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.

CRITICAL RELEVANCE STATEMENT

The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.

KEY POINTS

The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma

OBJECTIVES

This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs).

METHODS

CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation.

RESULTS

There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76).

CONCLUSIONS

Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC.

ADVANCES IN KNOWLEDGE

This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

An epidemiological and clinicopathological study of type 1 vs. type 2 morphological subtypes of papillary renal cell carcinoma- results from a nation-wide study covering 50 years in Iceland

INTRODUCTION

Papillary renal cell carcinoma (pRCC) is the second most common histology of renal cell carcinoma (RCC), accounting for 10-15% of cases. Traditionally, pRCC is divided into type 1 and type 2, although this division is currently debated as a prognostic factor of survival. Our aim was to investigate the epidemiology and survival of the pRCC subtypes in a whole nation cohort of patients during a 50-year period.

MATERIALS AND METHODS

A Population based retrospective study including consecutive cases of RCC in Iceland from 1971-2020. Comparisons were made between histological classifications of RCC, with emphasis on pRCC subtypes (type 1 vs. 2) for outcome estimation. Changes in RCC incidence were analyzed in 5-year intervals after age standardization. The Kaplan-Meier method and Cox regression were used for outcome analysis.

RESULTS

A total of 1.725 cases were identified, with 74.4%, 2.1% and 9.2% having clear cell (ccRCC), chromophobe (chRCC), and pRCC, respectively. The age standardized incidence (ASI) of pRCC was 1.97/100.000 for males and 0.5/100.000 for females, and the proportion of pRCC increased from 3.7% to 11.5% between the first and last intervals of the study (p < 0.001). Age standardized cancer specific mortality (ASCSM) of pRCC was 0.6/100.000 and 0.19/100.000 for males and females, respectively. The annual average increase in ASI was 3.6% for type 1 pRCC, but the ASI for type 2 pRCC and ASCSM for both subtypes did not change significantly. Male to female ratio was 4.4 for type 1 pRCC and 2.3 for type 2. The average tumor size for type 1 and 2 was 58.8 and 73.7 mm, respectively. Metastasis at diagnosis was found in 8.7% in the type 1 pRCC, compared to 30.0% of patients with type 2 pRCC (p < 0.001). Estimated 5-year cancer-specific survival (CSS) were 94.4%, 80.7%, and 69.3% for chRCC, pRCC and ccRCC, respectively (p < 0.001). For the pRCC subtypes, type 1 was associated with better 5-year CSS than type 2 (86.3% vs. 66.0%, p < 0.001), although this difference was not significant after adjusting for cancer stage and grading.

CONCLUSIONS

pRCC histology was slightly less common in Iceland than in other countries. Males are more than three times more likely to be diagnosed with pRCC, compared to other RCC histologies. The subtype of pRCC was not found to be an independent risk factor for worse survival, and as suggested by the most recent WHO Classification of Urinary Tumors, grade and TNM-stage seem to be the most important factors for estimation of survival for pRCC patients.

The long-term outcomes of local tumor destruction versus partial nephrectomy for cT1a non-clear cell renal cell carcinoma and development of prognostic nomograms

PURPOSE

There is a lack of authoritative opinions on local tumor destruction (LTD) for clinical T1a (cT1a) non-clear cell renal cell carcinoma (nccRCC). We aim to compare the outcomes of cT1a nccRCC after partial nephrectomy (PN) or LTD and explore prognostic factors.

METHODS

Patients diagnosed with cT1a nccRCC receiving LTD or PN between 2000 and 2020 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. A 1:1 propensity score matching (PSM) was performed for patients receiving LTD and PN. Kaplan-Meier survival analysis, Cox regression analysis, competing risk regression models, and subgroup analysis were used to compare outcomes and identify prognostic factors. Prognostic nomograms were established and evaluated based on the multivariate models.

RESULTS

A total of 3664 cT1a nccRCC patients were included. The LTD group had poorer overall survival (OS) and similar cancer-specific survival (CSS) compared with the PN group before and after PSM (p < 0.05), while the other-cause mortality rate of the LTD group was higher than that of the PN group. Age, marital status, household income, prior tumor history, interval between diagnosis and treatment, treatments, and tumor size were identified as independent predictive factors for OS. Age, tumor size, prior tumor history, and histological type were identified as independent predictive factors for CSS. Then the nomograms predicting OS and CSS were constructed based on these prognostic factors, which showed excellent performance in risk stratification and accuracy.

CONCLUSION

LTD could achieve comparable cancer-control effects as PN among cT1a nccRCC patients. The OS and CSS nomograms worked effectively for prognosis assessment.

Analysis of HIF-1α expression and genetic polymorphisms in human clear cell renal cell carcinoma

Introduction: Clear cell renal cell carcinoma (ccRCC) is mostly diagnosed incidentally and has relatively high recurrence rates. Alterations in VHL/HIF and mTOR pathways are commonly present in ccRCC. The present study attempted to identify potential diagnostic markers at the biochemical and molecular level. Methods: In total, 54 subjects (36 patients with ccRCC and 18 cancer-free controls) were enrolled. ELISA was used to measure the levels of HIF-1α in the tumor and healthy kidney tissue. The association between five selected SNPs (rs779805, rs11549465, rs2057482, rs2295080 and rs701848) located in genes of pathologically relevant pathways (VHL/HIF and mTOR) and the risk of ccRCC in the Slovak cohort was studied using real-time PCR. Results: Significant differences in HIF-1α tissue levels were observed between the tumor and healthy kidney tissue (p < 0.001). In the majority (69%) of cases, the levels of HIF-1α were higher in the kidney than in the tumor. Furthermore, the concentration of HIF-1α in the tumor showed a significant positive correlation with CCL3 and IL-1β (p (R2) 0.007 (0.47); p (R2) 0.011 (0.38). No relationship between intratumoral levels of HIF-1α and clinical tumor characteristics was observed. Rs11549465, rs2057482 in the HIF1A gene did not correlate with the expression of HIF-1α either in the tumor or in the normal kidney. None of the selected SNPs has influenced the susceptibility to ccRCC. Conclusion: More research is neccesary to elucidate the role of HIF-1α in the pathogenesis of ccRCC and the association between selected SNPs and susceptibility to this cancer.

Evaluation of the clinical behavior of unclassified renal cell carcinoma and its imaging findings on computed tomography and magnetic resonance imaging based on World Health Organization (WHO) 2022

OBJECTIVES

To ascertain the clinical behaviors of unclassified renal cell carcinoma (RCC) and its characteristic imaging findings on CT and MRI.

METHODS

Subjects in this retrospective study were 10 patients who had received a histological diagnosis of unclassified RCC based on World Health Organization (WHO) 2022 and who had undergone CT and/or MRI prior to surgery. In terms of clinical behaviors, TNM classification, stage, postoperative recurrence, time to recurrence, and postoperative survival were evaluated. In terms of imaging findings, tumor size, growth pattern, CT density, dynamic contrast-enhancement (DCE) pattern, internal appearance, presence of a pseudocapsule, and signal intensity on MRI were evaluated. We compared clinical behaviors and imaging findings, and investigated associations between them.

RESULTS

One patient could not be followed-up due to death from other causes. Postoperative recurrence was observed in 4 patients, all of whom had Stage 3 RCC. In the remaining 5 patients without recurrence, all 5 patients showed Stage 2 or below. On imaging, unclassified RCC tended to be large (58.7 mm) and solid (100%), and heterogeneous interiors (80%), cystic degeneration (80%) and high intensity on diffusion-weighted imaging (DWI) (71.4%) were common. Comparing patients with and without recurrence, the following findings tended to differ between recurrence and recurrence-free groups: tumor size (73.4 ± 33.9 mm vs. 50.2 ± 33.9 mm, P = 0.286), growth pattern (invasive: 100% vs. 0%, expansive: 0% vs. 100%, P = 0.008 each), DCE pattern (progressive enhancement pattern, 66.7% vs. 0%, washout pattern, 0% vs. 66.7%, P = 0.135 each) and presence of a pseudocapsule (25% vs. 80%, P = 0.167).

CONCLUSION

The clinical behavior of unclassified RCC varies widely. Although imaging findings are also variable, findings of large, heterogeneous tumors with cystic degeneration and high intensity on DWI were common. Several imaging findings such as large size, invasive growth, progressive enhancement pattern and no pseudocapsule may enable prediction of prognosis in unclassified RCC.

[Morphological variants of the invasive urothelial cell carcinoma.]

Urothelial cell carcinoma is the most common malignant tumor of the urinary tract, which develops in the renal pelvis, ureter, and bladder, and rarely it develops in the ureter. Histologically, urothelial cell carcinoma is categorized into non-invasive and invasive forms. Non-invasive urothelial cell carcinoma has papillary growth, it is usually well differentiated, and has a favorable outcome, while invasive urothelial cell carcinoma infiltratively spreads the organs of origin, it is typically poorly differentiated, and often associated with a poor prognosis. In the case of invasive urothelial cell carcinoma, the clinical course is primarily determined by the depth of invasion, but according to recent data, morphological variants of urothelial cell carcinoma respond differently to oncological treatments, and their biological behavior is also distinct. These subtypes and variants are significantly underdiagnosed in Hungary and internationally because the criteria for histological diagnosis are not clear for many subsets. The latest 2022 WHO classification of urinary tract tumors significantly clarified the definitions of various subtypes and variants. In this paper, utilizing the current classification, we review and explain these subtypes' morphological, immunohistochemical, differential diagnostic, prognostic, and predictive characteristics intending to make them appear as much as possible in everyday diagnostic practice. Also, the work aims to present the individual urothelial cell carcinoma subtypes and variants to the Hungarian community of pathologists, oncologists, and urologists, so that the previously high level of urological oncology care can become even more personalized. Orv Hetil. 2023; 164(40): 1567-1582.

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

BACKGROUND

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

METHODS

From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

RESULTS

After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

CONCLUSIONS

According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Renal Cell Carcinoma in End-Stage Renal Disease: A Retrospective Study in Patients from Hungary

INTRODUCTION

End-stage renal disease (ESRD) and acquired cystic kidney disease (ACKD) are known risk factors for renal cell carcinoma (RCC). Hereby, the clinicopathological features of RCCs developed in ESRD were investigated.

METHODS

A database consisting of 34 tumors from 31 patients with ESRD among 2,566 nephrectomy samples of RCC was built. The demographic, clinical, and follow-up data along with pathological parameters were analyzed. The RCCs were diagnosed according to the current WHO Classification of Urinary and Male Genital Tumors.

RESULTS

Twenty-two tumors developed in men and 12 in women, with a median age of 56 years (range: 27-75 years). The causes of ESRD were glomerulonephritis (n = 7), hypertensive kidney disease (n = 6), autosomal dominant polycystic kidney disease (n = 6), chronic pyelonephritis (n = 4), diabetic nephropathy (n = 3), chemotherapy-induced nephropathy (n = 1), and undetermined (n = 4). ACKD complicated ESRD in 12 patients. The following histological subtypes were identified: clear cell RCC (n = 19), papillary RCC (n = 5), clear cell papillary tumor (n = 5), ACKD RCC (n = 3), and eosinophilic solid and cystic RCC (n = 2). The median tumor size was 31 mm (range: 10-80 mm), and 32 tumors were confined to the kidney (pT1-pT2). There was no tumor-specific death during the period of this study. Progression was registered in 1 patient.

CONCLUSION

In our cohort, the most common RCC subtype was clear cell RCC (55%), with a frequency that exceeded international data appreciably (14-25%). The incidence of clear cell papillary tumor and ACKD RCC (14.7% and 8.5%) was lower than data reported in the literature (30% and 40%). Our results indicate a favorable prognosis of RCC in ESRD.

Dissection of tumor antigens and immune landscape in clear cell renal cell carcinoma: Preconditions for development and precision medicine of mRNA vaccine

Accumulating evidence reveals that mRNA-type cancer vaccines could be exploited as cancer immunotherapies in various solid tumors. However, the use of mRNA-type cancer vaccines in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to identify potential tumor antigens for the development of an anti-ccRCC mRNA vaccine. In addition, this study aimed to determine immune subtypes of ccRCC to guide the selection of patients to receive the vaccine. Raw sequencing and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Further, the cBioPortal website was used to visualize and compare genetic alterations. GEPIA2 was employed to evaluate the prognostic value of preliminary tumor antigens. Moreover, the TIMER web server was used to evaluate correlations between the expression of specific antigens and the abundance of infiltrated antigen-presenting cells (APCs). Single-cell RNA sequencing data of ccRCC was used to explore the expression of potential tumor antigens at single-cell resolution. The immune subtypes of patients were analyzed by the consensus clustering algorithm. Furthermore, the clinical and molecular discrepancies were further explored for a deep understanding of the immune subtypes. Weighted gene co-expression network analysis (WGCNA) was used to cluster the genes according to the immune subtypes. Finally, the sensitivity of drugs commonly used in ccRCC with diverse immune subtypes was investigated. The results revealed that the tumor antigen, LRP2, was associated with a good prognosis and enhanced the infiltration of APCs. ccRCC could be divided into two immune subtypes (IS1 and IS2) with distinct clinical and molecular characteristics. The IS1 group showed a poorer overall survival with an immune-suppressive phenotype than the IS2 group. Additionally, a large spectrum of differences in the expression of immune checkpoints and immunogenic cell death modulators were observed between the two subtypes. Lastly, the genes correlated with the immune subtypes were involved in multiple immune-related processes. Therefore, LRP2 is a potential tumor antigen that could be used to develop an mRNA-type cancer vaccine in ccRCC. Furthermore, patients in the IS2 group were more suitable for vaccination than those in the IS1 group.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma

Objectives

To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM).

Methods

175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test.

Results

The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p &lt;0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p &lt; 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration.

Conclusion

The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

Development of genomic instability-associated long non-coding RNA signature: A prognostic risk model of clear cell renal cell carcinoma

Purpose

Renal clear cell carcinoma (ccRCC) is the most lethal of all pathological subtypes of renal cell carcinoma (RCC). Genomic instability was recently reported to be related to the occurrence and development of kidney cancer. The biological roles of long non-coding RNAs (lncRNAs) in tumorigenesis have been increasingly valued, and various lncRNAs were found to be oncogenes or cancer suppressors. Herein, we identified a novel genomic instability-associated lncRNA (GILncs) model for ccRCC patients to predict the overall survival (OS).

Methods

The Cancer Genome Atlas (TCGA) database was utilized to obtain full transcriptome data, somatic mutation profiles, and clinical characteristics. The differentially expressed lncRNAs between the genome-unstable-like group (GU) and the genome-stable-like group (GS) were defined as GILncs, with |logFC| > 1 and an adjusted p-value< 0.05 for a false discovery rate. All samples were allocated into GU-like or GS-like types based on the expression of GILncs observed using hierarchical cluster analyses. A genomic instability-associated lncRNA signature (GILncSig) was constructed using parameters of the included lncRNAs. Quantitative real-time PCR analysis was used to detect the in vitro expression of the included lncRNAs. Validation of the risk model was performed by the log-rank test, time-dependent receiver operating characteristic (ROC) curves analysis, and multivariate Cox regression analysis.

Results

Forty-six lncRNAs were identified as GILncs. LINC00460, AL139351.1, and AC156455.1 were employed for GILncSig calculation based on the results of Cox analysis. GILncSig was confirmed as an independent predictor for OS of ccRCC patients. Additionally, it presented a higher efficiency and accuracy than other RCC prognostic models reported before.

Conclusion

GILncSig score was qualified as a critical indicator, independent of other clinical factors, for prognostic prediction of ccRCC patients.

A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis

Background

To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7).

Methods

In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7.

Results

A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson’s correlation coefficient was statistically significant between Rad-score and CK7.

Conclusion

We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01099-0.

Impact of Renal Cell Carcinoma Histological Variants on Recurrence After Partial Nephrectomy: A Multi-Institutional, Prospective Study (UROCCR Study 82)

BACKGROUND

The prognostic impact of renal cell carcinoma (RCC) morphotype remains unclear in patients who undergo partial nephrectomy (PN). Our objective was to determine the risk factors for recurrence after PN, including RCC morphotype.

METHODS

Patients with RCC who had undergone PN were extracted from the prospective, national French database, UroCCR. Patients with genetic predisposition, bilateral or multiple tumours, and those who had undergone secondary totalization were excluded. Primary endpoint was 5-year, recurrence-free survival (RFS), and secondary endpoint was overall survival (OS). Risk factors for recurrence were assessed by multivariable Cox regression analysis.

RESULTS

Overall, 2,767 patients were included (70% male; median age: 61 years [interquartile range (IQR) 51-69]). Most (71.5%) of the PN procedures were robot-assisted. Overall, 2,573 (93.0%) patients were recurrence free, and 74 died (2.7%). Five-year RFS was 84.9% (IQR 82.4-87.4). A significant difference in RFS was observed between RCC morphotypes (p < 0.001). Surgical margins (hazard ratio [HR] = 2.0 [95% confidence interval (CI): 1.3-3.2], p < 0.01), pT stage >1 (HR = 2.6 [95% CI: 1.8-3.7], p < 0.01]) and Fuhrmann grade >2 (HR = 1.9 [95% CI: 1.4-2.6], p < 0.001) were risk factors for recurrence, whereas chromophobe subtype was a protective factor (HR = 0.08 [95% CI: 0.01-0.6], p = 0.02). Five-year OS was 94.0% [92.4-95.7], and there were no significant differences between RCC subgroups (p = 0.06). The main study limitation was its design (multicentre national database), which may be responsible for declarative bias.

CONCLUSIONS

Chromophobe morphotype was significantly associated with better RFS in RCC patients who underwent PN. Conversely, pT stage, Fuhrman group and positive surgical margins were risk factors for recurrence.

Renal Cell Carcinoma in End-Stage Renal Disease: A Review and Update

Renal cell carcinoma (RCC) occurring in the setting of end-stage renal disease (ESRD) shows unique clinicopathological characteristics. The two most frequent types of ESRD-associated RCC are acquired cystic kidney disease-associated renal cell carcinoma (ACKD-RCC) and clear-cell papillary renal cell carcinoma (ccpRCC). Other types of RCC also occur in ESRD, albeit with different frequencies from the non-ESRD general population. The histological features of RCC do not vary in the setting of ESRD vs. non-ESRD, yet other findings, such as multifocality and multiple tumor types, are more frequent in ESRD. Studies have generated novel and important knowledge of the etiology, epidemiology, diagnosis, treatment, immunophenotype, and molecular characteristics of ESRD-associated RCC. Knowledge of these data is important for both pathologists and other physicians who may encounter ESRD patients with RCC. This review presents a comprehensive summary and update of the literature on RCC in ESRD, with a focus on the two most frequent types, ACKD-RCC and ccpRCC.

Clinicopathological features of non-conventional renal cell carcinoma histological subtypes: Learning points from a large contemporary series spanning over three decades

Purpose

To perform a retrospective review of the clinicopathological features of patients with conventional and non-conventional renal cell carcinoma (cRCC and ncRCC).

Materials and Methods

A large prospectively maintained uro-oncological registry was accessed to extract clinicopathological data of patients diagnosed with renal tumors who subsequently underwent nephrectomy from 1990–2019. Demographics and operative parameters were extracted. Analyses of overall survival (OS) and cancer-specific survival (CSS) were performed using the Kaplan–Meier method. Cox proportional-hazards analysis was used to identify risk factors which influenced survival.

Results

There were a total of 1,686 consecutive nephrectomies which was retrieved, with 1,286 cRCC and 400 ncRCC. The commonest ncRCC subtypes were papillary (n=198, 11.7%), clear cell papillary (n=50, 3.0%) and chromophobe (n=49, 2.9%) RCC. Kaplan–Meier estimates of OS were higher in cRCC (0.74; 95% confidence interval [CI], 0.71–0.78) than ncRCC (hazard ratio, 1.47; 95% CI, 1.16–1.87). Among individual subtypes, chromophobe RCC had the highest 5-year OS (0.90; 95% CI, 0.79–1.0). Among ncRCC subtypes, acquired cystic RCC demonstrated the highest association with end-stage renal failure and hypertension, with the highest CSS. MiT family translocation RCC had the youngest mean age at presentation (45.6±12.8 y) and excellent CSS. Factors associated with increased OS in the entire cohort included shorter operative time, partial nephrectomy and lower tumor stages.

Conclusions

This study provides a comprehensive contemporary overview of ncRCCs which are yet poorly characterized, in comparison to cRCCs. Data from this study would contribute towards tailored patient counseling and healthcare resource planning.

Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics

Objectives

This study aims to establish predictive logistic models for the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grades of clear cell renal cell carcinoma (ccRCC) based on tumoral and peritumoral radiomics.

Methods

A cohort of 370 patients with pathologically confirmed ccRCCs were included in this retrospective study between January 2014 and December 2020 according to the WHO/ISUP grading system. The volume of interests of triphasic computed tomography images were depicted manually using the “itk-SNAP” software, and the radiomics features were calculated. The cohort was segmented into the training cohort and validation cohort with a random proportion of 7:3. After extraction of radiomics features by analysis of variance (ANOVA) or Mann-Whitney U test, correlation analysis, and the least absolute shrinkage and selection operator (LASSO) method, the logistic models of tumoral radiomics (LR-tumor) and peritumoral radiomics (LR-peritumor) were developed. The LR-peritumor was subdivided into LR-peritumor-2mm, LR-peritumor-5mm, and LR-peritumor-10mm, and the LR-peritumor-2mm was subdivided into LR-peritumor-kid and LR-peritumor-fat based on the neighboring tissues of ccRCCs. Finally, an integrative model of tumoral and peritumoral radiomics (LR-tumor/peritumor) was built. The value of areas under the receiver operator characteristics curve (AUCs) was calculated to assess the efficacy of the models.

Results

There were 209 low-grade and 161 high-grade ccRCCs enrolled. The AUCs of LR-tumor in CT images of venous phase were 0.802 in the training cohort and 0.796 in the validation cohort. The AUCs were higher in the LR-peritumor-2mm than those in LR-peritumor-5mm and LR-peritumor-10mm (training cohort: 0.788 vs. 0.788 and 0.759; validation cohort: 0.787 vs. 0.785 and 0.758). Moreover, the AUCs of LR-peritumor-fat were higher compared with those of LR-peritumor-kid. The LR-tumor/peritumor displayed the highest AUCs of 0.812 in the training cohort and 0.804 in the validation cohort.

Conclusions

The tumoral and peritumoral radiomics helped to predict the WHO/ISUP grades of ccRCCs. On the diagnostic performance of peritumoral radiomics, better results were seen for the LR-peritumor-2mm and LR-peritumor-fat.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Purpose

To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).

Methods

This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.

Results

SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.

Conclusion

A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01107-1.

Predictors of postoperative recurrence in patients with non-metastatic pT3a renal cell carcinoma

OBJECTIVE

To analyze the effect of patterns of extrarenal tumor extension with other pathological factors on postoperative recurrence in patients with non-metastatic pT3a renal cell carcinoma.

METHODS

We retrospectively reviewed 587 non-metastatic renal cell carcinoma patients who underwent radical surgery between 2006 and 2017 at Kansai Medical University Hospital, Hirakata, Osaka, Japan. We extracted a subset of 114 patients with pT3a of predominant histological types: 93 with clear cell renal cell carcinoma (81.6%), 13 with unclassified renal cell carcinoma (11.4%), six with chromophobe renal cell carcinoma (5.3%) and two with papillary renal cell carcinoma. The primary end-point was recurrence-free survival. The Kaplan-Meier method and Cox proportional hazards model were used for statistical analysis.

RESULTS

Of the 114 patients with pT3a renal cell carcinoma, 42 patients (36.8%) experienced recurrence. Multivariate analysis showed that perinephric fat invasion (hazard ratio 2.36, P = 0.009), sarcomatoid or rhabdoid component (hazard ratio 2.88, P = 0.022) and necrosis (hazard ratio 2.34, P = 0.030) were independent factors for recurrence-free survival. The high-risk pT3a group, which had more than two independent predictors, had poor prognosis. Recurrence-free survival of the high-risk pT3a group and the pT3b or greater group were similar (median recurrence-free survival 23.0 and 10.8 months, respectively).

CONCLUSIONS

Perinephric fat invasion, sarcomatoid or rhabdoid component and necrosis are independent predictors of recurrence-free survival in patients with pT3a-predominant renal cell carcinoma. Patients with more than two of these predictors have poor oncological outcomes. These findings will aid in risk stratification for predicting recurrence and provide prognostic information for patient counseling.

Papillary renal cell carcinoma: Review

Kidney cancer is the 13th most common malignancy globally, and the incidence is rising. Papillary renal cell carcinoma is the second most common subtype, comprising 10-15% of renal cell carcinomas. Though the histologic features of this subtype were initially described in the 1990's, our understanding of the genetic and molecular characteristics of this disease have rapidly evolved over the past decade. In this review, we summarize the contemporary understanding of the clinical, morphologic, radiographic, and genetic characteristics of papillary renal cell carcinoma, as well as clinical considerations, current options for management, and prognosis.

Comparable survival outcome between acquired cystic disease associated renal cell carcinoma and clear cell carcinoma in patients with end-stage renal disease: a multi-institutional central pathology study

Acquired cystic disease (ACD) associated renal cell carcinoma (RCC) is designated as a new subtype unique to patients with end-stage renal disease (ESRD) according to the 2016 World Health Organization (WHO) classification. However, the oncological outcomes of the prognostic factors for patients with this subtype are not fully understood. In the present study, we compared the survival of ACD associated RCC patients who underwent nephrectomy with that of patients with other histological subtypes who developed ESRD. Over 378 patients who underwent nephrectomy at three Japanese institutes between 1987 and 2016 were included in this study. A central pathologist reviewed the sections from all patients according to the 2016 WHO classification. The central pathological review showed a clear cell subtype in 165 patients (43.6%), ACD associated RCC in 112 (29.6%), papillary in 61 (16.1%), and others in 40 (10.7%). The proportion of patients with pathological stage 1 was extremely high in both clear cell and ACD associated RCC cohorts (86.6%, 85.7%). The cancer specific survival (CSS) and recurrence free survival rates of patients with ACD associated RCC were comparable with those with clear cell carcinoma and significantly better than those with the papillary subtype. The factors predictive of unfavourable outcomes were long dialysis duration, tumour size, pathological stage, grade 4 tumour, and the presence of lymphovascular invasion or a sarcomatoid component. Patients with a pre-operative dialysis duration of 20 years or longer showed a significantly worse CSS than other patients, probably owing to sarcomatoid differentiation and stage migration during the advanced stages. In conclusion, this study included the largest number of patients with ACD associated RCC, showing a survival similar to that of clear cell histology patients with ESRD, except for the rarity of late recurrence. ACD associated RCC was not as indolent as initially recognised when patients were on long term dialysis.

Learnable image histograms-based deep radiomics for renal cell carcinoma grading and staging

Fuhrman cancer grading and tumor-node-metastasis (TNM) cancer staging systems are typically used by clinicians in the treatment planning of renal cell carcinoma (RCC), a common cancer in men and women worldwide. Pathologists typically use percutaneous renal biopsy for RCC grading, while staging is performed by volumetric medical image analysis before renal surgery. Recent studies suggest that clinicians can effectively perform these classification tasks non-invasively by analyzing image texture features of RCC from computed tomography (CT) data. However, image feature identification for RCC grading and staging often relies on laborious manual processes, which is error prone and time-intensive. To address this challenge, this paper proposes a learnable image histogram in the deep neural network framework that can learn task-specific image histograms with variable bin centers and widths. The proposed approach enables learning statistical context features from raw medical data, which cannot be performed by a conventional convolutional neural network (CNN). The linear basis function of our learnable image histogram is piece-wise differentiable, enabling back-propagating errors to update the variable bin centers and widths during training. This novel approach can segregate the CT textures of an RCC in different intensity spectra, which enables efficient Fuhrman low (I/II) and high (III/IV) grading as well as RCC low (I/II) and high (III/IV) staging. The proposed method is validated on a clinical CT dataset of 159 patients from The Cancer Imaging Archive (TCIA) database, and it demonstrates 80% and 83% accuracy in RCC grading and staging, respectively.

SLC39A8/Zinc Suppresses the Progression of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most frequent and lethal subtype, which has high risk of metastasis or recurrence, accounting for 75–83% of renal cell carcinoma (RCC). Zrt‐ and Irt‐like proteins (ZIP) family members (SLC39A1-14) function to pass zinc into the cytoplasm for many critical biological processes when cellular zinc is depleted. However, the functional analysis of individual ZIP family genes in ccRCC is not clarified. This study aimed to investigate whether ZIP family genes are related to the clinicopathological features and survival of ccRCC patients, and to identify the function of key gene of ZIP family in ccRCC in vitro. Through bioinformatics analysis of tumor databases, SLC39A8 was identified as a key gene of ZIP family in ccRCC, which could be used as an effective indicator for diagnosing ccRCC and judging its prognosis. With the progression of tumor, the expression of SLC39A8 decreased progressively. The prognosis of patients with low expression of SLC39A8 is significantly worse. Furthermore, we found that overexpression of SLC39A8 or treatment with low concentration of zinc chloride could effectively inhibit the proliferation, migration and invasion of ccRCC cells. Moreover, the inhibition effect of SLC39A8 overexpression could be enhanced by low concentration zinc supplement. Therefore, this study provides a novel understanding for the role of SLC39A8/zinc in the regulation of ccRCC progression. These findings provide a new direction and target for progressive ccRCC drug development and combination therapy strategies.

Computed Tomography-Based Radiomics Model for Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Preoperatively: A Multicenter Study

Purpose

To examine the ability of computed tomography radiomic features in multivariate analysis and construct radiomic model for identification of the the WHO/ISUP pathological grade of clear cell renal cell carcinoma (ccRCC).

Methods

This was a retrospective study using data of four hospitals from January 2018 to August 2019. There were 197 patients with a definitive diagnosis of ccRCC by post-surgery pathology or biopsy. These subjects were divided into the training set (n = 122) and the independent external validation set (n = 75). Two phases of Enhanced CT images (corticomedullary phase, nephrographic phase) of ccRCC were used for whole tumor Volume of interest (VOI) plots. The IBEX radiomic software package in Matlab was used to extract the radiomic features of whole tumor VOI images. Next, the Mann-Whitney U test and minimum redundancy-maximum relevance algorithm(mRMR) was used for feature dimensionality reduction. Next, logistic regression combined with Akaike information criterion was used to select the best prediction model. The performance of the prediction model was assessed in the independent external validation cohorts. Receiver Operating Characteristic curve (ROC) was used to evaluate the discrimination of ccRCC in the training and independent external validation sets.

Results

The logistic regression prediction model constructed with seven radiomic features showed the best performance in identification for WHO/ISUP pathological grades. The Area Under Curve (AUC) of the training set was 0.89, the sensitivity comes to 0.85 and specificity was 0.84. In the independent external validation set, the AUC of the prediction model was 0.81, the sensitivity comes to 0.58, and specificity was 0.95.

Conclusion

A radiological model constructed from CT radiomic features can effectively predict the WHO/ISUP pathological grade of CCRCC tumors and has a certain clinical generalization ability, which provides an effective value for patient prognosis and treatment.

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.

Methods

Patients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.

Results

A total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765–0.9585) and 0.8088 (95% CI: 0.7064–0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353–0.8987) and 0.8017 (95% CI: 0.6878–0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646–0.9824) and an AUC of 0.9099 (95% CI: 0.8324–0.9873) for the training cohort and validation cohort, respectively.

Conclusion

We developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.

Transmembrane protein ADAM29 facilitates cell proliferation, invasion and migration in clear cell renal cell carcinoma

Abnormal expression of ADAM29 has been frequently reported in several cancers, however, its role in clear cell renal cell carcinoma (ccRCC) has not evaluated in detail. Herein, we attempt to determine the biological role and the action mechanism of ADAM29 in ccRCC. Bioinformatics analysis based on the ccRCC RNA-Seq dataset from TCGA database revealed that ADAM29 was up-expressed in ccRCC tissues by comparison with normal tissues. And a significant increase of ADAM29 expression was also observed in 3 ccRCC cell lines (UT33A, Caki-1, and786-O) in comparison with normal cell line. Besides, high level of ADAM29 was found to be connected with the poor prognosis and could be considered as an independent prognosticator for patients with ccRCC. Furthermore, functional experiments in vitro demonstrated that ADAM29 promoted the growth, invasion and migration of ccRCC cells. Moreover, Western blot assays indicated that ADAM29 was positively correlated with the level of proliferation-related proteins Cyclin D1 and PCNA and motion-related proteins MMP9 and Snail. Our data indicate that ADAM29 acts as an oncogene that increases tumour cells proliferation, invasion and migration partly by regulating the expression of Cyclin D1/PCNA/MMP9/Snail, suggesting that ADAM29 may become a prognosticator and therapeutic candidate for ccRCC.

Ultrasonographic characteristics of thyroid metastasis from clear cell renal cell carcinoma: A case report

INTRODUCTION

Thyroid metastasis from clear cell renal cell carcinoma (ccRCC) is a very rare condition, and its ultrasonographic characteristics have not been summarized in the literature. We herein report a case of thyroid metastasis from ccRCC that occurred 11 years after the surgery and the ultrasonographic characteristics of it are described.

PATIENT CONCERNS

A 57-year-old male patient was admitted to our hospital in September 2018 due to discomfort in the neck. No other abnormalities were found during laboratory examination of thyroid function. The previous medical history of the patient included a right nephrectomy for the treatment of ccRCC in June 2007.

DIAGNOSIS

Ultrasound examinations revealed multiple thyroid nodules. After nephrectomy, there was no obvious abnormality in the right renal area. Computed tomography (CT) showed an oval lesion with slightly lower density in the right lobe of the thyroid, and the patient was initially diagnosed with nodular goiter.

INTERVENTIONS

Bilateral partial thyroidectomy under general anesthesia was conducted. Intraoperative frozen pathological examination showed clear cell carcinoma in the right lobe of the thyroid gland. Therefore, total thyroidectomy and lymph node dissection in the central neck area were performed.

OUTCOMES

The patient underwent surgical treatment and recovered successfully. The patient was followed up for 2 years with no further metastasis.

CONCLUSION

Ultrasound examination is a safe and convenient screening method. For patients with a renal malignant tumor, if the ultrasound image of thyroid nodule is found to have the characteristics of malignant tumors, the occurrence of metastasis of renal cancer to the thyroid should be highly suspected. Core needle biopsy (CNB) and histopathological diagnosis should be conducted subsequently for early diagnosis.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

Cultivation of Clear Cell Renal Cell Carcinoma Patient-Derived Organoids in an Air-Liquid Interface System as a Tool for Studying Individualized Therapy

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer accounting for 80% of all renal cancers as well as the majority of renal cancer-associated deaths. During the last decade, the treatment paradigm for ccRCC has radically changed. In particular, the recent development of immune checkpoint inhibitors (ICI) has led to an increased overall survival in the metastatic setting. Moreover, novel immune therapies targeting the tumor microenvironment have been developed. In this rapidly evolving treatment landscape, precise tools for personalized cancer therapy are needed. Here, we collected fresh tissue from 42 patients who underwent surgical resection for renal cell carcinoma. Part of the tissue was used to obtain formalin-fixed, paraffin-embedded samples or RNA. The remaining tissue was minced and cultured in a collagen-based three-dimensional, air-liquid interface (ALI) culture system. The generated patient-derived tumor organoids (ALI PDOs) were characterized by immunohistochemistry staining and RNA sequencing to validate their close similarity to the matched tumor. Immune cells and stromal cells within the microenvironment could be identified. Finally, we treated 10 ALI PDOs with the commonly used targeted cancer drug cabozantinib or the ICI nivolumab. Interestingly, we observed varying responses of ALI PDOs to these treatments and future studies are needed to investigate whether the ALI PDO approach could inform about treatment responses in patients. In conclusion, this three-dimensional ccRCC culture model represents a promising, facile tool for monitoring tumor responses to different types of therapies in a controlled manner, yet, still preserves the key features of the tumor of origin.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

Objective

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

Methods

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

Results

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

Conclusions

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

Elevated tumor-to-liver standardized uptake value ratio (TLR) from preoperative 18F-FDG PET/CT predicts poor prognosis of patients with clear cell renal cell carcinoma after nephrectomy

AIM

To assess the potential of using preoperative ¹⁸F-FDG PET/CT to predict the prognosis of patients with clear cell renal cell carcinoma (ccRCC) after nephrectomy.

METHODS

Sixty-nine patients with newly diagnosed ccRCC who underwent ¹⁸F-FDG PET/CT prior to surgery were retrospectively reviewed. The metabolic parameters of maximum standardized uptake value (SUVmax) and tumor-to-liver ratio (TLR) from ¹⁸F-FDG PET/CT were obtained. Clinicopathological characteristics, including the World Health Organization/the International Society of Urological Pathology (WHO/ISUP) grade, pathological tumor node metastasis (pTNM) stage, venous tumor thrombus, and so on, were acquired. Univariate and multivariate Cox proportional hazards analyses were performed to identify the prognostic factors for disease-free survival (DFS).

RESULTS

Of the 69 patients, 25 patients (36.2%) experienced disease progression during the follow-up period. In univariate analysis, the primary tumor size (>4.85 cm), pTNM stage (Ⅲ/Ⅳ), WHO/ISUP grade (G3/4), venous tumor thrombus, adjuvant therapy, SUVmax (>3.55), and TLR (>1.66) were found to correlate with the incidence of decreased DFS (P < 0.05). In multivariate analysis, TLR (P = 0.007, HR: 5.489, 95%CI: 1.605-18.774) and pTNM stage (P = 0.024, HR: 10.385, 95%CI: 1.361-79.238) were revealed to serve as independent prognostic predictors for DFS after adjustment for other variables. Only 3 cases (8.3%) with normal TLR showed disease progression, while 22 cases (66.7%) with elevated TLR experienced disease progression.

CONCLUSION

ccRCC patients with preoperatively elevated TLR (>1.66) and high pTNM stages (Ⅲ/Ⅳ) had significantly unfavorable survival outcomes. These patients should be carefully monitored to detect the possibility of disease progression after nephrectomy as early as possible.

Is percutaneous image-guided renal tumour ablation ready for prime time?

In the last few decades, thermal ablation (TA) techniques have been increasingly applied to treat small localised renal cell carcinomas. Despite this trend, there is still an underuse of TA compared to surgery and a substantial lack of high-quality evidence derived from large, prospective, randomised controlled trials comparing the long-term oncologic outcomes of TA and surgery. Therefore, in this narrative review, we assess published guidelines and recent literature concerning the diagnosis and management of kidney-confined renal cell carcinoma to understand whether percutaneous image-guided TA is ready to be proposed as a first-line treatment.

Grading of Clear Cell Renal Cell Carcinomas by Using Machine Learning Based on Artificial Neural Networks and Radiomic Signatures Extracted From Multidetector Computed Tomography Images

RATIONALE AND OBJECTIVES

To evaluate the ability of artificial neural networks (ANN) fed with radiomic signatures (RSs) extracted from multidetector computed tomography images in differentiating the histopathological grades of clear cell renal cell carcinomas (ccRCCs).

MATERIALS AND METHODS

The multidetector computed tomography images of 227 ccRCCs were retrospectively analyzed. For each ccRCC, 14 conventional image features (CIFs) were extracted manually by two radiologists, and 556 texture features (TFs) were extracted by a free software application, MaZda (version 4.6). The high-dimensional dataset of these RSs was reduced using the least absolute shrinkage and selection operator. Five minimum mean squared error models (minMSEMs) for predicting the ccRCC histopathological grades were constructed from the CIFs, the TFs of the corticomedullary phase images (CMP), and the TFs of the parenchyma phase (PP) images and their combinations, respectively abbreviated as CIF-minMSEM, CMP-minMSEM, PP-minMSEM, CIF+CMP-minMSEM, and CIF+PP-minMSEM. The RSs of each model were fed 30 times consecutively into an ANN for machine learning, and the predictive accuracy of each time ML was recorded for the statistical analysis.

RESULTS

The five predictive models were constructed from 12, 19, and 10 features selected from the CIFs, the TFs of the CMP images, and that of PP images, respectively. On the basis of their accuracy across the whole cohort, the five models were ranked as follows: CIF+CMP-minMSEM (accuracy: 94.06% ± 1.14%), CIF + PP-minMSEM (accuracy: 93.32% ± 1.23%), CIF-minMSEM (accuracy: 92.26% ± 1.65%), CMP-minMSEM (accuracy: 91.76% ± 1.74%), and PP-minMSEM (accuracy: 90.89% ± 1.47%).

CONCLUSION

Machine learning based on ANN helped establish an optimal predictive model, and TFs contributed to the development of high accuracy predictive models. The CIF+CMP-minMSEM showed the greatest accuracy for differentiating low- and high-grade ccRCCs.

miR-1293 Suppresses Tumor Malignancy by Targeting Hydrocyanic Oxidase 2: Therapeutic Potential of a miR-1293/Hydrocyanic Oxidase 2 Axis in Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a common cancer, and extensive research suggests that microRNA may play an important role in the progression of RCC. The emphasis of this article was to reveal the function and mechanism of microRNA-1293(miR-1293) in the development of RCC tumors. First, the authors carried out bioinformatics analysis. The differential expression of miR-1293 in RCC tumor and normal cells was analyzed using the data from The Cancer Genome Atlas database, and Kaplan-Meier survival analysis was carried out to test the survival rate. Subsequently, the miR-1293 expression in RCC cell lines was examined by quantitative real-time PCR. Then Cell counting kit-8 and Transwell assays were executed to detect the function of miR-1293 in RCC. Bioinformatics prediction, western blotting, and dual-luciferase reporter assay were set to check the target gene of miR-1293. Finally, they conducted rescue experiments to verify whether the regulation of miR-1293 on the biological function of RCC cells was achieved by regulating hydrocyanic oxidase 2 (HAO2). Bioinformatics results showed that miR-1293 was highly expressed in RCC, and the miR-1293 high-expression group showed a lower survival rate than the miR-1293 low-expression group, which suggested that the high expression of miR-1293 was related to unfavorable prognosis in RCC. Subsequent assays evidenced that upregulation of miR-1293 expression significantly increased the cell viability and promoted cell migration and invasion in RCC. Silencing miR-1293 expression showed opposite results. Furthermore, HAO2 was confirmed to be a direct target gene of miR-1293 by dual-luciferase reporter assay, and miR-1293 negatively regulated the expression of HAO2. Moreover, rescue experiments evidenced that miR-1293 reduced the cell viability, invasion, and migration of RCC by regulating HAO2. In sum, miR-1293 can regulate the viability, invasion, and migration of RCC tumor cells by targeting HAO2, suggesting that miR-1293 can be used as a new biomarker for clinical treatment of RCC.

Clinicopathological Findings on 28 Cases with XP11.2 Renal Cell Carcinoma

Xp11.2 translocation carcinoma is a distinct subtype of renal cell carcinoma characterized by translocations involving the TFE3 gene. Our study included the morphological, immunohistochemical and clinicopathological examination of 28 Xp11.2 RCCs. The immunophenotype has been assessed by using CA9, CK7, CD10, AMACR, MelanA, HMB45, Cathepsin K and TFE3 immunostainings. The diagnosis was confirmed by TFE3 break-apart FISH in 25 cases. The ages of 13 male and 15 female patients, without underlying renal disease or having undergone chemotherapy ranged from 8 to 72. The mean size of the tumors was 78.5 mm. Forty-three percent of patients were diagnosed in the pT3/pT4 stage with distant metastasis in 6 cases. Histological appearance was branching-papillary composed of clear cells with voluminous cytoplasm in 13 and variable in 15 cases, including one tumor with anaplastic carcinoma and another with rhabdoid morphology. Three tumors were labeled with CA9, while CK7 was negative in all cases. Diffuse CD10 reaction was observed in 17 tumors and diffuse AMACR positivity was described in 14 tumors. The expression of melanocytic markers and Cathepsin K were seen only in 7 and 6 cases, respectively. TFE3 immunohistochemistry displayed a positive reaction in 26/28 samples. TFE3 rearrangement was detected in all the analyzed cases (25/25), including one with the loss of the entire labeled break-point region. The follow-up time ranged from 2 to 300 months, with 7 cancer-related deaths. In summary, Xp11.2 carcinoma is an uncommon form of renal cell carcinoma with a variable histomorphology and rather aggressive clinical course.

TNIP1 Inhibits Proliferation And Promotes Apoptosis In Clear Cell Renal Carcinoma Through Targeting C/Ebpβ

Background/Purpose

TNF-α-induced protein 3-interacting protein 1 (TNIP1) is active in various cancers, but its expression and function in renal cell carcinoma (RCC) have not been described. This study investigated the role of TNIP1 in clear cell renal cell carcinomas (ccRCC), which accounts for 75–80% of RCC and has a poor prognosis.

Methods

The expression of TNIP1 in human ccRCC tissues and cells was detected by real-time quantitative reverse transcription–polymerase chain reaction (qRT-PCR), Western blot (WB), and immunohistochemical (IHC) staining. Cell proliferation was assayed by a cell counting kit (CCK)-8 assay; cell cycle analysis and apoptosis assay were done by flow cytometry.

Results

TNIP1 is downregulated in both ccRCC human tissues and cells. Besides, TNIP1 downregulation promoted cell proliferation with more cell cycle entry, and inhibited apoptosis. TNIP1 downregulation was associated with increased of expression of the Bcl-2 anti-apoptosis gene and decreased expression of the Bax apoptosis-promoting gene and cleaved-caspase-3 by negatively regulating C/EBPβ expression.

Conclusion

TNIP1 acted as a tumor-inhibitor in ccRCC by targeting C/EBPβ. The results warrant study of TNIP1 as a potential diagnostic marker and therapeutic target of ccRCC.

The Prognostic Significance of Protein Expression of CASZ1 in Clear Cell Renal Cell Carcinoma

Backgrounds

Clear cell renal cell carcinoma (ccRCC) is the most common histologic subtype of renal cell carcinoma (RCC) and shows a relatively poor prognosis among RCCs. Castor zinc finger 1 (CASZ1) is a transcription factor, prominently known for its tumor suppression role in neuroblastoma and other cancers. However, there has been no research about the prognostic significance of CASZ1 in ccRCC. In this study, we investigated CASZ1 expression in ccRCC and analyzed its prognostic implications.

Methods

A total of 896 ccRCC patients, who underwent surgical resection from 1995 to 2008, were included. We prepared tissue microarray blocks, evaluated CASZ1 nuclear expression by immunohistochemistry, and classified the cases into low or high expression categories.

Results

A low expression of CASZ1 was observed in 320 cases (35.7%) and was significantly associated with large tumor size, high World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade, and high T category and M category. In survival analysis, a low expression of CASZ1 was significantly correlated with unfavorable progression-free survival (PFS) (p < 0.001), overall survival (OS) (p < 0.001), and cancer-specific survival (CSS) (p < 0.001) and was an independent prognostic factor for PFS and CSS in multivariate analysis adjusted for tumor size, WHO/ISUP grade, T category, N category, and M category.

Conclusions

Our study is the first to show the prognostic significance of CASZ1 expression in ccRCC. Our results revealed that low expression of CASZ1 is associated with poor prognosis and may serve as a new prognostic indicator.

Prognosis of clear cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an investigation based on RNA-sequencing data

Background

The scientific understanding of long non-coding RNAs (lncRNAs) has improved in recent decades. Nevertheless, there has been little research into the role that lncRNAs play in clear cell renal cell carcinoma (ccRCC). More lncRNAs are assumed to influence the progression of ccRCC via their own molecular mechanisms.

Methods

This study investigated the prognostic significance of differentially expressed lncRNAs by mining high-throughput lncRNA-sequencing data from The Cancer Genome Atlas (TCGA) containing 13,198 lncRNAs from 539 patients. Differentially expressed lncRNAs were assessed using the R packages edgeR and DESeq. The prognostic significance of lncRNAs was measured using univariate Cox proportional hazards regression. ccRCC patients were then categorized into high- and low-score cohorts based on the cumulative distribution curve inflection point the of risk score, which was generated by the multivariate Cox regression model. Samples from the TCGA dataset were divided into training and validation subsets to verify the prognostic risk model. Bioinformatics methods, gene set enrichment analysis, and protein–protein interaction networks, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were subsequently used.

Results

It was found that the risk score based on 6 novel lncRNAs (CTA-384D8.35, CTD-2263F21.1, LINC01510, RP11-352G9.1, RP11-395B7.2, RP11-426C22.4) exhibited superior prognostic value for ccRCC. Moreover, we categorized the cases into two groups (high-risk and low-risk), and also examined related pathways and genetic differences between them. Kaplan–Meier curves indicated that the median survival time of patients in the high-risk group was 73.5 months, much shorter than that of the low-risk group (112.6 months; P < 0.05). Furthermore, the risk score predicted the 5-year survival of all 539 ccRCC patients (AUC at 5 years, 0.683; concordance index [C-index], 0.853; 95% CI 0.817–0.889). The training set and validation set also showed similar performance (AUC at 5 years, 0.649 and 0.681, respectively; C-index, 0.822 and 0.891; 95% CI 0.774–0.870 and 0.844–0.938).

Conclusions

The results of this study can be applied to analyzing various prognostic factors, leading to new possibilities for clinical diagnosis and prognosis of ccRCC.

Overexpressed pseudogenes, DUXAP8 and DUXAP9, promote growth of renal cell carcinoma and serve as unfavorable prognostic biomarkers

BACKGROUND

Growing studies have reported that pseudogenes play key roles in multiple human cancers. However, expression and roles of pseudogenes in renal cell carcinoma remains absent.

RESULTS

31 upregulated and 16 downregulated pseudogenes were screened. Higher expression of DUXAP8 and DUXAP9 indicated poorer prognosis of kidney cancer. 33 and 5 miRNAs were predicted to potentially binding to DUXAP8 and DUXAP9, respectively. miR-29c-3p was identified as the most potential binding miRNAs of DUXAP8 and DUXAP9 based on expression, survival and correlation analyses. 254 target genes of miR-29c-3p were forecast. 47 hub genes with node degree >= 10 were identified. Subsequent analysis for the top 10 hub genes demonstrated that COL1A1 and COL1A2 may be two functional targets of DUXAP8 and DUXAP9. Expression of DUXAP8, DUXAP9, COL1A1 and COL1A2 were significantly increased in cancer samples compared to normal controls while miR-29c-3p expression was decreased. Luciferase reporter assay revealed that miR-29c-3p could directly bind to DUXAP8, DUXAP9, COL1A1 and COL1A2. Functional experiments showed that DUXAP8 and DUXAP9 enhanced but miR-29c-3p weakened growth of renal cell carcinoma.

CONCLUSIONS

In conclusion, upregulated DUXAP8 and DUXAP9 promote growth of renal cell carcinoma and serve as two promising prognostic biomarkers.

METHODS

Dysregulated pseudogenes were obtained by dreamBase and GEPIA. The binding miRNAs of pseudogene and targets of miRNA were predicted using starBase and miRNet. Kaplan-Meier plotter was utilized to perform survival analysis, and Enrichr database was introduced to conduct functional enrichment analysis. Hub genes were identified through STRING and Cytoscape. qRT-PCR, luciferase reporter assay, cell counting assay and colony formation assay were performed to validate in silico analytic results.

Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment

Cystic renal cell carcinoma (RCC) is almost certainly overdiagnosed and overtreated. Efforts to diagnose and treat RCC at a curable stage result in many benign neoplasms and indolent cancers being resected without clear benefit. This is especially true for cystic masses, which compared with solid masses are more likely to be benign and, when malignant, less aggressive. For more than 30 years, the Bosniak classification has been used to stratify the risk of malignancy in cystic renal masses. Although it is widely used and still effective, the classification does not formally incorporate masses identified at MRI or US or masses that are incompletely characterized but are highly likely to be benign, and it is affected by interreader variability and variable reported malignancy rates. The Bosniak classification system cannot fully differentiate aggressive from indolent cancers and results in many benign masses being resected. This proposed update to the Bosniak classification addresses some of these shortcomings. The primary modifications incorporate MRI, establish definitions for previously vague imaging terms, and enable a greater proportion of masses to enter lower-risk classes. Although the update will require validation, it aims to expand the number of cystic masses to which the Bosniak classification can be applied while improving its precision and accuracy for the likelihood of cancer in each class.

The Association of Androgen Receptor Expression with Renal Cell Carcinoma Risk: a Systematic Review and Meta-Analysis

The relationship between androgen receptor expression and renal cell carcinoma risk remains controversial. This study is aimed to investigate the clinical significance of androgen receptor expression in renal cell carcinoma. A computerized bibliographic search of Embase, the PubMed, and Web of Science combined with manual research between 1977 and 2017 was conducted to explore the association between androgen receptor expression and clinicopathological features of renal cell carcinoma. Data were analyzed by a meta-analysis using RevMan 5.3 analysis software. Eleven retrospective studies with 1839 renal cell carcinoma cases were finally included according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. It was found that there was no significant difference between androgen receptor expression and susceptibility, pathological type, metastatic status, metastatic type (lymph or distant metastasis) and cancer-specific survival of renal cell carcinoma (P > 0.05). However, positive androgen receptor expression was demonstrated to be significantly associated with male patients, lower pathological grade, and earlier tumor stage of renal cell carcinoma (OR = 1.69, 95% CI = 1.30-2.19, P < 0.0001; OR = 2.06, 95% CI = 1.49-2.85, P < 0.0001; OR = 2.81, 95% CI = 1.30-6.12, P = 0.009; respectively). In conclusion, higher androgen receptor expression was correlated with male patients, low tumor grade and early stage of renal cell carcinoma. Based on current results, androgen receptor-inhibited target therapy for renal cell carcinoma patients may be of limited benefits and should be taken into more evaluations.

The miR-183/182/96 cluster functions as a potential carcinogenic factor and prognostic factor in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common type of renal cell carcinoma. While a number of treatments have been developed over the past few decades, the prognosis of patients with KIRC remains poor due to tumor metastasis and recurrence. Therefore, the molecular mechanisms of KIRC require to be elucidated in order to identify novel biomarkers. MicroRNAs (miRNAs/miRs) have been studied as important regulators of gene expression in a variety of cancer types. In the present study, a bioinformatics analysis of differentially expressed miRNAs in KIRC vs. normal tissues was performed based on raw miRNA expression data and patient information downloaded from the The Cancer Genome Atlas database. Furthermore, the clinical significance of differentially expressed miRNAs was evaluated, and their target genes and biological effects were further predicted. After applying the cut-off criteria of an absolute fold change of ≥2 and P<0.05, 127 differentially expressed miRNAs between KIRC and normal tissues were identified. The product of the miR-183/182/96 gene cluster, namely miR-183, miR-96 and miR-182, was revealed to be associated with multiple clinicopathological features of KIRC and to have a significant predictive and prognostic value. Subsequent functional enrichment analysis indicated that the target genes of the three miRNAs are associated with various Panther pathways, including the α-adrenergic receptor signaling pathway, metabotropic glutamate receptor group I pathway, histamine H1 receptor-mediated signaling pathway and thyrotropin-releasing hormone receptor signaling pathway. In addition, major enriched gene ontology terms in the category biological process included the intracellular signaling cascade, cellular macromolecule catabolic process and response to DNA damage stimulus. Taken together, the present study suggested that miR-183, miR-96 and miR-182 may function as potential carcinogenic factors in KIRC and may be utilized as prognostic predictors.

Predictive models composed by radiomic features extracted from multi-detector computed tomography images for predicting low- and high- grade clear cell renal cell carcinoma: A STARD-compliant article

To evaluate the values of conventional image features (CIFs) and radiomic features (RFs) extracted from multi-detector computed tomography (MDCT) images for predicting low- and high-grade clear cell renal cell carcinoma (ccRCC).Two hundred twenty-seven patients with ccRCC were retrospectively recruited. Five hundred seventy features including 14 CIFs and 556 RFs were extracted from MDCT images of each ccRCC. The CIFs were extracted manually and RFs by the free software-MaZda. Least absolute shrinkage and selection operator (Lasso) was applied to shrink the high-dimensional data set and select the features. Five predictive models for predicting low- and high-grade ccRCC were constructed by the selected CIFs and RFs. The 5 models were as follows: model of minimum mean squared error (minMSE) of CIFs (CIF-minMSE), minMSE of cortico-medullary phase (CMP) of kidney (CMP-minMSE), minMSE of parenchyma phase (PP) of kidney (PP-minMSE), the combined model of CIF-minMSE and CMP-minMSE (CIF-CMP-minMSE), and the combined model of CIF-minMSE and PP-minMSE (CIF-PP-minMSE). The Lasso regression equation of each model was constructed, and the predictive values were calculated. The receiver operating characteristic (ROC) curves of predictive values of the 5 models were drawn by SPSS19.0, and the areas under the curves (AUCs) were calculated.According to Lasso regression, 12, 19 and 10 features were respectively selected from the CIFs, RFs of CMP image and that of PP images to construct the 5 predictive models. The models ordered by their AUCs from large to small were CIF-CMP-minMSE (AUC: 0.986), CIF-PP-minMSE (AUC: 0.981), CIF-minMSE (AUC: 0.980), CMP-minMSE (AUC: 0.975), and PP-minMSE (AUC: 0.963). The maximum diameter of the largest axial section of ccRCC had a maximum weight in predicting the grade of ccRCC among all the features, and its cutoff value was 6.15 cm with a sensitivity of 0.901, a specificity of 0.963, and an AUC of 0.975.When combined with CIFs, RFs extracted from MDCT images contributed to the larger AUC of the predictive model, but were less valuable than CIFs when used alone. The CIF-CMP-minMSE was the optimal predictive model. The maximum diameter of the largest axial section of ccRCC had the largest weight in all features.

Circulating Tumor Cells for the Management of Renal Cell Carcinoma

Renal cell carcinoma is a highly malignant cancer that would benefit from non-invasive innovative markers providing early diagnosis and recurrence detection. Circulating tumor cells are a particularly promising marker of tumor invasion that could be used to improve the management of patients with RCC. However, the extensive genetic and immunophenotypic heterogeneity of cells from RCC and their trend to transition to the mesenchymal phenotype when they circulate in blood constitute a challenge for their sensitive and specific detection. This review analyzes published studies targeting CTC in patients with RCC, in the context of the biological, pathological, and molecular complexity of this particular cancer. Although further analytical and clinical studies are needed to pinpoint the most suitable approach for highly sensitive CTC detection in RCC patients, it is clear that this field can bring a relevant guide to clinicians and help to RCC patients. Furthermore, as described, a particular subtype of RCC-the ccRCC-can be used as a model to study the relationship between cytomorphological and genetic cellular markers of malignancy, an important issue for the study of CTC from any type of solid cancer.