Prognostic Factors and Predictive Models in Renal Cell Carcinoma: A Contemporary Review

Development and External Validation of Nomograms for Predicting Disease-Free Survival and Overall Survival in Patients with cT1-ccRCC After Partial Nephrectomy: A Multicenter Retrospective Study

BACKGROUND

To develop a novel nomogram for predicting 2-year and 5-year disease-free survival (DFS) and overall survival (OS) in patients with cT1-clear cell renal cell carcinoma (ccRCC) undergoing partial nephrectomy (PN).

METHODS

A retrospective study was conducted across five urological centers, including 940 patients who underwent PN for cT1N0M0-ccRCC. Four centers were randomly selected to constitute the training group, while the remaining center served as the testing group. We employed the LASSO and multivariate Cox regression to develop new nomograms. The 1,000 bootstrap-corrected c-index, net reclassification improvement (NRI) and receiver operating characteristic curve were employed to compare the predictive abilities of new nomograms with the widely used UUIS and SSIGN models. Finally, the novel nomograms underwent external validation.

RESULTS

The training group included 714 patients, while the testing group consisted of 226 patients. The bootstrap-corrected c-indexes for the DFS and OS model were 0.870 and 0.902, respectively. In the training cohort, the AUC for the DFS and OS models at 2 years and 5 years were 0.953, 0.902, 0.988, and 0.911, respectively. These values were also assessed in the testing cohort. The predictive capabilities of the new nomograms surpassed those of the UUIS and SSIGN models (NRI > 0). Decision curve analysis demonstrated that the novel nomograms provide greater net benefits compared to the UUIS and SSIGN models.

CONCLUSIONS

Our novel nomograms demonstrated strong predictive ability for forecasting oncological outcomes in cT1-ccRCC patients after PN. These user-friendly nomograms are simple and convenient for clinical application, providing tangible clinical benefits.

Development and validation of a nomogram to predict recurrence for clinical T1/2 clear cell renal cell carcinoma patients after nephrectomy

OBJECTIVE

To develop and validate a nomogram for predicting recurrence-free survival (RFS) for clinical T1/2 (cT1/2) clear cell renal cell carcinoma (ccRCC) patients after nephrectomy.

METHODS

Clinicopathological and survival data from 1289 cT1/2 ccRCC patients treated at the Second Hospital of Tianjin Medical University between 2017 and 2020 were included. Cox regression analysis was used to identify independent risk factors in 902 and 387 ccRCC patients in the training and validation cohorts, respectively, and construct the nomogram. The performance of the nomogram was assessed through calibration plots, time-dependent receiver operating characteristic (ROC) curves, C-index (concordance-index), and decision curve analysis (DCA). Kaplan-Meier curves were used to evaluate the probability of RFS in patients with different recurrence risks.

RESULTS

Age, tumor size, surgical approach, Fuhrman grade, and pT3a upstage were identified as independent predictors of RFS. The area under the curve (AUC) for the 3-year and 5-year RFS ROC curves were 0.791 and 0.835 in the training cohort, and 0.860 and 0.880 in the validation cohort. The DCA and calibration plots demonstrated the optimal application and excellent accuracy of the nomogram for predicting 3-year and 5-year RFS. Kaplan-Meier curves revealed significant differences in RFS among the three risk groups in both the training and validation cohorts. Clinically, the developed nomogram provides a more precise tool for risk stratification, enabling tailored postoperative management and surveillance strategies, ultimately aiming to improve patient outcomes.

CONCLUSIONS

We developed a nomogram for predicting RFS in cT1/2 ccRCC patients after nephrectomy with high accuracy. The clinical implementation of this nomogram can significantly enhance clinical decision-making, leading to improved patient outcomes and optimized resource utilization in the management of ccRCC.

MOF-Based Platform for Kidney Diseases: Advances, Challenges, and Prospects

Kidney diseases are important diseases that affect human health worldwide. According to the 2020 World Health Organization (WHO) report, kidney diseases have become the top 10 causes of death. Strengthening the prevention, primary diagnosis, and action of kidney-related diseases is of great significance in maintaining human health and improving the quality of life. It is increasingly challenging to address clinical needs with the present technologies for diagnosing and treating renal illness. Fortunately, metal-organic frameworks (MOFs) have shown great promise in the diagnosis and treatment of kidney diseases. This review summarizes the research progress of MOFs in the diagnosis and treatment of renal disease in recent years. Firstly, we introduce the basic structure and properties of MOFs. Secondly, we focus on the utilization of MOFs in the diagnosis and treatment of kidney diseases. In the diagnosis of kidney disease, MOFs are usually designed as biosensors to detect biomarkers related to kidney disease. In the treatment of kidney disease, MOFs can not only be used as an effective adsorbent for uremic toxins during hemodialysis but also as a precise treatment of intelligent drug delivery carriers. They can also be combined with nano-chelation technology to solve the problem of the imbalance of trace elements in kidney disease. Finally, we describe the current challenges and prospects of MOFs in the diagnosis and treatment of kidney diseases.

Concordance between renal tumour biopsy and robotic-assisted partial and radical nephrectomy histology: a 10-year experience

We aimed to assess concordance between renal tumour biopsy (RTB) and surgical pathology from robotic-assisted partial nephrectomy (RAPN) or robotic-assisted radical nephrectomy (RARN). Patients with preoperative RTB undergoing RAPN or RARN for suspected malignancy (9 September 2013-9 September 2023) were enrolled retrospectively from three sites. Patients were excluded if the tumour had prior cryotherapy or if biopsy or nephrectomy histology were unavailable or inconclusive. The primary outcome was concordance with the presence/absence of malignancy. Secondary outcomes were concordance with tumour subtype, World Health Organisation nuclear grade (patients with RTB clear cell or papillary RCC only), false-negative rate, false-positive rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the enrolment period, 332 and 132 patients underwent RAPN and RARN, respectively. Of these, 160 received preoperative RTB, with nine patients excluded, leaving 151 eligible patients. Median age was 63 years, and 49 (32%) were female. On surgical specimens, 144 patients had malignant histology. RTB was highly concordant with presence/absence of malignancy (147/151, 97%). Concordance with tumour subtype occurred in 141 patients (93%), while concordance with nuclear grade was seen in 42/66 patients (64%, RTB grade missing in 53 patients). False-negative rate, false-positive rate, sensitivity, specificity, PPV, and NPV were 2%, 14%, 98%, 86%, 99%, and 67%, respectively. Limitations include absence of complication data and exclusion of patients biopsied without surgery. In patients undergoing RAPN or RARN, preoperative RTB has high concordance with surgical pathology, both in the presence of malignancy and RCC subtype.

Biomarkers in renal cell carcinoma and their targeted therapies: a review

Renal cell carcinoma (RCC) is one of the most life-threatening urinary malignancies displaying poor response to radiotherapy and chemotherapy. Although in the recent past there have been tremendous advancements in using targeted therapies for RCC, despite that it remains the most lethal urogenital cancer with a 5-year survival rate of roughly 76%. Timely diagnosis is still the key to prevent the progression of RCC into metastatic stages as well as to treat it. But due to the lack of definitive and specific diagnostic biomarkers for RCC and its asymptomatic nature in its early stages, it becomes very difficult to diagnose it. Reliable and distinct molecular markers can not only refine the diagnosis but also classifies the tumors into thier sub-types which can escort subsequent management and possible treatment for patients. Potential biomarkers can permit a greater degree of stratification of patients affected by RCC and help tailor novel targeted therapies. The review summarizes the most promising epigenetic [DNA methylation, microRNA (miRNA; miR), and long noncoding RNA (lncRNA)] and protein biomarkers that have been known to be specifically involved in diagnosis, cancer progression, and metastasis of RCC, thereby highlighting their utilization as non-invasive molecular markers in RCC. Also, the rationale and development of novel molecular targeted drugs and immunotherapy drugs [such as tyrosine kinase inhibitors and immune checkpoint inhibitors (ICIs)] as potential RCC therapeutics along with the proposed implication of these biomarkers in predicting response to targeted therapies will be discussed.

PNI as a Potential Add-On Biomarker to Improve the IMDC Intermediate Prognostic Score

INTRODUCTION

This study aimed to assess the role of the adjusted PNI-IMDC risk scoring system in stratifying the intermediate group of metastatic RCC patients who received TKIS in the first-line setting.

METHODS

A total of 185 patients were included. The adjusted PNI and IMDC model was used to divide the intermediate group into two groups: intermediate PNI-high and intermediate PNI-low groups. The statistical data were analyzed using Kaplan-Meier and Cox regression analysis.

RESULTS

The results showed that the adjusted PNI-IMDC risk score, classic IMDC, and PNI had similar prognostic values. Adjusted PNI-IMDC risk score might be used for a more homogeneous differentiation of the classic intermediate group. On the other hand, multivariate analysis revealed that the presence of nephrectomy, adjusted favorable/intermediate (PNI-high) group, ECOG performance score, and presence of bone metastasis were independent predictors of OS.

CONCLUSIONS

Pre-treatment PNI, as a valuable and potential add-on biomarker to the adjusted PNI-IMDC classification model, can be helpful for establishing an improved prognostic model for intermediate group mRCC patients treated with first-line TKISs. Further validation studies are needed to clarify these findings.

Adjuvant and Neoadjuvant Therapy in Renal Cell Carcinoma

In locally advanced RCC, 6 phase 3 randomized controlled trials (RCTs) were designed in the perioperative setting with immune checkpoint inhibitor (ICI) monotherapy or combinations. Adjuvant trials with atezolizumab, pembrolizumab, and nivolumab with ipilimumab reported results, as did the only perioperative trial with nivolumab. Of these, only 1 year of adjuvant pembrolizumab improved disease-free survival (DFS) versus placebo, with the other trials showing no improvement in DFS. In the purely neoadjuvant setting, phase 1 b/2 ICI trials have demonstrated safety, efficacy, and dynamic changes of immune infiltrates, and provide a rationale for randomized trial concepts.

Radiomics and Ki-67 index predict survival in clear cell renal cell carcinoma

OBJECTIVE

To develop and validate predictive models based on Ki-67 index, radiomics, and Ki-67 index combined with radiomics for survival analysis of patients with clear cell renal cell carcinoma.

METHODS

This study enrolled 148 patients who were pathologically diagnosed as ccRCC between March 2010 and December 2018 at our institute. All tissue sections were collected and immunohistochemical staining was performed to calculate Ki-67 index. All patients were randomly divided into the training and validation sets in a 7:3 ratio. Regions of interests (ROIs) were segmented manually. Radiomics features were selected from ROIs in unenhanced, corticomedullary, and nephrographic phases. Multivariate Cox models based on the Ki-67 index and radiomics and univariate Cox models based on the Ki-67 index or radiomics alone were built; the predictive power was evaluated by the concordance (C)-index, integrated area under the curve, and integrated Brier Score.

RESULTS

Five features were selected to establish the prediction models of radiomics and combined model. The C-indexes of Ki-67 index model, radiomics model, and combined model were 0.741, 0.718, and 0.782 for disease-free survival (DFS); 0.941, 0.866, and 0.963 for overall survival, respectively. The predictive power of combined model was the best in both training and validation sets.

CONCLUSION

The survival prediction performance of combined model was better than Ki-67 model or radiomics model. The combined model is a promising tool for predicting the prognosis of patients with ccRCC in the future.

ADVANCES IN KNOWLEDGE

Both Ki-67 and radiomics have showed giant potential in prognosis prediction. There are few studies to investigate the predictive ability of Ki-67 combined with radiomics. This study intended to build a combined model and provide a reliable prognosis for ccRCC in clinical practice.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

Objective

This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).

Conclusion

The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Comparison and validation of different risk models for papillary renal cell carcinoma

BACKGROUND

Several prognostic algorithms were specifically or nonspecifically used for papillary renal cell carcinoma (PRCC). No consensus was reached upon their efficacy of discrimination. We aim to compare the stratifying ability of current models or systems in predicting the risk of recurrence of PRCC.

METHODS

A PRCC cohort consisting of 308 patients from our institution and 279 patients from The Cancer Genome Atlas (TCGA) was generated. With ISUP grade, TNM classification, UCLA Integrated Staging System (UISS), STAGE, SIZE, GRADE AND NECROSIS (SSIGN), Leibovich model and VENUSS system, recurrence-free survival (RFS), disease-specific survival (DSS) and overall survival (OS) were studied using Kaplan-Meier method and concordance index (c-index) was compared. Differences between risk groups in gene mutation and infiltration of inhibitory immune cells were studied with TCGA database.

RESULTS

All the algorithms were able to stratify patients in RFS as well as DSS and OS (all P < 0.001). VENUSS score and risk group generally had the highest and balanced c-index (0.815 and 0.797 for RFS). ISUP grade, TNM stage and Leibovich model had the lowest c-indexes in all analysis. Among the 25 most frequently mutated genes in PRCC, eight had different mutation frequency between VENUSS low- and intermediate-/high-risk patients and mutated KMT2D and PBRM1 resulted in worsened RFS (P = 0.053 and P = 0.007). Increased Treg cells in tumors of intermediate-/high- risk patients were also identified.

CONCLUSIONS

VENUSS system showed better predictive accuracy in RFS, DSS and OS compared with SSIGN, UISS and Leibovich risk models. VENUSS intermediate-/high-risk patients had increased frequency of mutation in KMT2D and PBRM1 and increased infiltration of Treg cells.

Recent Advances in the Management of Clear Cell Renal Cell Carcinoma: Novel Biomarkers and Targeted Therapies

Renal cell carcinoma (RCC) belongs to a heterogenous cancer group arising from renal tubular epithelial cells. Among RCC subtypes, clear cell renal cell carcinoma (ccRCC) is the most common variant, characterized by high aggressiveness, invasiveness and metastatic potential, features that lead to poor prognosis and high mortality rate. In addition, diagnosis of kidney cancer is incidental in the majority of cases, and this results in a late diagnosis, when the stage of the disease is advanced and the tumor has already metastasized. Furthermore, ccRCC treatment is complicated by its strong resistance to chemo- and radiotherapy. Therefore, there is active ongoing research focused on identifying novel biomarkers which could be useful for assessing a better prognosis, as well as new molecules which could be used for targeted therapy. In this light, several novel targeted therapies have been shown to be effective in prolonging the overall survival of ccRCC patients. Thus, the aim of this review is to analyze the actual state-of-the-art on ccRCC diagnosis, prognosis and therapeutic options, while also reporting the recent advances in novel biomarker discoveries, which could be exploited for a better prognosis or for targeted therapy.

The Metastatic Risk of Renal Cell Carcinoma by Primary Tumor Size and Subtype

Background

Current data on the association between tumor size, subtype, and metastases, and thresholds for intervention, for renal cell carcinoma (RCC), are largely based on single-center nephrectomy registries that may under-represent those presenting with metastatic disease.

Objective

We sought to assess tumor size and histologic subtype in relation to metastatic status at presentation for patients with RCC.

Design setting and participants

Using Surveillance, Epidemiology and End Results cancer registry data, we identified patients with a diagnosis of RCC made between 2004 and 2019, and a known size of primary tumor. We used nodal and metastatic TNM staging to assess metastatic disease at presentation.

Outcome measurements and statistical analysis

We report the proportion of metastatic disease across varying tumor sizes for clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) RCC. We also examine sarcomatoid RCC and RCC with sarcomatoid features (sarcRCC). Logistic regression models were used to model the likelihood of metastatic disease for each histologic subtype.

Results and limitations

Of 181 096 RCC patients included, 23 829 had metastatic disease. For any RCC, metastatic rates of 3.6%, 13.1%, 30.3%, and 45.1% were observed for tumors ≤4, 4-≤7, 7-≤10, and >10 cm, respectively. Metastatic rates of chRCC were low at even large sizes, 11.0% at >10 cm. In contrast, sarcRCC had high metastatic rates at all sizes, 27.1% at ≤4 cm. Metastatic rates for ccRCC and pRCC increased steadily above 3 cm. For any RCC and each evaluated subtype, tumor size was found to be associated with metastatic disease on logistic regression (p < 0.001).

Conclusions

The likelihood of a renal mass being metastatic varies greatly with both its subtype and size. We report higher likelihoods of metastatic disease across tumor sizes compared with what has been reported previously. These results may help clinicians pick appropriate thresholds for intervention and candidates for active surveillance.

Patient summary

We find that the metastatic probability of renal cell carcinoma varies greatly with subtype and increases with tumor size.

The significance of CD14 in clear cell renal cell carcinoma progression and survival prognosis

Purpose: CD14-positive tumour and immune cells have been implicated in cancer progression. This study evaluated the prognostic significance of CD14 immunostaining in clear cell renal cell carcinoma (ccRCC) compared to the adjacent non-cancer kidney, and serum soluble CD14 (sCD14) levels in patients versus controls.Methods: Immunohistochemistry was performed for CD14 on ccRCC and the corresponding adjacent non-cancer kidney tissue from 88 patients. Staining intensity was determined using Aperio ImageScope morphometry. Serum sCD14 was evaluated for 39 ccRCC patients and 38 non-cancer controls using ELISA. CD14 levels were compared with tumour characteristics and survival status.Results: CD14 overall and nuclear immunostaining was higher in ccRCC compared to the adjacent non-cancer kidney tissue. CD14 nuclear immunostaining in the adjacent non-cancer kidney was significantly associated with advanced stage and adverse RCC survival prognosis. Serum sCD14 concentration was elevated in ccRCC patients compared to non-cancer controls and was also significantly associated with tumour stage and worse survival prognosis. Higher CD14 expression, in particular CD14 positive immune cell infiltrates found in the adjacent non-RCC kidney tissue, were associated with tumour progression and poorer prognosis.Conclusion: The levels of CD14 in non-RCC adjacent kidney and serum could be potential prognostic indicators.

Markers of local kidney cancer recurrence: A surgeon's mistake or a pattern? Review

The influence of various morphological, anatomical, genetic and other factors on the local recurrence-free survival of patients who have undergone different renal cell cancer (RCC) treatment is still a rather complex, ambiguous and controversial issue for practicing oncourologists. This review evaluates the effect of several factors on both recurrence-free survival and local recurrence-free survival. The review includes articles, clinical cases, literature reviews, and meta-analyses highlighting the analysis of independent and interrelated predisposing factors for developing local recurrence of RCC from 1984 to 2020. The PubMed, Web of Science, and Scopus databases were searched in English, Spanish, and German. A review of the literature showed the role of the following indices in the local recurrence RCC: microvascular invasion (p = 0.001), tumor necrosis (p = 0.0001), high malignancy (Fuhrman III or IV) (HR = 38.3, 95% CI 3.1-467, p = 0.004) as histological factors, tumor size as an anatomical factor. Thus, the authors state that every centimeter of the tumor increases the risk of local recurrence (p < 0.05). A group from the Mayo Clinic showed the equivalence of different treatment methods in local RCC recurrence. Thus, in the group of patients with cT1a stage kidney cancer, the 5-year local recurrence-free survival rates were 97.7% (96.7-98.6), 95.9% (92.3-99.6), and 95.9% (92.3-99.6) for renal resection, RFA, and cryoablation, respectively. Surgical margin status is the most studied and controversial marker of local renal cell carcinoma recurrence. Researchers found a direct effect of PSM on the risk of local RCC recurrence (p < 0.01). The personalized approach with the search and evaluation of predisposing factors for the local recurrence, as well as further selection of the most optimal treatment, will allow oncourologists to improve both the effectiveness of primary treatment and the recurrence-free survival of patients.

Heterogeneity of Hippo signalling activity in different histopathologic subtypes of renal cell carcinoma

This study aimed to reveal the prognostic role of the Hippo pathway in different histopathological subtypes of renal cell carcinoma (RCC). The TCGA-KIRC (n = 537), TCGA-KIRP (n = 291) and TCGA-KICH (n = 113), which contain data about clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC), respectively, were investigated. Gene Set Variation Analysis was used to compare the activity of many pathways within a single sample. Oncogenic pathway-related expression differed between cases of ccRCC involving low and high Hippo pathway activity. There were two subsets of ccRCC, in which the cancer exhibited lower and higher Hippo signalling activity, respectively, compared with normal tissue. In the ccRCC cohort, lower Hippo pathway activity was associated with a higher clinical stage (p < 0.001). The Hippo pathway (HR = 0.29; 95% CI = 0.17-0.50, p < 0.001), apoptosis (HR = 6.02; 95% CI = 1.47-24.61; p = 0.013) and the p53 pathway (HR = 0.09; 95% CI = 0.02-0.36; p < 0.001) were identified as independent prognostic factors for ccRCC. The 5-year overall survival of the ccRCC patients with low and high Hippo pathway activity were 51.9% (95% CI = 45.0-59.9) and 73.6% (95% CI = 67.8-79.9), respectively. In conclusion, the Hippo pathway plays an important role in the progression of ccRCC. Low Hippo pathway activity is associated with poor outcomes in ccRCC, indicating the tumour suppressor function of this pathway.

Risk models for recurrence and survival after kidney cancer: a systematic review

OBJECTIVE

To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent.

MATERIALS AND METHODS

We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models.

RESULTS

Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival.

CONCLUSION

Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates.

Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis

Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 μL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R²Y(cum) = 0.744, Q² (cum) = 0.880), as well, from patients with other tumors (R²Y(cum) = 0.748, Q²(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R²Y(cum) = 0.620, Q²(cum) = 0.656), and the staging of RCC (R²Y(cum) = 0.755, Q²(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R²Y(cum) = 0.710, Q²(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database

Objectives

Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).

Patients and Methods

Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.

Results

Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.

Conclusion

In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

Nomograms for predicting overall and cancer-specific survival of patients with chromophobe renal cell carcinoma after nephrectomy: a retrospective SEER-based study

OBJECTIVE

We aimed to construct and validate nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with chromophobe renal cell carcinoma (chRCC) after nephrectomy.

DESIGN

This study is a retrospective cohort study.

SETTING AND PARTICIPANTS

There were 2810 patients with chRCC from Surveillance, Epidemiology and End Results database diagnosed between 2010 and 2015 included in the study who were randomly divided into a training cohort (n=1970) and a validation cohort (n=840). Another single-centre external validation cohort containing 124 patients from our hospital was also involved in our study.

PRIMARY AND SECONDARY OUTCOME MEASURES

OS and CSS.

RESULTS

Nomograms for OS and CSS include four and five variables, respectively, from the result of least absolute shrinkage and selection operator regression analyses. Nomograms reveal the accurate discrimination by the area under the curve of receiver operating characteristic (ROC) curves and C-indexes, with a C-index value of 0.777 (95% CI 0.728 to 0.826), 0.810 (95% CI 0.747 to 0.873) and 0.863 (95% CI 0.773 to 0.953) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for OS; and a C-index value of 0.884 (95% CI 0.829 to 0.939), 0.868 (95% CI 0.772 to 0.964) and 0.862 (95% CI 0.760 to 0.964) for the training cohort, the internal validation cohort and the external validation cohort in the nomogram for CSS. It was also proven that there was a high degree of conformance between the predicted and observation results by calibration plots. In addition, the comparison of ROC curves and C-indexes between nomograms and seventh tumour, node and metastasis stage demonstrated that nomograms were better in accuracy and efficacy ability.

CONCLUSIONS

We successfully constructed two accurate and effective nomograms to predict OS and CSS for patients with chRCC after nephrectomy, which can help clinical doctors choose individual treatment strategies for chRCC patients.

Prognostic nutritional index as a prognostic factor for renal cell carcinoma: A systematic review and meta-analysis

Background

Prognostic nutritional index (PNI) is a simple parameter which reflects patient’s nutritional and inflammatory status and reported as a prognostic factor for renal cell carcinoma (RCC). Studies were included from database inception until February 2, 2022. The aim of this study is to evaluate prognostic value of PNI by meta-analysis of the diagnostic test accuracy in RCC.

Methods and findings

Studies were retrieved from PubMed, Cochrane, and EMBASE databases and assessed sensitivity, specificity, summary receiver operating characteristic curve (SROC) and area under curve (AUC). Totally, we identified 11 studies with a total of 7,296 patients were included to evaluate the prognostic value of PNI in RCC finally. They indicated a pooled sensitivity of 0.733 (95% CI, 0.651–0.802), specificity of 0.615 (95% CI, 0.528–0.695), diagnostic odds ratio (DOR) of 4.382 (95% CI, 3.148–6.101) and AUC of 0.72 (95% CI, 0.68–0.76). Heterogeneity was significant and univariate meta-regression revealed that metastasis and cut-off value of PNI might be the potential source of heterogeneity. Multivariate meta-regression analysis also demonstrated that metastasis might be the source of heterogeneity.

Conclusions

PNI demonstrated a good diagnostic accuracy as a prognostic factor for RCC and especially in case of metastatic RCC.

Methylation of PCDH17 and NEFH as prognostic biomarker for nonmetastatic RCC: A cohort study

DNA methylation makes up a main part of the molecular mechanism of cancer evolution and has shown promising results in the prognosis of renal cell cancer (RCC). In this study, we investigated the possible association of promoter methylation of PCDH17, NEFH, RASSF1A, and FHIT, genes with the prognosis of nonmetastatic RCC patients. Cancerous and normal adjacent tissues from surgical specimens of 41 patients with long follow-up were treated for DNA isolation and bisulfite conversion. The gene promoter methylation was determined with quantitative methylation-specific PCR (qMSP). Wilcoxon signed-rank test was used for paired methylation comparisons, while univariate linear regression and Mann-Whitney test were applied for associating methylation status with clinical and disease characteristics. Cox regression proportional hazards models and Kaplan-Meier plots were used for survival analyses in reference to methylation status. Paired comparisons showed tissue-specific hypermethylation for PCDH17 (P < .001), NEFH (P < .001), RASSF1A (P = .032), while a positive association of methylation in normal tissues with age was demonstrated for PCDH17 (P < .001), RASSF1A (P < .001), FHIT (P < .001). PCDH17 was more methylated in cases with clear cell RCC (P = .015) and high-grade tumor (P = .013), while NEFH methylation was higher in locally advanced cases (P = .032). PCDH17 hypermethylation in cancerous and normal tissues was linked to shorter disease-specific survival (DSS, P = .026, P = .004), disease-free survival (DFS, P = .004, P = .019) while NEFH hypermethylation in cancerous tissues was related to shorter DSS (P = .032). Increased methylation difference of NEFH was also associated with shorter DSS (P = .041) and DFS (P = .020), while the corresponding parameter for PCDH17 was associated with poor DFS (P = .014). Kaplan-Meier curves for hypermethylation in cancer tissues demonstrated different clinical courses for PCDH17 (P = .017), NEFH (P = .023) regarding DSS, and PCDH17 (P = .001) regarding DFS. Our study not only highlights the prognostic value of promoter methylation of PCDH17 and NEFH in cancer tissues but also is the first report of the prognostic value of methylation alterations in normal tissues. Our findings are the first report of the prognostic value of methylation alterations in normal tissues, which can contribute to improved assessment of recurrence risk.

Long-term outcome of renal cell carcinoma in patients with HIV who undergo surgery

Background

People living with HIV (PLWH) have a higher risk for cancer compared to the general population. The prevalence of renal cell carcinoma (RCC) in PLWH has gradually increased in recent years, but relevant data on outcomes after surgery are scarce. We thus evaluated long-term outcomes after surgery in RCC patients with and without HIV.

Methods

This retrospective study included 67 patients with RCC, both HIV positive or negative, who underwent surgical treatment in our hospital between January 2012 and January 2021. Demographic details, clinical data, and cancer status were collected. We set the day of surgery as the baseline. The co-primary end points in this time-to-event analysis were overall survival and progression-free survival. We used a multivariate Cox model to compare the prognosis of PLWH and HIV-negative patients and present Kaplan–Meier curves for the co-primary end points.

Results

Of 261 consecutive patients, 18 patients who forwent treatment before surgery, 133 cases with incomplete data, 16 patients classified as clinical stage IV, 11 PLWH patients did not received antiretroviral therapy and 16 patients with metastasis were excluded from the main analysis. Of the remaining 67 patients, 33 individuals had HIV and the other 34 did not. The median overall survival was 74.9 months (95% confidence interval [CI] = 64.6 to 85.2) in PLWH and 96.4 months (95% CI = 90.0 to 102.9) in the HIV-negative group. Progression-free survival was 66.4 months (95% CI = 53.5 to 79.3) and 90.6 months (95% CI = 81.1 to 100.1), respectively. RCC patients with HIV who underwent surgery had a shorter survival time (hazard ratio [HR] = 2.8, 95% CI = 1.1 to 7.0, p = 0.016) and an increased incidence of tumor progression (HR = 2.7, 95% CI = 1.1 to 6.8, p = 0.028). Univariate and multivariate Cox regression analyses showed that a lower ratio of CD4⁺ T cells to CD8⁺ T cells (adjusted odds ratio = 0.092, 95% CI = 0.01 to 0.70, p = 0.022) was associated with worse survival among PLWH.

Conclusion

In this retrospective analysis of RCC patients who underwent surgery, PLWH had worse overall survival and shorter progression-free survival compared to HIV-negative cases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07592-z.

First Chinese study of HIV and RCC.

Prognostic risk factor in PLWH with RCC.

The Four-Feature Prognostic Models for Cancer-Specific and Overall Survival after Surgery for Localized Clear Cell Renal Cancer: Is There a Place for Inflammatory Markers?

We aimed at a determination of the relevance of comorbidities and selected inflammatory markers to the survival of patients with primary non-metastatic localized clear cell renal cancer (RCC). We retrospectively analyzed data from a single tertiary center on 294 patients who underwent a partial or radical nephrectomy in the years 2012–2018. The following parameters were incorporated in the risk score: tumor stage, grade, size, selected hematological markers (SIRI—systemic inflammatory response index; SII—systemic immune-inflammation index) and a comorbidities assessment tool (CCI—Charlson Comorbidity Index). For further analysis we compared our model with existing prognostic tools. In a multivariate analysis, tumor stage (p = 0.01), tumor grade (p = 0.03), tumor size (p = 0.006) and SII (p = 0.02) were significant predictors of CSS, while tumor grade (p = 0.02), CCI (p = 0.02), tumor size (p = 0.01) and SIRI (p = 0.03) were significant predictors of OS. We demonstrated that our model was characterized by higher accuracy in terms of OS prediction compared to the Leibovich and GRANT models and outperformed the GRANT model in terms of CSS prediction, while non-inferiority to the VENUSS model was revealed. Four different features were included in the predictive models for CSS (grade, size, stage and SII) and OS (grade, size, CCI and SIRI) and were characterized by adequate or even superior accuracy when compared with existing prognostic tools.

Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions

Radiogenomics is a field of translational radiology that aims to associate a disease's radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: "radiogenomics", "renal cell carcinoma", and "clear cell renal cell carcinoma". Articles included were limited to the English language and published between 2009-2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.

Long-Term Oncological and Functional Outcomes After Robot-Assisted Partial Nephrectomy for Clinically Localized Renal Cell Carcinoma

BACKGROUND

To evaluate long-term oncological and renal function outcomes in patients treated with robot-assisted partial nephrectomy (RAPN) for renal cell carcinoma (RCC).

PATIENTS AND METHODS

Patients undergoing RAPN for clinically localized RCC between January 2014 and December 2019 at a tertiary robotic reference center were evaluated. Clinical course, pathologic characteristics, and long-term outcomes were obtained from our institutional review board-approved RCC database.

RESULTS

A total of 234 patients were available for analysis. Median follow-up was 46 months (10.8-97.8 months), with 77 patients (32.9%) having at least 5-years of follow-up. Pathology revealed clear-cell RCC in 67.5% (n = 158). Among unfavorable factors, nuclear grades 3 or 4 were found in 67 (29.4%), lymphovascular invasion in 10 (4.3%), positive surgical margins in 22 (9.4%), necrosis in 21 (9%), and sarcomatoid pattern in 2 patients (0.9%). At 12 months, mean serum creatinine was 1.04 mg/dL and 12.9% of patients experienced upstaging in chronic kidney disease. Overall recurrence-free survival at 5-years was 97.8%. There were five local (2.1%) and two distant (0.9%) recurrences, none of them resulting in cancer-specific death. Median time to recurrence was 20 months (11-64 months). Warm ischemia time [hazard ratio (HR) = 1.14, p = 0.034] and sarcomatoid pattern (HR = 124.57, p = 0.001) were the only variables associated with local relapse.

CONCLUSIONS

Data from this large cohort demonstrate that patients undergoing RAPN have a low incidence of local and distant relapse, resulting in excellent long-term survival while preserving stable renal function in most patients.

Diagnostics and prognostic evaluation in renal cell tumors: the German S3 guidelines recommendations

The German guidelines on renal cell carcinoma (RCC) have been developed at highest level of evidence based on systematic literature review. In this paper, we are presenting the current recommendations on diagnostics including preoperative imaging and imaging for stage evaluation as well as histopathological classification. The role of tumor biopsy is further discussed. In addition, different prognostic scores and the status of biomarkers in RCC are critically evaluated.

Establishment and Validation of Coagulation Factor-Based Nomogram for Predicting the Recurrence-Free Survival of Prostate Cancer

<b><i>Introduction:</i></b> We aimed to establish and validate a coagulation feature-based nomogram to predict recurrence-free survival in prostate cancer patients. <b><i>Methods:</i></b> The study included 168 prostate cancer patients who had received radical prostatectomy between 2012 and 2018. Kaplan-Meier plot and log-rank analysis were used to screen recurrence-free survival-related features. The nomogram was established by combining the significant coagulation features with clinicopathological characteristics by using Cox regression analysis. The accuracy and clinical significance of the nomogram model were assessed by the receiver operating characteristic curve, Kaplan-Meier plot, and calibration plot. We explored the correlation between coagulation pathway activity and patient prognosis in public datasets by using gene set variation analysis (GSVA). <b><i>Results:</i></b> The results suggested that patients classified by the nomogram into the high-risk subgroup showed unfavorable prognoses compared with those in the low-risk subgroup in both the training (log-rank <i>p</i> &#x3c; 0.0001) and validation (log-rank <i>p</i> = 0.0004) cohorts. The nomogram model exhibited high discriminative accuracy in the training cohort (1-year area under the curve [AUC] of 0.74 and 3-year AUC of 0.69), which was confirmed in the internal validation cohort (C-index = 0.651). The calibration plots confirmed good concordance for the prediction of recurrence-free survival at 1 and 3 years. Subgroup analyses confirmed the utility of this model in different clinicopathological subgroups. Finally, GSVA suggested that patients with higher coagulation pathway scores mostly had unfavorable prognoses compared to those with lower scores, a result consistent with the findings above. <b><i>Conclusions:</i></b> We developed a practical nomogram model for predicting recurrence-free survival in prostate cancer patients. This model may offer clinicians prognostic assessments and facilitate personalized treatment.

Head-to-head comparison of all the prognostic models recommended by the European Association of Urology Guidelines to predict oncologic outcomes in patients with renal cell carcinoma

OBJECTIVES

European Urology Association guidelines suggest the use of integrated prognostic systems to assess oncologic outcomes after surgery in patients with localized renal cell carcinoma (RCC). We performed a head-to-head comparison among all the EAU guidelines recommended prognostic models in RCC.

METHODS

The study included 2,014 patients treated with surgery for clinically localized RCC. Patients were classified into prognostic risk groups, based on each of the five EAU guidelines recommended prognostic model definition, namely UISS, Leibovich 2003, VENUSS, GRANT, and Leibovich 2018 score. Prognostic accuracy of each prognostic model to predict clinical progression or cancer-specific mortality (CSM) was assessed, and ROC curves were calculated, according to histological subtype, namely clear-cell, papillary, and chromophobe RCC.

RESULTS

Of 2,014 patients, 1,575 (78%) harboured clear-cell, 312 (16%) papillary, and 127 (6%) chromophobe RCC. Median follow-up was 66 months [Interquartile range (IQR): 29-120]. In clear-cell RCC, low-risk patients rates ranged from 21% to 64%, according prognostic model. The same phenomenon was observed for papillary and chromophobe RCC. In clear-cell RCC, Leibovich 2018 resulted the most accurate model in predicting clinical progression (88.1%) and CSM (86.8%). Conversely, VENUSS or UISS prognostic models predicting oncologic outcomes represented the most accurate in papillary (88.7% and 84.8%) or chromophobe (87.8% and 89.1%) RCC, respectively.

CONCLUSIONS

A non-negligible difference in terms of performance accuracy exists among the EAU guidelines recommended prognostic models. Thus, their adoption in RCC should be histology-specific and follow-up strategies based on prognostic risk class appear justified only if the appropriate model is used to stratify patients into prognostic risk groups.

Potential Clinical Value of Pretreatment De Ritis Ratio as a Prognostic Biomarker for Renal Cell Carcinoma

Background

We performed this study to explore the prognostic value of the pretreatment aspartate transaminase to alanine transaminase (De Ritis) ratio in patients with renal cell carcinoma (RCC).

Methods

PubMed, EMBASE, Web of Science, and Cochrane Library were searched to identify all studies. The hazard ratio (HR) with a 95% confidence interval (CI) for overall survival (OS) and cancer-specific survival (CSS) were extracted to evaluate their correlation.

Results

A total of 6,528 patients from 11 studies were included in the pooled analysis. Patients with a higher pretreatment De Ritis ratio had worse OS (HR = 1.41, p < 0.001) and CSS (HR = 1.59, p < 0.001). Subgroup analysis according to ethnicity, disease stage, cutoff value, and sample size revealed that the De Ritis ratio had a significant prognostic value for OS and CSS in all subgroups.

Conclusions

The present study suggests that an elevated pretreatment De Ritis ratio is significantly correlated with worse survival in patients with RCC. The pretreatment De Ritis ratio may serve as a potential prognostic biomarker in patients with RCC, but further studies are warranted to support these results.

A Mitochondrial Dysfunction and Oxidative Stress Pathway-Based Prognostic Signature for Clear Cell Renal Cell Carcinoma

Mitochondria not only are the main source of ATP synthesis but also regulate cellular redox balance and calcium homeostasis. Its dysfunction can lead to a variety of diseases and promote cancer and metastasis. In this study, we aimed to explore the molecular characteristics and prognostic significance of mitochondrial genes (MTGs) related to oxidative stress in clear cell renal cell carcinoma (ccRCC). A total of 75 differentially expressed MTGs were analyzed from The Cancer Genome Atlas (TCGA) database, including 46 upregulated and 29 downregulated MTGs. Further analysis screened 6 prognostic-related MTGs (ACAD11, ACADSB, BID, PYCR1, SLC25A27, and STAR) and was used to develop a signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses showed that the signature could accurately distinguish patients with poor prognosis and had good individual risk stratification and prognostic potential. Stratified analysis based on different clinical variables indicated that the signature could be used to evaluate tumor progression in ccRCC. Moreover, we found that there were significant differences in immune cell infiltration between the low- and high-risk groups based on the signature and that ccRCC patients in the low-risk group responded better to immunotherapy than those in the high-risk group (46.59% vs 35.34%, P = 0.008). We also found that the expression levels of these prognostic MTGs were significantly associated with drug sensitivity in multiple ccRCC cell lines. Our study for the first time elucidates the biological function and prognostic significance of mitochondrial molecules associated with oxidative stress and provides a new protocol for evaluating treatment strategies targeting mitochondria in ccRCC patients.

High RSK4 expression constitutes a predictor of poor prognosis for patients with clear cell renal carcinoma

BACKGROUND

This research focuses on exploring RSK4 protein expression within Clear Cell Renal Cell Carcinoma (ccRCC), based on these investigations on level of expressions coupled with the relevance to clinicopathologic features and clinical outcomes.

METHODS

The expression of RSK4 in 48 ccRCC and 20 hydronephrosis samples were under the detection of immunohistochemistry; besides, its relevance to the combination of clinicopathologic features with prognosis was committed in virtue of statistical approaches.

RESULTS

The 48 ccRCC samples included 36 (75%, 36/48) positive for RSK4, while the positive rate in hydronephrosis samples were 5 (25%, 5/20). Statistical analysis showed that RSK4 in ccRCC samples express higher expression the hydronephrosis samples (P < 0.05). Furthermore, the expression of RSK4 in ccRCC samples weren't correlated with ages and genders (P > 0.05), while WHO/ISUP nucleolar grade harboured relevance to low survival rate (P = 0.018). Molecular researches demonstrated that over-expression of RSK4 was able to upgrade the proliferation capability of ccRCC cell lines.

CONCLUSIONS

According to the expression pattern and molecular systems featured RSK4 in ccRCCs, it performed the function of a latent independent prognostic factor performing the function of a newly built latent therapeutic aim oriented with the patients undergoing RCC. Moreover, the specific mechanism of action is expected to be revealed in the future research.

Potential prognostic value of miRNAs as biomarker for progression and recurrence after nephrectomy in renal cell carcinoma: a literature review

OBJECTIVES

We provide a systematic literature review on tissue miRNAs in patients with RCC to evaluate and summarize their usefulness as prognostic markers. We undertook a systematic search for articles in English using the PubMed-Medline database from January 2010 to December 2020. Studies were identified and selected according to the PRISMA criteria and the PICO methodology. The population consisted of RCC patients undergoing nephrectomy and the main outcome of interest was recurrence-free survival (RFS). Only studies providing hazard ratios (HRs) from multivariate or univariate analyzes with corresponding 95% confidence intervals (CI) and/or area under the curve (AUC) were considered.

CONTENT

All nine included studies (1,541 patients) analyzed the relationship between tissue miRNA expression levels (up or downregulated) and RFS. Some of these found that the methylation status of miR-9-1, miR-9-3 and miR-124 was associated with a high risk of relapse. Moreover, miR-200b overexpression was associated with OS. MiR-210 overexpression indicated a shorter OS than those who were miR-210 negative. Finally, patients with high miR-125b expression had shorter CSS than those with low expression; similarly, patients with low miR-126 expression also had shorter CSS time.

SUMMARY AND OUTLOOK

Several studies tested the usefulness of specific miRNAs to predict RCC recurrence. Some of them showed a fair accuracy and strong relationship between specific miRNA over or under-expression and survival outcomes. However, results from these studies are preliminary and miRNAs use in routine clinical practice is still far to come.

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Purpose

The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).

Methods

A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.

Results

A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.

Conclusions

The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma

Purpose

To develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation.

Materials and Methods

One hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination.

Results

Rad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808–0.940 vs. 0.803; 95% CI: 0.705–0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800–0.921 vs. 0.846; 95% CI: 0.777–0.915, P < 0.05).

Conclusions

The radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.

Transcription Factors BARX1 and DLX4 Contribute to Progression of Clear Cell Renal Cell Carcinoma via Promoting Proliferation and Epithelial-Mesenchymal Transition

Dysregulation of transcription factors contributes to the carcinogenesis and progression of cancers. However, their roles in clear cell renal cell carcinoma remain largely unknown. This study aimed to evaluate the clinical significance of TFs and investigate their potential molecular mechanisms in ccRCC. Data were accessed from the cancer genome atlas kidney clear cell carcinoma cohort. Bioinformatics algorithm was used in copy number alterations mutations, and differentially expressed TFs’ analysis. Univariate and multivariate Cox regression analyses were performed to identify clinically significant TFs and construct a six-TF prognostic panel. TFs’ expression was validated in human tissues. Gene set enrichment analysis (GSEA) was utilized to find enriched cancer hallmark pathways. Functional experiments were conducted to verify the cancer-promoting effect of BARX homeobox 1 (BARX1) and distal-less homeobox 4 (DLX4) in ccRCC, and Western blot was performed to explore their downstream pathways. As for results, many CNAs and mutations were identified in transcription factor genes. TFs were differentially expressed in ccRCC. An applicable predictive panel of six-TF genes was constructed to predict the overall survival for ccRCC patients, and its diagnostic efficiency was evaluated by the area under the curve (AUC). BARX1 and DLX4 were associated with poor prognosis, and they could promote the proliferation and migration of ccRCC. In conclusion, the six-TF panel can be used as a prognostic biomarker for ccRCC patients. BARX1 and DLX4 play oncogenic roles in ccRCC via promoting proliferation and epithelial–mesenchymal transition. They have the potential to be novel therapeutic targets for ccRCC.

Perioperative therapy in renal cancer in the era of immune checkpoint inhibitor therapy

PURPOSE OF REVIEW

Immune checkpoint inhibitor (ICI) combination therapy has revolutionized therapy of metastatic renal cancer. The success of immunotherapy has renewed an interest to study these agents in adjuvant and neoadjuvant settings and prior to cytoreductive nephrectomy. This narrative review will give an overview of ongoing trials and early translational research outcomes.

RECENT FINDINGS

In nonmetastatic renal cell carcinoma (RCC), five phase 3 adjuvant and neoadjuvant trials with ICI monotherapy or combinations are ongoing with atezolizumab (IMmotion 010; NCT03024996), pembrolizumab (KEYNOTE-564; NCT03142334), nivolumab (PROSPER; NCT03055013), nivolumab with or without ipilimumab (CheckMate 914; NCT03138512) and durvalumab with or without tremelimumab (RAMPART; NCT03288532). Phase 1b/2 neoadjuvant trials demonstrate safety, efficacy and dynamic changes of immune infiltrates and provide rationales for neoadjuvant trial concepts as well as prediction of response to therapy. In primary metastatic RCC, two phase 3 trials investigate the role of deferred cytoreductive nephrectomy following pretreatment with ICI combination (NORDICSUN; NCT03977571 and PROBE; NCT04510597).

SUMMARY

The outcomes of the major phase 3 trials are awaited as early as 2023. Meanwhile, translational data from phase 1b/2 studies enhance our understanding of the tumour immune microenvironment and its dynamic changes.

Effect of radiomics from different virtual monochromatic images in dual-energy spectral CT on the WHO/ISUP classification of clear cell renal cell carcinoma

AIM

To investigate the effect of radiomics obtained from different virtual monochromatic images (VMIs) in dual-energy spectral computed tomography (CT) on the World Health Organization/International Association for Urological Pathology (WHO/ISUP) classification of clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

A retrospective study of 99 ccRCC patients who underwent contrast-enhanced dual-energy CT was undertaken. ccRCC was confirmed at surgery or biopsy and graded according to the WHO/ISUP pathological grading criteria as low grade (n=68, grade I and II) or high grade (n=31, grade III and IV). Radiomics risk scores (RRSs) for differentiating high and low grades of ccRCC were constructed from 11 sets of VMI in (40-140 keV, 10 keV interval) the cortical phase. Receiver operating characteristic (ROC) curves were drawn and the area under the curves (AUCs) was calculated to evaluate the discriminatory power of RRS for each VMI. The Hosmer-Lemeshow test was used to evaluate the goodness-of-fit of each model and the decision curve was used to analyse its net benefit to patients.

RESULTS

The AUC values for distinguishing low-from high-grade ccRCC with RRS of 40-140 keV VMIs were all >0.920. The Hosmer-Lemeshow test showed that the p-values of RRS of VMIs were >0.05, suggesting good fits. In the decision curve analysis, RRS from the 40-140 keV VMIs had similar decision curves and provided better net benefits than considering all patients either as high-grade or low-grade.

CONCLUSIONS

The RRS obtained from multiple VMIs in dual-energy spectral CT have high diagnostic efficiencies for distinguishing between low- and high-grade ccRCC with no significant differences between different VMIs.

Biomarkers for Renal Cell Carcinoma Recurrence: State of the Art

PURPOSE OF REVIEW

We aim to summarize the current state of art about the possible use of biomarkers for predicting renal cell carcinoma (RCC) recurrence after curative treatment. In addition, we aim to provide a snapshot about the clinical implication of biomarkers use for follow-up planification.

RECENT FINDINGS

A wide variety of biomarkers have been proposed. RCC biomarkers have been individuated in tumoral tissue, blood, and urine. A variety of molecules, including proteins, DNA, and RNA, warrant a good accuracy for RCC recurrence and progression prediction. Their use in prediction models might warrant a better patients' risk stratification. Future prognostic models will probably include a combination of classical features (tumor grade, stage, etc.) and novel biomarkers. Such models might allow a more accurate treatment and follow-up planification.

STAM Prolongs Clear Cell Renal Cell Carcinoma Patients' Survival via Inhibiting Cell Growth and Invasion

Background: Signal transducing adaptor molecule 1 (STAM1) was considered to mediate cell growth and be involved in multiple signaling pathways; however, no research on the role of STAM1 in any tumors has been published yet. Our study aimed to investigate the prognostic value of STAM1 for clear cell renal cell carcinoma (ccRCC) and its role in modulating cancer cell function.

Methods: Data from The Cancer Genome Atlas (TCGA) in December 2019 were used to examine the role of STAM1 in indicating ccRCC patients' survival. A purchased tissue microarray (TM) and fresh ccRCC renal tissues were used for further validation. Then, STAM1 was overexpressed in human ccRCC cell lines for in vitro assays. Finally, bioinformatics was performed for STAM1 protein–protein interaction (PPI) network construction and functional analyses.

Results: A total of 539 ccRCC and 72 control samples were included for the TCGA cohort, and 149 ccRCC and 29 control slices were included for the TM cohort. In the TCGA and TM cohorts, we found that STAM1 expression was lower in ccRCC compared with normal adjacent non-cancerous renal tissues (P < 0.0001 for both cohorts). STAM1 downregulation was also related to significantly shorter overall survival (OS) (P < 0.0001 for both cohorts). In the TCGA cohort, reduced STAM1 expression was also associated with aggressive features of the tumor. Under multivariate analyses, STAM1 was demonstrated to be an independent prognostic factor for ccRCC survival in both TCGA (HR = 0.52, 95% CI: 0.33–0.84, P = 0.007) and TM cohorts (HR = 0.12, 95% CI: 0.04–0.32, P < 0.001). Our in vitro experiments showed that STAM1 inhibited cell viability, invasion, and migration in ccRCC cell lines. In PPI network, 10 candidate genes categorized into five biological processes were found to be closely related to STAM1.

Conclusion: STAM1 is a promising prognostic biomarker for predicting ccRCC survival outcomes. Preliminary pathogenesis is demonstrated by our in vitro experiments. Further pathological mechanisms of STAM1 in modulating ccRCC require comprehensive laboratory and clinical studies.

Construction autophagy-related prognostic risk signature combined with clinicopathological validation analysis for survival prediction of kidney renal papillary cell carcinoma patients

BACKGROUND

Little data is available on prognostic biomarkers and effective treatment options for Kidney Renal Papillary Cell Carcinoma (KIRP) patients, to find potential prognostic biomarkers and new targets was an urgent mission for KIRP therapy.

METHODS

The differentially expressed autophagy-related genes (DEARGs) were screened out according to the RNA sequencing data in The Cancer Genome Atlas database, then identified survival-related DEARGs to establish a prognostic model for survival predicting of KIRP patients. Then we verified the robustness and validity of the prognostic risk model through clinicopathological data. At last, we evaluate the prognostic value of genes that formed the prognostic risk model individually.

RESULTS

We analyzed the expression of 232 autophagy-related genes (ARGs) in 289 KIRP and 32 non-tumor tissue cases, and 40 mRNAs were screened out as DEARGs. The functional and pathway enrichment analysis was done and protein-protein interaction network was constructed for all DEARGs. To sift candidate DEARGs associated with KIRP patients' survival and create an autophagy-related risk prognostic model, univariate and multivariate Cox regression analysis were did separately. Eventually 3 desirable independent prognostic DEARGs (P4HB, NRG1, and BIRC5) were picked out and used for construct the autophagy-related risk model. The accuracy of the prognostic risk model for survival prediction was assessed by Kaplan-Meier plotter, receiver-operator characteristic curve, and clinicopathological correlational analyses. The prognostic value of above 3 genes was verified individually by survival analysis and expression analysis on mRNA and protein level.

CONCLUSIONS

The autophagy-related prognostic model is accurate and applicable, it can predict OS independently for KIRP patients. Three independent prognostic DEARGs can benefit for facilitate personalized target treatment too.

A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases

BACKGROUND

The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial.

METHODS

We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model.

RESULTS

A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively.

CONCLUSIONS

Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases.

Identification of a Risk Stratification Model to Predict Overall Survival and Surgical Benefit in Clear Cell Renal Cell Carcinoma With Distant Metastasis

Background: Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma and has different prognoses, especially in patients with metastasis. Here, we aimed to establish a novel model to predict overall survival (OS) and surgical benefit of ccRCC patients with distant metastasis. Methods: Using data from the Surveillance, Epidemiology, and End Results (SEER) databases, we identified 2185 ccRCC patients with distant metastasis diagnosed from 2010 to 2015. Univariate and multivariate Cox analysis were used to identify significant prognostic clinicopathological variables. By integrating these variables, a prognostic nomogram was constructed and evaluated using C-indexes and calibration curves. The discriminative ability of the nomogram was measured by analyses of receiver operating characteristic (ROC) curve. A risk stratification model was built according to each patient's total scores. Kaplan-Meier curves were performed in the low-, intermediate- and high-risk groups to evaluate the survival benefit of surgery. Results: Eight clinicopathological variables were included as independent prognostic factors in the nomogram: grade, marital status, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, and lung metastasis. The nomogram had a better discriminative ability for predicting OS than Tumor-Node-Metastasis (TNM) stage. The C-index was 0.71 (95% CI 0.68-0.74) in the training cohort. The calibration plots demonstrated that the nomogram-based predictive outcomes had good consistency with the actual prognosis results. Total nephrectomy improved prognosis in both the low-risk and intermediate-risk groups, but partial nephrectomy could only benefit the low-risk group. Conclusions: We constructed a predictive nomogram and risk stratification model to evaluate prognosis in ccRCC patients with distant metastasis, which was valuable for prognostic stratification and making therapeutic decisions.

Prognostic role of bland thrombus in patients treated with resection of renal cell carcinoma with inferior vena cava tumor thrombus

OBJECTIVES

To assess the impact of the presence of bland thrombus (BT) on prognosis of patients treated with resection of renal cell carcinoma (RCC) with inferior vena cava tumor thrombus (IVCTT).

MATERIALS AND METHODS

The medical records of a total of 145 consecutive postsurgical RCC patients with level I-IV IVCTT were reviewed from January 2008 to August 2018. Associations of BT with clinicopathological variables were estimated by chi-square test or Student's t-test. Kaplan-Meier method and multivariate Cox proportional hazard model were used. The eighth TNM staging system, "Spiess PE" model, University of California at Los Angeles Integrated Staging System and Stage, Size, Grade, and Necrosis (SSIGN) score were selected to assess whether BT could improve their predictive abilities.

RESULTS

BT was observed in 34 (23.4%) patients and was significantly associated with increased levels of IVCTT (P = 0.004) and invasion of IVC wall (P = 0.030). Multivariable Cox analyses revealed that tumor grade, T stage, M stage, tumor thrombus consistency and BT were independent risk factors for both progression-free survival and overall survival. The concordance indexes ranged from a low of 0.652 in TNM to a high of 0.731 in SSIGN, and integrating BT into each base model led to an increased predictive accuracies of 6.2% for TNM (P = 0.025), 4.0% for "Spiess PE" model (P = 0.069), 2.1% for University of California at Los Angeles Integrated Staging System (P = 0.149) and 1.2% for SSIGN (P = 0.290), respectively.

CONCLUSIONS

Presence of BT was independently associated with survival in postsurgical patients with RCC-IVCTT. Routine consideration of BT as an adjunct to TNM staging system may be suggested.

Preoperative anaemia and thrombocytosis predict adverse prognosis in non-metastatic renal cell carcinoma with tumour thrombus

BACKGROUND : This study aimed to determine the prognostic value of preoperative blood parameters in renal cell carcinoma (RCC) and tumour thrombus (TT) patients that were surgically treated.

METHOD

We retrospectively analysed clinicopathological data and blood parameters of 146 RCC and TT patients that were surgically treated. Univariate or multivariate Cox regression analyses were performed to determine the risk factors associated with progression-free survival (PFS) and overall survival (OS). Kaplan-Meier analysis and logistic regression were performed to study the risk factors. Receiver operating characteristic curves were applied to test improvements in the predictive accuracy of the established prognosis score.

RESULTS

On univariate and multivariate analysis, anaemia (HR 2.873, P = 0.008) and lymph node metastasis (HR 4.811, P = 0.015) were independent prognostic factors linked to OS. Besides, thrombocytosis (HR 2.324, P = 0.011), histologic subtype (HR 2.835, P = 0.004), nuclear grade (HR 2.069, P = 0.033), and lymph node metastasis (HR 5.739, P = 0.001) were independent prognostic factors associated with PFS. Kaplan-Meier curves revealed that patients with anaemia exhibited worse OS than those without it (P = 0.0033). Likewise, patients with thrombocytosis showed worse PFS than those without it (P < 0.0001). Adding the anaemia and thrombocytosis to the SSIGN score improved its predictive accuracy related to OS and PFS. Preoperative anaemia was linked to more symptom at presentation (OR 3.348, P = 0.006), longer surgical time (OR 1.005, P = 0.001), more blood loss (OR 1.000, P = 0.018), more transfusion (OR 2.734, P = 0.004), higher thrombus level (OR 4.750, P = 0.004) and higher nuclear grade (OR 3.449, P = 0.001) while thrombocytosis was associated with more symptom at presentation (OR 7.784, P = 0.007).

CONCLUSIONS

Preoperative anaemia and thrombocytosis were adverse prognostic factors in non-metastatic RCC patients with TT. Also, both preoperative anaemia and thrombocytosis can be clinically used for risk stratification of non-metastatic RCC and TT patients.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.