Can Gender-Specific Renal and Visceral Fat Be Evaluated by CT Predict Fuhrman Nuclear Classification of Clear Cell Renal Cell Carcinoma?

BACKGROUND: Evidence of the association between obesity and renal cell carcinoma progression is contradictory. The effects of renal cell carcinoma on fat distribution are still unknown. OBJECTIVE: The goal of this study was to determine the ability of various forms of fat deposition to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma [ccRCC]. METHODS: This retrospective study included 320 patients with pathologically proven ccRCC [215 men and 105 women; 263 low-grade ccRCC and 57 highgrade ccRCC]. Based on computed tomography scans, adipose tissue in various body regions was classified into the perirenal fat area [PFA], visceral fat area [VFA], total fat area [TFA], subcutaneous fat area [SFA], and hepatic steatosis [HS]. Subsequently, the relative VFA [rVFA] was computed. Age, sex, body mass index, and maximal tumor diameter were also regarded as clinical factors. Univariate and multivariate logistic regression studies were conducted to evaluate whether there was an association between body fat composition and the Fuhrman classification and whether it was related to gender. RESULTS: After correcting for age, males with low-grade ccRCC exhibited higher TFA [257.6 vs. 203.0, p = 0.002], VFA [151.6 vs.115.5, p = 0.007], SFA [106.0 vs. 87.5, p = 0.015], PFA [55.1 vs. 30.4, p < 0.001], and HS [18% vs. 0%, p = 0.031] than those with high-grade ccRCC. There was no significant difference among rVFA in males. In females, there was no significant difference in any of the parameters. VFA and PFA remained independent predictors for high-grade ccRCC in males in both the monovariate [VFA: odds ratio [OR] 0.992, 95% confidence interval [CI] 0.987-0.997, p = 0.004; PFA: OR 0.949, 95% CI 0.930-0.970, p < 0.001] and multivariate [VFA: OR 1.028, 95% CI 1.001-1.074, p < 0.001; PFA: OR 0.878, 95% CI 0.833-0.926, p < 0.001] models. CONCLUSION: Gender-specific adipose tissue in different locations demonstrated varied values for predicting high-grade ccRCC and may be utilized as an independent predictor of high-grade ccRCC in male patients.

SPP1 expression indicates outcome of immunotherapy plus tyrosine kinase inhibition in advanced renal cell carcinoma.

Clinical guidelines have recently advised combination therapy involving immunotherapy (IO) and tyrosine kinase inhibitors (TKI) as the first-line therapy approach for advanced renal cell carcinoma (RCC). Nevertheless, there is currently no available biomarker that can effectively distinguish the progression-free survival (PFS). RNA-sequencing and immunohistochemistry were conducted on our cohort of metastatic RCC patients, namely ZS-MRCC, who received combination therapy consisting of IO and TKI. We further applied RNA-sequencing, immunohistochemistry, and flow cytometry to examine the immune cell infiltration and functionality inside the tumor microenvironment of high-risk localized RCC samples. SPP1 expression was significantly higher in non-responders to IO-TKI therapy. Elevated levels of SPP1 were associated with poor PFS in both the ZS-MRCC cohort (HR = 2.73, p = .018) and validated in the JAVELIN Renal 101 cohort (HR = 1.61, p = .004). By multivariate Cox analysis, SPP1 was identified as a significant independent prognosticator. Furthermore, there existed a negative correlation between elevated levels of SPP1 and the presence of GZMB+CD8+ T cells (Spearman's ρ= -0.48, p < .001). Conversely, SPP1 expression is associated with T cell exhaustion markers. A significant increase in the abundance of Tregs was observed in tumors with high levels of SPP1. Additionally, a machine-learning-based model was constructed to predict the benefit of IO-TKI treatment. High SPP1 is associated with therapeutic resistance and unfavorable PFS in IO-TKI therapy. SPP1 expression have also been observed to be indicative of malfunction and exhaustion in T cells. Increased SPP1 expression has the potential to serve as a potential biomarker for treatment selection of metastatic RCC.

Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Effect of Annexin A2 on prognosis and sensitivity to immune checkpoint plus tyrosine kinase inhibition in metastatic renal cell carcinoma.

BACKGROUND: Immunotherapy (IO) plus tyrosine kinase inhibitor (TKI) therapy is the first-line recommendation for advanced renal cell carcinoma (RCC), but no biomarker has been approved for it. Annexin A2 (ANXA2) can induce immune escape in tumors. METHODS: Two independent cohorts of advanced RCC treated by IO + TKI were utilized for survival analysis (ZS-MRCC, n = 45; Javelin-101, n = 726). ANXA2 expression was determined by RNA-sequencing. The impact of ANXA2 on the tumor microenvironment was assessed by RNA-sequencing, flow cytometry and immunohistochemistry in two localized RCC datasets (ZS-HRRCC, n = 40; TCGA-KIRC, n = 530). RESULTS: ANXA2 was upregulated in non-responders of IO + TKI therapy (p = 0.027). High-ANXA2 group showed poor progression-free survival (PFS) in both the ZS-MRCC cohort (HR, 2.348; 95% CI 1.084-5.085; P = 0.025) and the Javelin-101 cohort (HR, 1.472; 95% CI 1.043-2.077; P = 0.027). Multivariate Cox regression determined ANXA2 as an independent prognostic factor (HR, 2.619; 95% CI 1.194-5.746; P = 0.016). High-ANXA2 was correlated with decreased proportion of granzyme B+ CD8+ T cells (Spearman's ρ = - 0.40, P = 0.01), and increased TIM-3+ (Spearman's ρ = 0.43, P < 0.001) and CTLA4+ (Spearman's ρ = 0.49, P < 0.001) tumor-infiltrating lymphocytes. A random forest (RF) score was further build by integrating ANXA2 and immune genes, which stratified patients who would benefit from IO + TKI therapy (low-RF score, IO + TKI vs TKI, HR = 0.453, 95% CI 0.328-0.626; high-RF score, IO + TKI vs TKI, HR = 0.877, 95% CI 0.661-1.165; interaction P = 0.003). CONCLUSIONS: Upregulated ANXA2 was associated with poor PFS and therapeutic resistance in RCC treated by IO + TKI therapy, and related with T cell exhaustion. The integrated RF score could stratify patients who would benefit from IO + TKI therapy.

Associations between R.E.N.A.L. nephrometry score and survival outcomes in renal tumours.

OBJECTIVE: The radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry score could be used to predict surgical outcomes and renal tumour aggressiveness. We aimed to analyse its associations with survival outcomes. METHODS: We included 1368 patients with sporadic, unilateral and non-metastatic renal tumours who received curative nephrectomy in Zhongshan Hospital from January 2009 to September 2019. Radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry scores were assigned by three urologists based on preoperative CT/MRI scans. Correlations between parameters or sum of radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry scores, overall survival and recurrence-free survival were analysed by Kaplan-Meier analyses and the multivariate Cox regression model. We further compared survival outcomes between patients who received partial nephrectomy and patients who received radical nephrectomy. RESULTS: We observed statistically significant associations between all components of radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry scores and oncologic outcomes, including R (radius) (overall survival, P < 0.001; recurrence-free survival , P < 0.001), E (exophytic/endophytic) (overall survival, P = 0.003; recurrence-free survival, P < 0.001), N (nearness) (overall survival, P = 0.063; recurrence-free survival, P < 0.001), A (anterior/posterior) (overall survival, P < 0.001; recurrence-free survival, P = 0.005), L (location) (overall survival, P = 0.008; recurrence-free survival, P < 0.001) and suffix 'h' (overall survival, P = 0.237; recurrence-free survival, P = 0.034). Kaplan-Meier curves of overall survival and recurrence-free survival rates were significantly different when stratified by radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry score complexity group (overall survival, P < 0.001; recurrence-free survival, P < 0.001). After adjusting for tumour stage and grade, radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry score as continuous variables was an adverse independent risk factor for survival outcomes [P = 0.027, hazard ratio (95% confidence interval) = 1.151 (1.016-1.303)] and recurrence-free survival [P < 0.001, hazard ratio (95% confidence interval) = 1.299 (1.125-1.501)]. For tumours with radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry scores of 4 and 5, partial nephrectomy showed a survival benefit than radical nephrectomy. CONCLUSION: Both components and complexity groups of the radius-exophytic/endophytic-nearness-anterior/posterior-location nephrometry score are associated with survival outcomes in renal tumour patients.

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.

Development and validation of a nomogram to evaluate the therapeutic effects of second-line axitinib in patients with metastatic renal cell carcinoma.

The unpredictable biological behavior and tumor heterogeneity of metastatic renal cell carcinoma (mRCC) cause significant differences in axitinib efficacy. The aim of this study is to establish a predictive model based on clinicopathological features to screen patients with mRCC who can benefit from axitinib treatment. A total of 44 patients with mRCC were enrolled and divided into the training set and validation set. In the training set, variables related with the therapeutic efficacy of second-line treatment with axitinib were screened through univariate Cox proportional hazards regression and least absolute shrinkage and selection operator analyses. A predictive model was subsequently established to assess the therapeutic efficacy of second-line treatment with axitinib. The predictive performance of the model was evaluated by analyzing the concordance index and time-dependent receiver operating characteristic, calibration, and decision curves. The accuracy of the model was similarly verified in the validation set. The International Metastatic RCC Database Consortium (IMDC) grade, albumin, calcium, and adverse reaction grade were identified as the best predictors of the efficacy of second-line axitinib treatment. Adverse reaction grade was an independent prognostic index that correlated with the therapeutic effects of second-line treatment with axitinib. Concordance index value of the model was 0.84. Area under curve values for the prediction of 3-, 6-, and 12-month progression-free survival after axitinib treatment were 0.975, 0.909, and 0.911, respectively. The calibration curve showed a good fit between the predicted and actual probabilities of progression-free survival at 3, 6, and 12 months. The results were verified in the validation set. Decision curve analysis revealed that the nomogram based on a combination of four clinical parameters (IMDC grade, albumin, calcium, and adverse reaction grade) had more net benefit than adverse reaction grade alone. Our predictive model can be useful for clinicians to identify patients with mRCC who can benefit from second-line treatment with axitinib.

Preoperative prognostic nomogram for prophylactic steroid treatment of patients with subclinical Cushing's syndrome.

BACKGROUND: Subclinical Cushing's syndrome (SCS) is incidentally detected in a growing number of patients by advanced imaging technology. However, there is no consensus on the clinical management of SCS, especially in terms of whether prophylactic steroid treatment is necessary following adrenalectomy. In this study we developed a model based on preoperative indices for predicting postoperative adrenal insufficiency (AI) that can guide therapeutic decision-making. METHODS: A total of 27 patients with SCS who underwent adrenalectomy between August 2016 and August 2019 were enrolled and divided into AI and non-AI groups. Cox proportional hazards regression and least absolute shrinkage and selection operator analyses were performed to select relevant clinical parameters. The predictive performance of our model was evaluated by time-dependent receiver operating characteristic (ROC) curve and calibration curve analyses. RESULTS: Five clinical parameters (apolipoprotein A1, neutrophil-lymphocyte ratio, total cholesterol, platelet count, and homocysteine) were identified as the best predictors of replacement therapy (RT). The areas under the ROC curve for our prognostic model were 0.833, 0.945, and 0.967 for 3-, 4-, and 5-day non-(N)RT, respectively. The calibration curve of the 5 independent RT-related markers showed a good fit between nomogram-predicted probability of NRT and actual NRT, suggesting that our model has good predictive value. CONCLUSIONS: Our prognostic nomogram can help clinicians identify patients with AI who would benefit from RT so that timely treatment can be initiated. KEYWORDS: Subclinical Cushing's syndrome (SCS); Replacement therapy (RT); Adrenal insufficiency (AI); Nomogram; Receiver operating characteristic (ROC).

The Identification of Differentially Expressed Genes Showing Aberrant Methylation Patterns in Pheochromocytoma by Integrated Bioinformatics Analysis.

Malignant pheochromocytoma (PHEO) can only be fully diagnosed when metastatic foci develop. However, at this point in time, patients gain little benefit from traditional therapeutic methods. Methylation plays an important role in the pathogenesis of PHEO. The aim of this research was to use integrated bioinformatics analysis to identify differentially expressed genes (DEGs) showing aberrant methylation patterns in PHEO and therefore provide further understanding of the molecular mechanisms underlying this condition. Aberrantly methylated DEGs were first identified by using R software (version 3.6) to combine gene expression microarray data (GSE19422) with gene methylation microarray data (GSE43293). An online bioinformatics database (DAVID) was then used to identify all overlapping DEGs showing aberrant methylation; these were annotated and then functional enrichment was ascertained by gene ontology (GO) analysis. The online STRING tool was then used to analyze interactions between all overlapping DEGs showing aberrant methylation; these results were then visualized by Cytoscape (version 3.61). Next, using the cytoHubba plugin within Cytoscape, we identified the top 10 hub genes and found that these were predominantly enriched in pathways related to cancer. Reference to The Cancer Genome Atlas (TCGA) further confirmed our results and further identified an upregulated hypomethylated gene (SCN2A) and a downregulated hypermethylated gene (KCNQ1). Logistic regression analysis and receiver operating characteristic (ROC) curve analysis indicated that KCNQ1 and SCN2A represent promising differential diagnostic biomarkers between benign and malignant PHEO. Finally, clinical data showed that there were significant differences in the concentrations of potassium and sodium when compared between pre-surgery and post-surgery day 1. These suggest that KCNQ1 and SCN2A, genes that encode potassium and sodium channels, respectively, may serve as putative diagnostic targets for the diagnosis and prognosis of PHEO and therefore facilitate the clinical management of PHEO.

Association of polymorphisms in AGTR1 and AGTR2 genes with primary aldosteronism in the Chinese Han population.

HYPOTHESIS: Polymorphisms in angiotensin II type-1/2 receptor genes (AGTR1/AGTR2) may be involved in the pathogenesis of primary aldosteronism. The present study aims to reveal some loci susceptible to the disease on the genes in a group of Chinese Han nationality. MATERIALS AND METHODS: A case-control study was conducted in 202 patients and 188 controls. Ten tagging SNPs on AGTR1/AGTR2 were genotyped for all subjects via the method of multiplex PCR-ligase detection reaction. Statistical analysis was performed with chi-square test and logistic regression analysis. RESULTS: rs3772616 on the AGTR1 gene was a factor for susceptibility to primary aldosteronism (p<0.001), and the TT genotype significantly decreased the risk of primary aldosteronism compared with the CC homozygote (p=0.008, adjusted OR=0.13; 95%CI: 0.03-0.59). The rs3772616 polymorphism was associated with primary aldosteronism under the additive and dominant models. The female carriers of the G allele in rs5193 showed a significant difference compared with the T allele. CONCLUSIONS: The AGTR1 rs3772616 polymorphism can be considered as a hereditary marker for primary aldosteronism, and in the Chinese Han population the rs5193 G allele seems to predispose to it only in women.