Accuracy of the revised 2010 TNM classification in predicting the prognosis of patients treated for renal cell cancer in the north east of England

N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.

Construction and Verification of a Hypoxia-Related 4-lncRNA Model for Prediction of Breast Cancer

Introduction

Breast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer.

Methods

Here, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan–Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model.

Results

Our results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes.

Discussion

In summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Identification and Validation of a Novel Prognosis Prediction Model in Adrenocortical Carcinoma by Integrative Bioinformatics Analysis, Statistics, and Machine Learning

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = -0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

Identification of a Novel Signature and Construction of a Nomogram Predicting Overall Survival in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), which accounts for majority of RCC-related deaths. It is clearly essential to further identify more novel prognostic signatures and therapeutic targets.

Material and Methods

We identified differentially expressed genes (DEGs) between ccRCC and adjacent normal tissues in GEO database using a Robust Rank Aggregation (RRA) method. An mRNA signature (mRNASig) based on DEGs was developed using Cox and LASSO analysis in the TCGA database and validated in the ICGC database. Afterward, the influence of mRNASig mRNAs on the immune microenvironment in ccRCC was explored using comprehensive bioinformatics analysis.

Results

A total of 957 robust DEGs were identified using the RRA method. mRNASig comprised CEP55, IFI44, NCF4, and TCIRG1 and was developed and validated to identify high-risk patients who had poorer prognosis than low-risk patients. A nomogram was also constructed based on mRNASig, AJCC stage, and tumor grade. The mRNASig were closely related to a variety of tumor-infiltrating lymphocytes, especially including CD8+ T cells, activated CD4+ memory T cells, regulatory T cells, activated NK cells, and resting NK cells. The mRNASig were also correlated positively with the expression of CTLA4, LAG3, PDCD1, TIGIT, and HAVCR2.

Conclusion

We developed and validated mRNASig to assist clinicians in making personalized treatment decisions. Furthermore, CEP55, IFI44, NCF4, and TCIRG1 may be novel potential targets for future treatment of ccRCC.

Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.

METHODS

Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.

RESULTS

PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer.

CONCLUSIONS

In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients' prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509-3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601-4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113-8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243-4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.

Prognostic heterogeneity in T3aN0M0 renal cell carcinoma according to the site of invasion

PURPOSE

We investigated the influence of the site of invasion on recurrence and survival in patients with pT3aN0M0 renal cell carcinoma (RCC).

MATERIALS AND METHODS

We reviewed the data of 266 patients with pT3aN0M0 RCC who underwent nephrectomy and divided them into the following 5 groups according to the site of invasion: perinephric invasion (PNI), sinus fat invasion (SFI), PNI and SFI without renal vein invasion (RVI) (i.e., PNI+SFI), RVI, and RVI with PNI and/or SFI (RVI+PNI±SFI). Subgroup analysis was performed to verify the differences in prognosis according to the extent of renal vein invasion using Cox regression models.

RESULTS

A total of 111 patients (41.7%) experienced recurrence and 59 patients (22.2%) died of disease during follow-up (median = 58.1mo; interquartile range: 37.2-86.5). Patients with RVI showed significantly poorer outcomes than those with fat invasion in terms of 5-year recurrence-free survival (34.3% vs. 62.2%, P<0.001) and cancer-specific survival (62.8% vs. 84.1%; P<0.001). In multivariate analysis, RVI was an independent prognostic factor for recurrence and survival. In 94 patients with RVI, the 5-year recurrence-free survival rates were 50.0%, 33.9%, and 8.9% for the thrombus-only, the vascular wall invasion with negative surgical margin, and the vascular wall invasion with positive surgical margin groups, respectively (P<0.001), and the cancer-specific survival rates were 82.3%, 56.6%, and 20.0%, respectively (P<0.001). Wall invasion was the only independent prognostic factor for cancer-specific survival in these patients.

CONCLUSIONS

Patients with pT3aN0M0 RCC with RVI have a significantly poorer prognosis than those with fat invasion. The prognosis differs according to the extent of RVI. Wall invasion should be considered a negative prognostic indicator in patients with T3a RCC.

Prognostic and discriminative power of the 7th TNM classification for patients with surgically treated papillary renal cell carcinoma: results of a multi-institutional validation study (CORONA subtype project)

OBJECTIVE

Studies on the prognostic reliability of the Union for International Cancer Control tumor, node, metastasis (TNM) staging system for renal cell carcinoma (RCC) predominantly focus on clear-cell RCC. Therefore, the aim of this study was to investigate whether the oncological prognosis of surgically treated papillary RCC (papRCC) patients is reliably given by the current TNM system, by analyzing the largest database reported to date.

MATERIALS AND METHODS

Data on 2325 papRCC patients who underwent surgical treatment in 1984- 2015 were collated from 17 international centers (median follow-up 47 months). Tumor stage was adapted to the 7th edition of the TNM system. Multivariable, bootstrap-corrected Cox regression models were applied to assess the independent impact of the TNM system on cancer-specific mortality (CSM) and all-cause mortality (ACM).

RESULTS

The median age at diagnosis was 63 years (interquartile range 54-70 years) and 77% of patients were male. Nephron-sparing surgery was performed in 42%, and 82% were with symptom free at diagnosis. In 6.7% (n = 156), organ metastasis (stage M1) was present at the time of surgery. On multivariable analysis, the TNM system and Fuhrman grade had an independent impact on both CSM and ACM, while patient age affected ACM only. The discriminative ability of the pT classification was significant for both endpoints: 5 year CSM rates were 5%, 17%, 36% and 56% for stages pT1, pT2, pT3 and pT4, respectively (each p < 0.001). The pT classification contributed significantly to the predictive accuracy of the CSM and ACM models by 6.3% and 2.5%, respectively (each p < 0.001).

CONCLUSIONS

The 2010 TNM staging system can be reliably applied to papRCC patients and allows certain prognostic discrimination.

Is Robotic Partial Nephrectomy Safe for T3a Renal Cell Carcinoma? Experience of a High-Volume Center

PURPOSE

To compare the oncological and functional outcomes of robotic partial nephrectomy (RPN) with radical nephrectomy (RN) in renal-cell carcinoma (RCC) cases with pT3a staging.

PATIENTS AND METHODS

A retrospective analysis of our IRB-approved nephrectomy database from 2005 to 2015 was performed. RPN and RN cases with confirmed RCC and pT3a staging were matched. Preoperative variables, functional, and oncological outcomes were compared between the groups, as well as Kaplan-Meier estimated overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). A multivariable Cox proportional hazards regression model for overall mortality rate was generated to evaluate hazard ratios (HRs) of potential risk factors.

RESULTS

Seventy patients with pT3a tumors composed each group. Preoperative variables were comparable between groups. The median follow-up time for the cohort was 20 (9-38) months and the renal function preservation was higher in the RPN group (86% vs 70%; p < 0.001). The estimated 3 years of OS (90% vs 84%; p = 0.42), CSS (94% vs 95%; p = 0.78), and RFS (95% vs. 100%; p = 0.06) were similar between RPN and RN groups, respectively. On multivariable Cox regression model, the presence of ≥2 aggressive tumor features was the only factor associated with increased risk of overall mortality rate (HR 4.01 95% confidence interval [1.13, 14.27)]; p = 0.03).

CONCLUSION

Patients with localized pT3a RCC treated with RPN had similar short-term oncological and better renal functional outcomes compared with similar cases treated by RN. In the minimally invasive robotic surgery era, renal masses suspicious for pathological T3a disease should not be a deterring factor for performing nephron-sparing surgery when technically feasible by skilled surgeons.

Low level of PDZ domain containing 1 (PDZK1) predicts poor clinical outcome in patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most lethal neoplasm of the urologic system. Clinical therapeutic effect varies greatly between individual ccRCC patients, so there is an urgent need to develop prognostic molecular biomarkers to help clinicians identify patients in need of early aggressive management. In this study, samples from primary ccRCC tumor and their corresponding nontumor adjacent tissues (n=18) were analyzed by quantitative proteomic assay. Proteins downregulated in tumors were studied by GO and KEGG pathways enrichment analyses. Six proteins were found both downregulated and annotated with cell proliferation in ccRCC patients. Of these proteins, PDZK1 and FABP1 were also involved in the lipid metabolism pathway. The downregulation of PDZK1 was further validated in TCGA_KIRC dataset (n=532) and independent set (n=202). PDZK1 could discriminate recurrence, metastasis and prognosis between ccRCC patients. Low level of PDZK1 in both mRNA and protein was associated with reduced overall survival (OS) and disease-free survival (DFS) in two independent sets. In univariate and multivariate analyses, PDZK1 was defined as an independent prognostic factor for both OS and DFS. These findings indicated that low level of PDZK1 could predict poor clinical outcome in patients with ccRCC.

HCRP-1 regulates cell migration and invasion via EGFR-ERK mediated up-regulation of MMP-2 with prognostic significance in human renal cell carcinoma

Previous studies indicated a role of hepatocellular carcinoma-related protein-1(HCRP-1) in human cancers, however, its expression pattern in renal cell carcinoma (RCC) and the molecular mechanism of HCRP-1 on cancer progression have not been characterized. In the present study, HCRP-1 expression was examined in a RCC tissue microarray. The negative expression of HCRP-1 was significantly correlated with tumor grade (P = 0.002), TNM stage (P = 0.001) and pT status (P = 0.003). Furthermore, we showed a strong correlation between negative HCRP-1 expression and worse overall and disease-specific survival (P = 0.0003 and P = 0.0012, respectively). Knockdown of HCRP-1 promoted cell migration and invasion in 786-O and OS-RC-2 cell lines. HCRP-1 depletion increased matrix metalloproteinase (MMP)-2 protein level, with increased extracellular signal-regulatedkinase (ERK) phosphorylation, which could be reversed by ERK siRNA or ERK inhibitor, PD98059. Further analysis showed that HCRP-1 knockdown induced epidermal growth factor receptor (EGFR) phosphorylation. Treatment with EGFR inhibitor or EGFR siRNA blocked HCRP-1-mediated up-regulation of EGFR, ERK phosphorylation and MMP-2 expression. In summary, our results showed that negative HCRP-1 expression is an independent prognostic factor for RCC patients and promotes migration and invasion by EGFR-ERK-mediated up-regulation of MMP-2. HCRP-1 may serve as a therapeutic target for RCC.

Pathological Stage T3a Significantly Increases Disease Recurrence across All Tumor Sizes in Renal Cell Carcinoma

PURPOSE

Tumor size and stage are important prognostic parameters in renal cell carcinoma. While pathological stage T1 and T2 are defined by size alone, the presence of certain intrinsic features can up stage a tumor to pathological stage T3a regardless of size. We investigate the effect of pathological tumor stage on the relationship between tumor size and risk of disease recurrence.

MATERIALS AND METHODS

Data were reviewed on patients who underwent nephrectomy at our institution between 2006 and 2013 to identify all those with pathological stage T1, T2 and T3a tumors. A proportional hazards Cox model was built with time to recurrence as outcome, and pathological stage and tumor size as covariates. An interaction term for stage and tumor size was included.

RESULTS

The final cohort included 1,809 patients. On multivariable analysis, when adjusted for tumor size, patients with pT3a tumors had a greater risk of tumor recurrence compared to those with pT1/T2 tumors (HR 3.70; 95% CI 2.31, 5.92; p <0.0001). The risk of disease recurrence increased more rapidly as tumor size increased only with the presence of perinephric fat invasion (p=0.006).

CONCLUSIONS

Using the AJCC 2010 staging criteria we validated pathological stage T3a as a poor prognostic factor in renal cell carcinoma regardless of tumor size. Our results also demonstrated an increased rate of risk of recurrence with perinephric fat invasion. Given this increased risk of recurrence, even in tumors less than 4 cm, closer surveillance is warranted in such cases and the role of perinephric involvement necessitates further investigation.

Prognostic impact of urokinase-type plasminogen activator system components in clear cell renal cell carcinoma patients without distant metastasis

Background

Members of the urokinase-type plasminogen activator (uPA) system including uPA, its receptor uPAR and the plasminogen activator inhibitor 1 (PAI-1) play an important role in tumour invasion and progression in a variety of tumour types. Since the majority of clear cell renal cell carcinoma (ccRCC) shows distant metastasis at time of diagnosis or later, the interplay of uPA, uPAR and PAI-1 might be of importance in this process determining the patients’ outcome.

Methods

Corresponding pairs of malignant and non-malignant renal tissue specimens were obtained from 112 ccRCC patients without distant metastasis who underwent tumour nephrectomy. Tissue extracts prepared from fresh-frozen tissue samples by detergent extraction were used for the determination of antigen levels of uPA, uPAR and PAI-1 by ELISA. Antigen levels were normalised to protein concentrations and expressed as ng per mg of total protein.

Results

Antigen levels of uPA, uPAR, and PAI-1 correlated with each other in the malignant tissue specimens (r_s=0.51-0.65; all P<0.001). Antigen levels of uPA system components were significantly higher in tissue extracts of non-organ confined tumours (pT3+4) compared to organ-confined tumours (pT1+2; all P<0.05). Significantly elevated levels of uPAR and PAI-1 were also observed in high grade ccRCC. When using median antigen levels as cut-off points, all three uPA system factors were significant predictors for disease-specific survival (DSS) in univariate Cox’s regression analyses. High levels of uPA and uPAR remained independent predictors for DSS with HR=2.86 (95% CI 1.07-7.67, P=0.037) and HR=4.70 (95% CI 1.51-14.6, P=0.008), respectively, in multivariate Cox’s regression analyses. A combination of high antigen levels of uPA and/or uPAR further improved the prediction of DSS in multivariate analysis (HR=14.5, 95% CI 1.88-111.1, P=0.010). Moreover, high uPA and/or uPAR levels defined a patient subgroup of high risk for tumour-related death in ccRCC patients with organ-confined disease (pT1+2) (HR=9.83, 95% CI 1.21-79.6, P=0.032).

Conclusions

High levels of uPA and uPAR in tumour tissue extracts are associated with a significantly shorter DSS of ccRCC patients without distant metastases.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-974) contains supplementary material, which is available to authorized users.

Proteomics identified nuclear N-myc downstream-regulated gene 1 as a prognostic tissue biomarker candidate in renal cell carcinoma

The aim of this study was to identify proteins with aberrant expression in clear cell renal cell carcinoma (ccRCC), and elucidate their clinical utilities. The protein expression profiles of primary ccRCC tumor tissues and neighboring non-tumor tissues were obtained from 9 patients by two-dimensional difference gel electrophoresis and mass spectrometry. Comparative analysis of 3771 protein spots led to the identification of 73 proteins that were expressed at aberrant levels in tumor tissues compared with non-tumor tissues. Among these 73 proteins, we further focused on N-myc downstream-regulated gene 1 protein (NDRG1). NDRG1 expression is regulated by members of myc family as well as by p53, HIF1A, and SGK1. The biological and clinical significance of NDRG1 is controversial for various malignancies and no detailed studies on NDRG1 have been reported in ccRCC until our study. For the 82 newly enrolled ccRCC patients, immunohistochemical analysis revealed a significant association between nuclear NDRG1 and favorable prognosis (p<0.05). Multivariate analysis demonstrated the role of NDRG1 as an independent factor of progression-free survival (p=0.01). Subsequent in vitro gene suppression assay demonstrated that NDRG1 silencing significantly enhanced cell proliferation and invasion of RCC cells. The cytotoxic effects of NDRG1 up-regulation induced by an iron chelator were also confirmed. These findings suggest that nuclear NDRG1 has tumor suppressive effects, and the NDRG1 expression may have clinical values in ccRCC. Nuclear NDRG1 may provide additional insights on molecular backgrounds of ccRCC progression, and contribute to the development of novel therapeutic strategy.

Application of the revised Tumour Node Metastasis (TNM) staging system of clear cell renal cell carcinoma in eastern China: advantages and limitations

This study was designed to evaluate whether the revised 2010 Tumour Node Metastasis (TNM) staging system could lead to a more accurate prediction of the prognosis of renal cell carcinoma (RCC) patients. A total of 1216 patients who had undergone radical nephrectomy or partial nephrectomy for RCC from 2003 to 2011 were enrolled. All of the patients had pathologically confirmed clear cell RCC (ccRCC). All cases were staged by both the 2002 and 2010 TNM staging systems after pathological review, and survival data were collected. Univariate and multivariate Cox regression models were used to evaluate cancer-specific survival (CSS) and progression-free survival (PFS) after surgery. Continuous variables, such as age and tumour diameter, were calculated as mean values and standard deviations (s.d.) or as median values. Survival was calculated by the Kaplan-Meier method, and the log-rank test assessed differences between groups. Statistically significant differences in CSS and PFS were noted among patients in T3 subgroups using the new 2010 staging system. Therefore, the revised 2010 TNM staging system can lead to a more accurate prediction of the prognosis of ccRCC patients. However, when using the revised 2010 staging system, we found that more than 92% of patients (288/313) with T3 tumours were staged in the T3a subgroup, and their survival data were not significantly different from those of patients with T2b tumours. In addition, T2 subclassification failed to independently predict survival in RCC patients.

Predictive ability of the 2002 and 2010 versions of the Tumour-Node-Metastasis classification system regarding metastasis-free, cancer-specific and overall survival in a European renal cell carcinoma single-centre series

OBJECTIVE

To compare the predictive ability of the Tumour-Node-Metastasis (TNM) classification systems for renal cell carcinoma (RCC) using three different endpoints: metastasis-free (MFS); overall (OS); and cancer-specific survival (CSS).

PATIENTS AND METHODS

Data from 2739 consecutive patients with RCC, who underwent surgery at a single academic centre, were evaluated using multivariate Cox proportional models, Harrell's concordance (c)-index and by applying decision curve analysis (DCA) with regard to MFS, OS and CSS.

RESULTS

According to TNM 2010, significant differences for MFS were observed for pT1a vs pT1b, pT1b vs pT2a, pT3a vs pT3b and pT3b vs pT3c stages, respectively (all P < 0.05). With regard to OS, significant differences could be observed in pT1a vs pT1b and pT3a vs pT3b stages, respectively (all P < 0.05). The c-index for CSS, OS and MFS was slightly higher for the 2002 than for the 2010 version of the TNM classification system. Non-inferiority of the 2002 TNM system is supported by the results of the DCA.

CONCLUSION

According to our data, the predictive ability of the 2010 version of the TNM classification system regarding three different clinical endpoints is not superior to the 2002 version of this staging system.