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Summary Background Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p Interpretation Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

We previously demonstrated that amplified in breast cancer 1 (AIB1) and eukaryotic initiation factor 2 (EIF5A2) overexpression was an independent predictor of poor clinical outcomes for patients with bladder cancer (BCa). In this study, we evaluated the usefulness of AIB1 and EIF5A2 alone and in combination with nuclear matrix protein 22 (NMP22) as noninvasive diagnostic tests for BCa. Using urine samples from 135 patients (training set, controls [n = 50] and BCa [n = 85]), we detected the AIB1, EIF5A2, and NMP22 concentrations using enzyme-linked immunosorbent assay. We applied multivariate logistic regression analysis to build a model based on the three biomarkers for BCa diagnosis. The diagnostic accuracy of the three biomarkers and the model were assessed and compared by the area under the curve (AUC) of the receiver operating characteristic. We validated the diagnostic accuracy of these biomarkers and the model in an independent validation cohort of 210 patients. In the training set, urinary concentrations of AIB1, EIF5A2, and NMP22 were significantly elevated in BCa. The AUCs of AIB1, EIF5A2, NMP22, and the model were 0.846, 0.761, 0.794, and 0.919, respectively. The model had the highest diagnostic accuracy when compared with AIB1, EIF5A2, or NMP22 (p < 0.05 for all). The model had 92% sensitivity and 92% specificity. We obtained similar results in the independent validation cohort. AIB1 and EIF5A2 show promise for the noninvasive detection of BCa. The model based on AIB1, EIF5A2, and NMP22 outperformed each of the three individual biomarkers for detecting BCa.

Current staging methods do not accurately predict the risk of disease recurrence and benefit of adjuvant chemotherapy for patients who have had surgery for stage II colon cancer. We postulated that expression patterns of multiple microRNAs (miRNAs) could, if combined into a single model, improve postoperative risk stratification and prediction of chemotherapy benefit for these patients.Using miRNA microarrays, we analysed 40 paired stage II colon cancer tumours and adjacent normal mucosa tissues, and identified 35 miRNAs that were differentially expressed between tumours and normal tissue. Using paraffin-embedded specimens from a further 138 patients with stage II colon cancer, we confirmed differential expression of these miRNAs using qRT-PCR. We then built a six-miRNA-based classifier using the LASSO Cox regression model, based on the association between the expression of every miRNA and the duration of individual patients' disease-free survival. We validated the prognostic and predictive accuracy of this classifier in both the internal testing group of 138 patients, and an external independent group of 460 patients.Using the LASSO model, we built a classifier based on the six miRNAs: miR-21-5p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. Using this tool, we were able to classify patients between those at high risk of disease progression (high-risk group), and those at low risk of disease progression (low-risk group). Disease-free survival was significantly different between these groups in every set of patients. In the initial training group of patients, 5-year disease-free survival was 89% (95% CI 77·3-94·4) for the low-risk group, and 60% (46·3-71·0) for the high-risk group (hazard ratio [HR] 4·24, 95% CI 2·13-8·47; p0·0001). In the internal testing set of patients, 5-year disease-free survival was 85% (95% CI 74·3-91·8) for the low-risk group, and 57% (42·8-68·5) for the high-risk group (HR 3·63, 1·86-7·01; p0·0001), and in the independent validation set of patients, was 85% (79·6-89·0) for the low-risk group and 54% (46·4-61·1) for the high-risk group (HR 3·70, 2·56-5·35; p0·0001). The six-miRNA-based classifier was an independent prognostic factor for, and had better prognostic value than, clinicopathological risk factors and mismatch repair status. In an ad-hoc analysis, the patients in the high-risk group were found to have a favourable response to adjuvant chemotherapy (HR 1·69, 1·17-2·45; p=0·0054). We developed two nomograms for clinical use that integrated the six-miRNA-based classifier and four clinicopathological risk factors to predict which patients might benefit from adjuvant chemotherapy after surgery for stage II colon cancer.Our six-miRNA-based classifier is a reliable prognostic and predictive tool for disease recurrence in patients with stage II colon cancer, and might be able to predict which patients benefit from adjuvant chemotherapy. It might facilitate patient counselling and individualise management of patients with this disease.Natural Science Foundation of China.

// Zong-Ren Wang 1, 2, * , Jin-Huan Wei 1, * , Jian-Cheng Zhou 3, * , Ahmed Haddad 2, * , Liang-Yun Zhao 4 , Payal Kapur 5 , Kai-Jie Wu 6 , Bin Wang 2 , Yan-Hong Yu 4 , Bing Liao 7 , Da-Lin He 6 , Wei Chen 1 , Vitaly Margulis 2 , Jer-Tsong Hsieh 2 , Jun-Hang Luo 1 1 Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangdong, China 2 Department of Urology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA 3 Department of Urology, Shaanxi Provincial People’s Hospital, Shaanxi, China 4 Department of Urology, Affiliated Hospital of Kunming University of Science and Technology, Yunnan, China 5 Department of Pathology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX, USA 6 Department of Urology, First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China 7 Department of Pathology, First Affiliated Hospital, Sun Yat-sen University, Guangdong, China * These authors have contributed equally to this work Correspondence to: Jer-Tsong Hsieh, email: jt.hsieh@utsouthwestern.edu Jun-Hang Luo, email: luojunh@mail.sysu.edu.cn Keywords: DAB2IP, DNA methylation, intratumour heterogeneity, prognosis, renal cell carcinoma Received: October 22, 2015 Accepted: April 02, 2016 Published: April 25, 2016 ABSTRACT We have recently reported tumor suppressive role of DAB2IP in RCC development. In this study, We identified one CpG methylation biomarker (DAB2IP CpG1) located UTSS of DAB2IP that was associated with poor overall survival in a cohort of 318 ccRCC patients from the Cancer Genome Atlas (TCGA). We further validated the prognostic accuracy of DAB2IP CpG methylation by pyrosequencing quantitative methylation assay in 224 ccRCC patients from multiple Chinese centers (MCHC set), and 239 patients from University of Texas Southwestern Medical Center at Dallas (UTSW set) by using FFPE samples. DAB2IP CpG1 can predict the overall survival of patients in TCGA, MCHC, and UTSW sets independent of patient age, Fuhrman grade and TNM stage (all p

Clear cell renal cell carcinomas (ccRCCs) display divergent clinical behaviours. Molecular markers might improve risk stratification of ccRCC. Here we use, based on genome-wide CpG methylation profiling, a LASSO model to develop a five-CpG-based assay for ccRCC prognosis that can be used with formalin-fixed paraffin-embedded specimens. The five-CpG-based classifier was validated in three independent sets from China, United States and the Cancer Genome Atlas data set. The classifier predicts the overall survival of ccRCC patients (hazard ratio=2.96−4.82; P=3.9 × 10−6−2.2 × 10−9), independent of standard clinical prognostic factors. The five-CpG-based classifier successfully categorizes patients into high-risk and low-risk groups, with significant differences of clinical outcome in respective clinical stages and individual ‘stage, size, grade and necrosis' scores. Moreover, methylation at the five CpGs correlates with expression of five genes: PITX1, FOXE3, TWF2, EHBP1L1 and RIN1. Our five-CpG-based classifier is a practical and reliable prognostic tool for ccRCC that can add prognostic value to the staging system., Using molecular markers is a useful way to predict the prognosis of cancer patients. Here, Wei et al. describe a five gene methylation signature that can predict the prognosis of renal clear cell cancer and validate its use in multiple patient cohorts.

It is controversial whether chromophobe renal cell carcinoma (chRCC) or clear cell renal cell carcinoma (ccRCC) is associated with better survival. We conducted a clinical-based cohort study and meta-analysis to evaluate the prognostic role of histology between chRCC and ccRCC. A cohort of 1540 patients (166 with chRCC and 1374 with ccRCC) were selected from Sun Yat-sen University and The Cancer Genome Atlas databases. The clinicopathological parameters and overall survival (OS) were compared between patients with chRCC and those with ccRCC. For the meta-analysis, we searched the PubMed, Cochrane Library, and Ovid databases for studies comparing OS or cancer-specific survival (CSS) between chRCC and ccRCC. The cohort study revealed that patients with chRCC were younger (median 52 vs. 55 years, P

This study was aimed to investigate the potential role of microRNA-29c (miR-29c) in regulating the sensitivities of nasopharyngeal carcinoma (NPC) to ionizing radiation (IR) and cisplatin. Low expression of miR-29c was positively associated with therapeutic resistance in 159 NPC cases. Our further in vitro and in vivo studies illustrated ectopic restoration of miR-29c substantially enhanced the sensitivity of NPC cells to IR and cisplatin treatment by promoting apoptosis. Furthermore, we detected miR-29c repressed expression of anti-apoptotic factors, Mcl-1 and Bcl-2 in NPC tissues and cell lines. These data indicate miR-29c might serve as a potential therapeutic sensitizer in NPC treatment.