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Expression of long non-coding RNA MFI2-AS1 is a strong predictor of recurrence in sporadic localized clear-cell renal cell carcinoma.

ABSTRACT: Prediction of recurrence is a challenge for the development of adjuvant treatments in clear-cell renal cell carcinoma (ccRCC). In these tumors, expression of long non-coding RNAs (lncRNAs) are deregulated and closely associated with prognosis. Thus, we aimed to predict ccRCC recurrence risk using lncRNA expression. We identified prognostic lncRNAs in a training set of 351 localized ccRCCs from The Cancer Genome Atlas and validated lncRNA-based recurrence classification in an independent cohort of 167 localized ccRCCs. We identified lncRNA MFI2-AS1 as best candidate in the training set. In the validation cohort, MFI2-AS1 expression was independently associated with shorter disease-free survival (Hazard Ratio (HR) for relapse 3.5, p = 0.0001). Combined with Leibovich classification, MFI2-AS1 status improved prediction of recurrence (C-index 0.70) compared to MFI2-AS1 alone (0.67) and Leibovich classification alone (0.66). In patients with aggressive tumors (Leibovich ≥5), MFI2-AS1 expression was associated with dramatically increased risk of relapse (HR 12.16, p < 0.0001) compared to patients with undetectable MFI2-AS1 who had favorable outcomes. Compared to normal samples, MFI2-AS1 was upregulated in tumor tissue, and higher expression was associated with metastatic dissemination. Overall, MFI2-AS1 status improves patient stratification in localized ccRCC, which supports further integration of lncRNAs in molecular cancer classifications.

SUBMITTER: Flippot R

PROVIDER: S-EPMC5561098 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.The subtypes were significantly associated with RFS (p<0.01), CSS (p<0.01), and OS (p<0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

Project description:OBJECTIVES:To examine whether serum adiponectin or leptin level has the ability to differentiate clear cell renal cell carcinoma (ccRCC) from other subtypes of renal cell carcinoma (RCC) in a Chinese population. PATIENTS AND METHODS:We recruited 198 consecutive patients who were treated with radical or partial nephrectomy in our department from September 2011 to June 2013. Their histological types were all malignant, including clear cell, papillary, chromophobe and unclassified RCC. We also enrolled 86 people with no cancer or cancer-related diseases as normal controls. We measured patients' preoperative blood samples for plasma adiponectin and leptin concentrations using an enzyme-linked immunosorbent assay method. Statistical methods were used to analyze ccRCC and other subtypes as they relate to serum adiponectin/leptin level and other factors such as body mass index or visceral fat area. RESULTS:In our database, normal controls had significantly higher circulating adiponectin (p < 0.001) and leptin levels (p < 0.001) than patients with RCC. Among the 198 RCC patients, 156 patients had ccRCC while 42 patients had other histological types. Serum adiponectin levels were lower in ccRCC patients than in non-clear-cell RCC patients (p = 0.004). However, the plasma leptin level was not differently distributed between ccRCC and non-ccRCC patients (p = 0.940). In multivariate analysis, we found that serum adiponectin level may be an independent predictor for discriminating ccRCC patients from others (p = 0.004). Furthermore, in the ccRCC subgroup, we observed that men with ccRCC had lower leptin (p < 0.001) and adiponectin (p = 0.002) levels, and diabetic patients had lower plasma adiponectin levels (p = 0.001). CONCLUSIONS:Lower plasma adiponectin concentration was related to an increased incidence of ccRCC and may act as an independent predictor for ccRCC. Our study may help define the process from obesity to adipose tissue, to cytokines and finally to ccRCC.

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Expression of long non-coding RNA MFI2-AS1 is a strong predictor of recurrence in sporadic localized clear-cell renal cell carcinoma.

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