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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 | 2017 Aug

REPOSITORIES: biostudies-literature

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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.

Flippot Ronan R Mouawad Roger R Spano Jean-Philippe JP Rouprêt Morgan M Compérat Eva E Bitker Marc-Olivier MO Parra Jérôme J Vaessen Christophe C Allanic Frederick F Manach Quentin Q Tannir Nizar M NM Khayat David D Su Xiaoping X Malouf Gabriel G GG

Scientific reports 20170817 1

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 o ...[more]

PMID: 28819235

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Project description:BackgroundGene 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.ObjectiveTo 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.Design, setting, and participantsA 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.Outcome measurements and statistical analysisKaplan-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.Results and limitationsThe 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.ConclusionsThe ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.Patient summaryWe 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.

Dataset Information

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

Publications

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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