Abstract
BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.ResultsA four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.ConclusionsA novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.
Highlights
Clear cell renal cell carcinoma is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate
We identified a four miRNA signature associated with the prognosis of Clear cell renal cell carcinoma (ccRCC) from high-dimensional miRNA expression profiles using multivariate Cox regression with Elastic-net, Least absolute shrinkage and selection operator (Lasso) and Adaptive lasso penalties followed by best subset regression analysis
We proposed a computational method including penalized Cox models and machine learning approach to identify miRNA signature for risk and tumor stage prediction using miRNA profiles, which consists of several steps as described in detail in the “Methods” section
Summary
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA bio‐ markers for predicting the tumor stage of ccRCC are still limitedly identified. We proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype and it represents 70–80%, of all renal malignant tumors [3]. Despite many advances in effective therapeutic and diagnostic strategies in ccRCC, and the overall survival rate is still poor, for advanced-stage ccRCC patients[4]. Late tumor staging is the main risk factor of ccRCC patients [6] and detection of ccRCC patients at early-stage is crucial for better diagnosis and treatment options
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