Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five XXXX Cancers.

@entity5 @entity1
INTRODUCTION: Advances in high-throughput technologies have generated diverse informative molecular markers for @entity5 outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in @entity1 @entity5 ; however, the prognostic utility of such diverse molecular data remains to be explored. MATERIALS AND METHODS: We proposed a computational pipeline (IDFO) to predict @entity1 survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five @entity5 types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in @entity1 survival stratification, and compared their relative importance in each type of @entity5 , respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome. RESULTS: Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71-0.97) in five @entity5 types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four @entity5 types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two @entity5 . Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables. CONCLUSION: Our study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.
