5 research outputs found
Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers
<div><p>Introduction</p><p>Advances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored.</p><p>Materials and Methods</p><p>We proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome.</p><p>Results</p><p>Using the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71–0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer 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 cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.</p><p>Conclusion</p><p>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.</p></div
Correction: Comparison of the Prognostic Utility of Diverse Molecular Data among lncRNA, DNA Methylation, microRNA and mRNA across Five Human Cancers
<p>Correction: Comparison of the Prognostic Utility of Diverse Molecular Data among lncRNA, DNA Methylation, microRNA and mRNA across Five Human Cancers</p
Flowchart of the IDFO approach.
<p>This flowchart contains three basic steps: (i) PRP ranking of molecular features, (ii) model construction and (iii) feature optimization and validation.</p
Survival analysis on IDFO predictors of four types of molecular data in five cancers.
<p>The Kaplan-Meier overall survival curves of two outcome groups classified by MCPHR models using IDFO-identified predictors of each molecular data of each cancer. (a) the BRCA lncRNA cohort; (b) the BRCA DNA methylation cohort; (c) the BRCA microRNA cohort; (d) the BRCA mRNA cohort; (e) the COAD lncRNA cohort; (f) the COAD DNA methylation cohort; (g) the COAD microRNA cohort; (h) the COAD mRNA cohort; (i) the LUSC lncRNA cohort; (j) the LUSC DNA methylation cohort; (k) the LUSC microRNA cohort; (l) the LUSC mRNA cohort;(m) the OV lncRNA cohort; (n) the OV DNA methylation cohort; (o) the OV microRNA cohort; (p) the OV mRNA cohort;(q) the UCEC lncRNA cohort; (r) the UCEC DNA methylation cohort; (s) the UCEC microRNA cohort; (t) the UCEC mRNA cohort. The difference in outcome of two outcome groups was tested using Kaplan-Meier survival analysis. Likelihood ratio = the likelihood ratio test.</p
Comparison of the predictive performance of integrated multi-type molecular data and single molecular data in cancer outcome stratification.
<p>(A) BRCA (N<sub><i>overlaps</i></sub> = 178), (B) COAD (N<sub><i>overlaps</i></sub> = 161), (C) LUSC (N<sub><i>overlaps</i></sub> = 97), (D) OV (N<sub><i>overlaps</i></sub> = 145), (E) UCEC (N<sub><i>overlaps</i></sub> = 84). For the respective models in each type of cancer, we performed 10,000 times of random splitting with 2/3 training and 1/3 testing using IDFO pipeline. The dotted red box indicated the significantly improved performance of two integrated models in (D) OV and (E) UCEC compared with individual data type models (two-sided Wilcoxon signed rank test, <i>P</i> < 0.01); the dotted blue box indicated the three individual data type models of mr, lnr and meth. The integrated group are composed of both double-combination and triple-combination molecular signature models. Individual group contained the three individual molecular data type models. The gray line across seven boxes shows the predictive patterns of integrated groups and individual groups. N<sub><i>overlaps</i></sub> is the number of overlap sample occurred in all three molecular data profiles (mRNA, lncRNA and DNA methylation), lnr = lncRNA, mr = mRNA, meth = DNA methylation, mr+lnr = mRNA + lncRNA, mr+meth = mRNA + DNA methylation, lnr+meth = lncRNA + DNA methylation, mr+lnr+ meth = mRNA + lncRNA +DNA methylation.</p