Dataset Information

A comprehensive analysis of prognosis prediction models based on pathway?level, gene?level and clinical information for glioblastoma.

ABSTRACT: The present study aimed to develop a pathway?based prognosis prediction model for glioblastoma (GBM). Univariate and multivariate Cox regression analysis were used to identify prognosis?related genes and clinical factors using mRNA?seq data of GBM samples from The Cancer Genome Atlas (TCGA) database. The expression matrix of prognosis?related genes was transformed into pathway deregulation score (PDS) based on the Gene Set Enrichment Analysis (GSEA) repository using Pathifier software. With PDS scores as input, L1?penalized estimation?based Cox?proportional hazards (PH) model was used to identify prognostic pathways. Consequently, a prognosis prediction model based on these prognostic pathways was constructed for classifying patients in the TCGA set or each of the three validation sets into two risk groups. The survival difference between these risk groups was then analyzed using Kaplan?Meier survival analysis and log?rank test. In addition, a gene?based prognostic model was constructed using the Cox?PH model. The model of prognostic pathway combined with clinical factors was also evaluated. In total, 148 genes were discovered to be associated with prognosis. The Cox?PH model identified 13 prognostic pathways. Subsequently, a prognostic model based on the 13 pathways was constructed, and was demonstrated to successfully differentiate overall survival in the TCGA set and in three independent sets. However, the gene?based prognosis model was validated in only two of the three independent sets. Furthermore, the pathway+clinic factor?based model exhibited better predictive results compared with the pathway?based model. In conclusion, the present study suggests a promising prognosis prediction model of 13 pathways for GBM, which may be superior to the gene?level information?based prognostic model.

SUBMITTER: Liang R

PROVIDER: S-EPMC6108889 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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A comprehensive analysis of prognosis prediction models based on pathway‑level, gene‑level and clinical information for glioblastoma.

Liang Ruqing R Wang Meng M Zheng Guizhi G Zhu Hua H Zhi Yaqin Y Sun Zongwen Z

International journal of molecular medicine 20180711 4

The present study aimed to develop a pathway‑based prognosis prediction model for glioblastoma (GBM). Univariate and multivariate Cox regression analysis were used to identify prognosis‑related genes and clinical factors using mRNA‑seq data of GBM samples from The Cancer Genome Atlas (TCGA) database. The expression matrix of prognosis‑related genes was transformed into pathway deregulation score (PDS) based on the Gene Set Enrichment Analysis (GSEA) repository using Pathifier software. With PDS ...[more]

PMID: 30015853

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Project description:BACKGROUND:Cancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway-level and gene-level prognostic models has not been performed. To address this gap, we characterized the performance of penalized Cox proportional hazard models built using either pathway- or gene-level predictors for the cancers profiled in The Cancer Genome Atlas (TCGA) and pathways from the Molecular Signatures Database (MSigDB). RESULTS:When analyzing TCGA data, we found that pathway-level models are more parsimonious, more robust, more computationally efficient and easier to interpret than gene-level models with similar predictive performance. For example, both pathway-level and gene-level models have an average Cox concordance index of ~ 0.85 for the TCGA glioma cohort, however, the gene-level model has twice as many predictors on average, the predictor composition is less stable across cross-validation folds and estimation takes 40 times as long as compared to the pathway-level model. When the complex correlation structure of the data is broken by permutation, the pathway-level model has greater predictive performance while still retaining superior interpretative power, robustness, parsimony and computational efficiency relative to the gene-level models. For example, the average concordance index of the pathway-level model increases to 0.88 while the gene-level model falls to 0.56 for the TCGA glioma cohort using survival times simulated from uncorrelated gene expression data. CONCLUSION:The results of this study show that when the correlations among gene expression values are low, pathway-level analyses can yield better predictive performance, greater interpretative power, more robust models and less computational cost relative to a gene-level model. When correlations among genes are high, a pathway-level analysis provides equivalent predictive power compared to a gene-level analysis while retaining the advantages of interpretability, robustness and computational efficiency.

Project description:BackgroundGenomic profiling of solid human tumors by projects such as The Cancer Genome Atlas (TCGA) has provided important information regarding the somatic alterations that drive cancer progression and patient survival. Although researchers have successfully leveraged TCGA data to build prognostic models, most efforts have focused on specific cancer types and a targeted set of gene-level predictors. Less is known about the prognostic ability of pathway-level variables in a pan-cancer setting. To address these limitations, we systematically evaluated and compared the prognostic ability of somatic point mutation (SPM) and copy number variation (CNV) data, gene-level and pathway-level models for a diverse set of TCGA cancer types and predictive modeling approaches.ResultsWe evaluated gene-level and pathway-level penalized Cox proportional hazards models using SPM and CNV data for 29 different TCGA cohorts. We measured predictive accuracy as the concordance index for predicting survival outcomes. Our comprehensive analysis suggests that the use of pathway-level predictors did not offer superior predictive power relative to gene-level models for all cancer types but had the advantages of robustness and parsimony. We identified a set of cohorts for which somatic alterations could not predict prognosis, and a unique cohort LGG, for which SPM data was more predictive than CNV data and the predictive accuracy is good for all model types. We found that the pathway-level predictors provide superior interpretative value and that there is often a serious collinearity issue for the gene-level models while pathway-level models avoided this issue.ConclusionOur comprehensive analysis suggests that when using somatic alterations data for cancer prognosis prediction, pathway-level models are more interpretable, stable and parsimonious compared to gene-level models. Pathway-level models also avoid the issue of collinearity, which can be serious for gene-level somatic alterations. The prognostic power of somatic alterations is highly variable across different cancer types and we have identified a set of cohorts for which somatic alterations could not predict prognosis. In general, CNV data predicts prognosis better than SPM data with the exception of the LGG cohort.

Dataset Information

A comprehensive analysis of prognosis prediction models based on pathway?level, gene?level and clinical information for glioblastoma.

Publications

A comprehensive analysis of prognosis prediction models based on pathway‑level, gene‑level and clinical information for glioblastoma.

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