J Cancer 2016; 7(12):1701-1710. doi:10.7150/jca.15486 This issue

Research Paper

Sample Level Enrichment Analysis of KEGG Pathways Identifies Clinically Relevant Subtypes of Glioblastoma

Siyi Wanggou, Chengyuan Feng, Yuanyang Xie, Linrong Ye, Feiyifan Wang, Xuejun Li

Department of Neurosurgery, Xiangya Hospital, Central South University.

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Wanggou S, Feng C, Xie Y, Ye L, Wang F, Li X. Sample Level Enrichment Analysis of KEGG Pathways Identifies Clinically Relevant Subtypes of Glioblastoma. J Cancer 2016; 7(12):1701-1710. doi:10.7150/jca.15486. Available from https://www.jcancer.org/v07p1701.htm

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Background: Glioblastoma is the most lethal primary brain tumor in adults. Aberrant signal transduction pathways, associated with the progression of glioblastoma, have been identified recently and may offer a potential gene therapy strategy.

Methods and Findings: We first used the sample level enrichment analysis to transfer gene expression profile of TCGA dataset into pathway enrichment z-score matrix. Then, we classified glioblastoma into five subtypes (Cluster A to Cluster E) by the consensus clustering and silhouette analysis. Principle component analysis showed the five subtype could be separated by first three principle components. Integrative omics data showed that mesenchymal subtype was rich in Cluster A, neural subtype was centered in Cluster D and proneural subtype was gathered in Cluster E, while Cluster E showed a high percentage of G-CIMP subtype. Additionally, according to analyze the overall survival and progression free survival of each subtype by Kaplan-Merie analysis and Cox hazard proportion model, we identified Cluster D and Cluster E received a better prognosis.

Conclusions: We report a clinically relevant classification of glioblastoma based on sample level KEGG pathway enrichment profile and this novel classification system provided new insights into the heterogeneity of glioblastoma, and may be used as an important clinical tool to predict the prognosis.

Keywords: Glioblastoma, KEGG pathway, Sample level enrichment analysis, Classification, Prognosis.