Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

Frontiers in Cell and Developmental Biology
Jianyang DuShaoshan Hu

Abstract

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, R...Continue Reading

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Citations

May 1, 2021·International Journal of Molecular Sciences·Vincenzo MatteiClaudio Festuccia

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Datasets Mentioned

BETA
GSE124145
GSE22866

Methods Mentioned

BETA
chips
PCR
flow cytometry
transfection
gene knockdown
xenograft

Software Mentioned

limma
Perl
ESTIMATE
GSEA
R package “ gelnet ”
clusterProfiler
SVA
EREG
GraphPad Prism
Ensemble

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