Abstract
Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers. Here we interrogated microarray gene expression profiles (GEP) of 268 diffuse astrocytic gliomas—33 diffuse astrocytomas (DA), 52 anaplastic astrocytomas (AA) and 183 primary glioblastoma (GBM)—based on multivariate analysis, to identify discriminatory GEP that might support precise histopathological tumor stratification, particularly among inconclusive cases with II–III grade diagnosed, which have different prognosis and treatment strategies. Microarrays based GEP was analyzed on 155 diffuse astrocytic gliomas (discovery cohort) and validated in another 113 tumors (validation set) via sequential univariate analysis (pairwise comparison) for discriminatory gene selection, followed by nonnegative matrix factorization and canonical biplot for identification of discriminatory GEP among the distinct histological tumor subtypes. GEP data analysis identified a set of 27 genes capable of differentiating among distinct subtypes of gliomas that might support current histological classification. DA + AA showed similar molecular profiles with only a few discriminatory genes overexpressed (FSTL5 and SFRP2) and underexpressed (XIST, TOP2A and SHOX2) in DA vs AA and GBM. Compared to DA + AA, GBM displayed underexpression of ETNPPL, SH3GL2, GABRG2, SPX, DPP10, GABRB2 and CNTN3 and overexpression of CHI3L1, IGFBP3, COL1A1 and VEGFA, among other differentially expressed genes.
Highlights
Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers
Despite the expression levels of specific genes, such as CHI3L1 and TOP2A, which have been related to necrosis in GBM14,15, and IGFBP2 and VEGFA involved in tumor p rogression[12], mRNA-based gene expression profiling (GEP) has frequently shown discrepant results in gliomas, hampering application of gene expression profiles (GEP) in clinical practice
Once we investigated the relevance of each gene to the formation of both clusters (Fig. 2A) we confirmed that the ETNPPL, SH3GL2, GABRB2, CNTN3, SPX, GABRG2, DPP10, SFRP2, FSTL5 genes where those most contributing to the diffuse astrocytomas (DA) + AA cluster, followed by a few genes displaying a lower contribution (XIST, IGFBP3, ANXA1, TOP2A, PDPN and VEGFA) (Fig. 2B)
Summary
Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers. Other genetic markers that have been associated with specific subtypes of astrocytomas and diffuse astrocytic tumors could be useful for glioma c lassification[10,11], include gains and losses of specific chromosomal regions together with mutations of the EGFR, MDM4, PTEN, PDGFRA and CDKN2A genes, but they are not considered in the WHO2016 classification[2] This highlights the need for deeper genomic analysis of astrocytic tumors to gain further insight in those gene profiles that might help to unequivocally distinguish among the different subtypes of astrocytic tumors and support the differential diagnosis and subclassification of diffuse gliomas, in cases[12,13] with an inconclusive histopathological diagnosis. Variable selection techniques together with other matrix factorization algorithms, such as nonnegative matrix factorization (NMF)[19], have been proposed for the discovery of clusters that might gather important biological information, as recently demonstrated in pancreatic c ancer[20]
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