Meta-Analysis and Experimental Validation Identified FREM2 and SPRY1 as New Glioblastoma Marker Candidates

Abstract

Glioblastoma (GB) is the most aggressive brain malignancy. Although some potential glioblastoma biomarkers have already been identified, there is a lack of cell membrane-bound biomarkers capable of distinguishing brain tissue from glioblastoma and/or glioblastoma stem cells (GSC), which are responsible for the rapid post-operative tumor reoccurrence. In order to find new GB/GSC marker candidates that would be cell surface proteins (CSP), we have performed meta-analysis of genome-scale mRNA expression data from three data repositories (GEO, ArrayExpress and GLIOMASdb). The search yielded ten appropriate datasets, and three (GSE4290/GDS1962, GSE23806/GDS3885, and GLIOMASdb) were used for selection of new GB/GSC marker candidates, while the other seven (GSE4412/GDS1975, GSE4412/GDS1976, E-GEOD-52009, E-GEOD-68848, E-GEOD-16011, E-GEOD-4536, and E-GEOD-74571) were used for bioinformatic validation. The selection identified four new CSP-encoding candidate genes—CD276, FREM2, SPRY1, and SLC47A1—and the bioinformatic validation confirmed these findings. A review of the literature revealed that CD276 is not a novel candidate, while SLC47A1 had lower validation test scores than the other new candidates and was therefore not considered for experimental validation. This validation revealed that the expression of FREM2—but not SPRY1—is higher in glioblastoma cell lines when compared to non-malignant astrocytes. In addition, FREM2 gene and protein expression levels are higher in GB stem-like cell lines than in conventional glioblastoma cell lines. FREM2 is thus proposed as a novel GB biomarker and a putative biomarker of glioblastoma stem cells. Both FREM2 and SPRY1 are expressed on the surface of the GB cells, while SPRY1 alone was found overexpressed in the cytosol of non-malignant astrocytes.

Analysis of miR-9-5p, miR-124-3p, miR-21-5p, miR-138-5p, and miR-1-3p in Glioblastoma Cell Lines and Extracellular Vesicles

“…In this study, we selected candidate miRNAs that regulate genes associated with GBM, as confirmed by our previous studies. FREM2 and SPRY1 have been proposed as potential new markers of GBM, and have been demonstrated to be significantly upregulated in GBM at the mRNA and/or protein level in GBM cell lines and GBM tissues compared to references (astrocytes or reference non-malignant brain tissue) [6,19]. Furthermore, VIM, NCL, and NAP1L1 were significantly overexpressed in GBM tissues and/or cells compared to the reference [20,21].…”

“…We then compared the gene expression in sEVs with the expression in cells types, as previously published [19,21]. The expression profile of VIM gene in sEVs followed the expression in cells, while the expression profile of the NAP1L1, NCL, SPRY1, and FREM2 genes in sEVs did not follow the expression profile of the same genes in different cell types [19,21].…”

“…Finally, we investigated whether the target genes of the selected miRNAs-NAP1L1, FREM2, SPRY1, VIM, and NCL-that have been associated with GBM in our previous studies [6, [19][20][21] were also present in the sEVs of different cell types and showed any particular expression pattern in GBM-isolated sEVs compared to normal astrocyte cells. qPCR was used for analysis.…”

“…Candidate miRNAs were selected based on their interaction with NCL, VIM NAP1L1, FREM2, and SPRY1 [6, [19][20][21] target genes using the TargetScan [22], miRDB [23,24], and microT-CDS datasets [25,26].…”

“…1. Selection of a Set of miRNAs (miR-9-5p, miR-21-5p, miR-124-3p, miR-138-5p, and miR-1-3p) That Target VIM, NCL, NAP1L1, FREM2, and SPRY1 Genes Candidate miRNAs were selected based on their target genes, vimentin (VIM), nucleolin (NCL), nucleosome assembly protein-1 like-1 (NAP1L1), FRAS1-related extracellular matrix protein 2 (FREM2), and sprouty RTK signaling antagonist 1 (SPRY1) [6, [19][20][21], which were previously associated with GBM in our studies. The selection was based on the prediction of the high probability and consistency of miRNA candidates among TargetScan [22], miRDB [23,24], and microT-CDS datasets [25,26].…”