Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas

Abstract

Background: Glioblastoma (GBM) is an aggressive brain tumor that exhibits resistance to current treatment, making the identification of novel therapeutic targets essential. In this context, cellular prion protein (PrP^C) stands out as a potential candidate for new therapies. Encoded by the PRNP gene, PrP^C can present increased expression levels in GBM, impacting cell proliferation, growth, migration, invasion and stemness. Nevertheless, the exact molecular mechanisms through which PRNP/PrP^C modulates key aspects of GBM biology remain elusive.

Methods: To elucidate the implications of PRNP/PrP^C in the biology of this cancer, we analyzed publicly available RNA sequencing (RNA-seq) data of patient-derived GBMs from four independent studies. First, we ranked samples profiled by bulk RNA-seq as PRNP^high and PRNP^low and compared their transcriptomic landscape. Then, we analyzed PRNP⁺ and PRNP^- GBM cells profiled by single-cell RNA-seq to further understand the molecular context within which PRNP/PrP^C might function in this tumor. We explored an additional proteomics dataset, applying similar comparative approaches, to corroborate our findings.

Results: Functional profiling revealed that vesicular dynamics signatures are strongly correlated with PRNP/PrP^C levels in GBM. We found a panel of 73 genes, enriched in vesicle-related pathways, whose expression levels are increased in PRNP^high/PRNP⁺ cells across all RNA-seq datasets. Vesicle-associated genes, ANXA1, RAB31, DSTN and SYPL1, were found to be upregulated in vitro in an in-house collection of patient-derived GBM. Moreover, proteome analysis of patient-derived samples reinforces the findings of enhanced vesicle biogenesis, processing and trafficking in PRNP^high/PRNP⁺ GBM cells.

Conclusions: Together, our findings shed light on a novel role for PrP^C as a potential modulator of vesicle biology in GBM, which is pivotal for intercellular communication and cancer maintenance. We also introduce GBMdiscovery, a novel user-friendly tool that allows the investigation of specific genes in GBM biology.

Keywords: Glioblastoma; Intracellular trafficking; Prion protein; Transcriptomics; Vesicle dynamics.

Fig. 1

Impact of PRNP expression at bulk resolution in GBM samples from TCGA. A Schematic workflow of bulk RNA-seq data analyses of patient-derived primary GBM samples from TCGA (n=157). B Violin plots of PRNP normalized expression, according to log10 (counts per million [CPM]+1), in samples classified as IDH-mutant (n=11), IDHwt (n=142), or unclassified (n=4) (left, p=0.00016); and samples classified as classical (n=50), mesenchymal (n=67), proneural (n=18), or unclassified (n=22) (right, p=0.01). Kruskal-Wallis test. C GBM samples (n=124, after filtering) were ranked according to quartiles of PRNP expression. Those below the lower (PRNP^low, n=31) and above the upper quartile (PRNP^high, n=31) were selected, as shown in the density plot of PRNP expression. D GBM molecular subtype composition of the PRNP^low and PRNP^high groups. E Volcano plot of upregulated (red) and downregulated (blue) transcripts in PRNP^high relative to PRNP^low. (The PRNP was removed from the plot). F Gene set enrichment analysis (GSEA) show enriched terms (Gene Ontology, GO) in the upregulated and downregulated transcripts of PRNP^high GBM, sorted by normalized enrichment score (NES) and colored by adjusted p-value

Fig. 2

Integrated analysis of single-cell GBM datasets shows the impact of PRNP expression. A Schematic workflow of single-cell RNA-seq (scRNA-seq) data analyses carried out on three independent and publicly available datasets. B UMAP representation of PRNP expression in GBM cells from Darmanis et al., Neftel et al., and Richards et al. (C) Venn diagram of common marker genes of PRNP⁺ cells in all scRNA-seq datasets. D Functional profiling (ORA, GO) of the 840 common genes found in (C), ranked by -log₁₀ (Adjusted p-value)

Fig. 3

PRNP positively correlates with vesicle-associated genes at single-cell resolution. A Schematic workflow of the correlation analysis between PRNP expression and all genes identified in the scRNA-seq of the three independent patient-derived GBM datasets. B Pearson correlation shows significant positively (red) and negatively (blue) correlated genes with PRNP for the three scRNA-seq datasets. C Venn diagram of common positively correlated genes identified in (B). D ORA analysis (gProfiler2 and GO) of genes positively correlated with PRNP

Fig. 4

Identification of a 73-gene panel enriched in traffic-related structures and vesicle dynamics in PRNP^high samples and PRNP⁺ GBM cells. A Venn diagram of the 73 common genes with increased expression levels in PRNP^high GBM cells (TCGA bulk RNA-seq) and upregulated in PRNP⁺ cells (Darmanis et al., Neftel et al. and Richards et al.). B Over-representation analysis (gProfiler2 - GO, KEGG, Reactome, and WikiPathways) of common genes found in (A). C Schematic workflow of the analysis of an in-house cohort of GBM patient-derived samples. D Relative expression of PRNP, SYPL1, DSTN, ANXA1 and RAB31 in patient-derived GBM samples expressing Low (n=26) or High (n=26) PRNP levels by RT-qPCR and 2^-ΔΔCt, normalized by HPRT, GUSB and TBP. Student t-test, ****p<0.0001

Fig. 5

Impact of PRNP and vesicle dynamics signatures on patients’ overall survival. A Kaplan-Meier curves demonstrate the overall survival of GBM patients either below or above the optimal cutoff, calculated for PRNP expression levels. A risk table is shown at the left. Statistical significance was assessed using a log-rank test, and p-value is stated in the graph. B Kaplan-Meier curves demonstrate the overall survival of GBM patients either below or above the optimal cutoff, calculated for gene signatures of traffic- or vesicle-related processes (GO). Statistical significance was assessed using log-rank tests, and p-values are stated in the graphs. Inserts show PRNP normalized expression in samples above and under the cutoff of each gene signature

Fig. 6

Leading page and example analysis of GBMdiscovery. GBMdiscovery is an R-based shiny app that allows users to discover the implications of their genes of interest in GBM biology, combining the four publicly available patient-derived bulk- and scRNA-seq datasets analyzed in the present study

Fig. 7

Schematic diagram of the mechanisms involved in extra- and intracellular trafficking by which PrP^C may affect GBM biology. PrP^C is transiently found in the ER and Golgi during its biogenesis and is internalized by a vesicle-mediated process to be recycled or degraded. Vesicular biogenesis, endocytic path and exocytosis are processes enriched in GBM cells with high PRNP expression