Expression of key unfolded protein response genes predicts patient survival and an immunosuppressive microenvironment in glioblastoma

doi:10.21203/rs.3.rs-3407414/v1

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Expression of key unfolded protein response genes predicts patient survival and an immunosuppressive microenvironment in glioblastoma

https://doi.org/10.21203/rs.3.rs-3407414/v1

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published 23 Feb, 2024

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Background

Dysregulation of cellular processes related to protein folding and trafficking leads to the accumulation of misfolded proteins in the endoplasmic reticulum (ER), triggering ER stress. Cells cope with ER stress by activating the unfolded protein response (UPR), a signaling pathway that has been implicated in a variety of diseases, including cancer. However, the role of the UPR in cancer initiation and progression is still unclear.

Methods

Here we used bulk and single cell RNA sequencing data to investigate ER stress-related gene expression in glioblastoma, as well as the impact key UPR genes have on patient survival.

Results

ER stress-related genes are highly expressed in both cancer cells and tumor-associated macrophages, with evidence of high intra- and inter-tumor heterogeneity. Elevated levels of the UPR-related genes, namely HSPA5, P4HB, and PDIA4, have been identified as risk factors, while high MAPK8 (JNK1) expression emerges as a protective factor in glioblastoma patients, underscoring the prognostic relevance of UPR genes in this cancer type. Finally, the expression patterns of XBP1 and MAPK8, crucial downstream targets of the ER sentinel IRE1α, show significant correlation with the presence of immune cell subtypes associated with immunosuppression and a worse patient outcome. These findings suggest a link between the expression of these genes, the immunosuppressive tumor microenvironment, and the overall survival of individuals with glioblastoma.

Conclusions

We performed a comprehensive transcriptional characterization of the unfolded protein response in glioblastoma patients and identified UPR-related genes associated with glioblastoma patient survival. These findings hold promise for the development of valuable prognostic and predictive biomarkers, offering insights into potential targets for the advancement of therapeutic approaches in glioblastoma treatment.

glioblastoma

unfolded protein response

endoplasmic reticulum stress

transcriptomics

single cell RNA-sequencing

The endoplasmic reticulum (ER) is the major site for protein folding and quality control for approximately one-third of all cellular proteins [1]. An imbalance between the protein load entering the ER and the ER’s protein folding capacity results in ER stress and subsequent activation of the unfolded protein response (UPR). The UPR is initiated through the activation of three ER transmembrane sensors within the ER: PERK (protein kinase RNA-like ER kinase), IRE1α (Inositol-requiring protein 1 alpha), and ATF6 (activating transcription factor 6). The UPR’s primary objective is to restore protein homeostasis by increasing ER folding capacity, attenuating general translation, and facilitating the removal of misfolded proteins through the ER-associated degradation (ERAD) system [2, 3]. However, chronic or irreversible ER stress leads to cell death by apoptosis [4].

Glioblastoma is an aggressive and often fatal brain tumor with a median survival of 15 to 18 months [5]. The standard treatment is surgical resection, followed by radiotherapy and temozolomide chemotherapy [6]. Several studies have proposed a role for the UPR in mediating chemoresistance across different cancer types, including glioma [7–12]. Pharmacological modulation of the UPR has shown potential in cell culture models [10–14]. Consequently, targeting specific UPR components or inducing acute ER stress may hold therapeutic potential for managing glioblastoma.

Glioblastoma cells, xenograft tumors, and patient samples show elevated transcript and protein levels of chaperones [7, 9, 15], especially HSPA5, a gene whose elevated expression has been correlated with poor prognosis in glioblastoma patients [9]. In addition, previous studies have identified a role for the IRE1α branch in angiogenesis [16], tumor cell invasion [17], and immune infiltration [18] in glioblastoma, as well as associated the IRE1α-XBP1 pathway with reduced patient survival [18, 19]. Yet, a comprehensive characterization of UPR-related gene expression in glioblastoma patient samples is still lacking [13]. Moreover, it is crucial to recognize that the UPR exerts a substantial influence on non-malignant cell types and interactions within the tumor microenvironment [20, 21]. Therefore, to gain a comprehensive understanding of how the UPR influences glioblastoma, we must consider the entire tumor ecosystem.

Here, we harnessed both bulk and single cell RNA sequencing (scRNA-seq) data to delve into the expression patterns of ER stress-related genes in glioblastoma, as well as the impact key UPR genes have on patient survival. Our investigation unveiled a substantial upregulation of the UPR in glioblastoma patient samples and pinpointed factors that could either increase or decrease the risk associated with the disease. Importantly, our analysis of both single-cell and bulk data highlighted that the gene expression of IRE1α downstream targets, specifically XBP1 and JNK1, yields contrasting effects on the composition of the immunosuppressive tumor microenvironment (TME). This finding suggests a potential mechanism by which these UPR pathways influence the survival of glioblastoma patients.

Data origin

scRNA-seq data processed with 10X Genomics were obtained from Neftel et al. 2019 [22]. We excluded the 539 cells from pediatric patients and therefore analyzed 30,401 genes across 15,662 glioblastoma cells from 9 tumors (Table S1). In addition, bulk microarray and RNA-seq expression data of glioblastoma patients with clinical information were obtained from The Cancer Genome Atlas (TCGA) Firehose Legacy dataset and TCGA PanCancer Atlas dataset (Table S2), respectively, through the cBioPortal for Cancer Genomics [23, 24]. Expression data from low-grade glioma patients was also from PanCancer Atlas dataset. Microarray expression data of normal brain tissue and glioma patients with different tumor grades were obtained from the Gene Expression Omnibus (GEO) Platform GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) through the Gene Expression database of Normal and Tumor tissues 2 (Gent2) [25]. The expression data of normal brain samples was obtained from 33 different GEO series, and the expression data of the glioblastoma tissues was from 11 GEO series (Table S3).

scRNA-seq processing and analysis

Single cell data analysis was performed in R software using the Seurat package (version 4.0) [26]. Normalization and variance stabilization of molecular count data was carried out by SCTransform. Following a Seurat workflow, we assigned each cell a cell-cycle score, based on its expression of G2/M and S phase markers [27], and regressed out the difference between these two scores. Subsequently, data dimensionality reduction was performed using a principal component analysis (PCA), and the first 25 principal components were used for clustering through a graph-based approach, by which we constructed a k-nearest neighbor graph based on distances in PCA space and then applied the Louvain algorithm. The resolution parameter was set to 1. Clustering results were visualized using two-dimensional Uniform Manifold Approximation and Projection (UMAP). Clusters were identified based on the expression of reported markers [22].

ER stress gene signature

Transcriptome profiles of glioma cells treated for 2 and 6 hours with the classical ER stress inducers, thapsigargin and tunicamycin, were obtained from Reich et al. 2020 [28] via GEO (GSE129757). The most significantly upregulated genes (all p < 0.005 and log2FC > 1.60) across these conditions were selected and only those upregulated in at least two experimental conditions were chosen for the signature. To validate the meta-signature, we analyzed the differential expression of the signature genes in four GEO series (GSE7806, GSE24497, GSE21979 and GSE107859) that treated different brain tumor cell lines with tunicamycin, thapsigargin or erstressin (Fig. S1, Table S4). This 37-gene signature was used for further analyses of the single cell expression data (Table S5).

Differential expression analysis

For both differential expression and survival analyses, we examined the behavior of an additional set of 25 UPR genes curated from the literature and that are well known components of the different UPR branches [1, 4]. We compared expression levels in normal brain tissue samples and glioblastoma tumor samples from GEO, as well as between low-grade glioma and glioblastoma samples from TCGA. Normality was rejected using the Shapiro-Wilk normality test and therefore the nonparametric Mann-Whitney U test was chosen. To access differential expression across glioma grades, Kruskal Wallis test was carried out comparing data among grade II, III and IV glioma samples, and Dunn’s test was performed for multiple comparisons.

Kaplan-Meier analysis

Kaplan-Meier analyses were performed individually for each of the 25 UPR genes, using the median to separate patients with low and high expression. In addition, Kaplan-Meier analysis using two genes (XBP1 and MAPK8) was performed dividing patients into 4 groups according to Pereira et al. 2018 [29].

Cox regression analysis

Cox regression models were performed to calculate the hazard ratio using mRNA expression z-scores and overall survival data. The models were adjusted for the impact of the clinical variable Sex or adjusted for the impact of both Sex and all other 24 genes. Diagnosis analysis based on weighted Schoenfeld residuals was used to test the proportional hazard assumption. Cox models were carried out using the R survival package (version 3.2–13).

Immune cell meta-signatures

Meta-signatures representing different cells of the immune system were previously described [29]. The meta-signatures were obtained by the average expression z-scores of the genes corresponding to each cell type.

Statistical analyses

Statistical analyses were carried out using GraphPad Prism 8.0.2 Software. Two-sided p-values < 0.05 were statistically significant.

Characterization of UPR gene expression in the TME

Through single cell transcriptomic profiling encompassing 30,401 genes across 15,662 cells from 9 tumors, distinct clusters representing tumor cells, macrophages, oligodendrocytes, and T cells were identified based on the expression of established cell-type specific markers[22] (Fig. 1A and B). Notably, within these clusters, a marked variation in gene expression patterns was observed across cells from different patients, particularly within the tumor cell and macrophage populations, as previously reported[22] (Fig. 1C). To investigate the heterogeneity in ER stress levels, we devised an ER stress gene signature derived from genes upregulated in brain tumor cells following treatment with thapsigargin, tunicamicin, and erstressin (Table S5). This ER stress gene signature exhibited notable expression across all four clusters, with heightened levels in both tumor cells and macrophages (Fig. 1D and E, Table S6). (Fig. 1D and E, Table S6). Examination of four representative individual genes involved in UPR pathways and responsive to ER stress revealed a similar distribution, with expression evident across all clusters but predominantly concentrated in macrophages and tumor cells (Fig. 1F and G). Furthermore, the expression of both the signature and individual genes exhibited substantial variability among patients, with some tumors displaying minimal ER stress marker expression while others exhibited pronounced signatures (Fig. 1C and D, Fig. S2). Intriguingly, even within the same patient, heterogeneity was observed, with sub-groups of cells within a single tumor displaying distinct expression patterns (Fig. 1C and D). Of note, patients demonstrating high signature expression in tumor cells also exhibited elevated expression in macrophage cells (Fig. S2A and B).

UPR is highly upregulated in glioblastoma patient samples

To comprehensively study the UPR landscape in glioblastoma patients, we evaluated the differential expression of major UPR genes in glioblastoma patient samples. We analyzed a set of 25 genes [1, 4], comparing microarray expression data of 844 normal brain tissue samples and 408 glioblastoma tumor samples obtained from GEO Platform GPL570 (Table S3). Almost all genes analyzed showed elevated expression in tumor samples relative to normal brain tissue (Fig. 2A, Fig. S3A). These upregulated genes include the three UPR transducers EIF2AK3 (PERK), ATF6, and ERN1 (IRE1α); the transcription factors XBP1, ATF4, and DDIT3 (CHOP); and their targets, such as the ER chaperones HSPA5 (GRP78) and HSP90B1 (GRP74), and other genes involved in the folding machinery and the ERAD pathway. The only two downregulated genes in tumor samples were MAPK8, which encodes the JNK1 protein, implicated in IRE1α-mediated apoptosis [30, 31], and SEL1L, whose product is a member of the ERAD system that associates with HRD1 (SYVN1) and OS9 to translocate misfolded proteins to the cytosol [32]. Similar results were obtained when comparing low-grade glioma and glioblastoma samples as well as across glioma grades (Fig. 2B, Fig. S3B and C). Likewise, most UPR genes within the subset studied were overexpressed in glioblastoma compared to low-grade glioma and their expression levels correlated with tumor grade. SEL1L and MAPK8 were also downregulated in glioblastoma when compared to low-grade glioma, and MAPK8 expression was negatively correlated with tumor grade.

Identification of novel risk and protective UPR factors in glioblastoma

Next, we evaluated the impact of individual UPR genes on patient survival. Utilizing microarray data from the TCGA cohort, Kaplan-Meier analysis was employed for each of the 25 UPR genes comprising the UPR subset, revealing that nine of these genes held significant prognostic relevance in glioblastoma patients (Fig. 2C, Fig. S4A). To further determine the prognostic implications of these UPR genes, we performed multivariate Cox proportional hazard ratio analysis for the same set of 25 genes, adjusted only to the clinical variable Sex or adjusted to Sex and all other genes, using microarray and RNA-seq data from TCGA cohorts (Fig. 2D and E, Fig. S4B and C, Table S7). The Kaplan-Meier curves showed that elevated expression levels of six genes were significantly associated with shorter survival, while heightened expression of three genes was linked to a better prognosis. Similarly, Cox survival analysis identified seven genes as risk factors and four as protective factors. By considering genes that exhibited significance in both Kaplan-Meier and Cox analyses across both microarray and RNA seq datasets, we identified three risk factors (HSPA5, P4HB, and PDIA4) and one protective factor (MAPK8).

Opposing roles of IRE1α downstream pathways in patient prognosis and presence of immune cells in TME

Given the opposing roles of the IRE1α branch, which on one side leads to the induction of apoptosis via JNK1 and on the other triggers adaptive responses through XBP1 [30, 31, 33], we next focused on the role of XBP1 and MAPK8 in glioblastoma. We performed a Kaplan-Meier analysis to investigate the impact of XBP1 and MAPK8 gene interaction on survival. Patients were divided into 4 equal groups, as shown in Fig. S5A [29]. Patients with high MAPK8 and low XBP1 expression had significantly better prognoses than patients with low MAPK8 and high XBP1 expression (Fig. 3A). IRE1α downstream pathways are associated with immune infiltration and tumor-stroma interaction mainly through the regulation of cytokines in tumor and immune cells [20, 34]. JNK1 can regulate the inflammatory responses in cancer cells through NF-κB pathway, and IRE1α-XBP1 signaling in glioblastoma cells can enhance expression of cytokines that participate in the chemoattraction of macrophages to the TME [18, 20]. We measured the expression of immune cell markers and meta-signatures in the Low-High and High-Low patient groups, as well as in the XBP1 and MAPK8 individual low and high expression groups. High XBP1 and low MAPK8 was associated with an elevated expression of immune markers and immune cell meta-signatures (Fig. 3B and C, Fig. S5B and C). The highly overexpressed immune meta-signatures (p < 0.05 and FoldChange > 1.5) in patients with high XBP1 and low MAPK8 expression were all of cell types that were previously associated with immunosuppression and a worst prognosis in glioblastoma patients (Treg, Th1, MDSC, macrophages, NK, and NK T) [29]. We then performed further analyses with scRNA-seq data to investigate if these findings were not due to the intrinsic expression of these genes in immune cells. XBP1 and MAPK8 were expressed across all clusters, mainly in the macrophage population (Fig. 3D, Fig. S5D). In fact, the ratio of XBP1 and MAPK8 average expression only in tumor cells positively correlated with the percentage of immune cells in the tumor for each sample (Fig. 3E, Table S6). XBP1 and MAPK8 expression in tumor cells alone correlated with the percent of immune cells in the tumor (XBP1: r = 0.59; MAPK8: r=-0.26), but their ratio showed a much stronger correlation (r = 0.79) (Fig. S5E). Thus, XBP1 and MAPK8 expression ratio can be an indicative of an immunosuppressive TME.

In this investigation, we have substantiated the upregulation of UPR (unfolded protein response) stress pathways within glioblastoma tumors. We have also pinpointed UPR-related genes whose expression levels exhibit significant associations with patient prognosis. Furthermore, we have unraveled a compelling correlation between the expression levels of two pivotal UPR genes and the enrichment of immune cells within the TME.

Our analysis has unveiled an increase in the expression of ER stress-related genes in glioblastoma when compared to normal tissue and low-grade gliomas. This upregulation stems not only from tumor cells but also from non-tumor cells within the TME, primarily macrophages. Importantly, we observed pronounced intra- and inter-tumor heterogeneity in the expression of ER stress signatures and markers. This heightened expression of ER stress-related genes encompassed nearly all the UPR-related genes we examined, with one noteworthy exception being MAPK8.

Crucially, we have identified certain UPR-related genes, including HSPA5, P4HB, and PDIA4, as robust risk factors, demonstrating their significant prognostic relevance across all three survival analyses conducted. Additionally, OS9 and HSP90B1 emerged as prognostically relevant in two out of the three analyses. These findings align with previous research, which has highlighted the overexpression of HSPA5 and the PDI (protein disulfide isomerase) family in glioblastoma models. This consistency reinforces the notion that UPR's cytoprotective branches play a pivotal role in facilitating the rapid growth of tumors and their ability to adapt to the challenging conditions of the microenvironment [9, 10].

While several ERAD genes exhibited upregulation in glioblastoma tumors and were designated as risk factors, a notable exception was SEL1L. In contrast to previous findings that indicated an escalation in SEL1L protein expression with increasing malignancy in glioma cells and tissues [35, 36], we observed a downregulation of SEL1L in glioblastoma tissue when compared to normal brain and low-grade glioma samples. Notably, SEL1L presents multiple alternative transcripts encoding putative protein isoforms, suggesting that its role may oscillate between oncogenic and tumor-suppressive, contingent upon the cellular context and the prevailing isoform predominance [35].

Another gene exhibiting downregulation in glioblastoma, MAPK8, emerged as a protective factor in all three survival analyses conducted. Previous studies have alluded to the prognostic significance of the MAPK8 gene, postulating that this relevance is rooted in its involvement in programmed cell death [37, 38]. ERN1 (IRE1α), which serves as the activator of MAPK8 within the context of UPR signaling, was also identified as a protective factor in two of the analyses. This finding diverges from a prior report that associated high ERN1 expression with poor prognosis in glioblastoma patients [18]. Notably, in our Cox regression analysis, ERN1 did not emerge as a prognostic factor when adjusted for MAPK8 as a covariate (data not shown), implying that its prognostic relevance may be contingent upon the protective role of MAPK8.

Regarding the PERK/eIF2α/ATF4 branch, our analysis revealed that all the scrutinized genes exhibited upregulation in glioblastoma compared to normal tissue. This finding agrees with previous reports that showed elevated protein and transcript levels of both ATF4 and its target CHOP (DDIT3) in xenograft tumors. Additionally, these findings align with the observed positive correlation between PERK activation and tumor grade in glioma [9, 39].

Although none of the analyzed genes of the PERK/eIF2α/ATF4 branch had a significant impact on prognosis across all three survival analyses, EIF2A and TRIB3, the latter being an apoptosis-related gene activated by CHOP [40], emerged as protective factors based on Kaplan-Meier curve and Cox analysis, respectively. In contrast, ATF3 and ATF5, which are induced by the eIF2α/ATF4 pathway, were identified as risk factors in one and two of the survival analyses, respectively. These findings are consistent with previous reports in other cancer types [41, 42]. These results underscore the complexity of this pathway, that leads to an intricate balance between cytoprotective and cytotoxic outcomes.

Intrigued by the contrasting functions of the IRE1α branch, which can either induce apoptosis via JNK1 or promote survival through the XBP1-dependent activation of adaptive responses, we conducted a survival analysis involving these two genes. In line with their antagonistic roles within the UPR signaling pathway, we observed that patients with elevated XBP1 expression and reduced MAPK8 expression experienced shorter survival compared to patients exhibiting low XBP1 and high MAPK8 expression. However, it is important to note that our standard transcriptomic methods did not provide access to crucial details such as XBP1 mRNA splicing or JNK1 (MAPK8) phosphorylation levels. Additionally, multiple pathways distinct from the UPR can converge on the phosphorylation of JNK1, making it an indirect indicator of IRE1α activation. Therefore, a comprehensive understanding of how these pathways interplay and contribute to either cell survival or cell death is still lacking.

While the precise mechanisms underpinning the connection between IRE1α downstream pathways and immune infiltration remain elusive, prior studies have indicated that IRE1α can either promote immunosurveillance or facilitate immune system evasion through the regulation of damage-associated molecular patterns, cytokines, and the propagation of ER stress signals [20, 34]. We observed that heightened XBP1 and diminished MAPK8 expression correlated with elevated expression of immune cell meta-signatures, which have previously been associated with unfavorable prognosis in glioblastoma. These meta-signatures encompassed cell types such as macrophages, MDSC, Th1, and Treg [29]. This result aligns with recent studies proposing a link between ER stress and the expression of immune cell markers in glioma patient samples [43–45].

Furthermore, when analyzing single cell data, we noted that, although both XBP1 and MAPK8 were highly expressed in immune cells, the XBP1/MAPK8 expression ratio within tumor cells correlated strongly with the abundance of immune cells in each sample. This suggests a potential role for the IRE1α pathways in shaping the TME and influencing the infiltration of immune cell types that may potentiate immunosuppression and are typically associated with worse patient outcomes. However, it is important to emphasize that a more in-depth investigation is warranted to elucidate the intricate mechanisms governing this relationship.

In summary, we performed a comprehensive transcriptional characterization of the unfolded protein response in glioblastoma patients. Our study identified UPR-related genes associated with glioblastoma patient survival, providing potential prognostic and predictive biomarkers as well as promising targets for developing new therapeutic interventions in glioblastoma treatment.

ATF6 Activating transcription factor 6

ER Endoplasmic reticulum

ERAD ER-associated degradation

Gent2 Gene Expression database of Normal and Tumor tissues 2

GEO Gene Expression Omnibus

IRE1α Inositol-requiring protein 1 alpha

PERK Protein kinase RNA-like ER kinase

scRNA-seq Single cell RNA sequencing

TCGA The Cancer Genome Atlas

TME Tumor microenvironment

UPR Unfolded protein response

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The scRNA-seq dataset is available in the GEO repository under accession number GSE131928. Firehouse Legacy and PanCancer Atlas datasets are available in the TCGA repository. Microarray expression data analysed during the current study are available in the GEO repository, under the accession numbers included in Table S3. The code to reproduce the analyses and figures described in this study can be found at www.github.com/fernandaoliveiraa/2022_GBMsinglecell.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was done without formal funding sources.

Authors' contributions

FDO: Conceptualization, formal analysis, investigation, data curation, writing - original

draft, visualization. RPC: Conceptualization, writing - review & editing. LCP: Conceptualization, writing - review & editing. LM: Conceptualization, writing - review & editing, supervision. GL: Conceptualization, writing – review & editing, supervision. All authors read and approved the final manuscript.

Acknowledgements

LCP received a CAPES fellowship and Guido Lenz received a CNPq fellowship.

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published 23 Feb, 2024

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Editorial decision: Major revision
05 Nov, 2023
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07 Oct, 2023
Reviewers invited by journal
07 Oct, 2023
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04 Oct, 2023
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Expression of key unfolded protein response genes predicts patient survival and an immunosuppressive microenvironment in glioblastoma

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