EIF2S2 as a Prognostic Marker and Therapeutic Target in Glioblastoma: Insights into its Role and Downstream Mechanisms

doi:10.21203/rs.3.rs-5297353/v1

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EIF2S2 as a Prognostic Marker and Therapeutic Target in Glioblastoma: Insights into its Role and Downstream Mechanisms

https://doi.org/10.21203/rs.3.rs-5297353/v1

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Glioblastoma (GBM) the most common and most aggressive primary brain tumor has a five-year survival rate of less than 5%. The onset of GBM is very complicated and has always been the focus of researchers. This study analyzed data from 155 GBM and 5 normal tissues from The Cancer Genome Atlas (TCGA), and patients were categorized into high and low EIF2S2 expression groups. The Overall survival and disease-free survival of GBM patients in low expression of EIF2S2 group were significantly higher than those in high expression of EIF2S2 group (p < 0.001), and the expression level of EIF2S2 was significantly correlated with tumor grade (p < 0.001) and tumor recurrence (p < 0.001). The study designed three different short hairpin RNA (shRNA) sequence vectors, identifying shEIF2S2-1 as the most effective. This vector significantly reduced EIF2S2 expression, cell proliferation, and migration while increasing apoptosis in SHG-44 and U251 cells (p < 0.01). By detecting SHG-44 cells infected with shEIF2S2 vector and shCtrl with human whole gene expression chip, we identified WNT5A that is a downstream target gene of EIF2S2. Interfering with WNT5A and overexpressing EIF2S2 in SHG-44 and U251 cells revealed that EIF2S2 regulates WNT5A expression. This regulation led to an increased apoptosis rate (p < 0.05) and a significant reduction in cell migration (p < 0.05) in both the EIF2S2 overexpression and shWNT5A interference groups, confirming that WNT5A is a downstream regulatory target of EIF2S2. This study revealed the key role of EIF2S2 in GBM and its potential molecular mechanism.

GBM

EIF2S2

WNT5A

SHG-44

U251

Cerebral glioma (CG) is the most common central nervous system tumor, accounting for about 85%-90% of all primary central nervous system tumors ¹. CG occurs in important brain areas such as the central sulcus, basal ganglia, thalamus and brainstem ². About 55% of gliomas will deteriorate into glioblastoma (GBM) ³, and the average survival of patients who deteriorate into glioblastoma is less than one year. Most brain tumor lesions are cancer metastases outside the central nervous system ³, and the incidence is 5–10 times that of primary brain tumors. Among them, glioma is the most common primary brain tumor ⁴, accounting for 30% of all primary brain tumors and 80% of malignant tumors which is the cause of death from most primary brain tumors. And GBM is highly aggressive that leads to the recurrence of residual tumor cells after surgical removal ⁵. The blood-brain barrier ⁶ also restricts many anticancer drugs from entering the brain tissue that can reduce the effectiveness of chemotherapy.

The EIF2S2 (eukaryotic translation initiation factor 2 subunit 2) gene encodes a subunit of eukaryotic translation initiation factor 2 ⁷ which plays a key role in the initiation stage of protein synthesis. EIF2S2 plays an important role in regulating translation initiation and cell stress response and its abnormal expression is closely related to the occurrence, development and prognosis of various cancers⁸, such as hepatocellular carcinoma ⁷, breast cancer ⁹ and colorectal cancer ¹⁰. In hepatocellular carcinoma, overexpression of EIF2S2 promotes the proliferation and anti-apoptosis ability of tumor cells and is associated with poor prognosis of patients ⁷. In breast cancer research, EIF2S2 can promote tumor growth and metastasis by regulating the cell cycle and inhibiting cell apoptosis ⁹. EIF2S2 regulates protein synthesis by regulating the activity of translation initiation factors, which can help cells adapt and survive ¹¹. In cancer cells, this mechanism is often hijacked to promote the survival and growth of tumor cells. EIF2S2 can also affect tumor progression through multiple signaling pathways, such as EIF2S2 regulating protein synthesis and cell growth in the mTOR signaling pathway ¹².

Wingless-Type MMTV Integration Site Family, Member 5A (WNT5A) ¹³ is an important member of the Wnt signaling pathway and plays a key role in biological processes such as embryonic development, cell proliferation, migration and differentiation ¹⁴. Studies have found that WNT5A may play a dual role in different types of cancer ¹⁵ that can act as both a tumor suppressor gene and an oncogene, and its specific function depends on the type of cancer and the cell environment. In some types of cancer, WNT5A exhibits the characteristics of a tumor suppressor gene ¹⁶. Its low expression or loss is closely related to the occurrence, development and poor prognosis of cancer, such as colorectal cancer ¹⁶ and gastric cancer¹⁷. In other types of cancer, WNT5A exhibits the characteristics of an oncogene. Its high expression promotes the growth, migration and invasion of tumor cells, such as breast cancer ¹⁸ and melanoma ¹⁹. Some studies have shown that WNT5A is highly expressed in GBM and is associated with increased tumor invasiveness and metastasis ²⁰.

In this study, we downloaded data of 155 GBM and 5 normal paracancerous tissues from The Cancer Genome Atlas (TCGA) database and divided GBM patients into high expression of EIF2S2 group and low expression of EIF2S2 group according to the average expression level of EIF2S2 gene to determine the relationship between EIF2S2 gene expression level and Overall survival and disease-free survival of GBM patients. Three different shRNA sequence vectors (shEIF2S2-1, shEIF2S2-2, and shEIF2S2-3) were designed, and the shRNA sequence vector with the highest silencing efficiency was screened in SHG-44 cells. By analyzing the EIF2S2 mRNA and protein expression levels, cell proliferation, apoptosis, cell cycle, and cell mobility of SHG-44 and U251 cells infected with shEIF2S2 vectors, we explored the specific mechanism of action of EIF2S2 in GBM. In addition, by detecting SHG-44 cells with human whole gene expression chips, we identified the downstream target gene WNT5A of EIF2S2. Further, by interfering with WNT5A and overexpressing EIF2S2, the interaction mechanism between WNT5A and EIF2S2 in GBM was explored. This study reveals the key role of EIF2S2 in GBM and provides new perspectives and potential targets for future GBM treatment.

Cell and data sources

U251 Human Glioblastoma Cell Line,SHG-44 Human Glioblastoma Cell Line),U373 Human Glioblastoma Cell Line, U87 Human Glioblastoma Cell Line, HEB Human Normal Brain Cell Line.

In this study, RNAseq data of 155 GBM and 5 normal para-cancerous tissues were downloaded from The Cancer Genome Atlas (TCGA) database ²¹, and the phenotypic data of the corresponding patients, such as the demographic information (age) and clinical opathological characteristics (tumor grade, stage).

PCA analysis and differential expression analysis

The R package TCGAbiolinks ²¹ was used to preprocess the RNA-seq data of GBM patients and normal para-cancerous tissues. The estimate the dispersion method in DEseq2 ²² was used to standardize the expression profiles of GBM patients and normal para-cancerous tissues. In order to avoid the errors caused by inappropriate sample grouping, principal component analysis (PCA) analysis ²³ was performed on the gene expression levels of 160 patients. The R package DEseq2 was used to identify differentially expressed genes (DEGs) in 139 cases and normal para-cancerous tissues. For the DEseq2 results, genes with Fold Change greater than or equal to 1.3 or less than or equal to -1.3 were screened.

EIF2S2 gene expression level and patients' clinical data

The mean expression levels of EIF2S2 gene in 139 GBM patients were calculated, and GBM patients were divided into high EIF2S2 expression group and low EIF2S2 expression group according to the mean expression levels of EIF2S2 gene. We used to Chi-square test ²⁴ evaluate the relationship between EIF2S2 gene expression level and tumor characteristics of glioma patients, namely age, gender, tumor recurrence and tumor grade.

Overall survival and disease-free survival analysis

The R packages survival ²⁵ and survminer ²⁶ used the Kaplan-Meier method ²⁷ to plot the overall survival curve and disease-free survival curve of the EIF2S2 high expression group and the low EIF2S2 expression group. The Log-Rank test ²⁸ was used to evaluate the effect of EIF2S2 expression level on patient survival time.

Real-time qPCR detection

Total RNA was extracted from five cell samples using Sigma's Trizol method. The RNA was reverse transcribed into cDNA following Vazyme's Hiscript QRT supermix instructions. The qPCR reaction mix included SYBR Green mastermix, primers, Dye2, cDNA, and RNase-Free H2O. The reaction was run in triplicate on a qPCR plate, with the program set to 95°C for 10 minutes for initial denaturation, followed by 45 cycles of 95°C for 15 seconds and 60°C for 60 seconds.

Construction of EIF2S2 gene RNA interference lentiviral vector

The EIF2S2 gene was cloned using the BR-V108 vector and TOP10 E. coli competent cells. Based on the design principles of RNA interference, three 19-21nt RNA interference target sequences were designed using the EIF2S2 gene as a template (Table S1). ShRNA sequences were created with restriction sites added for vector construction, including a TTTTT termination signal on the 3' end of the positive strand and its complementary sequence on the 5' end of the reverse strand. These sequences were synthesized by Biotech and annealed to form double-stranded DNA (Table S2).

The BR-V108 vector was linearized by double digestion with Age I and EcoR I, and the target fragment was recovered by gel cutting. The double-stranded DNA was then ligated into the linearized vector using T4 DNA ligase. The ligation product was transformed into E. coli, followed by heat shock and culture. Positive clones were identified and sequenced to ensure consistency with the target sequence. Correctly sequenced clones were grown in LB medium with Amp antibiotics, and plasmids were extracted using the EndoFree Maxi Plasmid Kit for downstream processes.

Infection of target cells by lentivirus

SHG-44 and U251 cells (1×10⁵) in the logarithmic growth phase were infected with lentiviral vectors containing shEIF2S2 (shEIF2S2-1 plasmid) and shCtrl (EIF2S2 plasmid without RNA interference sequence). Infection efficiency and fluorescence were observed under a microscope 72 hours later

Western Blot

SHG-44 and U251 cells infected with shEIF2S2 and shCtrl vectors were washed three times with PBS, lysed with Western and IP cell lysis buffer and PMSF (100:1 ratio), and incubated on ice for 10–15 minutes. After centrifugation at 4°C, 12000 rpm for 10 minutes, the supernatant was collected, mixed with loading buffer, heated at 100°C for 20 minutes, centrifuged briefly, and stored at -20°C. The protein samples were separated by SDS-PAGE and transferred to a PVDF membrane at 4°C, 300 mA for 90 minutes. The membrane was blocked with 5% skim milk in TBST for 1 hour at room temperature, then incubated overnight at 4°C with primary antibodies (EIF2S2: Rabbit polyclonal, GAPDH: Mouse monoclonal). After washing, the membrane was incubated with secondary antibodies (EIF2S2: Goat Anti-Rabbit HRP, GAPDH: Mouse monoclonal) for 1 hour at room temperature, washed again, and developed using the Millipore Immobilon Western Chemiluminescent HRP Substrate Kit.

Celigo system for detecting cell proliferation

The cells in the logarithmic growth phase were digested with trypsin and resuspended in complete medium into a cell suspension. The number of cells per well was set to 2000 cells/well, with 3 replicates per group, and the culture system was 100 µL/well. The cells were cultured in a 37°C, 5% CO2 incubator. Starting from the second day after plating, the Celigo assay was read once a day for 5 consecutive days.

Cell cycle detection

Cells were grown to ~ 80% confluence, collected after trypsin digestion, and centrifuged. After washing with cold PBS, the cells were fixed in 70% ethanol for 1 hour. Following centrifugation and PBS wash, the cells were stained with cell staining solution (0×PI (propidium iodide) stock solution (2 mg/ml): 100×RNase stock solution (10 mg/ml): 1×PBS = 25: 10: 1000) and resuspended for analysis.

Wound-healing Assay

Cells in the logarithmic growth phase were digested, resuspended, and plated at 50,000 cells/well in a 96-well plate with 100 µL of medium per well. After 24 hours, the medium was replaced with low-serum medium. A scratch was made using a scratch instrument, rinsed with serum-free medium, and then cultured in low-serum medium. The plate was scanned using Cellomics to analyze the migration area over time.

Transwell migration experiment

Prepare a 24-well plate by adding 100µL serum-free medium to the chambers and incubate for 1–2 hours. Digest cells in the logarithmic phase, create a suspension, and count the cells. Remove the medium from the chambers, add 600µL of 30% FBS medium to the lower chamber, and add 100µL of diluted cell suspension (100,000-200,000 cells) to each chamber. Place the chambers in the lower chamber and incubate for 24 hours. Stain the transferred cells by soaking the chamber in staining solution for 5 minutes, then rinse and capture images under a microscope

Apoptosis detection

Cells in each group were grown to ~ 70% confluence, digested, and resuspended. Drug-induced apoptosis was initiated, and cells were collected with the supernatant. After centrifugation and washing, the cell pellet was resuspended in 200 µL binding buffer, stained with Annexin V-APC, incubated in the dark for 10–15 minutes and finally detect on the machine.

Detection of downstream genes of shEIF2S2 by human whole gene expression microarray

Three SHG-44 cell samples from the shCtrl (NC) and shEIF2S2 (KD) groups were collected (Table S3). Total RNA was extracted and quality tested, then synthesized into cDNA and hybridized to a human gene expression chip. The oligo package ²⁹ in R was used for background correction and normalization. DEseq2a constructed the design and comparison matrices, and the empirical Bayesian linear model calculated p-values, with FDR correction by the Benjamini-Hochberg method. DEG were identified with |Fold Change|≥1.3 and FDR < 0.05, and clustered using hierarchical clustering. Functional and disease enrichment analyses were performed with clusterProfiler ³⁰ and DisGeNET ³¹, respectively. A heatmap of DEG was created with Complex Heatmap ³², and STRINGdb ³³ was used for EIF2S2 protein interaction analysis.

Reaction experiment to verify downstream genes

This experiment used the LV-013 vector and the experimental strain was TOP10 Escherichia coli competent cells (TIANGEN, Cat. #CB104-03). Using the WNT5A gene as a template, three interference target sequences were designed (shWNT5A-1, shWNT5A-2, shWNT5A-3). Four cell groups were established for investigating the roles of WNT5A and EIF2S2 in GBM: NC(OE + KD): Cells infected with empty vectors (LV-013 and BR-V108), EIF2S2 + NC(KD): Cells infected with EIF2S2 and NC-shWNT5A lentivirus to over-express EIF2S2, shWNT5A + NC(OE): Cells infected with shWNT5A and NC-EIF2S2 lentivirus to down-regulate WNT5A, EIF2S2 + shWNT5A: Cells infected with lentiviruses to simultaneously down-regulate WNT5A and over-express EIF2S2.

PCA analysis and differential expression analysis

This study first performed PCA analysis on the gene expression profiles of RNA-seq data from 155 cases of GBM patient cancer tissues and 5 cases of normal para-cancerous tissues (Fig. 1A). PC1 and PC2 accounted for more than 45% of the variation in the principal components, of which PC1 accounted for 31.9%. PCA results show that Normal (para-cancerous tissues) and Tumor (cancer) samples can be clearly separated at PC1, proving that the sample data of different batches are highly stable. The DEG results between the Tumor group and the Normal group (Fig. 1B) showed that the expression levels of 2302 genes were up-regulated and the expression levels of 1907 genes were down-regulated in cancer tissues.

The expression of EIF2S2 gene in glioma tissue is significantly higher than that in normal tissue (p < 0.001) (Table S4) which can be used for subsequent statistical analysis of clinic pathological data. The expression level of EIF2S2 gene was not significantly related to the age (p = 0.060) and gender (p = 0.229) of GBM patients. And there was a significant association between tumor grade (p < 0.001) and tumor recurrence (p < 0.001) and EIF2S2 expression level (Table S5). These results indicate that the higher the expression level of EIF2S2, the greater the probability of tumor recurrence and the higher the tumor grade. Kaplan-Meier survival analysis found that the expression of EIF2S2 gene was significantly related to the overall survival (Fig. 1C) and disease-free survival (Fig. 1D) of GBM (p < 0.05). Patients with high expression levels of the EIF2S2 gene have significantly shorter overall survival and disease-free survival than patients with low expression levels of the EIF2S2 gene. Therefore, EIF2S2 could potentially serve as a prognostic biomarker in GBM, with higher expression levels predicting worse clinical outcomes.

Effect of RNA interference on EIF2S2 gene expression

This study used qRT-PCR to measure EIF2S2 mRNA levels in GBM cells (U251, SHG-44, U373, U87) and normal brain cells (HEB) (Fig. 2A). EIF2S2 expression was significantly higher in GBM cells than in HEB cells (p < 0.001), consistent with previous findings that EIF2S2 is elevated in GBM tissues compared to para-cancerous tissues. To assess shRNA-mediated silencing, three shRNA sequences (shEIF2S2-1, shEIF2S2-2, shEIF2S2-3) were tested in SHG-44 cells. ShEIF2S2-1 and shEIF2S2-2 effectively reduced EIF2S2 expression by 57.4% and 74.0%, respectively (p < 0.001) (Fig. 2B), while shEIF2S2-3 was ineffective. ShEIF2S2-1 exhibited the highest silencing efficiency, so we chose the shEIF2S2-1 plasmid for further experimental validation.

To investigate the infection efficiency of lentiviral vectors (shCtrl or shEIF2S2) in GBM cells, microscopy was performed 72 hours post-infection in SHG-44 and U251 cells (Fig. 2C-D). Fluorescence microscopy revealed an infection efficiency exceeding 80%, indicating effective vector function and successful introduction of shRNA. qRT-PCR analysis showed that EIF2S2/GAPDH mRNA levels were significantly lower in the shEIF2S2 group compared to the shCtrl group (p < 0.001) in both SHG-44 (Fig. 2E) and U251 (Fig. 2F) cells. Silencing efficiencies were 87.00% in SHG-44 and 91.21% in U251 cells. Western blot results further confirmed significant down-regulation of EIF2S2 protein in the shEIF2S2 group relative to the shCtrl group (Fig. 2G-H), validating the effectiveness of shRNA-mediated EIF2S2 silencing.

EIF2S2 interference impairs GBM cell proliferation, induces apoptosis and inhibits migration

Previous research demonstrated that shEIF2S2 significantly impacts the mRNA and protein expression levels of EIF2S2 in SHG-44 and U251 cells. This study further explored the role of EIF2S2 in GBM cells through cell proliferation experiments (Fig. 3A-D). SHG-44 and U251 cells infected with shEIF2S2 displayed a significantly slower proliferation rate compared to the shCtrl group. Specifically, SHG-44 cells showed a fold change of 2.398 (p < 0.01), and U251 cells exhibited a fold change of 3.468 (p < 0.001). Additionally, flow cytometry revealed that both early and late apoptotic cell populations were significantly increased in the shEIF2S2 group compared to the shCtrl group (p < 0.01) (Fig. 3E-H). These results suggested that interference with EIF2S2 inhibits cell proliferation by promoting apoptosis.

Further analysis of the cell cycle distribution (Fig. 4A-D) indicated that EIF2S2 silencing in SHG-44 and U251 cells resulted in cell cycle arrest, with a significant reduction in the proportion of cells in the S phase and an increase in the proportion of cells in the G2 phase (p < 0.001). This cell cycle arrest was a critical factor contributing to the reduced proliferation observed. Wound healing (Fig. 4E-H) and Transwell assays (Figure S1A-D) demonstrated that EIF2S2 interference significantly inhibited the migration ability of SHG-44 and U251 cells. SHG-44 cells in the shEIF2S2 group showed an 87% reduction in migration rate (p < 0.001) in the wound healing assay, and a 44% reduction in the Transwell assay (p < 0.01). U251 cells exhibited a 36% reduction in migration rate in both assays (p < 0.01 and p < 0.001, respectively). These findings suggest that EIF2S2 plays a crucial role in the migration and invasiveness of GBM cells, with a more pronounced effect in SHG-44 cells.

Analysis of the expression level of the chip

To investigate downstream target genes regulated by EIF2S2, this study used human whole gene expression chips to analyze SHG-44 cells with shCtrl and shEIF2S. The relative logarithmic signal intensities for the NC and KD groups (Fig. 5A) showed that the median Z-scores were below 2, confirming good repeatability of the chip experiment. Intra-group correlation coefficients for KD and NC were over 0.99 (Fig. 5B), indicating high similarity in gene expression within each group, while inter-group coefficients were significantly lower, highlighting large group differences. PCA analysis showed PC1 and PC2 explained over 90% of the variation (Figure S2), with clear clustering of KD and NC samples. A total of 5630 differentially expressed genes (DEGs) were identified (Fig. 5C), with 2787 upregulated and 2843 downregulated. The heat map of the cluster analysis of significantly DEG in the chips of the NC group and the KD group (Fig. 5D) showed the differences between the shEIF2S2 group and the shCtrl group. This suggests that DEGs are likely associated with EIF2S2's cellular functions.

Classical path analysis of IPA

Enrichment analysis of DEGs using Ingenuity Pathway Analysis (IPA) revealed that these genes are primarily involved in pathways such as Cholesterol Biosynthesis (I, II, and III), Aryl Hydrocarbon Receptor Signaling, Cell Cycle Control, NF-kB Signaling, and Xenobiotic Metabolism CAR Signaling (Fig. 6A).These results reveal that the EIF2S2 gene is shown to exert its role in cellular function through these key biological processes and signaling pathways.

Disease and function enrichment analysis (Fig. 6B) identified major categories including cancer, organismal injury, endocrine system disorders, gastrointestinal and reproductive system diseases, neurological diseases, and cell survival. These findings suggest that EIF2S2 interference affects crucial biological processes and disease-related pathways, notably cancer. A network relationship analysis (Figure S3) showed that EIF2S2 interacts with various signaling pathways such as ATM Signaling, EIF2 Signaling, ERK/MAPK Signaling, mTOR Signaling, Senescence Pathway and AMPK Signaling. EIF2S2 influences genes like ATF2, CDK1, DLD, EIF2B5 and EIF3J by affecting the expression of genes involved in these pathways, including APP, BIRC3, CUL7, EIF2B5, EIF3M, and G3BP1.

Analysis of downstream targeted genes

This study identified five potential EIF2S2 downstream target genes through IPA pathway analysis, namely Cluster of Differentiation 44 (CD44), DEP Domain Containing 1B (DEPDC1B), Matrix Metalloproteinase 7 (MMP7), Myeloid Differentiation Primary Response 88 (MYD88), Wingless-Type MMTV Integration Site Family, Member 5A (WNT5A). Western blot results (Fig. 7A) showed no change in MMP7 and MYD88 protein levels between SHG-44 cells with shCtrl and shEIF2S2. However, CD44 protein levels were higher in SHG-44 with shCtrl, while DEPDC1B and WNT5A levels were lower compared to shEIF2S2. Additionally, WNT5A mRNA expression in SHG-44 cells aligned with EIF2S2 mRNA expression.MeRIP-qRT-PCR experiments further revealed that WNT5A gene interference significantly reduced the m6A level of the EIF2S2 gene (Fig. 7C). And the interaction diagram between EIF2S2 and APP, BIRC3, CUL7, EIF2B5, EIF3M, and WNT5A genes (Fig. 7D) also shows that the WNT5A gene is a key downstream target gene of the EIF2S2 gene.

Reaction experiment to verify downstream genes

This study examined the effects of three shRNA sequences (shEIF2S2-1, shEIF2S2-2, shEIF2S2-3) on WNT5A expression in SHG-44 cells (Figure S4). Post-lentiviral infection, WNT5A knockout rates were 93.5% for shWNT5A-1, 75.1% for shWNT5A-2, and 87.6% for shWNT5A-3 (p < 0.001). The highest knockout efficiency was observed with shWNT5A-1, which was selected for further experiments. To explore the infection efficiency of shWNT5A-1 and EIF2S2 plasmids in GBM cells, SHG-44 and U251 cells were observed under a microscope after 72 hours of infection (Fig. 8A-B). Fluorescence indicated over 80% infection efficiency, confirming the functionality of the lentiviral vectors. MRNA expression levels of EIF2S2/GAPDH and WNT5A/GAPDH were assessed in SHG-44 and U251 cells (Figs. 8C-F). In the EIF2S2 + NC (KD) group, EIF2S2 and WNT5A mRNA levels significantly increased (p < 0.001), while in the shWNT5A + NC (OE) group, both decreased significantly (p < 0.05). In the EIF2S2 + shWNT5A group, WNT5A mRNA levels significantly decreased (p < 0.001), but EIF2S2 levels remained unchanged. Conversely, in the shWNT5A + NC (OE) group, EIF2S2 mRNA levels increased significantly, while WNT5A decreased (p < 0.05). These results suggest a mutual regulatory relationship between EIF2S2 and WNT5A. Western blot analysis (Figs. 8G-H) showed that in SHG-44 cells, EIF2S2 protein levels significantly increased in the EIF2S2 + NC (KD) group, while WNT5A decreased. In the shWNT5A + NC (OE) group, WNT5A decreased significantly, with no significant change in EIF2S2. In U251 cells, the EIF2S2 + NC (KD) group showed a significant increase in EIF2S2 protein without a significant change in WNT5A. In the shWNT5A + NC (OE) group, both proteins decreased significantly. These findings reinforce the mutual regulatory relationship between EIF2S2 and WNT5A.

To further explore the effects of EIF2S2 and WNT5A genes on GBM cells, this study conducted apoptosis and wound-healing assay experiments (Fig. 9). In SHG44 cells (Fig. 9A, 9C) and U251 cells (Fig. 9B, 9D), compared with the NC(OE + KD) group, the cell apoptosis in the EIF2S2 + NC (KD) group was reduced (P < 0.001). Compared with the NC (OE) + KD group, the cell apoptosis in the shWNT5A + NC(OE) group was significantly increased (p < 0.05). Compared with the EIF2S2 + NC (KD) group, the cell apoptosis in the EIF2S2 + shWNT5A group was significantly increased (p < 0.001). Compared with the shWNT5A + NC (OE) group, the cell apoptosis in the EIF2S2 + shWNT5A group was reduced (p < 0.001). These results reveal the complex interaction between EIF2S2 and WNT5A in regulating apoptosis, providing important clues for further understanding their roles in cell biological functions.

The scratch healing assay in SHG-44 cells (Fig. 9E, 9G) showed that the cell migration rate in the EIF2S2 + NC (KD) group was significantly increased compared with the NC (OE + KD) group (p < 0.05). There was no significant difference in the cell migration rate in the shWNT5A + NC (OE) group compared with the NC (OE + KD) group. Further comparison showed that the cell migration rate in the EIF2S2 + shWNT5A group was significantly decreased compared with the shWNT5A + NC (OE) group (p < 0.05). The cell migration rate in the EIF2S2 + shWNT5A group (24 hours) was significantly increased compared with the EIF2S2 + NC(KD) group (p < 0.05). The scratch healing assay in U251 cells (Fig. 9F, 9H) found that there was no significant difference in the cell migration rate among the groups.

The effective of EIF2S2 in cancer

The role of EIF2S2 in cancer is mainly achieved through its role in translation regulation ⁸. The activity regulation of the eIF2 complex is a key link in protein synthesis, EIF2S2 as an important subunit of this complex which plays an important role in this process ³⁴. In addition, EIF2S2 promotes the growth and survival of cancer cells by affecting the selective translation of specific mRNAs and regulating key genes related to cell cycle, apoptosis and metabolism ³⁵. High expression of EIF2S2 is associated with tumor invasiveness and poor prognosis in breast cancer ³⁶. By inhibiting EIF2S2, the proliferation and migration ability of breast cancer cells is significantly reduced, while the sensitivity of cells to chemotherapeutic drugs is enhanced. This suggests that EIF2S2 may promote the malignant behavior of breast cancer cells by regulating cell stress response and translation initiation process. High expression of EIF2S2 in hepatocellular carcinoma is closely related to tumor grade and patient survival rate ³⁷. Studies have found that by regulating the expression of EIF2S2, the proliferation and apoptosis of liver cancer cells can be significantly affected ⁷. Studies have found that EIF2S2 is highly expressed in GBM which is associated with tumor invasiveness and poor prognosis of patients ³⁸. Highly expressed EIF2S2 may promote the growth and proliferation of GBM cells. This is consistent with the results of this study.

EIF2S2 affects the expression of multiple downstream genes involved in cell growth, apoptosis, metabolism, and stress response by regulating translation initiation ^39,40. EIF2S2 can indirectly regulate the stability and function of certain proteins by affecting their translation speed and folding process ⁴¹. Moreover, EIF2S2 regulates the expression of certain non-coding RNAs (such as miRNA and lncRNA) which play an important role in post-transcriptional regulation of gene expression ⁴². EIF2S2 regulates the expression of activating transcription factor 4 (ATF4) through selective translation which can help cells cope with various stress conditions, such as oxidative stress and amino acid deficiency ³⁶. EIF2S2 participates in the apoptosis process triggered by endoplasmic reticulum stress by regulating the expression of CHOP, a pro-apoptotic transcription factor ⁴³.

EIF2S2 affects cell proliferation, survival, and metabolism not only through its basic translation function, but also through interactions with multiple signaling pathways, such as ATM Signaling, EIF2 Signaling⁴⁴, ERK/MAPK Signaling ⁴⁵, mTOR Signaling ³⁸, Senescence Pathway and AMPK Signaling ⁴⁴. Studies have found that EIF2S2 can affect the ATM signaling pathway by regulating stress responses during translation ⁴⁴. The translation inhibition of EIF2S2 under stress conditions may reduce protein synthesis and reduce the cell's ability to repair DNA damage that increasing the cell's sensitivity to DNA damage. The interaction between EIF2S2 and the ERK/MAPK signaling pathway may affect cell behavior by regulating protein synthesis and stress responses ⁴⁵. Studies have found that high expression of EIF2S2 can activate the ERK/MAPK pathway which can promote cell proliferation and survival ⁴⁵. The mTOR signaling pathway plays a key role in cell growth, metabolism, and survival. Abnormal expression of EIF2S2 can affect cell proliferation and survival through the mTOR pathway ³⁸.

The effective of WNT5A in cancer

WNT5A is involved in a variety of biological processes, such as embryonic development, tissue regeneration, and tumorigenesis and development ^46,47. In cancer, the role of WNT5A is complex and diverse, and it may promote tumor development or inhibit tumor growth ⁴⁸. WNT5A has been found to promote cancer cell invasion and migration in certain types of cancer, such as breast cancer ¹⁸ and melanoma ¹⁹. Moreover, high expression of WNT5A in breast cancer and melanoma is associated with the malignancy and prognosis of the tumor. WNT5A can affect the development of tumors by affecting the formation and function of the tumor microenvironment. Studies have found that WNT5A can regulate the activation state and infiltration of tumor-associated macrophages ⁴⁹ that can affect the immune escape and treatment sensitivity of tumors. WNT5A has also been found to inhibit tumor growth and metastasis in some cases¹⁷. In colorectal cancerand lung cancer, the expression of WNT5A is associated with a better prognosis, and may inhibit the development of tumors by inhibiting the proliferation and invasion of tumor cells ¹⁶.

WNT5A can affect tumor progression by regulating cell senescence ⁵⁰. Studies have found that in the epithelial ovarian cancer cell line OVCAR5, primary human ovarian cancer epithelial cells and mouse in vivo models, the expression of WNT5A was upregulated by retroviral transduction to increase the recruitment of histone repressor A (HIR A) to promyelocytic bodies (PML) to induce cell senescence ⁵¹.The WNT5A signaling pathway regulates the interaction between tumor cells and tumor microenvironment cells ¹⁸. In colorectal cancer, WNT5A is mainly expressed in its stroma, especially in tumor-associated macrophages in the tumor microenvironment ⁵². Studies have found that WNT5A promotes the secretion of IL-10, induces tumor-associated macrophages to polarize to M2, and ultimately promotes tumor growth and metastasis of colorectal cancer by regulating the CaKMII-ERK1/2-STAT3 pathway. ⁴⁹ In malignant peripheral nerve sheath tumors (MPNST), WNT5A mRNA is significantly upregulated in tumor cells and tissues ⁵³. After WNT5A knockdown, mRNAs related to extracellular matrix remodeling and immune cell communication are significantly upregulated, the secretion of proinflammatory cytokines is increased, and the occurrence and development of tumors are promoted, indicating that Wnt5a inhibits the formation of MPNST tumors by regulating the MPNST microenvironment.

The development and metastasis of tumor cells is a series of continuous events, including tumor cell proliferation, increased migration, invasion of basement membrane, entry into the circulatory system, and finally tumor cell invasion of distant organs ⁵⁴. In some cancers, WNT5A has been found to promote cell proliferation and migration to enhance tumor cell invasion ⁵⁵. WNT5A was found to be overexpressed and was associated with the proliferation, migration and invasion of tumor cells in GBM ⁵⁶. In colorectal cancer (CSC), compared with CRC patients with WNT5A deletion, CRC patients with WNT5A overexpression in tumor tissue have a longer survival time, which may be related to the upregulation of 15-PGDH mediated by WNT5A ⁵⁷. WNT5A has different effects on the proliferation, migration and invasion of different types of tumor cells which may depend on the differential expression of Wnt5a receptors or different downstream pathways.

This study revealed the key role of EIF2S2 gene in the pathogenesis of glioblastoma (GBM) and its potential molecular mechanism through in-depth study of the EIF2S2 gene in GBM. In cell experiments, we designed three different shRNA sequence vectors and identified shEIF2S2-1 with the highest silencing efficiency. Cell experiments showed that the mRNA and protein expression levels of EIF2S2 were significantly decreased in cells infected with shEIF2S2 vector, accompanied by a slowdown in cell proliferation, an increase in apoptosis rate, and a decrease in migration rate. In addition, through whole gene expression chip detection, we identified the downstream target gene WNT5A of EIF2S2 and confirmed the interaction between them. The findings of this study not only deepen our understanding of the pathogenesis of GBM, but also provide important inspiration for the development of new therapeutic strategies. This study provides a new perspective for understanding the pathogenesis of GBM, and EIF2S2 and WNT5A may become potential targets for the treatment of GBM.

Supplement: Table S1: Three 19-21 nt RNA interference target sequences of EIF2S2 gene. Table S2: Three 19-21nt RNA interference target sequences of EIF2S2 gene single-stranded DNA oligo. Table S3: The samples Negative Control group (NC) and Knockdown group (KD). Table S4: LogFC value and p value of EIF2S2 gene in cancer tissues and normal tissues. Table S5: Relationship between EIF2S2 expression and tumor characteristics in patients with glioma cancer. Figure S1: A. Transwell migration assay images of SHG-44 cells with shCtrl and shEIF2S2. B. Transwell migration assay images of U251 cells with shCtrl and shEIF2S2. C. The migration cell per field (Left) and migration fold change (Right) of SHG-44 with shCtrl and shEIF2S2. D. The migration cell per field (Left) and migration fold change (Right) of U251 with shCtrl and shEIF2S2. Figure S2: PCA analysis of chip signal strength in NC group and KD group. Figure S3: Gene interaction network diagram. Figure S4: Effect of three different shRNA sequences on the Ratio of WNT5A/GAPDH mRNA expression levels.

CRediT authorship contribution statement: Zhongjie Yan conceived and designed the research. Bo Fan analyzed the transcriptome data and wrote the original draft. Qing Pan, Xiaokai Yuan and Du Wei revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Declaration of competing interest: The authors declare no conflict of interest.

Ethics statement: This study was approved by the Ethics Committee of the Second Hospital of Hebei Medical University.

Funding: Not applicable.

Institutional Review Board Statement: Not applicable.

Data Availability Statement: Not applicable.

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EIF2S2 as a Prognostic Marker and Therapeutic Target in Glioblastoma: Insights into its Role and Downstream Mechanisms

Status:

Version 1

Abstract

Figures

Introduction

Method

Cell and data sources

PCA analysis and differential expression analysis

EIF2S2 gene expression level and patients' clinical data

Overall survival and disease-free survival analysis

Real-time qPCR detection

Construction of EIF2S2 gene RNA interference lentiviral vector

Infection of target cells by lentivirus

Western Blot

Celigo system for detecting cell proliferation

Cell cycle detection

Wound-healing Assay

Transwell migration experiment

Apoptosis detection

Detection of downstream genes of shEIF2S2 by human whole gene expression microarray

Reaction experiment to verify downstream genes

Result

PCA analysis and differential expression analysis

Effect of RNA interference on EIF2S2 gene expression

EIF2S2 interference impairs GBM cell proliferation, induces apoptosis and inhibits migration

Analysis of the expression level of the chip

Classical path analysis of IPA

Analysis of downstream targeted genes

Reaction experiment to verify downstream genes

Discussion

The effective of EIF2S2 in cancer

The effective of WNT5A in cancer

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1