Introduction
Gliomas, the most prevalent form of primary brain tumors, present a significant clinical challenge due to their aggressive nature and resistance to treatment. Despite advancements in surgery, radiation therapy, and chemotherapy, the median survival for patients remains distressingly low, ranging from 12 to 18 months [1-4]. This grim prognosis is largely attributed to the intricate biology of gliomas, characterized by their infiltrative growth and high propensity for recurrence [5, 6]. The urgent need to identify novel therapeutic targets to combat these tumors has become a central focus in neuro-oncology research.
Among the promising avenues of investigation is the Ring finger protein 5 (RNF5). This ubiquitin ligase, belonging to the RING finger family, is anchored to the endoplasmic reticulum (ER) membrane and is a crucial component of the ER-associated degradation (ERAD) pathway [7, 8]. RNF5 is known to play a role in monitoring the folding of the cystic fibrosis transmembrane conductance regulator (CFTR) within the ER membrane [9, 10]. Beyond ERAD, RNF5 has been implicated in diverse cellular processes. Studies have shown its involvement in regulating cell movement through paxillin ubiquitination [11], and in the inflammatory response to viral infections by modulating interferon regulatory factor 3 activation [12]. Intriguingly, in breast cancer, RNF5 has been shown to degrade glutamine carrier proteins, impacting cellular metabolism and proliferation [13]. Furthermore, RNF5 is often found to be highly expressed in breast cancer, and its inhibition reduces cancer cell proliferation [13].
This study delves into the functional role of RNF5 in human glioma, aiming to elucidate its relationship with tumor grade and patient survival. We hypothesize that RNF5 plays a significant, yet complex, role in glioma biology, potentially intertwined with key regulatory pathways such as the TP53 pathway, which is well-established as a critical tumor suppressor. The Tp53 Gene Serves An Important Role In Regulating: cellular stress responses, DNA repair, cell cycle arrest, and apoptosis, and its mutations are frequently observed in various cancers, including gliomas. Understanding the interplay between RNF5 and the TP53 pathway could provide crucial insights into glioma pathogenesis and potential therapeutic strategies.
This research utilizes patient data from the Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases, along with in vitro experiments, to characterize RNF5 expression patterns, its prognostic significance, and associated signaling pathways in glioma. By integrating bioinformatics analysis with experimental validation, we aim to provide a comprehensive understanding of RNF5’s role in this deadly brain cancer.
Materials and Methods
Patient Datasets
Gene expression data from glioma patients were sourced from publicly available databases: the Chinese Glioma Genome Atlas (CGGA; cgga.org.cn) and the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo). The CGGA dataset comprised 301 glioma samples, categorized into 84 astrocytomas, 89 oligodendrogliomas, and 128 glioblastomas. Tumor grades were classified as low-grade glioma (LGG), anaplastic glioma (AG), and glioblastoma multiforma (GBM), as per established criteria [14]. Two GEO datasets were also utilized: GSE16011, containing 276 glioma samples (24 astrocytoma, 85 oligodendroglioma, and 159 glioblastoma) and 8 control samples from epilepsy patients [15], and GSE4290, including 157 tumor samples (26 astrocytoma, 50 oligodendrogliomas, and 81 glioblastoma) and 23 non-cancerous samples from epilepsy patients [16]. Gene mutation data for TP53 and RNF5 were retrieved from The Cancer Genome Atlas (TCGA; cancergenome.nih.gov).
Analysis of RNF5 Expression and Patient Prognosis
RNF5 expression levels across different glioma grades and in non-cancerous brain tissue were analyzed using GraphPad Prism software (version 6.01, GraphPad Software, Inc.). To assess the prognostic value of RNF5 expression, data from the CGGA database were used. Patients were stratified into two groups based on RNF5 expression levels (high vs. low), and survival analysis was performed.
Cell Culture and Transfection
The human glioblastoma cell line U251, obtained from The Type Culture Collection of The Chinese Academy of Sciences, was cultured in DMEM (Invitrogen; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (Gibco; Thermo Fisher Scientific, Inc.) at 37°C in a 5% CO2 atmosphere. For RNF5 overexpression, the RNF5 cDNA sequence was cloned into the p3XFLAG-CMV-14 vector (Shanghai GenePharma Co., Ltd.). U251 cells were transfected with 3 µg of either the RNF5-expressing plasmid or the empty vector control using Polyjet transfection reagent (SignaGen Laboratories), following the manufacturer’s instructions. The culture medium was replaced after 12 hours, and cells were incubated for a total of 72 hours post-transfection.
Reverse Transcription-Quantitative PCR (RT-qPCR)
RNF5 mRNA expression levels were quantified using RT-qPCR. Total RNA was extracted from U251 cells using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) and reverse-transcribed into cDNA using the Quant One-Step RT-PCR kit (Tiangen Biotech Co., Ltd.). qPCR was performed using FastStart Universal SYBR Green Mix (Roche Diagnostics) on an ABI 7300 real-time PCR instrument (Applied Biosystems; Thermo Fisher Scientific, Inc.). Primer sequences were: RNF5 forward 5′-GTACCCATACGATGTTCCAGATTACGC-3′, reverse 5′-CTGAGCAGCCAGAAAAAGAAAAAGATG-3′; β-actin forward 5′-CATGTACGTTGCTATCCAGGC-3′, reverse 5′-CGCTCGGTGAGGATCTTCATG-3′. Thermocycling conditions were: 95°C for 3 min, followed by 35 cycles of 95°C for 15 sec, 60°C for 15 sec, and 72°C for 1 min. RNF5 expression was normalized to β-actin and calculated using the 2−ΔΔCq method [17].
Colony Formation Assay
To assess the effect of RNF5 overexpression on cell proliferation, U251 cells (1×105) transfected with RNF5-expressing plasmid or control vector were seeded in 6 cm dishes and cultured for 14 days at 37°C. Colonies were fixed with 4% paraformaldehyde for 30 min and stained with 0.05% crystal violet for 30 min at room temperature. Images were captured using a Canon 70D camera.
Gene Set Enrichment Analysis (GSEA)
To identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with RNF5 in glioma, GSEA software (version 6.2; software.broadinstitute.org/gsea/login.jsp) was used with CGGA database data. Samples were ranked based on RNF5 expression and divided into four groups (A-D) from low to high expression. GSEA was performed to identify KEGG pathways significantly enriched in the high RNF5 expression group (Group D).
Identification of Differentially Expressed Genes (DEGs)
To predict potential RNF5 ubiquitination substrates, CGGA, GSE16011, and GSE4290 datasets were sorted by RNF5 expression level. Data were divided into quartiles (Groups A-D). DEGs were identified by comparing Group A (low RNF5) with Group D (high RNF5) in each dataset using the limma package in R software (version 3.4.4; www.r-project.org). DEGs were defined as genes with |logFC| > 1. Overlapping DEGs across the three datasets were identified.
Correlation Analysis
Pearson’s correlation coefficient was calculated using R and GraphPad Prism software to determine the correlation between RNF5 expression and the expression of overlapping DEGs.
Statistical Analysis
Statistical analyses were performed using GraphPad Prism software. Data are presented as mean ± standard error of the mean (SEM). Student’s t-test (for two groups) or one-way ANOVA followed by Dunnett’s post hoc test (for multiple groups) were used to determine statistical significance. Kaplan-Meier survival analysis and log-rank test were used for survival comparisons. P < 0.05 was considered statistically significant.
Results
RNF5 Expression Varies with Glioma Grade and Correlates with Prognosis
Analysis of the CGGA database and GSE16011 and GSE4290 datasets revealed that RNF5 expression is differentially regulated across different grades of glioma. Notably, RNF5 expression was significantly higher in low-grade glioma (LGG) and anaplastic glioma (AG) compared to glioblastoma multiforme (GBM) (Figure 1A). While datasets GSE16011 and GSE4290 were explored to compare RNF5 expression between non-cancerous brain tissue and glioma, inconsistent results were obtained, likely due to the limited number of non-cancerous samples in these datasets (Figure 1B and C).
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Figure 1. Expression levels of RNF5 in non-cancerous brain tissue and glioma samples from patients with different disease grades and association with patient prognosis. (A) RNF5 was differentially expressed in different grades of glioma and exhibited the highest and lowest expression in AG and GBM, respectively. (B) RNF5 expression significantly differed between non-cancerous brain and glioma tissues in the GSE16011 dataset. (C) Expression of RNF5 did not significantly differ between non-cancerous brain tissue and glioma tissues in the GSE4290 dataset. Kaplan-Meier survival curve analysis of the prognostic significance of RNF5 expression in patients with (D) LGG, (E) AG and (F) GBM. RNF5, ring finger protein 5; AG, anaplastic glioma; GBM, glioblastoma multiforme; LGG, low-grade glioma; CGGA, Chinese Glioma Genome Atlas. |
|—|—|](/article_images/ol/18/5/ol-18-05-4659-g00.jpg “Figure 1. – Expression levels of RNF5 in non-cancerous brain tissue and glioma samples from patients with different disease grades and association with patient prognosis. (A) RNF5 was differentially expressed in different grades of glioma and exhibited the highest and lowest expression in AG and GBM, respectively. (B) RNF5 expression significantly differed between non-cancerous brain and glioma tissues in the GSE16011 dataset. (C) Expression of RNF5 did not significantly differ between non-cancerous brain tissue and glioma tissues in the GSE4290 dataset. Kaplan-Meier survival curve analysis of the prognostic significance of RNF5 expression in patients with (D) LGG, (E) AG and (F) GBM. RNF5, ring finger protein 5; AG, anaplastic glioma; GBM, glioblastoma multiforme; LGG, low-grade glioma; CGGA, Chinese Glioma Genome Atlas.”)
Prognostic analysis using the CGGA database revealed a significant association between RNF5 expression and patient survival in specific glioma subtypes. In LGG, RNF5 expression did not significantly correlate with prognosis. However, in AG and GBM, elevated RNF5 expression was associated with improved overall survival compared to patients with low RNF5 expression (Figure 1D-F).
To further explore the relationship between RNF5 and glioma prognosis, particularly considering the context of tumor suppressor genes, we examined mutations in tumor protein 53 (TP53), a gene known to be frequently mutated in gliomas. Analysis of the TCGA database showed a high mutation frequency of TP53 in GBM, while RNF5 mutations were rare (Figure 2A). In vitro experiments revealed that overexpression of RNF5 in U251 glioma cells significantly increased colony formation ability compared to control cells (Figure 2B-D), suggesting a potential role for RNF5 in promoting glioma cell proliferation.
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Figure 2. RNF5 and TP53 mutations identified in low-grade glioma and GBM and colony formation assay. (A) A total of 117 mutations in TP53 and two mutations in RNF5 in GBM were identified in The Cancer Genome Atlas. (B) Cell colony forming ability was increased in U251 cells overexpressing RNF5 compared with control cells. (C) Cell colony formation analysis. (D) Reverse transcription-quantitative PCR analysis of the expression levels of RNF5 in U251 cells overexpressing RNF5 compared with control cells. **P |
|—|—|](/article_images/ol/18/5/ol-18-05-4659-g01.jpg “Figure 2. – RNF5 and TP53 mutations identified in low-grade glioma and GBM and colony formation assay. (A) A total of 117 mutations in TP53 and two mutations in RNF5 in GBM were identified in The Cancer Genome Atlas. (B) Cell colony forming ability was increased in U251 cells overexpressing RNF5 compared with control cells. (C) Cell colony formation analysis. (D) Reverse transcription-quantitative PCR analysis of the expression levels of RNF5 in U251 cells overexpressing RNF5 compared with control cells. **P<0.01. RNF5, ring finger protein 5; TP53, tumor protein 53; 3′UTR, 3′untranslated region; GBM, glioblastoma multiforme.”)
RNF5 Enrichment in Specific KEGG Signaling Pathways
To further elucidate the functional mechanisms of RNF5 in glioma, GSEA was performed using the CGGA database. This analysis identified several KEGG signaling pathways significantly enriched in samples with high RNF5 expression. These pathways included ‘Wnt signaling pathway’, ‘apoptosis’, ‘cell adhesion molecules (CAMs)’, ‘cytokine-cytokine receptor interaction’, ‘focal adhesion’, and ‘ECM-receptor interaction’ (Figure 3). These findings suggest that RNF5 may exert its influence on glioma development through modulation of these critical cellular pathways.
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Figure 3. GSEA to identify significant RNF5-enriched KEGG signaling pathways. GSEA revealed that RNF5 is enriched in the following pathways: (A) The ‘Wnt signaling pathway’, (B) ‘apoptosis’, (C) ‘cell adhesion molecules CAMs’, (D) ‘cytokine-cytokine receptor interaction’, (E) ‘focal adhesion’ and (F) ‘ECM-receptor interaction’. GSEA, Gene Set Enrichment Analysis; RNF5, ring finger protein 5; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; NES, normalized enrichment score; NOM, nominal; FDR, false discovery rate. |
|—|—|](/article_images/ol/18/5/ol-18-05-4659-g02.jpg “Figure 3. – GSEA to identify significant RNF5-enriched KEGG signaling pathways. GSEA revealed that RNF5 is enriched in the following pathways: (A) The ‘Wnt signaling pathway’, (B) ‘apoptosis’, (C) ‘cell adhesion molecules CAMs’, (D) ‘cytokine-cytokine receptor interaction’, (E) ‘focal adhesion’ and (F) ‘ECM-receptor interaction’. GSEA, Gene Set Enrichment Analysis; RNF5, ring finger protein 5; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; NES, normalized enrichment score; NOM, nominal; FDR, false discovery rate.”)
Prediction of Potential RNF5 Ubiquitination Substrates
To identify potential targets of RNF5’s ubiquitin ligase activity in glioma, we conducted differential gene expression analysis and identified genes with expression patterns inversely correlated with RNF5. By analyzing overlapping DEGs across the CGGA, GSE16011, and GSE4290 datasets, we narrowed down a list of potential RNF5 substrates. Four overlapping genes were identified between CGGA and GSE16011 datasets, and one overlapping gene between GSE16011 and GSE4290 datasets. No overlapping genes were found between CGGA and GSE4290 (Figure 4A). The five overlapping genes were contactin 3 (CNTN3), leucine rich glioma inactivated 1 (LGI1), proprotein convertase subtilisin/kexin type 1 (PCSK1), sorting nexin 10 (SNX10), and solute carrier family 39 member 12 (SLC39A12) (Figure 4B). Correlation analysis revealed a positive association between RNF5 and CNTN3 expression, while negative correlations were observed between RNF5 and SNX10, PCSK1, LGI1, and SLC39A12 (Figure 4B and Figure 5). These negatively correlated genes are candidate ubiquitination substrates of RNF5 in glioma.
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Figure 4. RNF5 ubiquitin substrate prediction. (A) Venn diagram showing 4 overlapping genes between the CGGA database and the GSE16011 dataset and 1 overlapping gene between the GSE4290 and GSE16011 datasets. (B) Associations between RNF5 and these 5 genes. Red and blue dots indicate a negative and positive association, respectively. The greater the size and color intensity of the dot, the stronger the association. RNF5, ring finger protein 5; CGGA, Chinese Glioma Genome Atlas; CNTN3, contactin 3; LGI1, leucine rich glioma inactivated 1; SLC39A12, solute carrier family 39 member 12; PCSK1, proprotein convertase subtilisin/kexin type 1; SNX10, sorting nexin 10. |
|—|—|](/article_images/ol/18/5/ol-18-05-4659-g03.jpg “Figure 4. – RNF5 ubiquitin substrate prediction. (A) Venn diagram showing 4 overlapping genes between the CGGA database and the GSE16011 dataset and 1 overlapping gene between the GSE4290 and GSE16011 datasets. (B) Associations between RNF5 and these 5 genes. Red and blue dots indicate a negative and positive association, respectively. The greater the size and color intensity of the dot, the stronger the association. RNF5, ring finger protein 5; CGGA, Chinese Glioma Genome Atlas; CNTN3, contactin 3; LGI1, leucine rich glioma inactivated 1; SLC39A12, solute carrier family 39 member 12; PCSK1, proprotein convertase subtilisin/kexin type 1; SNX10, sorting nexin 10.”)
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Figure 5. Correlation between RNF5 and the 4 negatively correlated genes. Correlation between RNF5 and (A) SNX10, (B) PCSK1, (C) LGI1 and (D) SLC39A12. RNF5, ring finger protein 5; SNX10, sorting nexin 10; PCSK1, proprotein convertase subtilisin/kexin type 1; LGI1, leucine rich glioma inactivated 1; SLC39A12, solute carrier family 39 member 12. |
|—|—|](/article_images/ol/18/5/ol-18-05-4659-g04.jpg “Figure 5. – Correlation between RNF5 and the 4 negatively correlated genes. Correlation between RNF5 and (A) SNX10, (B) PCSK1, (C) LGI1 and (D) SLC39A12. RNF5, ring finger protein 5; SNX10, sorting nexin 10; PCSK1, proprotein convertase subtilisin/kexin type 1; LGI1, leucine rich glioma inactivated 1; SLC39A12, solute carrier family 39 member 12.”)
Discussion
Ubiquitin ligases have emerged as critical regulators in tumor development and metastasis [13, 22-25]. This study investigated the role of RNF5, an E3 ubiquitin ligase, in human glioma. Our findings demonstrate that RNF5 expression is not uniform across glioma grades and is significantly associated with patient prognosis in AG and GBM. Contrary to some cancers where high RNF5 expression is linked to poor prognosis, we observed that higher RNF5 expression correlated with improved survival in AG and GBM patients. In vitro, RNF5 overexpression in U251 cells enhanced colony formation, suggesting a role in promoting cell proliferation, which appears paradoxical given the improved survival outcome associated with high RNF5 expression in certain glioma subtypes. This highlights the complex and potentially context-dependent role of RNF5 in cancer.
GSEA revealed that RNF5 is associated with several key KEGG signaling pathways, including the Wnt signaling pathway, apoptosis, cell adhesion molecules, cytokine-cytokine receptor interaction, focal adhesion, and ECM-receptor interaction. These pathways are central to cancer development and progression, suggesting that RNF5 may influence glioma biology through multiple mechanisms affecting cell growth, survival, and interaction with the tumor microenvironment.
To identify potential downstream targets of RNF5, we predicted candidate ubiquitination substrates through correlation analysis. We identified four genes (SNX10, PCSK1, LGI1, and SLC39A12) that were negatively correlated with RNF5 expression. These genes may be directly targeted for ubiquitination and degradation by RNF5 in glioma cells. Further research is needed to validate these genes as direct RNF5 substrates and to determine the functional consequences of their regulation by RNF5 in glioma.
Previous research in breast cancer has shown a contrasting role for RNF5, where high expression is associated with poorer prognosis and increased cell proliferation [13, 21]. In MCF-7 breast cancer cells, silencing RNF5 inhibited cell proliferation and increased TP53 expression [21]. This inverse relationship between RNF5 and TP53 in breast cancer, and the known role of the tp53 gene serves an important role in regulating cell cycle arrest and apoptosis in response to cellular stress, prompted us to consider the TP53 status in glioma. Gliomas, particularly GBM, are characterized by a high frequency of TP53 mutations [28, 29]. It is plausible that the presence of TP53 mutations in glioma may alter the cellular response to RNF5. In glioma cells with mutated TP53, RNF5 might promote cell survival and proliferation through pathways that are normally suppressed by functional TP53. This could explain the observed association of higher RNF5 expression with better prognosis in AG and GBM – it might be indicative of a different, perhaps less aggressive, tumor biology in the context of TP53 mutations. However, this remains speculative and requires further investigation.
The seemingly contradictory prognostic effect of RNF5 expression in glioma compared to breast cancer underscores the need for a deeper understanding of RNF5’s mechanism of action in different cancer types. While this study provides valuable insights, it is limited by the lack of in vivo validation and functional studies beyond colony formation. Future research should investigate the impact of RNF5 on apoptosis, invasion, and migration in glioma, and validate these findings in clinical glioma samples.
Conclusion
This study provides evidence that RNF5 expression is differentially regulated in glioma and is associated with patient prognosis in AG and GBM. GSEA analysis revealed the involvement of RNF5 in key cancer-related signaling pathways. Bioinformatics analysis identified potential ubiquitination substrates of RNF5 in glioma. These findings contribute to our understanding of the complex role of RNF5 in glioma biology and suggest that RNF5 and its downstream targets may represent potential therapeutic avenues for this challenging disease. Further research is warranted to fully elucidate the mechanisms of RNF5 action and its interaction with pathways like the TP53 pathway in glioma pathogenesis.
Acknowledgements
The authors gratefully acknowledge Mr. Xue Shengbai (Clinical Department of Nanjing Medical University) for his technical assistance.
Funding
This work was supported by grants from the Foundation of Jiangsu Provincial Health Department (grant no. YG201514), Xuzhou Municipal Bureau on Science and Technology (grant no. XM12B055), and Xuzhou Medical University (grant no. 2018KJ09).
Availability of Data and Materials
The datasets analyzed during this study are available from the corresponding author upon reasonable request.
Authors’ Contributions
YG, CX, MJ, QS, and YS conceived and designed the study. YG, CX, MJ, QA, BZ, XC, LW, and YW performed the experiments. YG, CX, and LW conducted the statistical analysis. YG, CX, QA, QS, and YS wrote the manuscript. All authors have read and approved the final manuscript.
Ethics Approval and Consent to Participate
The study protocol was approved by the Ethics Committee of Xuzhou Children’s Hospital.
Patient Consent for Publication
Not applicable.
Competing Interests
The authors declare no competing interests.
