Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma
- Authors:
- Published online on: April 18, 2023 https://doi.org/10.3892/etm.2023.11952
- Article Number: 253
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Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Throughout the last 10 years, glioma has persisted as the foremost prevalent and lethal primary brain tumor among adult populations worldwide, exhibiting an annual incidence of 6 cases per 100,000 individuals and a 5-year overall survival rate not exceeding 35% (1,2). According to recent studies, low-grade gliomas (LGG) account for 15-20% of all adult gliomas and correlate with a median overall survival of 10 years, which is higher compared with the median overall survival of high-grade glioma (HGG) (3,4). Tumor-associated epilepsy is a common symptom in patients with LGG (5). Nevertheless, patients with LGG have a higher mortality rate when compared with the general population (6). Despite improved advancements in diagnostics and therapeutic techniques, the majority of LGGs in adults invariably progress to glioblastoma (GBM) over time (7). Moreover, high-risk LGG patients display shorter survival outcomes when compared with low-risk LGG patients (8,9). Thus, it is necessary to elucidate the prognostic predictors and underlying molecular mechanisms in patients with LGG.
Spectrin β non-erythrocytic 2 (SPTBN2), also termed β-III spectrin, is highly expressed in the brain and plays an important role in the neuronal membrane skeleton (10). SPTBN2 regulates glutamate-associated pathways by stabilizing excitatory amino-acid transporter 4(11). SPTBN2 is detected in numerous tumors and is involved in tumor occurrence and metastasis (12-14). The expression of SPTBN2 is higher in lung cancer compared with in normal lung tissues (14). In addition, SPTBN2 expression is correlated with the prognosis of patients with lung adenocarcinoma (14). SPTBN2 is significantly overexpressed in endometrioid endometrial cancer and is positively associated with poor prognosis (15).
The SPTBN2 expression, prognosis and regulatory mechanism in LGG remain elusive. A prior study revealed that SPTBN2 has an adverse effect on reduced infiltration of CD4+ T cells, contributing to a suboptimal prognosis for patients with ovarian cancer (16). Nevertheless, the potential function of SPTBN2 in regulating tumor immune infiltration in LGG is poorly understood. The present study performed expression and survival analyses for SPTBN2 in a pan-cancer study. Next, the potential upstream noncoding RNAs (ncRNAs) of SPTBN2 were investigated in LGG, including microRNAs (miRNAs/miRs) and long noncoding RNAs (lncRNAs). Finally, the relationship of SPTBN2 expression to immune infiltration, immune biomarkers, and immune checkpoints in LGG was determined. The aim of the present study was to investigate the association between ncRNA-mediated downregulation of SPTBN2 and tumor immune infiltration and prognosis in patients with LGG.
Materials and methods
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (approval no. MRCTA, ECFAH of FMU [2022]509).
The Cancer Genome Atlas (TCGA) data download, process, and analysis
Pan-cancer gene expression data were obtained from TCGA database (https://tcga-data.nci.nih.gov/tcga/; V33.0; accession no. phs001145). The 33 TCGA cancer types analyzed are presented in Table SI. A differential expression analysis of SPTBN2 was performed using the R package (version 3.6.3) (17). Weighted Pearson correlations and P-values were also calculated using the R package (version 3.6.3) ‘weights’ (https://CRAN.R-project.org/package=weights) (18). P<0.05 was considered to indicate a statistically significant difference. The clinicopathological features of patients with LGG are displayed in Table I. Patients with incomplete clinical information were excluded.
 | Table ICorrelation of SPTBN2 mRNA with clinicopathological features in The Cancer Genome Atlas cohort. |
Gene Expression Profiling Interactive Analysis (GEPIA) database analysis
GEPIA (http://gepia.cancer-pku.cn/detail.php; accessed on 16 August 2022; accession no. GEPIA2) is a web server for gene-expression profiling and correlation analysis based on The Genotype-Tissue Expression (GTEx) data and TCGA (19). GEPIA was used to analyze SPTBN2 and lncRNA expression in various types of cancer. An appropriate expression threshold was selected to split the high and low expression cohorts by grouping cut-offs. High cut-off values were considered to be samples with expression levels above this threshold, and were the high expression cohorts. Samples with lower cut-off values were considered to have an expression level below this threshold and were considered to be the low-expression cohort. A comparison of high and low-expression groups was completed using GEPIA. P<0.05 was considered to indicate a statistically significant difference. GEPIA was used to generate survival analysis for SPTBN2 pan-cancer studies, including overall survival (OS) and disease-free survival (DFS). Also, candidate lncRNAs in SPTBN2 were assessed prognostically using GEPIA. Cluster of differentiation 274 (CD274), programmed cell death 1 (PDCD1), cytotoxic T lymphocyte antigen 4 (CTLA4), sialic acid-binding immunoglobulin-like lectin 15 (SIGLEC15), T cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory domains (TIGIT), hepatitis A virus cellular receptor 2 (HAVCR2), lymphocyte activation gene-3 (LAG3), indoleamine 2,3-dioxygenase 1 (IDO1) and programmed cell death 1 ligand 2 (PDCD1LG2) were selected to be immune checkpoints. The GEPIA database investigated the relationship between SPTBN2 and immune checkpoints in LGG.
Encyclopedia of RNA Interactomes (ENCORI) database analysis
ENCORI (http://starbase.sysu.edu.cn/; accessed on 16 August 2022, version 2.0) is an online publicly accessed platform for studying the interactions between various RNAs (20). Candidate miRNAs were generated using ENCORI. Several target prediction programs were used to obtain upstream binding miRNAs of SPTBN2, including RNA22, PITA, miRmap, microT, PicTar, miRanda and TargetScan (http://starbase.sysu.edu.cn; version 2.0) (21). In addition, parameters for degradome data (low stringency) and pan-cancer type (one cancer type) were set. Only the predicted miRNAs obtained in at least three programs were considered candidate miRNAs of SPTBN2 and included for subsequent analysis. ENCORI was also used to generate the correlation between miRNAs and SPTBN2 in LGG. miRNAs negatively correlated with SPTBN2 were selected for subsequent survival analysis. Survival analysis of candidate miRNAs was performed by the ggplot2 R package (version 3.6.3) (https://cran.r-project.org/package=ggplot2) (22). Besides, candidate lncRNAs that could potentially bind to candidate miRNAs were generated using ENCORI.
Prediction of lncRNA and ceRNA network construction
Analysis of miRNet2.0 (www.mirnet.ca/miRNet/home.xhtml; version Primeface 11) and ENCORI was implemented to predict targeted lncRNAs of miRNAs. The positive correlation between SPTBN2 and targeted lncRNAs was analyzed using miRNet2.0 databases following the ceRNA hypothesis. Moreover, a lncRNA-miRNA-mRNA interaction network of SPTBN2 was constructed using ENCORI to understand post-transcriptional gene regulation. Overall survival analysis of these candidate miRNAs was performed using R package. The Sankey diagram was generated using SankeyMATIC (www.sankymatic.com).
University of California, Santa Cruz (UCSC) Xena database analysis and Kaplan-Meier plotter analysis
The UCSC Xena database (http://xena.ucsc.edu/; accessed on 16 August 2022) supports the visualization and analysis of correlations between genomic and/or phenotypic variables. The database contains numerous public datasets, including data from TCGA. The database provides information on gene expression and survival outcomes. The expression and survival curve of lncRNAs was obtained by combining the GEPIA database (http://gepia.cancer-pku.cn/detail.php; accession no. GEPIA2) (19) and the ‘survival’ package-derived R Project (http://cran.r-project.org/package=survival) (23).
TIMER database analysis
TIMER (https://cistrome.shinyapps.io/timer/; accessed on 16 August 2022; version 2.0) is a comprehensive database established for the systematical analysis of tumor-infiltrating immune cells and their clinical impact. TIMER was also employed to analyze the relationship between SPTBN2 expression and immune infiltrates in LGG. P<0.05 was considered to indicate a statistically significant difference. The survival module assessed the association between clinical outcomes and the abundance of immune infiltrates.
Immune infiltration analysis
The level of tumor immune infiltrates was identified using a single sample GSEA (ssGSEA) method with the Gene Set Variation Analysis R package (17) based on TCGA data sets (https://tcga-data.nci.nih.gov/tcga/; V33.0; accession no. phs001145) (24). The Spearman correlation test was used to calculate the correlation analysis between SPTBN2 and 24 immune cell types. Graphs and figures were generated using the ggplot2 R package (version 3.6.3) (https://cran.r-project.org/package=ggplot2) (23). The correlation between SPTBN2 and gene markers of immune cells was derived from GEPIA.
Enrichment analysis of Gene Set Enrichment Analysis (GSEA)
The tumor samples were divided into SPTBN2-low and SPTBN2-high groups according to the data downloaded from the TCGA database (https://tcga-data.nci.nih.gov/tcga/; V33.0; accession no. phs001145) (24). The R package DESeq2 (version 1.26.0) was used to conduct the GSEA between SPTBN2-low and SPTBN2-high groups (25). Heatmap generation was performed with the R package (version 3.6.3) (22). The top 25 negative and top 25 positive correlations and these genes were selected as the top 50 correlation-ranked probes. Adjusted P-value <0.05 and false discovery rate (FDR) q-value <0.25 were considered statistically significant.
University of Alabama at Birmingham Cancer (UALCAN) data analysis portal
UALCAN is a comprehensive and interactive web resource for analyzing cancer OMICS data (26). UALCAN was used to generate graphs and plots depicting survival information of miRNAs and lncRNAs in patients with LGG.
The Human Protein Atlas (THPA) analysis
THPA (version 21.1), a roadmap to generate renewable protein binders to the human proteome by integrating various omics technologies (including antibody-based imaging, mass spectrometry-based proteomics and transcriptomics), was used to assess SPTBN2 expression of LGG and normal tissues. The SPTBN2 expression of normal brain tissues and glioma tissues were detected using immunohistochemical data from THPA (27).
Tissue samples
The tissues of the patients (recruited June 2021 to January 2022) were obtained from the Department of Neurosurgery, The First Hospital of Fujian Medical University (Fuzhou, China). Glioma tissues were from first-onset cases that had not received any treatment before surgery. A total of five glioma tissues [4 World Health Organization (WHO) grade 2 glioma tissues and 1 WHO grade 3 glioma tissue] were used. The normal cerebral tissues were obtained from patients with severe traumatic brain injury undergoing internal decompression surgery. The inclusion criteria were as follows: i) The patients were >18 years of age; and ii) the patients had severe traumatic brain injury and required internal decompression surgery. The exclusion criteria were as follows: i) The patient was <18 years old; ii) the patient had other tumors in combination; iii) the patient did not provide consent; and iv) there was no serious damage or bleeding in the brain tissue taken. A total of 3 normal cerebral tissues were obtained from patients with severe traumatic brain injury undergoing internal decompression operation. The group of glioma samples comprised 5 patients (3 males and 2 females; age, 45.67±18.18 years), and the group of internal decompression samples comprised 3 patients (1 male and 2 females; age, 32.20±14.62 years). Resected samples were immediately frozen by liquid nitrogen and stored at -80˚C until use. The diagnosis of gliomas was confirmed by the pathologist through postoperative histological examination according to The 2021 WHO Classification of Tumors of the Central Nervous System (28). The pathologist was independent from the study. The diagnosis of human tumors is based on codes specified by the International Classification of Diseases (ICD) (29). The ICD was available from http://www.who.int/classifications/icd/en/. The Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Fuzhou, China) approved the study protocol. All patients provided written informed consent.
Western blotting assay
Cells were lysed in NP-40 buffer (Wuhan Boster Biological Technology, Ltd.) with protease inhibitor cocktail (MedChemExpress; cat. no. HY-K0010; 1:99) and phosphatase inhibitor cocktail III (MedChemExpress; cat. no. HY-K0023; 1:99). The proteins were extracted from tissue samples using RIPA lysis buffer (Beyotime Institute of Biotechnology; cat. no. P0013B) with protease inhibitor cocktail (MedChemExpress; cat. no. HY-K0010; 1:99) and phosphatase inhibitor cocktail III (MedChemExpress; cat. no. HY-K0023; 1:99). Protein levels were determined by bicinchoninic acid assay. Equal amounts of proteins (10 µg) extracted from tissue samples and cells were separated by 12% SDS-PAGE and transferred onto a 0.45-µm PVDF membrane (Amersham; Cytiva). Membranes were blocked with 5% skimmed milk [Beijing Solarbio Science & Technology Co., Ltd.; cat. no. D8340; with 1X TBST (TBS with 0.1% Tween-20)] for 2 h at room temperature. Next, the membranes were probed with primary antibodies for β-actin (1:50,000; cat. no. AC026; ABclonal Biotech Co., Ltd.) and anti-SPTBN2 (1:1,000; cat. no. 55107-1-AP; ProteinTech Group, Inc.) overnight at 4˚C. After three washes (1X TBST), the membranes were incubated with goat anti-rabbit IgG horseradish peroxidase-conjugated secondary antibodies (1:5,000; cat. no. SA00001-2; ProteinTech Group, Inc.) for 1 h at 37˚C. After three washes (1X TBST), target proteins were detected by ECL solution (Vazyme Biotech Co., Ltd.) on Amersham Imager 680 System (Amersham; Cytiva).
Statistical analysis
SPTBN2 expression analysis was conducted with the GEPIA, TIMER, THPA, and R projects using the ‘ggplot2’ package (version 3.6.3) (https://cran.r-project.org/package=ggplot2). Analysis of correlation was performed using Spearman's test. Survivals, including OS and DFS, were performed with GEPIA, ENCORI, TIMER and R projects (version 3.6.3) (23). The association between SPTBN2 expression and clinicopathologic features was evaluated using Fisher's exact test, χ2 test, Wilcoxon signed-rank test and logistic regression. In addition, the Kaplan-Meier method and Cox regression were used to evaluate the role of SPTBN2 expression in prognosis. P<0.05 was considered to indicate a statistically significant difference.
Results
Expression and survival analysis for SPTBN2 in pan-cancer studies
To explore the potential roles of SPTBN2 in carcinogenesis, the expression of SPTBN2 in various types of human cancer and corresponding TCGA and GTEx normal tissues were analyzed. Differences in SPTBN2 were detected in 27 types of cancer, except cholangiocarcinoma (CHOL), kidney chromosome cancer (KICH), mesothelioma (MESO), pheochromocytoma and paraganglioma (PCPG), sarcomas (SARC) and uveal melanoma (UVM) (Fig. 1A). SPTBN2 expression was downregulated In LGG samples compared with corresponding TCGA and GTEx normal tissues (Fig. 1). For OS, higher expression of SPTBN2 had an unfavorable prognosis in kidney renal clear cell carcinoma (KIRC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PAAD) and UVM (Fig. 1B-F). Moreover, higher expression of SPTBN2 was significantly associated with short DFS in KIRC and PAAD (Fig. 1G-K). However, lower expression of SPTBN2 was associated with short OS and DFS in LGG. Higher expression of SPTBN2 was associated with poor prognosis in KIRC and PAAD (Fig. 1B, E, G and J), while lower expression of SPTBN2 was associated with an unfavorable outcome in LGG (Fig. 1C and H).
Low SPTBN2 expression is associated with poor clinicopathological features of LGG
As shown in Table I, 528 LGG cases were collected from TCGA datasets with complete clinical and gene expression data. Patients with LGG were categorized into SPTBN2-high (n=264) and SPTBN2-low (n=264) groups. The association between SPTBN2 expression and clinicopathological characteristics of patients with LGG was evaluated (Table I and Fig. 2). Immunohistochemical analysis of SPTBN2 was conducted on normal brain and glioma tissues using the THPA database (Fig. 2A-C). A significant correlation between low SPTBN2 mRNA expression and poor clinicopathological features was detected, including elders (P=0.019; Fig. 2D), males (P<0.05; Fig. 2E), 1p/19q non-codeletion (P<0.001; Fig. 2F), wild-type isocitrate dehydrogenase (IDH) status (P<0.001; Fig. 2G), primary therapy (P<0.05; Fig. 2H) and WHO grade (P<0.05; Fig. 2I). The western blotting assay results demonstrated that the expression of SPTBN2 in LGG was significantly lower compared with that in normal brain tissues (P=0.0266; Fig. 2J). Furthermore, univariate logistic regression analysis (Table II) indicated that SPTBN2 mRNA expression was closely associated with 1p/19q codeletion [OR=0.323; 95% confidence interval (CI), 0.219-0.473; P<0.001], IDH status (OR=4.664; 95% CI, 2.822-8.014; P<0.001) and older ages (OR=0.653; 95% CI, 0.463-0.920; P=0.015).
| Table IIMultivariate logistic regression analysis of how SPTBN2 is associated with clinicopathological parameters in LGG. |
Predicted biological function and pathways of SPTBN2 in LGG
GSEA analysis was performed to identify the possible biological pathways regulated by SPTBN2 between SPTBN2-high and SPTBN2-low groups. As shown in Fig. 3A-D, several signal KEGG pathways were significantly associated with SPTBN2 expression, including ‘neuroactive ligand-receptor interaction’, ‘cytokine-cytokine receptor interaction’, ‘calcium signaling pathway’ and ‘cAMP signaling pathway’. A heatmap showed the top 50 genes in LGGs that were positively and negatively associated with SPTBN2 (Fig. 4A). The red color denoted positively correlated genes, and the blue color denoted negatively correlated (Fig. 4A). Briefly, SPTBN2 was positively associated with CDK5R1, PAK5, UNC5A, DLGAP3 and CELF5. By contrast, SPTBN2 was negatively associated with CD99, HOMER3, PTLP, SLC8B1 and CD44.
Analysis of candidate miRNAs and lncRNAs that bind to SPTBN2
According to the ceRNA hypothesis (30), miRNAs negatively correlate with SPTBN2, while lncRNAs correlate positively with SPTBN2. Candidate miRNAs must negatively correlate with SPTBN2 expression and be statistically associated with prognosis in LGG (30). Subsequently, candidate lncRNAs that might bind to the candidate miRNAs were generated using ENCORI. The enrolled lncRNAs must positively correlate with the expression of SPTBN2 and the prognosis of low-grade gliomas. MiRNAs and lncRNAs were rigorously screened out based on the ceRNA hypothesis.
Predicted miRNAs that could competitively bind to SPTBN2 were investigated. A total of six candidate miRNAs, including hsa-miR-214-3p, hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-15b-5p, hsa-miR-34c-5p and hsa-miR-424-5p, were revealed (Fig. 4B). Higher expression of five miRNAs, hsa-miR-15a-5p (Fig. 4C), hsa-miR-15b-5p (Fig. 4D), hsa-miR-16-5p (Fig. 4E), hsa-miR-34c-5p (Fig. 4F) and hsa-miR-424-5p (Fig. 4G) were associated with poor prognosis in LGG. Subsequently, 48 candidate lncRNAs associated with hsa-miR-15a-5p were regulated in LGG (Fig. 5A), of which 22 candidate lncRNAs related to hsa-miR-15a-5p were downregulated in LGG. The statistical significances of four lncRNAs (BASP1-AS1, EPB41L4A-AS1, LINC00641 and ARMCX5-GPRASP2) for predicting the prognosis of LGG were obtained, while the statistical significance of the other 18 LncRNAs were not. In addition, a positive correlation of candidate lncRNAs and SPTBN2 was revealed by ENCORI (Fig. 5B-E). A total of 43 candidate lncRNAs associated with hsa-miR-15b-5p were regulated in LGG (Fig. 5A), of which 21 candidate lncRNAs related to hsa-miR-15b-5p were downregulated in LGG. The statistical significances of two lncRNAs (LINC00641 and ARMCX5-GPRASP2) for predicting the prognosis of LGG were detected, while the statistical significance of the other 19 lncRNAs were not. A total of 49 lncRNAs that were associated with hsa-miR-16-5p underwent regulation in LGG (Fig. 5A), with 22 of these lncRNAs experiencing downregulation. The statistical significances of ARMCX5-GPRASP2 (Fig. 5F), BASP1-AS1 (Fig. 5G), EPB41L4A-AS1 (Fig. 5H), LINC00641 (Fig. 5I) were investigated to predict the prognosis of LGG, while the statistical significance of the other 18 lncRNAs were not. In LGG, a total of 27 candidate lncRNAs that were linked to hsa-miR-34c-5p exhibited regulation, as demonstrated in Fig. 5A. Among these, 15 candidate lncRNAs that were associated with hsa-miR-34c-5p were observed to be downregulated. A significant correlation was not found between all lncRNAs and prognosis for LGG. A total of 22 candidate lncRNAs associated with hsa-miR-424-5p were downregulated in LGG. A total of 47 candidate lncRNAs associated with hsa-miR-424-5p were downregulated in LGG (Fig. 5A), of which 22 candidate lncRNAs associated with hsa-miR-424-5p were downregulated. Finally, the statistical significance of four lncRNAs, including ARMCX5-GPRASP2, BASP1-AS1, EPB41L4A-AS1 and LINC00641, were detected to predict predict the prognosis of high and low expression levels in LGG (Fig. 5F-I).. In LGG patients, high expression of ARMCX5-GPRASP2 was associated with shorter survival time, while low expression of BASP1-AS1, EPB41L4A-AS1 and LINC00641 was associated with shorter survival time.
The Sankey diagram presents the lncRNA-miRNA-SPTBN2 regulatory network according to the ceRNA hypothesis (Fig. 5J). Overall, four lncRNAs (ARMCX5-GPRASP2, BASP1-AS1, EPB41L4A-AS1 and LINC00641) were involved in the regulation of SPTBN2 via five miRNAs (hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p and hsa-miR-424-5p) in LGG.
Association of immune cell infiltration, overall survival, immune checkpoints and SPTBN2 level in LGG
Infiltration of immune cells, including ‘macrophages’, ‘neutrophils’, ‘dendritic cells’, ‘NK cells’ and ‘T cells’, were negatively correlated with SPTBN2 expression (Fig. 6A). In addition, significant changes in immune cell infiltration with various copy numbers of SPTBN2 in LGG, including B cells, CD8+ T cells, CD4+ T cells, macrophage, neutrophils, and dendritic cells were observed (Fig. 6B). Correlation analysis between SPTBN2 and biomarkers of immune cells in LGG are presented in Table III. The survival module explored the association between clinical outcomes and the abundance of SPTBN2-related immune infiltrates (Fig. 6C). Higher immune infiltrates (B cells, CD4+ cells, neutrophils) and low expression of SPTBN2 were significantly positively associated with poor outcomes (Fig. 6C). Moreover, SPTBN2 mRNA expression was negatively correlated with nine immune checkpoints: CD274, CTLA4, HAVCR2, IDO1, LAG3, PDCD1, PDCD1LG2, SIGLEC15 and TIGIT (Fig. 7).
Discussion
Management of patients with low-grade gliomas is mainly based on clinical prognostic factors. Median survival varies from 3.2 (high-risk LGG) to 7.8 years (low-risk LGG) (31). Despite improved advances in diagnosis and therapeutic techniques, the majority of LGGs progress clinically to GBM over time (7). Moreover, high-risk LGGs resemble GBM and correlate with poor outcomes (32). Evidence suggests that SPTBN2 plays important roles in tumor initiation and progression in multiple types of human cancer, including ovarian cancer, endometrioid endometrial cancer and lung cancer adenocarcinoma (13,15). However, the expression, function, and molecular mechanism of SPTBN2 in LGG remain unclear.
The present study conducted a pan-cancer analysis of the expression of SPTBN2 using TCGA and GTEx data. Previous studies have indicated that SPTBN2 is highly expressed in lung adenocarcinoma and endometrioid endometrial cancer, is positively correlated with unfavorable prognosis and promotes cancer proliferation, invasion, and migration of cells (13,15). In the present study, the TCGA and GTEx databases analysis revealed a statistically significant decrease in SPTBN2 expression in LGG samples compared with normal tissues. SPTBN2 has been previously reported in the development of neurological disorders and cancer (13,15). The low expression of SPTBN2 may be associated with the expression of tumor suppressors in LGG (16). Additionally, SPTBN2 has recently been identified as a key gene in the development of seven different types of cancer, and it has been identified as a marker for the recognition of cancer patterns (33). However, survival analysis indicated that patients with LGG with low expression SPTBN2 had a worse prognosis. An age >40 years has been reported to be associated with an inferior prognosis (34). Known favorable molecular prognostic factors of LGG contain codeletion of chromosome 1p/19q and isocitrate dehydrogenase mutation (31). IDH wild-type LGGs mimicking high-grade gliomas are associated with poor outcomes (32). The present study revealed that low expression of SPTBN2 was significantly correlated with older adults (>40 years), 1p/19q non-codeletion and wild-type IDH status, which were associated with an unfavorable outcome.
It is well known that ncRNAs, including miRNAs and lncRNAs, play key roles in regulating gene expression via the ceRNA mechanism (14,15,35). The present study used seven prediction programs to identify the candidate miRNAs of SPTBN2. Lastly, six miRNAs were obtained, including hsa-miR-214-3p, hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-15b-5p, hsa-miR-34c-5p, and hsa-miR-424-5p. In addition, 5 miRNAs, including hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-15b-5p, hsa-miR-34c-5p and hsa-miR-424-5p, had pro-tumorigenic effects and were correlated with poor prognosis in LGG. Higher expression of hsa-miR-15b-5p has been reported to be associated with a short survival time of patients with LGG (36). Has-miR-15a-5p promotes the proliferation and invasion of colorectal cancer by targeting CCND1(37). Hsa-mir-16-5p is downregulated in giant cell tumors (38). Ectopic expression of hsa-miR-424-5p leads to enhanced growth of gastric cancer cells by targeting LATS1(39). The present study revealed that higher expression levels of hsa-miR-15a-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, were correlated with lower SPTBN2 mRNA expression and unfavorable prognosis in LGG. SPTBN2 was associated with poor prognosis and regulated by miRNA-1827 in ovarian cancer (16). SPTBN2 was also a target of miR-424-5p and promoted endometrial cancer metastasis via the PI3K/AKT pathway (15).
Based on the ceRNA hypothesis, the potential lncRNAs were positively related to SPTBN2 in LGG (30). A comprehensive, integrated analysis of lncRNAs indicated that the four most promising lncRNAs, including ARMCX5-GPRASP2, BASP1-AS1, EPB41L4A-AS1 and LINC00641, were associated with SPTBN2 mRNA expression and prognosis of LGG. Higher expression levels of BASP-AS1, EPB41L4A-AS1 and LINC00641 were associated with a favorable outcome of LGG, while higher expression of ARMCX5-GPRASP2 correlated with poor prognosis in LGG. BASP1-AS1 is a protective lncRNA and significantly impacts the proliferation of glioma cells (40). Functionally, the ectopic expression of BASP1-AS1 promotes cell proliferation and invasion in melanoma (41). LINC00641 is differentially expressed in various tumors and is associated with a poor prognosis (42). Decreased LINC00641 leads to changes in tumor proliferation (42). Yang et al revealed that reduced expression of LINC00641 is observed in glioma, and overexpression of LINC00641 promotes apoptosis of glioma (43).
Nevertheless, the role of ARMCX5-GPRASP2 and EPB41L4A-AS1 in predicting prognosis in LGG remains unclear. Low expression of EPB41L4A-AS1 has been detected in multiple types of human cancer and is associated with poor prognosis (44). EPB41L4A-AS1 functions as a repressor of the Warburg effect and plays a notable role in the metabolic reprogramming of cancer (44). EPB41L4A-AS1 also functions as an oncogene by regulating the Rho/ROCK pathway in colon cancer (45). The deletion of ARMCX5-GPRASP2 has been associated with the novel Xq22.1 deletion syndrome in a male patient with multiple congenital abnormalities (46). The present study revealed that higher expression of ARMCX5-GPRASP2 correlated with poor prognosis in LGG.
Previous studies have indicated that LINC01605 can regulate m6A modification of SPTBN2 mRNA in colorectal cancer (27). In a lncRNA-miRNA-mRNA network of bladder cancer, SPTBN2 and hsa-miR-590-3p affect the prognosis of patients with bladder cancer (14). Collectively, according to the ceRNA hypothesis, the Sankey plot can demonstrate the pathways by which low SPTBN2 expression is associated with poor prognosis in LGG. The Sankey plot illustrates the lncRNA-miRNA-SPTBN2 regulatory network based on the ceRNA hypothesis. In LGG, four lncRNAs (ARMCX5-GPRASP2, BASP1-AS1, EPB41L4A-AS1 and LINC00641) were regulated through five miRNAs (hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c -5p and hsa-miR-424-5p) were involved in the regulation of SPTBN2. Candidate miRNAs, lncRNAs, and SPTBN2 expression were observed to correlate with poor LGG prognosis.
Tumor cells frequently interact with the microenvironment and a variety of immune cells. Moreover, the tumor microenvironment has been shown to affect response to immune checkpoint blockade (47). An immune checkpoint blockade takes advantage of tumor immune infiltration to launch an effective immune response (47). The present study suggested that significant changes in immune infiltration with various copy numbers of SPTBN2, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells, were observed in LGG. Furthermore, higher immune cell infiltration (B cells, CD8+ cells, CD4+ cells, macrophages, neutrophils and dendritic cells) and low expression of SPTBN2 were significantly positively associated with poor outcomes. A previous study revealed that SPTBN2 generates adverse effects on the reduced infiltration of CD4+ T cells and leads to an unsatisfactory outcome in ovarian cancer (16). The present study also assessed the relationship between SPTBN2 and immune checkpoints. The results demonstrated that SPTBN2 mRNA expression was significantly negatively correlated with nine immune checkpoints, indicating that targeting SPTBN2 might increase the efficacy of immunotherapy in LGG. However, future experiments are required to ascertain the correlation between SPTBN2 and tumor immunity in LGG.
In summary, we elucidated that SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. We identified 6miRNAs and four lncRNAs being able to modulate SPTBN2 in an lncRNA-miRNA-mRNA network of LGG. Furthermore, our current findings also indicated that SPTBN2 possessed anti-tumor roles via regulating tumor immune infiltration and immune checkpoint expression. However, these results should be validated by more basic experiments and clinical trials in the future.
Supplementary Material
The Cancer Genome Atlas cancer types analyzed (n=33).
Acknowledgements
Not applicable.
Funding
Funding: The study was supported by the Excellent Talent Project of the First Affiliated Hospital of Fujian Medical University (grant no. YYXQN-YPS2021), Fujian Clinical Research Center for Neurological Disease (grant no. SSJ-YJZX-1) and Fujian Key Laboratory of Precision Medicine for Cancer (grant no. ZLZDSYS-2020).
Availability of data and materials
The datasets analyzed in this study are available in the following open access repositories. TGGA: The Cancer Genome Atlas (TCGA) database (https://tcga-data.nci.nih.gov/tcga/; version V33.0; release date, May 3, 2022; dbGaP Study accession no. phs001145), GEPIA (http://gepia.cancer-pku.cn/detail.php; accessed on 16 August 2022; accession no. GEPIA2), ENCORI (http://starbase.sysu.edu.cn; accessed on 16 August 2022), miRNet2.0 (www.mirnet.ca/miRNet/home.xhtml; accessed on 16 August 2022), The UCSC Xena database (http://xena.ucsc.edu/; accessed on 16 August 2022) and TIMER (https://cistrome.shinyapps.io/timer/; accessed on 16 August 2022). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
GRC, YBZ, SFZ, DZK and PSY designed the study. YBZ, GRC, PL and HCSG performed all bioinformatic analyses. GRC, YBZ and YWX completed the experiments. GRC, YXL, and PSY confirm the authenticity of all the raw data. YXL and DZK acquired the data, and analyzed and interpreted the data. GRC, YBZ, SFZ, and PSY drafted the manuscript. YBZ, YWX, PL and HCSG prepared the figures and interpreted the results. PSY and DZK were identified as the guarantors of the paper, taking responsibility for the integrity of the work as a whole. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The present study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University [approval no. MRCTA, ECFAH of FMU(2022)509]. Written informed consents were obtained from all enrolled individuals prior to their participation.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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