The role of ASIC2 in glioma progression: implications for prognosis and therapeutic targeting

Abstract

Glioma, the most frequent primary intracranial tumor, is characterized by infiltrative growth in the central nervous system, pronounced invasiveness, high malignancy, and poor clinical prognosis. The existing treatment methods include surgery, radiotherapy and chemotherapy, but the efficacy is still limited. Analysis of The Cancer Genome Atlas (TCGA) dataset reveals marked downregulation of acid-sensing ion channel 2 (ASIC2) expression in glioma tissues, which significantly correlates with reduced patient survival. Moreover, ASIC2 expression is inversely associated with the extent of immune cell infiltration and glioma stem cell markers. Functional experiments demonstrate that both knockdown and overexpression of ASIC2 critically regulate glioma cell proliferation, invasion, and metastatic potential through mechanisms mediated by matrix metalloproteinase 2 (MMP2), calcineurin, and nuclear factor of activated T cells 1 (NFAT1) signaling pathways. These findings delineate a pivotal role for ASIC2 in governing glioma malignant behavior and establish its relevance as a potential molecular target for therapeutic intervention.

Introduction

Glioma is the most common primary intracranial tumor, constituting about 81% of malignant brain tumors (Ostrom et al., 2014). Classified by the World Health Organization (WHO) into four grades (I–IV), its malignancy increases with grade, leading to heightened invasion, faster proliferation, and poorer survival (Louis et al., 2007). Standard treatments include surgery, radiotherapy, and chemotherapy. A key challenge is its early and extensive infiltration of the brain parenchyma, which makes treatment difficult and contributes to its high malignancy (Giese et al., 2003). Paradoxically, while highly invasive within the central nervous system, extracranial metastasis of glioblastoma is very rares of only 0.4%–2% (Kurdi et al., 2023). Therefore, developing strategies to curb glioma invasion is a critical and urgent goal.

Changes in the tumor microenvironment influence how cells become cancerous and develop diverse traits. Solid tumors are known for increased glycolysis and glucose uptake, which creates an acidic environment (Gatenby & Gillies, 2008). This acidity can activate acid-sensing ion channels (ASICs), as can the inflammation that occurs during tumor growth (Yu et al., 2015; Kellenberger & Schild, 2015). These results suggest that ASICs may play an important role in the development of tumors. ASICs, a member of the Degenerin/Epithelial Sodium Channels (DEG/ENAC) family, can sense the change of extracellular pH and play an important role in the regulation of liquid/ion homeostasis (Waldmann et al., 1997). ASICs are encoded by four genes and comprise six identified subunits—ASIC1a, ASIC1b, ASIC2a, ASIC2b, ASIC3, and ASIC4. Each subunit, which contains two transmembrane domains, a large extracellular loop, and intracellular N- and C-termini, is involved in diverse neuronal processes (Grifoni et al., 2008; Canessa, 2007).

Recent studies indicate that acid-sensing ion channel 2 (ASIC2) is highly expressed in neurons of both the peripheral and central nervous systems. In contrast to other ASIC subtypes present in the brain, which are generally confined to discrete regions or unique cellular populations, ASIC2 is less sensitive to extracellular pH and generates markedly prolonged currents when activated (Hesselager, Timmermann & Ahring, 2004; Bencheva et al., 2019). These findings imply that the loss of ASIC2 may facilitate tumor cell adaptation to acidic microenvironments and is potentially linked to chronic pathogenesis. Consequently, ASIC2 appears to be significantly correlated with glioma progression, representing a potential biomarker for therapeutic strategies.

While ASIC2 is a potential therapeutic target in various cancers (Liu et al., 2016; Zhou et al., 2017), its role in glioma remains incompletely understood. Existing studies have focused on its electrophysiological properties, leaving its mechanistic role in glioma pathogenesis largely unknown. We analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) and observed a significant downregulation of ASIC2 expression in glioma tissues. To investigate its function, we modulated ASIC2 protein levels and assessed its impact on glioma proliferation, invasion, and metastasis. We further examined how ASIC2 influences key proteins like matrix metalloproteinase 2 (MMP2), calcineurin, and nuclear factor of activated T cells 1 (NFAT1). Additionally, using multiple bioinformatics databases, we analyzed the correlation of ASIC2 with glioma immunity and prognosis, providing insights for future clinical therapy.

Materials and Methods

GEPIA

Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/index.html) is an interactive online analysis tool for the analysis of expression profile data in TCGA and Genotype-Tissue Expression (GTEx) databases, which can be used for gene correlation analysis, differential expression analysis and survival prognosis analysis (Li et al., 2021). Our study analyzed the differential expression of ASICs in glioma and ASIC2 in pan-cancer.

Kaplan–Meier Plotter

The Kaplan–Meier Plotter (https://kmplot.com/analysis/) covers gene expression profile data and clinical sample information data, and is a tool to evaluate the survival and prognosis of genes and cancer (Liu et al., 2018). In this study, we evaluated the correlation between ASICs and the survival and prognosis of glioma.

Sanger Box

SangerBox (http://sangerbox.com/) is an online interactive analysis platform for bioinformatics data, covering gene expression profile data, clinical sample information data, immune infiltration analysis and other modules in TCGA, GEPIA, International Cancer Genome Consortium (ICGC) and other databases (Guo et al., 2022; Wei et al., 2022). We evaluated ASIC2 and the survival and prognosis of cancer, and analyzed the dryness of tumor.

Bioinformatics

Bioinformatics (https://www.bioinformatics.com.cn/) is an online PPI analysis tool (Xia et al., 2022). In this study, we analyzed the gene analysis related to ASIC2, and used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis modules to analyze the related biological behaviors involved in ASICs.

TIMER

The TIMER database (https://cistrome.shinyapps.io/timer/) was employed as a publicly accessible platform to evaluate immune cell infiltration in tumor samples. Using this resource, we investigated the associations between ASIC2 expression in glioblastoma (GBM) and tumor purity, as well as the levels of diverse immune cell populations. TIMER utilizes RNA sequencing expression profiles to estimate the infiltration degrees of six immune cell subtypes: B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells.

Cell culture

The human glioma cell line LN229 was obtained from the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, and the U87MG cell line was acquired from the American Type Culture Collection (ATCC). Both cell lines were maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (Life Technologies, Carlsbad, CA, USA), 100 U/mL penicillin, and 100 µg/mL streptomycin. All cultures were incubated at 37 °C in a humidified atmosphere containing 5% CO₂. All cell lines were validated by STR profiling and confirmed free of mycoplasma contamination by Immunofluorescence assays. Throughout the study, aseptic techniques were strictly followed, with regular mycoplasma testing and incubator sanitation performed to ensure experimental quality.

Construction of short hairpin RNA targeting ASIC2

According to the target gene sequence of GenBank ASIC2 (NM_183377), the interference sequence was designed and ASIC2 targeted short hairpin RNA was constructed, in which the interfering lentivirus vector was GV493, which was synthesized by Shanghai Genechem Co., LTD (91059-1; Shanghai Genechem Co., LTD, Shanghai, China). The negative control sequence and vector synthesis were also completed by Shanghai Genechem Co., LTD. When the fusion degree of LN229 and U87MG cells reached 80%, 6 ×10⁴ cells were inoculated in 6-well plates. Cells were transfected with negative control lentivirus shRNA-Con and interfering lentivirus shRNA-AS, respectively. The transfection was observed by fluorescence microscope after 48 h of transfection, and the interference effect was detected.

Construction of targeted ASIC2 overexpression

The ASIC2 gene (ENST00000359872.6, CDS region) with EcoRI and BamHI restriction sites was amplified by nested PCR, and then ligated to PLVX-Puro vector. The primers were as follows: ASIC2 forward, 5′-GTTTAAGCAGAGCCCGAGGAC-3′, ASIC2 reverse, 5′-TCCCACCTGAGCTTGCTGTTC-3′, ASIC2-EcoRI forward, 5′-AGTCGAATTCGCCACCATGGACCTCAAGGAAAGCCC-3′, and ASIC2-BamHI reverse, 5′-AGTCGGATCCTCAGCAGGCAATCTCCTCCA-3′. After sequencing the plasmids, the overexpression plasmids, PSPAX2 and PMD2.G plasmids were transfected into glioma cells by Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA), and the overexpression effect was detected.

Construction of targeted ASIC2 overexpression

The ASIC2 gene (ENST00000359872.6, CDS region) with EcoRI and BamHI restriction sites was amplified by nested PCR, and then ligated to PLVX-Puro vector. The primers were as follows: ASIC2 forward, 5′-GTTTAAGCAGAGCCCGAGGAC-3′, and reverse, 5′-TCCCACCTGAGCTTGCTGTTC-3′, ASIC2-EcoRI forward, 5′-AGTCGAATTCGCCACCATGGACCTCAAGGAAAGCCC-3′, and ASIC2-BamHI reverse, 5′-AGTCGGATCCTCAGCAGGCAATCTCCTCCA-3′. After sequencing the plasmids, the overexpression plasmids, PSPAX2 and PMD2.G plasmids were transfected into glioma cells by Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA), and the overexpression effect was detected.

Quantitative Real Time Polymerase Chain Reaction (qPCR)

Cell treatment groups: Blank Control: Con; Knockdown Group: shRNA-AS, Knockdown Negative Control: shRNA-Con, Overexpression Group: Over-AS, Overexpression Negative Control: Over-Con.

RNA extraction

Following trypsinization, cells were counted with a microscope and 1 × 10⁷ cells were pelleted. The cell pellet was washed once with phosphate-buffered saline (PBS) and subsequently lysed in one mL of TRIzol reagent (Invitrogen) on ice for 10 min. After adding 200 µL of chloroform, the mixture was vigorously vortexed for 15 s and incubated on ice for 5 min. Samples were then centrifuged at 12,000 for 15 min at 4 °C. The aqueous upper phase was carefully transferred to a new tube. An equal volume of isopropanol was added to the aqueous phase, and the solution was gently mixed. RNA was precipitated by incubation at room temperature for 10 min, followed by centrifugation at 12,000 g for 10 min at 4 °C. The supernatant was discarded. The RNA pellet was washed with one mL of 75% ethanol and centrifuged at 7,500 g for 5 min at 4 °C. After discarding the supernatant, the pellet was air-dried briefly and dissolved in an appropriate volume of DEPC-treated water (with incubation at 65 °C for 5 min to facilitate dissolution). RNA concentration was determined using a microvolume spectrophotometer. This was repeated four times for each group. RNA with an A260/A280 ratio between 1.8 and 2.0 is of good quality for downstream applications.

cDNA synthesis

Total RNA (500 ng) was reverse transcribed into cDNA using the Evo M-MLV RT Master Mix Kit (AG11706; Accurate Biology, Hunan, China). The reaction mixture (10 µL final volume) contained two µL of 5X Evo M-MLV RT Master Mix, an appropriate volume of RNA, and nuclease-free water. Reverse transcription was performed at 37 °C for 15 min, followed by enzyme inactivation at 85 °C for 15 s. All RNA were prepared fresh before use. Repeat 4 times for each group.

Quantitative Real-Time PCR (qRT-PCR)

The qPCR reaction was performed using ABI Prism7500 fluorescence quantitative PCR instrument (Applied Biosystems, Foster City, CA, USA). The matching SYBR^® Green premix pro Taq HS qPCR kit (AG11701; Accurate Biology, Hunan, China) was used to prepare the amplification mixture. Each reaction (20 µL final volume) contained 100 ng of cDNA template, 0.4 µM of each forward and reverse primer, 0.08 µM ROX Reference Dye II, 10 µL of 2 × SYBR Green Pro Taq HS Premix II, and nuclease-free water. The reaction condition was 95 °C for 30 s, and then it went through 40 cycles of 95 °C for 5 s and 60 °C for 30 s. A dissociation curve analysis was performed to confirm amplification specificity.

The amplicon length is 97 bp, the primer sequences are as follows:

ASIC2 forward: 5′-GGAGCAGAGGCTCACATACC-3′

ASIC2 reverse: 5′-ACAGGCGGTGATGCTGTAAA-3′

β-actin forward: 5′-CATGTACGTTGCTATCCAGGC-3′

β-actin reverse: 5′-CTCCTTAATGTCACGCACGAT-3′.

The relative expression level of the gene was calculated by (2^−△△Ct).

Cell counting Kit-8 analysis

Glioma cells in the logarithmic growth phase were seeded into 96-well plates at a density of 2 ×10³ cells per well and assessed using a Cell Counting Kit-8 (CCK-8) assay. Cells were divided into five experimental groups: blank control, scramble shRNA control (shRNA-Con), ASIC2 knockdown (shRNA-AS), empty vector control (Over-Con), and ASIC2 overexpression (Over-AS). Following the addition of CCK-8 reagent (Solarbio, Beijing, China), cells were incubated for 2 h, and the absorbance at 450 nm was measured daily for five consecutive days. Each experimental condition was performed in triplicate.

Matrigel invasion assay

A total of 100 µL Matrigel (1:10; Sigma-Aldrich) was mixed with 300 µL precooled serum-free DMEM medium, and 50 µL was added to the upper chamber (Corning, Corning, NY, USA) respectively. After standing for 1 h, a suspension made of 4 × 10⁴ glioma cells was added, and 600 µL medium, 37 °C, 5% CO₂ was added to the lower chamber. After being fixed with 4% paraformaldehyde for 30 min, crystal violet staining was performed, and after being rinsed with PBS, five different fields of vision were counted, and the average value was calculated. Prior to the formal experiments, the selected Matrigel concentration was confirmed to form a complete and uninterrupted basement membrane through pilot experiments verified by crystal violet staining, thereby ensuring the validity of the invasion assay.

Colony formation assay

Glioma cells in the logarithmic growth phase were seeded into 6-well plates at a density of 200 cells per well. The cells were cultured in a humidified atmosphere of 5% CO₂ at 37 °C for 12 days, with the DMEM medium replaced every other day. Following the incubation period, the resulting cell colonies were fixed with 4% paraformaldehyde for 30 min and stained with 0.1% crystal violet solution. After rinsing with PBS to remove excess stain, the number of colonies (typically defined as clusters >50 cells) was counted manually. The experiment was independently repeated three times, and the results are presented as the mean ± standard deviation.

Scratch test

After digestion and counting with cell trypsin, glioma cell suspension was made, and 5 × 10⁵ cells were inoculated in 6-well plate. When the cells adhere to the wall, draw a line smoothly with the tip of the gun head. Cell migration was observed at 0 h and 24 h after changing fresh culture medium. Scratch closure rate is expressed as the average standard deviation (SD).

Flow cytometry analysis of cell cycle

Following trypsinization, glioma cells were collected and adjusted to a density of 1 × 10⁶ cells per sample in a 15 mL conical centrifuge tube. The cells were washed twice with pre-cooled PBS and fixed in 70% ethanol overnight at 4 °C. Subsequently, the fixed cells were stained with propidium iodide according to the manufacturer’s instructions of the cell cycle detection kit (Beyotime, Shanghai, China). DNA content analysis was then performed using a flow cytometer (BD Accuri C6 Plus), and the resulting data were analyzed to determine the cell cycle distribution.

Western blot

Equal amounts of protein samples were separated by SDS-PAGE and subsequently transferred onto nitrocellulose (NC) membranes. The membranes were blocked with 5% skim milk for 1 h at room temperature and then incubated with specific primary antibodies overnight at 4 °C. The primary antibodies used were as follows: anti-MMP2 (Cell Signaling Technology, cat. #13132, 1:1,000), anti-calcineurin (cat. #13422-1-AP, 1:1,000; Proteintech), anti-ASIC2 (Abcam, cat. #ab169768, 1:1,000), mouse anti-β-actin (cat. #66009-1-IG, 1:1,000; Proteintech), anti-NFAT1 (cat. #22023-1-AP, 1:1,000; Proteintech), and anti-GAPDH (cat. #60004-1-IG, 1:1,000; Proteintech). Following incubation, the membranes were washed three times for 10 min each with TBST. They were then incubated with appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies for 1.5 h at room temperature. After another three washes with TBST, protein bands were visualized using a chemiluminescent substrate and imaged with a digital imaging system. The band intensity was quantified by densitometric analysis. The expression level of the target protein was normalized using dual internal references, β-actin and GAPDH, with confirmed stability.

Statistical analysis

RNA-seq expression data for tumor tissues were obtained from TCGA database, with matched normal samples included as controls. All values are presented as mean ± SD. The Spearman method was employed to assess the correlation between ASIC expression and tumor mutational burden (TMB). Survival curves were plotted using the Kaplan–Meier method, and differences in ASIC2 and ASIC3 expression across glioma grades were analyzed by the Kruskal–Wallis test. Additional statistical analyses were performed using IBM SPSS Statistics (Version 19.0) and GraphPad Prism 5.0 (USA). Between-group comparisons were conducted using the Student’s t-test or one-way ANOVA, as appropriate. A p-value < 0.05 was considered statistically significant. All experiments were independently repeated three times.

Results

Expression of ASIC2 in tumor

Based on integrated RNA-seq data from TCGA and GTEx databases, ASIC2 expression was significantly altered in only three cancer types: cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma (GBM), and lower-grade glioma (LGG). Tumor tissues consistently showed downregulated ASIC2 expression compared to normal counterparts (Fig. 1). Collectively, these results suggest that ASIC2 may function as a tumor suppressor in glioma.

Figure 1. The expression of ASIC2 in pan carcinoma.

(A) The relative expression of ASIC2 in ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC. (B) The relative expression of ASIC2 in ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML. (C) The relative expression of ASIC2 in LGG, LIHC, LUAD, LUSC, OV, PAAD, and PCPG. (D) The relative expression of ASIC2 in PRAD, READ, SARC, SKCM, STAD, TGCT, THCA. (E) The relative expression of ASIC2 in THYM, UCEC and UCS. * P < 0.05.

Correlation between ASIC2 and tumor survival and prognosis

To evaluate the prognostic value of ASIC2, we analyzed its association with patient survival across multiple cancers using hazard ratios (HRs) for overall survival (OS) and disease-specific survival (DSS). We found that decreased ASIC2 expression was significantly associated with poorer survival outcomes in specific cancers. This was supported by HRs less than 1 (indicating a favorable prognostic role) and corroborated by Kaplan–Meier survival curves (Figs. 2A–2B, Figs. S2A–S2B). Notably, this association was particularly significant in skin cutaneous melanoma (SKCM) and glioma (Fig. 2C, Fig. S2C). The consistent protective role of ASIC2 in glioma suggests it may function as a tumor suppressor in this malignancy. Furthermore, expanding the analysis to the entire ASIC gene family in glioma revealed that tumors with low expression of the four genes within this family exhibited a worse survival prognosis (Figs. S1E–S1H).

Figure 2. Analysis of the survival and prognosis of ASIC2 in overall survival.

(A) OS curve of ASIC2 in glioma. (B) OS curve expressed by ASIC 2 in SKCM. (C) OS-related HR analysis of ASIC2 in cancers. GBMLGG represents a combined sample set of GBM and brain LGG from the TCGA database. The HR denotes the relative event risk between groups (HR>1: increased risk; HR<1: decreased risk), and the 95% confidence interval (CI) represents the statistical precision of the HR estimate.

Correlation analysis of ASIC2 in glioma

We next profiled the gene expression dynamics during glioma progression. This analysis revealed that several key genes, including ASIC2, synuclein beta (SNCB), transmembrane protein 130 (TMEM130), synaptosome associated protein 25 (SNAP25), SV2 related protein (SVOP), and complexin 2 (CPLX2), were significantly downregulated (Figs. 3A–3B). Functional enrichment analysis (GO/KEGG) indicated that ASICs are involved in processes including neutrophil regulation, vesicle phagocytosis, and synaptic signaling (Figs. 3C–3F). Correlation analysis showed significant negative associations between ASIC2 expression and multiple processes—including tumor inflammation, proliferation, epithelial-mesenchymal transition (EMT) markers, extracellular matrix (ECM) degradation, DNA repair, and replication (Figs. 4A–4F). Moreover, we found that ASIC2 expression was inversely correlated with tumor desiccation in glioma, breast cancer (BRCA), liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD), but positively correlated in pheochromocytoma and paraganglioma (PCPG), acute myeloid leukemia (LAML), and kidney renal clear cell carcinoma (KIRC) (Fig. 4G). These results indicate that ASIC2 exerts distinct, context-dependent functions across cancer types, negatively regulating glioma progression while potentially contributing to tumorigenesis in other malignancies.

Figure 3. The correlation analysis of ASIC2.

(A) Analysis of up-regulated and down-regulated genes in glioma. (B) Analysis of up-regulated and down-regulated genes in different pathological stages of glioma. (C–D) ASICs-related up-regulation and down-regulation KEGG analysis. (E–F) ASICs-related up-regulation and down-regulation GO analysis. The circle represents the enrichment degree, and the color represents the correlation degree.

Figure 4. Regulation of biological function of ASIC2 in glioma and analysis of tumor dryness.

(A) Correlation analysis between ASIC2 and ECM degradation. (B) Correlation analysis between ASIC2 and DNA repair. (C) Correlation analysis between ASIC 2 and DNA replication. (D) Correlation analysis between ASIC2 and epithelial mesenchymal transition. (E) Correlation analysis between ASIC2 and glioma inflammation. (F) Correlation analysis between ASIC2 and glioma proliferation. (G) Correlation analysis between ASIC2 and tumor stem cells.

ASIC2 participates in malignant biological behavior of glioma

The effect of ASIC2 expression on malignant behaviors was investigated in glioma cell lines LN229 and U87MG. Results demonstrated that knockdown of ASIC2 promoted proliferation, clonogenicity, invasion, and migration of glioma cells. In contrast, ASIC2 overexpression attenuated these malignant phenotypes (Figs. 5E–5J, 6A–6C). Based on these findings, we propose that ASIC2 may function as a tumor suppressor in glioma, which aligns with the bioinformatic analysis from the database. Knockdown of ASIC2 resulted in an increased malignant phenotype in glioma cells.

Figure 5. ASIC2 regulates the malignant biological behavior of glioma.

(A) Green fluorescent protein detection in LN229 and U87MG cell lines. (B) The detection of expression level of ASIC2 by shRNA plasmid. (C) Detection of ASIC2 expression level by shRNA plasmid. (D) Detection of ASIC2 expression level by overexpression plasmid. (E) Detection of cell proliferation ability with U87MG cells. (F) Detection of cell proliferation ability of LN229 cells in each group. (G) Detection of cell clone forming ability of U87MG and LN229 cells. (H) Statistical analysis of colony formation in U87MG and LN229 cells. (I) Detection of cell invasion ability of U87MG and LN229 cells. (J) Statistical analysis of the number of U87MG and LN229 cells migrating through the chamber. Data are expressed as the mean ± SD. * P < 0.05, ** P < 0.01 vs control, # P < 0.05, ## P < 0.01 vs plasmid control.

Figure 6. ASIC2 regulates the migration and invasion of glioma.

(A) Detection of cell migration ability of glioma groups. (B) Statistical analysis of the wound closure rate in U87MG cells. (C) Statistical analysis of the wound closure rate in LN229 cells. (D–E) Regulation of cell cycle by ASIC2 on LN229 cells. (F, H) ASIC2 regulates the expression levels of P21, cycline D1 proteins in LN229. (G, I) Statistical analysis of protein expression in LN229 cells after ASIC2 interference and overexpression. (J–K) ASIC2 regulates the expression levels of MMP2, calcineurin and NFAT1 proteins in glioma cells. (L–O) Statistical analysis of protein expression in U87MG and LN229 cells after ASIC2 interference and overexpression. Data are expressed as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001 vs control, #P < 0.05, ##P < 0.01, ###P < 0.001 vs plasmid control.

To further investigate the underlying mechanism, cell cycle distribution was analyzed. The results revealed that ASIC2 knockdown significantly reduced the proportion of cells in G1 phase (from 63.93% ± 1.33% to 52.86% ± 2.91%) and increased those in S phase (from 19.06% ± 2.43% to 29.76% ± 2.08%). Conversely, ASIC2 overexpression increased the G1 population (from 63.93 ± 1.33% to 73.2 ± 2.8%) and decreased the G2/M population (from 18.03 ± 2.27% to 11.61 ± 2.20%) (Figs. 6D–6E). This indicates that ASIC2 regulates the G1/S transition in glioma cells.

Alkylaminophenol has been shown to arrest LN229 cells at the G1/S transition, exerting antitumor effects (Doan et al., 2019). However, the role of ASIC2 in the glioma cell cycle remains unknown. Here, we found that ASIC2 knockdown upregulated cyclin D1 and downregulated p21, while its overexpression had the opposite effects (Figs. 6F–6I). These results identify ASIC2 as a key regulator of the G1/S transition in glioma, highlighting its potential as a therapeutic target for cell cycle intervention.

Effect of ASIC2 on the Invasiveness of Gliomas

Subsequently, we evaluated the expression levels of MMP2, calcineurin, and NFAT1—proteins implicated in tumor invasion—following ASIC2 modulation in LN229 and U87MG glioma cell lines. Downregulation of ASIC2 significantly promoted the expression of calcineurin, MMP2, and NFAT1 in both U87MG and LN229 cells (P < 0.05; Figs. 6J, 6L, 6M). Conversely, ASIC2 overexpression suppressed the expression of calcineurin, MMP2, and NFAT1 in these cells (P < 0.05; Figs. 6K, 6N, 6O). These results suggest that ASIC2 may influence glioma invasion and metastasis by regulating the expression of MMP2, calcineurin, and NFAT1.

Discussion

Glioma, the most common primary intracranial tumor, exhibits high invasiveness driven by multi-gene programs. Since current treatments are often ineffective, preventing its invasion and metastasis remains a major clinical challenge. The ASIC family, sensors of extracellular proton concentration, contribute to a range of pathologies, including tumors (Wu et al., 2017; Baron & Lingueglia, 2015). Consequently, targeting ASICs represents a promising avenue for developing novel cancer prevention and therapeutic strategies.

Accumulating evidence implicates ASIC2 in various cancers, including glioma (Zhou et al., 2017; Wang et al., 2019). Furthermore, its broad distribution within the nervous system implies a role in regulating neural functions. Analysis of ASIC2 expression using RNA-seq data from the TCGA and GTEx databases demonstrated a marked reduction in glioma tissues compared to normal counterparts (Fig. 1). Strikingly, this decreased expression of ASIC2 was correlated with diminished survival rates, indicating its adverse prognostic implications in glioma patients (Fig. 2, Fig. S2), suggesting it acts as a favorable prognostic marker. However, research has primarily focused on its electrophysiological role, and a specific pharmacological inhibitor is lacking (Hesselager, Timmermann & Ahring, 2004). The fact that the broad-spectrum ASIC inhibitor amiloride triggers currents only in high-grade glioma cell simplies that ASIC2 may mediate this effect and contribute to progression (Vila-Carriles et al., 2006).

As pivotal components of the tumor microenvironment, immune cells regulate key processes such as immune escape, sparking broad interest in cancer vaccines that reactivate the host immune system to prevent recurrence (Qin et al., 2021; Accolla & Tosi, 2013). Cytokines such as interleukin 6 (IL-6), transforming growth factor beta (TGF-β), tumor necrosis factor-alpha (TNF-α) secreted by immune cells in the course of cancer are related to the invasion and metastasis of the tumor (Taniguchi & Karin, 2014; Candido & Hagemann, 2013). Among them, the expression of IL-6 is often up-regulated in tumors, which can protect tumor cells from DNA damage caused by radiotherapy and chemotherapy. At present, drugs against IL-6 have been used in cancer treatment research (Yao et al., 2014; Kumari et al., 2016). In glioblastoma, tumor cells downregulate ASIC2 expression, leading to impaired activation of effector immune cells and insufficient chemokine signaling within the tumor microenvironment. We evaluated the correlation between ASIC2 expression and tumor-infiltrating immune cells, revealing a significant inverse association with B cells, T cells, neutrophils, macrophages, and dendritic cells (DCs). Further, ASIC2 exhibited extensively negative associations with major immune chemokines and receptors—such as CCL22, CXCL12, CCR7, CXCR3, and CXCL10 (Figs. S3, S4). Furthermore, GO and KEGG analyses indicated a link between ASICs and the regulation of neutrophil nuclear activation (Fig. 3). These findings suggest that ASIC2 may facilitate tumor progression by negatively regulating anti-glioma immunity through the modulation of immune cell infiltration.

The calcineurin-NFAT1 axis transduces diverse signals to the nucleus, regulating processes from immune responses to axon growth (Crabtree & Schreiber, 2009). This signaling is initiated by calcineurin, a heterodimeric Ca (Louis et al., 2007)⁺/calmodulin-dependent serine/threonine phosphatase (Creamer, 2020; Rusnak & Mertz, 2000). Calcineurin activates NFAT1 by dephosphorylation, triggering its nuclear translocation and the subsequent regulation of gene expression (Ren et al., 2021; Crabtree & Olson, 2002). In high-grade glioma, where calcineurin is upregulated this axis drives tumor progression by promoting proliferation, invasion, and metastasis (Brun et al., 2013). Matrix metalloproteinase MMP2, defined as a key pro-invasive effector frequently overexpressed in cancer and inflammation, constitutes a critical downstream target of this pathway. Its elevated expression is considered a contributing factor to enhanced tumor invasiveness. Our findings indicate that knockdown of ASIC2 leads to increased MMP2 expression, which correlates with accelerated wound closure in scratch assays and an elevated number of tumor cells migrating through the extracellular matrix (Fig. 6).

Glioma invasion into adjacent normal tissue is driven by the coordinated activation of multiple gene programs governing distinct pathological processes, such as cell proliferation, apoptosis, necrosis, invasion, and angiogenesis. Consequently, complete surgical eradication is often unattainable, leading to a poor prognosis with a median survival of only about 14 months for patients with high-grade glioma (Westphal et al., 2003). We evaluated the proliferative capacity of U87MG and LN229 cells using the CCK-8 assay. The results demonstrated that downregulation of ASIC2 expression promoted tumor cell proliferation, whereas ASIC2 overexpression suppressed proliferation (Fig. 5). In LN229 glioma cells, our findings establish ASIC2 as a critical negative regulator of cell cycle progression at the G1/S transition. Functionally, ASIC2 interference reduced the G0/G1 phase population while increasing the S phase fraction, suggesting facilitated G1/S transition and enhanced proliferation. Conversely, ASIC2 overexpression induced G0/G1 phase arrest and diminished the G2/M population (Figs. 6D–6E). Mechanistically, these effects are mediated through the regulation of key cell cycle proteins: ASIC2 knockdown promoted the G1/S phase transition by downregulating p21 and upregulating Cyclin D1, whereas its overexpression exerted the opposite effect, leading to G1 arrest (Figs. 6F–6I). Thus, our results provide definitive molecular evidence that ASIC2 constrains glioma progression by controlling the G1/S checkpoint.

In conclusion, this study further confirms the downregulation of ASIC2 expression in gliomas and extends the investigation into its role in other cancer types. At the cellular level, we demonstrate that ASIC2 contributes to the malignant phenotype of glioma by modulating proliferation, invasion, and migration in U87MG and LN229 cell lines. Furthermore, we explore the regulatory effects of ASIC2 on cell cycle progression and key proteins involved in invasion and metastasis. While this study reveals the diverse roles of ASIC2 across different cancers, the precise molecular mechanisms underlying these functions remain to be fully elucidated. While conventional drug development has primarily focused on inhibiting overactivated oncoproteins, restoring or mimicking the function of tumor suppressor genes has emerged as a key frontier strategy in contemporary cancer therapeutics. “restoring” or “imitating” the normal function of ASIC2 in glioma highlights an alternative and valuable intervention strategy.

Abbreviations

ASICs

acid-sensing channels

DEG/ENAC

Degenerin/Epithelial Sodium Channels

ASIC2

acid-sensing ion channel 2

TCGA

The Cancer Genome Atlas

MMP2

matrix metalloproteinase 2

NFAT1

nuclear factor of activated T cells 1

ICGC

International Cancer Genome Consortium

ATCC

American Type Culture Collection

TMB

tumor mutation burden

CESC

cervical squamous cell carcinoma and endocervical adenocarcinoma

GBM

glioblastoma multiforme

LGG

lower-grade glioma

HR

hazard ratio

OS

overall survival

DSS

disease-specific survival

SKCM

skin cancer melanoma

SNCB

synuclein beta

TMEM130

transmembrane protein 130

SNAP25

synaptosome associated protein 25

SVOP

SV2 related protein

CPLX2

complexin 2

PCPG

pheochromocytoma and paraganglioma

LAML

acute myeloid leukemia

KIRC

kidney renal clear cell carcinoma

IL-6

interleukin 6

TGF-β

transforming growth factor beta

TNF-α

tumor necrosis factor-alpha

ECM

extracellular matrix

Funding Statement

This work was financially supported by the National Natural Science Foundation of China (8257052788), the Key Scientific Research Plan Projects of Shaanxi Education Department (23JS009, 22JS004), the Shandong provincial Natural Science Foundation of China (ZR2021QH367, ZR2024MH082), and the Research and Development Fund of the Affiliated Hospital of Shandong Second Medical University (2024FYM038). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.