Choline kinase α interacts with epidermal growth factor receptor to activate the mitogen-activated protein kinase pathway and contributes to glioma tumorigenesis

Abstract

Background:

Glioma is a common malignant brain tumor with poor prognosis. Choline kinase α (CHKA) has been implicated in glioma progression, but its regulatory mechanisms remain unclear.

Methods:

Single-cell ribonucleic acid (RNA) sequencing was performed to assess the coexpression of CHKA and epidermal growth factor receptor (EGFR) in glioma subpopulations. Public datasets were analyzed to evaluate their clinical relevance, which was verified in a cohort from Ningxia Medical Hospital (November 2019–October 2024). Immunohistochemical staining confirmed their expression and subcellular localization. The interaction between CHKA and EGFR was examined using mass spectrometry, coimmunoprecipitation, polymerase chain reaction, and Western blotting. The effects of the CHKA/EGFR axis on the mitogen-activated protein kinase (MAPK) pathway were explored using polymerase chain reaction and Western blotting. Cell Counting Kit-8 (CCK-8), transwell, and wound-healing assays were conducted in glioma cell lines following CHKA knockdown and EGFR rescue. Finally, a nude mouse xenograft model was established to validate in vivo tumorigenicity and MAPK pathway activation.

Results:

CHKA was highly coexpressed with EGFR in specific glioma subpopulations, and their expression levels were positively correlated. Both genes were associated with advanced tumor grade and poor prognosis. CHKA interacted with EGFR and promoted its expression and phosphorylation. Silencing CHKA reduced EGFR levels and suppressed MAPK signaling. Functionally, CHKA enhanced glioma cell proliferation, migration, and invasion through EGFR upregulation. Moreover, CHKA knockdown inhibited tumor growth and MAPK activation in vivo, while EGFR overexpression restored tumorigenesis.

Conclusions:

CHKA drives glioma malignancy by regulating EGFR and activating the MAPK pathway. The CHKA/EGFR/MAPK axis represents a potential therapeutic target for glioma treatment.

Introduction

Glioma is the most prevalent primary tumor of the central nervous system (CNS), accounting for approximately 80% of all malignant brain tumors.^[1,2] Glioblastoma multiforme (GBM), classified as a World Health Organization (WHO) grade IV tumor, exhibits the highest incidence. Despite multimodal therapeutic approaches, the prognosis for patients with GBM remains poor, with a median overall survival of less than 2 years and a 5-year survival rate of approximately 10%.^[3] Current therapeutic strategies, including surgical resection, chemotherapy, and radiotherapy,^[4] are limited by multiple factors. The diffuse infiltrative growth of glioma cells prevents complete surgical excision, and the blood–brain barrier restricts the delivery of chemotherapeutic agents. Additionally, the intrinsic radioresistance of tumor cells poses a challenge, as dose escalation risks damaging normal brain parenchyma.^[5,6] Furthermore, the aggressive nature of gliomas, their high recurrence rates, and their propensity for progression significantly impair patient’s survival and quality of life, imposing a substantial socioeconomic burden. Although recent advances in immunotherapy and targeted therapy have modestly improved outcomes, therapeutic efficacy remains unsatisfactory.^[7] Therefore, elucidating the molecular mechanisms underlying glioma pathogenesis is essential for identifying novel therapeutic targets and developing more effective treatment strategies.

Choline, a hydrophilic quaternary ammonium compound, plays critical roles in physiological processes such as memory consolidation, cholinergic neurotransmission, and membrane biosynthesis and metabolism. In the Kennedy pathway of phosphatidylcholine biosynthesis, choline kinases (CHKs) catalyze the initial and rate-limiting reaction by converting choline into phosphocholine through phosphorylation. Phosphocholine is subsequently converted to cytidine diphosphate–choline.^[8] A hallmark of tumor metabolism is the aberrant phosphorylation of choline by CHKs, which drives phosphatidylcholine synthesis.^[9] The CHK family comprises two isoforms: CHK-α (CHKA) and CHK-β (CHKB). Accumulating evidence implicates CHKA in tumorigenesis and cancer progression, where it facilitates tumor cell proliferation.^[9] CHKA overexpression is observed in 40–60% of human cancers and is strongly associated with poor prognosis in patients with prostate cancer, early-stage non-small cell lung cancer, and hepatocellular carcinoma,^[10,11,12] establishing it as a potential diagnostic and prognostic biomarker.^[13] The development of CHKA inhibitors as anticancer agents is an active area of research, with several promising candidates advancing to clinical trials.^[14] Nevertheless, the role of CHKA in glioma pathogenesis and the mechanistic basis of its oncogenic function remain incompletely understood.

The human epidermal growth factor receptor (HER) tyrosine kinase family comprises four structurally related members: (1) epidermal growth factor receptor (EGFR), (2) HER2, (3) HER3, and (4) HER4. These receptors are pivotal regulators of apoptosis, proliferation, angiogenesis, and migration, and they contribute to the pathogenesis of various cancers. In many malignancies, EGFR undergoes molecular alterations such as overexpression, gene amplification, and mutations. In GBM, EGFR amplification drives tumor invasion, proliferation, and resistance to conventional therapies. Notably, 24–67% of GBMs harbor EGFR mutations, 40% exhibit gene amplification, and 60% demonstrate EGFR overexpression, making EGFR a prognostic marker in GBM. The therapeutic targeting of EGFR has garnered significant attention, with two main classes of inhibitors: (1) small-molecule tyrosine kinase inhibitors, which target the intracellular catalytic domain and (2) monoclonal antibodies, which block receptor dimerization by binding to the extracellular domain.^[15] EGFR inhibitors have been used in the treatment of various cancers, including breast, colorectal, lung, and pancreatic carcinomas. EGFR regulates several key pathways, including phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT), rat sarcoma virus (RAS)/mitogen-activated protein kinase (MAPK), and Janus kinase 2 (JAK2) signal transducer and activator of transcription, thereby modulating diverse cellular processes.^[16] MAPKs are serine/threonine kinases that orchestrate cellular responses to diverse stimuli, including growth factors, cytokines, and environmental stressors. In mammals, 14 MAPKs have been identified and classified into seven groups. The canonical MAPK pathways include extracellular signal-regulated kinase (ERK)1/2, ERK5, p38 MAPK, and c-Jun N-terminal kinase (JNK)1/2/3. Dysregulation of the MAPK pathway is a hallmark of gliomas, where it drives tumor cell proliferation, invasion, and survival.^[17] Activation of the MAPK pathway correlates with glioma grade and is implicated in tumor development and therapeutic resistance.^[18]

This study aims to delineate the expression patterns of CHKA in glioma using bioinformatics analysis and patient tissue samples. Using in vitro and in vivo models, we investigate the role of CHKA in glioma progression and elucidate its underlying molecular mechanisms.

Methods

Clinical sample collection

Patients diagnosed with glioma who underwent surgery at General Hospital of Ningxia Medical University from November 2019 to October 2024 were enrolled in this study. Eligible participants had a first-time diagnosis of glioma confirmed by pathological examination and magnetic resonance imaging, were classified according to the World Health Organization (WHO) CNS tumor classification, underwent surgical treatment, and provided written informed consent. Exclusion criteria included comorbid brain diseases; significant hepatic, renal, or cardiac impairment; psychiatric disorders; additional malignant tumors; or pregnancy/lactation. Three glioblastoma tissue samples were collected, and intraoperative neuronavigation was used to obtain high-metabolic areas within the tumor core, high-metabolic areas at the tumor edge, and low-metabolic areas adjacent to the tumor. Tumor tissues collected during surgery were stored at −80°C or embedded in paraffin for subsequent experiments. The clinical characteristics of patients and their correlations with CHKA, EGFR, and phosphorylated EGFR (p-EGFR) expression are presented in Supplementary Tables 1–3, http://links.lww.com/CM9/C761. The study involving human tissues was approved by the Ethics Committee of General Hospital of Ningxia Medical University (No. KYLL-2021-636). All experimental procedures complied with the Declaration of Helsinki.

Bioinformatics analysis

RNA expression data from glioma tissues were downloaded from the GSE19728 and GSE29796 datasets. The Tumor IMmune Estimation Resource (TIMER) database was used to analyze the correlation between CHKA and EGFR expression in glioma tissues. The Chinese Glioma Genome Atlas (CGGA),^[19] the University of ALabama at Birmingham CANcer (UALCAN),^[20] and The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) databases were used to evaluate the relationship between CHKA/EGFR expression and glioma patient prognosis.

Reagents and facilities

Reagents and facilities used in this study are listed in Supplementary Tables 4 and 5, http://links.lww.com/CM9/C761. Protocols for cell culture, cell transfection, Western blotting analysis, colony formation assay, wound-healing assay, transwell assay are provided in Supplementary Materials, http://links.lww.com/CM9/C761.

Immunohistochemistry

Paraffin sections were baked at 65°C overnight. Xylene was used for dewaxing, followed by rehydration through a graded ethanol series (100%, 95%, and 85%) and rinsing with distilled water for 1 min. Slides were immersed in distilled water for 1–3 min. Antigen retrieval was performed in ethylenediaminetetraacetic acid (EDTA) buffer (pH 9.0) at 2000 W for 3 min in a pressure cooker. Sections were rinsed with phosphate buffered saline (PBS) for 30 s and treated with hydrogen peroxide solution for 8 min, followed by PBS washes (3 times for 3 min each). Excess PBS was removed, and primary antibodies anti-isocitrate dehydrogenase 1 (IDH1) (1:200), anti-Ki67 (1:5000), anti-CHKA (1:200), anti-p-EGFR (1:200), and anti-EGFR (1:2000) were applied and incubated at 37.5°C for 60 min. After washing with PBS (3 times for 1 min each), a secondary antibody (1:1000) was applied and incubated at 37.5°C for 30 min, followed by three PBS washes (1 min each). 3,3′-diaminobenzidine (DAB) chromogenic solution was added at room temperature (RT) for 8–10 min in the dark. The reaction was stopped with running water for 30 s. Slides were counterstained with fresh hematoxylin for 10–15 s and washed for 30 s. Sections were differentiated with 0.3% acid ethanol for 3 s, washed for 30 s, and treated with bluing reagent for 30 s. Dehydration was performed with graded ethanol (85% and 95%, and absolute ethanol) and xylene. Mounting medium was applied, and images were captured using a microscope.

Tissue processing and single-cell sequencing

Fresh glioma specimens were cut into 0.5 mm² pieces and rinsed with PBS. Enzymatic dissociation was performed at 37°C for 20 min using a digestion cocktail containing 0.35% collagenase IV, 2 mg/mL papain, and 120 U/mL DNase I. The reaction was quenched with PBS containing 10% fetal bovine serum (FBS). The resulting cell suspension was filtered, pelleted by centrifugation (300 × g, 5 min), and resuspended in PBS. Nonviable cells and erythrocytes were removed using viability reagents. Cell integrity was verified by trypan blue exclusion (>85% viability) followed by quantitative assessment.

For single-cell analysis, processed cells were loaded onto the Chromium system (10X Genomics, Pleasanton, CA, USA) following the manufacturer’s workflow. Amplified complementary DNA products were converted into sequencing libraries using the Single Cell 3′ Kit V3 (10 × Genomics). Sequencing was performed on an Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA), achieving 20,000 read pairs per cell according to quality control metrics.

Reverse transcription–quantitative polymerase chain reaction (RT-qPCR)

Cell pellets were lysed with 200 μL TRK lysis buffer and homogenized. The lysate was centrifuged at 14,000 × g for 2 min, and the supernatant was mixed with an equal volume of 70% ethanol. The mixture was transferred to an RNA Homogenizer Spin Column and centrifuged at 10,000 × g for 1 min. The column was washed with RNA Wash Buffers I and II and centrifuged at 10,000 × g for 2 min. RNA was eluted with RNase-free water, and concentration was measured spectrophotometrically. Genomic DNA was removed with gDNA Eraser at 42°C for 2 min. Reverse transcription was performed with PrimeScript RT Enzyme Mix I at 37°C for 15 min and 85°C for 5 s. Polymerase chain reaction (PCR) reactions were set up using TB Green Premix Ex Taq II, forward and reverse primers, and complementary DNA (cDNA) template under the following conditions: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the internal control, and gene expression was quantified using the 2^−ΔΔCT method. Primer sequences were as follows: CHKA, F: 5′-TCTGCAGAAATCGCCGAGAA-3′, R: 5′-TCCATTGTGCCAAAAAGCCA-3′; EGFR, F: 5′-TTGCCGCAAAGTGTGTAACG-3′, R: 5′-GAGATCGCCACTGATGGAGG-3′; MAPK1, F: 5′-CACAACACCTCAGCAATGACC-3′, R: 5′-CAGGTTGGAAGGCTTGAGGT-3′; MAPK14, F: 5′-TGGATTTTGGACTGGCTCGG-3′, R: 5′-CAGCATGATCTCAGGAGCCC-3′; MAPK8, F: 5′-AGGACTGCAGGAACGAGTTT-3′, R: 5′-CTTGTAGCCCATGCCAAGGA-3′; GAPDH, F: 5′-CAGGAGGCATTGCTGATGAT-3′, R: 5′-GAAGGCTGGGGCTCATTT-3′. Three unique short hairpin RNAs (shRNAs) targeting CHKA were designed and synthesized; two of them significantly reduced CHKA expression [Supplementary Figure 1A, http://links.lww.com/CM9/C761].

CCK-8 assay

Cells from the wild type (WT), sh-negative control (NC), sh-CHKA, sh-CHKA + OE-NC, and sh-CHKA + OE-EGFR groups were seeded into 96-well plates at a density of 5000 cells in 100 μL/well and cultured for 24, 48, or 72 h. CCK-8 reagent (10 μL) was added to each well, followed by 2 h incubation. Absorbance at 450 nm was measured using a microplate reader. Since sh-CHKA-2 exhibited a stronger inhibitory effect on cell viability than sh-CHKA-3 [Supplementary Figure 1B and C, http://links.lww.com/CM9/C761], the sh-CHKA-2 construct was selected for further functional studies due to the scale and complexity of subsequent experiments.

Subcutaneous glioma xenograft model

Four- to five-week-old female Balb/c-nu mice (18–20 g) were obtained from Beijing Huafukang Biotechnology (Beijing, China) and housed under controlled conditions (25°C, 60% humidity, and 12-h light/dark cycle) with ad libitum access to food and water. After one week of acclimatization, the animals were randomly allocated into five experimental groups (n = 6/group): WT, sh-NC, sh-CHKA, sh-CHKA + OE-NC, and sh-CHKA + OE-EGFR. For tumor induction, each mouse received a subcutaneous injection of 150 μL U87 cell suspension (5 × 10⁷ cells/mL) stably expressing the respective genetic modifications.

Tumor progression was monitored weekly using caliper measurements, and tumor volume was calculated as (length × width²)/2. The study was terminated at 4 weeks postinoculation, after which euthanasia was performed through cervical dislocation. Excised tumors were photographed and weighed for comparative analysis. All experimental protocols were conducted in compliance with international animal welfare standards and approved by the Animal Ethics Committee of Ningxia Medical University (No. KYLL-2021-636).

RNA-seq analysis

RNA sequencing was performed by GeneChem (Shanghai, China) following standardized protocols. RNA quality was assessed by evaluating integrity (agarose gel electrophoresis) using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) and purity (NanoPhotometer spectrophotometry). Poly(A)-enriched mRNA was isolated, fragmented, and converted into cDNA using the NEBNext^® Ultra^™ RNA Library Prep Kit (Illumina, San Diego, CA, USA), followed by end repair, adapter ligation, and PCR amplification to construct sequencing libraries. Library quality was verified by Qubit 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and size distribution analysis using Agilent 2100 before paired-end sequencing (150 bp) on an Illumina platform. Bioinformatics analyses included raw data quality control, reference genome alignment (HISAT2 v2.0.5), transcript assembly (StringTie v1.3.3b), and gene expression quantification (featureCounts v1.5.0-p3). Differentially expressed genes (adj. P <0.05, DESeq2 v1.16.1) were functionally annotated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (clusterProfiler v3.4.4).

Phosphoproteomics analysis

Phosphoproteomics analysis was performed by Wuhan Ruixing Biotechnology Co., Ltd (Wuhan, China). U87 cells from different groups (U87-si-NC No. 1/2/3 and U87-si-CHKA No. 1/2/3) were lysed, sonicated, and centrifuged at 12,000 × g for 5 min to collect the supernatant. Protein concentration was determined using the bicinchoninic acid (BCA) method and adjusted to ensure consistent loading. Samples were treated with 400 mmol/L tris(2-carboxyethyl) phosphine hydrochloride and 80 mmol/L chloroacetamide, incubated at 60°C for 30 min, and digested with trypsin (enzyme-to-protein ratio 1:50) overnight at 37°C. The reaction was terminated with trifluoroacetic acid, and peptides were desalted, dried, and stored at −20°C. Phosphopeptide enrichment was performed using titanium dioxide magnetic beads, followed by desalting and vacuum drying. Mass spectrometry analysis was conducted using a Bruker timsTOF Pro coupled with an UltiMate 3000 RSLCnano system (Thermo Fisher Scientific, Waltham, MA, USA). Peptides were loaded onto a trap column, eluted onto an analytical column, and analyzed using a gradient elution with two mobile phases (0.1% formic acid in water and 0.1% formic acid in acetonitrile) at a flow rate of 300 nL/min. Data-dependent acquisition was used for tandem mass spectrometry scanning. Data were processed using Fragpipe (v20.0) with the MSFragger search algorithm and the UniProt human proteome reference database. Fixed modifications included carbamidomethylation (C), while variable modifications included oxidation (M), phosphorylation (S/T/Y), and acetylation (protein N-terminus). Proteins and peptides were filtered at a 1% false discovery rate for downstream analysis. A complete list of proteins identified as potential CHKA interactors is provided in Supplementary Table 6, http://links.lww.com/CM9/C762.

Statistical analysis

Statistical analysis was performed using GraphPad Prism 10 software (GraphPad Software, Boston, MA, USA). Categorical data were analyzed using chi-squared tests. Continuous data were analyzed using Student’s t-test (for two-group comparisons) or analysis of variance (for multiple-group comparisons) and expressed as mean ± standard deviation. Patients with glioma were divided into high and low gene expression groups based on the average expression of CHKA, EGFR, and p-EGFR. The clinical relevance of CHKA, EGFR, and p-EGFR expression with glioma was analyzed using Pearson’s correlation analysis. A P <0.05 was considered statistically significant.

Results

Single-cell sequencing reveals coexpression and functional correlation of EGFR and CHKA in glioma subpopulations

Single-cell sequencing data showed that EGFR is highly expressed in tumor cell subpopulations, which also express CHKA [Supplementary Figure 1D, http://links.lww.com/CM9/C761]. Supplementary Figure 2A, http://links.lww.com/CM9/C761 shows the distribution of EGFR and CHKA gene expression across various tumor subpopulations. Tumor cell subpopulations were further classified into EGFR high-expression (EGFR_hi) and EGFR low-expression (EGFR_lo) groups based on the average EGFR expression level. Visualization of EGFR and CHKA gene expression in these groups revealed that CHKA expression was relatively higher in the EGFR high-expression subpopulations [Supplementary Figure 2B, http://links.lww.com/CM9/C761]. Differential expression analysis between EGFR high- and low-expression tumor cell groups identified differentially expressed genes that were subjected to KEGG enrichment analysis, which revealed significant enrichment of the MAPK signaling pathway [Supplementary Figure 2C and D, http://links.lww.com/CM9/C761]. To clarify the correlation between EGFR and CHKA gene expression in tumor cells, we conducted a Spearman correlation analysis, which showed a significant positive correlation between the two genes in glioma (r = 0.51, P <0.001) [Supplementary Figure 2E, http://links.lww.com/CM9/C761].

CHKA and EGFR are upregulated in glioma

Analysis of the GSE19728 and GSE29796 datasets showed that CHKA expression was significantly upregulated in glioma [Figure 1A]. Gene Expression Profiling Interactive Analysis (GEPIA) analysis also indicated that EGFR expression was significantly elevated in glioma [Figure 1B]. Western blotting analysis of patient tissues revealed that CHKA, p-EGFR, and EGFR expression levels were highest in the highly metabolic areas at the tumor edge, followed by those within the tumor core, and lowest in low-metabolic areas adjacent to the tumor [Figure 1C]. Immunohistochemical staining showed that IDH1, CHKA, EGFR, and p-EGFR were mainly located in the cytoplasm, whereas Ki67 was confined to the nucleus. As tissue malignancy increased, the expression levels of these proteins also increased. Notably, p-EGFR and CHKA showed relatively strong expression even in low-grade glioma tissues [Figure 1D].

Figure 1.

Expression of CHKA and EGFR in patients with glioma. (A) Expression pattern of CHKA in normal and glioma samples based on GSE19728 and GSE29796 datasets. (B) GEPIA database showing EGFR expression in GBM and normal tissues. (C) Western blotting detecting CHKA, EGFR, and p-EGFR levels in tumor core, tumor edge, and para-tumor tissues. GAPDH served as the loading control. (D) IHC staining showing the expression of IDH1, p-EGFR, EGFR, Ki67, and CHKA in glioma tissues of different WHO grades. ^*P <0.05, ^†P <0.01, ^‡P <0.001. CHKA: Choline kinase α; EGFR: Epidermal growth factor receptor; GBM: Glioblastoma multiforme; GEPIA: Gene Expression Profiling Interactive Analysis; IHC: Immunohistochemistry; WHO: World Health Organization.

High expression of CHKA and EGFR is associated with glioma malignancy

High CHKA expression was significantly associated with higher WHO grade (P = 0.0168) and lower Karnofsky Performance Status (KPS) score (P = 0.0015) [Supplementary Table 1, http://links.lww.com/CM9/C762]. High EGFR expression correlated with higher WHO grade (P = 0.0414), larger tumor volume (P = 0.0375), and lower KPS score (P = 0.0083) [Supplementary Table 2, http://links.lww.com/CM9/C762]. Elevated p-EGFR levels were also associated with higher WHO grade (P = 0.0195) and lower KPS score (P = 0.0082) [Supplementary Table 3, http://links.lww.com/CM9/C762].

High expression of CHKA and EGFR is associated with poor prognosis in patients with glioma

The prognostic impact of CHKA and EGFR expression in patients with glioma was analyzed using online databases. Analysis of the CGGA database showed that high CHKA expression was associated with poor prognosis in recurrent glioma across all WHO grades [Supplementary Figure 3A, http://links.lww.com/CM9/C761]. According to the UALCAN database, high EGFR expression correlated with poor prognosis in patients with low-grade glioma [Supplementary Figure 3B, http://links.lww.com/CM9/C761]. TCGA database analysis indicated that high CHKA expression was linked to poor prognosis in patients with low-grade glioma, with an area under the curve (AUC) value of 0.600 for predicting 5-year survival [Supplementary Figure 3C, http://links.lww.com/CM9/C761]. Similarly, patients with low-grade glioma with EGFR high expression had poorer outcomes [Supplementary Figure 3D, http://links.lww.com/CM9/C761], with the 5-year AUC for EGFR prognosis prediction being 0.613 [Supplementary Figure 3D, http://links.lww.com/CM9/C761].

CHKA binds to EGFR and positively regulates EGFR expression

To explore the potential mechanisms by which CHKA regulates glioma progression, quantitative phosphoproteomics analysis was conducted. EGFR expression was significantly downregulated in si-CHKA samples (P = 0.001, log₂FC = –2.0187), and CHKA knockdown affected the enrichment of EGFR and MAPK pathway-related proteins [Supplementary Figure 4A, http://links.lww.com/CM9/C761]. KEGG pathway enrichment analysis of differentially expressed proteins indicated that EGFR tyrosine kinase inhibitor resistance was the most significantly enriched pathway [Supplementary Figure 4B, http://links.lww.com/CM9/C761]. A clustering heatmap confirmed downregulated EGFR expression in CHKA-knockdown glioma cells [Supplementary Figure 4C, http://links.lww.com/CM9/C761]. Mass spectrometry analysis verified the interaction between CHKA and EGFR proteins [Supplementary Figure 4D, http://links.lww.com/CM9/C761]. Western blotting analysis showed that CHKA silencing significantly reduced p-EGFR protein expression [Supplementary Figure 4E, http://links.lww.com/CM9/C761]. TIMER database analysis revealed a positive expression correlation between CHKA and EGFR [Supplementary Figure 4F, http://links.lww.com/CM9/C761]. Coimmunoprecipitation (Co-IP) results further confirmed the interaction between EGFR and CHKA in glioma cells and tissues [Supplementary Figure 4G, http://links.lww.com/CM9/C761]. CHKA and EGFR mRNA levels decreased in U87 and U251 cells of the sh-CHKA group [Figure 2A and B]. Western blotting [Figure 2C and D] confirmed that CHKA positively regulates EGFR protein expression.

Figure 2.

CHKA binds to EGFR in glioma cells and positively regulates EGFR expression. (A and B) RT-qPCR determining the effects of CHKA knockdown on CHKA and EGFR mRNA levels in U87 and U251 cells under the indicated transfections. (C and D) Western blot determining the effects of CHKA knockdown on CHKA and EGFR protein levels in U87 and U251 cells under the indicated transfections. ^*P <0.05, ^†P <0.001, ^‡P <0.001. CHKA: Choline kinase α; EGFR: Epidermal growth factor receptor; NC: Negative control; RT-qPCR: Reverse transcription–quantitative polymerase chain reaction; sh-: Short hairpin RNA; WT: Wild type.

CHKA promotes the malignant phenotype of glioma cells by positively regulating EGFR

Rescue experiments were conducted to investigate the role of EGFR in CHKA-mediated regulation of glioma cell malignancy. RT-qPCR confirmed EGFR overexpression efficiency [Figure 3A]. Cell viability was significantly reduced in the sh-CHKA group and restored in the sh-CHKA + OE-EGFR group, indicating that CHKA regulates glioma cell viability through EGFR [Figure 3B]. Similarly, colony formation assays showed that EGFR overexpression rescued the impaired colony formation capacity caused by CHKA knockdown [Figure 3C]. Wound-healing and Transwell assays demonstrated that CHKA modulates glioma cell migration through EGFR [Figure 3D and E]. Collectively, CHKA regulates glioma cell proliferation, migration, and invasion in vitro via EGFR.

Figure 3.

CHKA promotes glioma cell proliferation, migration, and invasion in vitro by positively regulating EGFR. (A) RT-qPCR detecting EGFR expression across transfection groups. (B) CCK-8 assay evaluating U87 and U251 cell viability at 0, 24, 48, and 72 h under the indicated transfections. (C) Colony formation assay assessing U87 and U251 cell proliferation. (D) Wound-healing assay measuring wound closure rates in U87 and U251 cells at 24 h. (E) Transwell assay evaluating invasion ability of U87 and U251 cells under the indicated transfections. *P <0.05, ^†P <0.001, ^‡P <0.0001. CCK-8: Cell Counting Kit-8; CHKA: Choline kinase α; EGFR: Epidermal growth factor receptor; NC: negative control; OE: overexpression; RT-qPCR: Reverse transcription–quantitative polymerase chain reaction; sh-: Short hairpin RNA; WT: Wild type.

CHKA promotes MAPK pathway activation by positively regulating EGFR

Proteomic analysis suggested that CHKA silencing affects EGFR and MAPK pathway-related proteins [Supplementary Figure 4A, http://links.lww.com/CM9/C761]. RNA-seq analysis showed that compared with control glioma cells, CHKA-silenced glioma cells revealed significant KEGG enrichment of the MAPK pathway [Supplementary Figure 4H, http://links.lww.com/CM9/C761]. The KEGG database indicated that the MAPK pathway is downstream of EGFR in glioma [Supplementary Figure 4I, http://links.lww.com/CM9/C761]. RT-qPCR results showed that EGFR overexpression reversed the inhibitory effects of CHKA knockdown on the mRNA levels of MAPK1, MAPK14, and MAPK8 [Figure 4A]. Western blotting results confirmed that EGFR overexpression restored the expression levels of p-ERK, ERK, p-p38, p38, p-JNK, and JNK [Figure 4B and C]. Thus, CHKA activates the MAPK pathway by positively regulating EGFR, enhancing the proliferative, migratory, and invasive abilities of glioma cells in vitro [Figure 4D].

Figure 4.

CHKA regulates EGFR to activate the MAPK signaling pathway. (A) RT-qPCR detecting MAPK1, MAPK14, and MAPK8 mRNA levels in U87 and U251 cells after CHKA silencing and EGFR overexpression. (B and C) Western blot detecting MAPK pathway-related proteins in U87 and U251 cells after CHKA knockdown and EGFR overexpression. (D) Graphical abstract: CHKA upregulates EGFR to activate the MAPK pathway and promote malignant behaviors of glioma cells. ^*P <0.05, ^†P <0.01, ^‡P <0.001. CHKA: Choline kinase α; EGFR: Epidermal growth factor receptor; ERK: Extracellular signal-regulated kinases; KEGG: Kyoto Encyclopedia of Genes and Genomes; MAPK: Mitogen-activated protein kinase; NC: Negative control; OE: Overexpression; RT-qPCR: Reverse transcription–quantitative polymerase chain reaction; sh-: Short hairpin RNA; WT: Wild type.

CHKA promotes glioma growth in mice by regulating EGFR

A nude mouse xenograft model using stably transfected U87 cells demonstrated that CHKA silencing reduced subcutaneous tumor volume by approximately 66.1% and tumor weight by approximately 78.8% at week four. Conversely, EGFR overexpression significantly increased both tumor volume and weight [Figure 5A and B]. Immunohistochemical staining revealed strong expression of CHKA, EGFR, p-EGFR, and Ki67 in tumor tissues, with CHKA, EGFR, and p-EGFR localized in the cytoplasm and Ki67 in the nucleus. CHKA knockdown reduced CHKA, EGFR, p-EGFR, and Ki67 expression, whereas EGFR overexpression increased EGFR, p-EGFR, and Ki67 levels without altering CHKA levels, indicating that CHKA promotes glioma proliferation by upregulating EGFR [Figure 5C].

Figure 5.

CHKA promotes xenograft tumor growth by regulating EGFR. (A) Nude mice were subcutaneously injected with stably transfected U87 cells to establish xenograft tumor models. Tumor volume was measured weekly using a caliper. (B) Tumor images and weights after 28 days of U87 cell injection. (C) IHC staining showing CHKA, EGFR, p-EGFR, and Ki67 levels in mouse tumor tissues. ^*P <0.05, ^†P <0.001, ^‡P <0.0001. CHKA: Choline kinase α; EGFR: Epidermal growth factor receptor; IHC: Immunohistochemistry; NC: Negative control; OE: Overexpression; sh-: Short hairpin RNA; WT: Wild type.

CHKA promotes MAPK pathway activation by regulating EGFR

In mouse tumor tissues, EGFR mRNA and protein expression, as well as p-EGFR levels, were significantly reduced in the CHKA-knockdown group, while EGFR overexpression increased their levels [Figure 6A and B]. Co-IP results confirmed the protein interaction between CHKA and EGFR in mouse tumor tissues [Figure 6C]. CHKA knockdown decreased MAPK1, MAPK14, and MAPK8 mRNA expression, while EGFR overexpression reversed this effect [Figure 6D]. CHKA knockdown also reduced p-ERK, ERK, p-p38, p38, p-JNK, and JNK protein expression, whereas EGFR overexpression increased them [Figure 6E]. These findings are consistent with the in vitro results, demonstrating that CHKA activates the MAPK pathway by positively regulating EGFR in glioma tumor tissues.

Figure 6.

CHKA regulates EGFR to activate the MAPK pathway in tumor tissues. (A) RT-qPCR detecting EGFR mRNA expression in mouse tumor tissues of WT, sh-NC, sh-CHKA, sh-CHKA + OE-NC, and sh-CHKA + OE-EGFR groups. (B) Western blot detecting p-EGFR and EGFR protein expression in mouse tumor tissues of different groups. (C) Co-IP assays exploring the interaction between CHKA and EGFR in mouse tumor tissues. (D) RT-qPCR detecting MAPK pathway-related genes (MAPK1, MAPK14, and MAPK8) in mouse tumor tissues of each group. (E) Western blot detecting MAPK pathway-related proteins (p-ERK, ERK, p-p38, p38, p-JNK, and JNK) in mouse tumor tissues of each group. ^*P <0.05, ^†P <0.01, ^‡P <0.001. CHKA: Choline kinase α; Co-IP: Coimmunoprecipitation; EGFR: Epidermal growth factor receptor; MAPK: Mitogen-activated protein kinase; NC: Negative control; OE: Overexpression; p-EGFR: Phosphorylated EGFR; p-ERK: Phosphorylated extracellular signal-regulated kinases; p-JNK: Phosphorylated c-Jun N-terminal kinase; RT-qPCR: Reverse transcription–quantitative polymerase chain reaction; sh-: Short hairpin RNA; WT: Wild type.

Discussion

We analyzed the expression of CHKA and EGFR in glioma and normal tissues using the Gene Expression Omnibus and GEPIA databases. Both CHKA and EGFR were significantly upregulated in glioma tissues. This finding was further validated in clinical glioma specimens, which consistently showed elevated CHKA and EGFR expression in tumor tissues. Additionally, CHKA and EGFR expression levels were positively correlated with glioma malignancy, as indicated by higher WHO grades and lower KPS scores. Patients with high CHKA or EGFR expression exhibited poorer overall survival. Single-cell RNA sequencing further confirmed the positive correlation between CHKA and EGFR expression in glioma cells, and EGFR upregulation was associated with MAPK pathway enrichment.

CHKA is frequently overexpressed in multiple cancers, including colon, lung, breast, and prostate cancers, where it is implicated in tumorigenesis.^[9,11,21] CHKA promotes glioma cell proliferation and migration through exosome-mediated mechanisms,^[22] and CHKA knockdown suppresses glioma cell proliferation, invasion, and migration.^[23] Under glucose deprivation, CHKA2 binds to lipid droplets and, through posttranslational modifications and phosphorylation events, promotes lipid droplet lipolysis and fatty acid oxidation, thereby supporting brain tumor growth. CHKA2-related modifications are associated with poor prognosis in patients with glioma.^[13] Moreover, CHKA enhances glioma cell-derived exosome secretion by regulating autophagy-dependent pathways, facilitating glioma cell proliferation and motility.^[22] Treatment with the PI3K inhibitor PI-103 reduces lactate and phosphocholine levels, possibly due to decreased CHKA protein expression.^[24] In GBM cells, CHKA inhibition significantly suppresses cell viability, invasiveness, clonogenicity, and the expression of epithelial–mesenchymal transition-related genes. Notably, combined treatment with temozolomide and the CHKA inhibitor V-11-0711 markedly enhances GBM cytotoxicity.^[25]

EGFR is a well-known oncogene that is frequently overexpressed or aberrantly activated in breast, head and neck, colorectal, and nonsmall cell lung cancers, while its expression in normal epithelial tissues remains relatively low.^{[26,27,28,29]} The disruption of cell–cell and cell–matrix contacts in tumor cells renders them more dependent on EGFR-mediated survival signals.^[30] EGFR amplification is also one of the molecular hallmarks of GBM.^[31] Multiple studies have demonstrated that the EGFR signaling network, either through overexpression, ligand coexpression, dephosphorylation impairment, or constitutive activation of mutant forms, contributes to glioma progression.^{[32,33,34,35]}

Furthermore, CHKA has been shown to interact with the kinase domain of EGFR when coexpressed with c-Src and undergoes c-Src-dependent phosphorylation at tyrosine residues 197 and 333.^[36] CHKA also mediates EGFR transactivation upon stimulation with thrombin, a G protein-coupled receptor ligand.^[37] Conversely, EGFR can activate CHKA.^[38] Importantly, CHKA serves as a critical mediator of the physical interaction between EGFR and mechanistic target of rapamycin complex 2 (mTORC2), which facilitates the formation of metastatic lesions in vivo and contributes to resistance to EGFR inhibitors in tumor cells.^[39] A novel CHKA inhibitor, CK37, suppresses CHKA activity, reduces phosphocholine levels in transformed cells, inhibits tumor growth, and attenuates ERK activation in mouse models. These findings indicate that CHKA regulates the MAPK pathway and represents a promising therapeutic target.^[40] However, whether CHKA regulates EGFR in glioma has not been previously explored. Our data demonstrate that CHKA and EGFR exhibit a positive correlation in clinical glioma tissues. Co-IP and mass spectrometry confirmed the direct interaction between CHKA and EGFR. Functional studies revealed that CHKA promotes EGFR phosphorylation and activates the EGFR–MAPK signaling pathway. Rescue experiments further showed that EGFR overexpression could reverse the inhibitory effects of CHKA knockdown on glioma cell proliferation, migration, and invasion, suggesting that CHKA exerts oncogenic effects in glioma by upregulating EGFR–MAPK signaling. Previous studies have shown that CHKA also activates PI3K/AKT signaling.^[40,41] The EGFR–MAPK and PI3K/AKT pathways may act in parallel or converge to promote tumor progression, and CHKA may function as an important node connecting multiple oncogenic signaling networks.

This study has certain limitations. First, the number of clinical samples was relatively small, which may have reduced the statistical power of our analyses. Multicenter studies with larger cohorts are needed to validate the clinical significance of CHKA in glioma. Second, the glioma xenograft model cannot fully recapitulate the interactions between tumor cells and the immune microenvironment. Future studies will focus on elucidating the role of CHKA in glioma immunity using immunocompetent models. Finally, further investigation is required to clarify the molecular mechanisms underlying CHKA-mediated EGFR regulation in glioma.

In conclusion, our findings demonstrate that CHKA promotes glioma progression by enhancing EGFR–MAPK signaling and suggest that targeting CHKA may represent a promising therapeutic strategy for glioma treatment.

Funding

This study was supported by grants from the Ningxia Natural Science Foundation project (Nos. 2022AAC03592; 2022AAC03559), Central Government Guided Local Science and Technology Development Fund Project (No. 2024FRD05099) and the National Natural Science Foundation of China (No. 82460469).

Conflicts of interest

None.

Supplementary Material

cm9-139-741-s001.pdf ^{(949.5KB, pdf)}

cm9-139-741-s002.xlsx ^{(1.2MB, xlsx)}