Integrative analysis of polo-like kinase family identifies a prognostic signature and validates PLK2 as a therapeutic target in glioma

Introduction

Glioma is one of the most common malignant tumors of the central nervous system (1), characterized by high invasiveness and poor prognosis, significantly compromising patient survival and quality of life (2). The current standard of care for glioma includes maximal safe surgical resection followed by radiotherapy and concomitant temozolomide-based chemotherapy tailored to the tumor’s molecular characteristics (3). However, glioblastoma (GBM), the most prevalent and aggressive subtype of glioma, remains highly infiltrative and resistant to therapy. Despite standard treatment, the median overall survival of GBM patients is less than 15 months, and the 5-year survival rate remains below 10% (4). Most patients eventually develop treatment resistance and experience tumor recurrence (5).

Dysregulation of the cell cycle is a hallmark of tumorigenesis (6). This disruption leads to an imbalance between cellular proliferation and differentiation, granting tumor cells the ability to proliferate uncontrollably while impeding normal differentiation (7). Additionally, it alters the equilibrium between oncogenes and tumor suppressor genes, activating pro-proliferative signaling pathways and promoting tumor progression (8). Therefore, investigating the mechanisms underlying cell cycle dysregulation is critical for advancing our understanding of glioma pathogenesis and identifying novel diagnostic biomarkers and therapeutic targets, ultimately contributing to the development of more individualized and effective treatment strategies (9,10).

The polo-like kinase (PLK) family comprises five highly conserved serine/threonine kinases—PLK1 to PLK5—characterized by an N-terminal kinase domain and a conserved C-terminal polo-box domain (11-13). These kinases play essential roles in mitotic processes, including spindle formation, chromosome segregation, CDC2 activation, and cytokinesis (14,15). Among them, PLK1 has been extensively studied as an anti-cancer target, with several small-molecule inhibitors under preclinical and clinical evaluation (16). As the first PLK1 inhibitor to enter clinical trials, BI-2536 has demonstrated promising anti-tumor potential across various human cancers. In a study on breast cancer, both treatment with BI-2536 and genetic knockdown of PLK1 effectively induced apoptosis in triple-negative breast cancer cell lines (17). Volasertib, another PLK1 inhibitor currently under clinical investigation, exhibits greater selectivity for PLK1 and possesses a longer half-life compared to BI-2536. Clinical studies have shown that Volasertib holds therapeutic promise against a range of malignancies, including acute myeloid leukemia, ovarian cancer, and melanoma (18-20). PLK2, a key regulator of centrosome duplication and the G1-to-S phase transition (21), has been implicated in various cancers. However, its role in glioma remains largely unexplored (22,23).

This study aims to systematically investigate the expression and prognostic relevance of PLK family genes in glioma using transcriptomic data, to explore their associations with molecular pathological features and clinical outcomes, and to experimentally validate the therapeutic potential of a PLK2-specific small-molecule inhibitor in glioma models. We present this article in accordance with the TRIPOD and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1078/rc).

Methods

Data acquisition

Messenger RNA (mRNA) transcriptome data and corresponding clinical and molecular pathology information for 1,018 glioma patients were downloaded from the Chinese Glioma Genome Atlas (CGGA, http://www.cgga.org.cn/) database (24). The Human Protein Atlas (HPA, https://www.proteinatlas.org/) was utilized to validate the differential expression of PLK family proteins across glioma grades at the protein level. Single-cell RNA-seq data were obtained from Gene Expression Omnibus (GEO) dataset (https://www.ncbi.nlm.nih.gov/geo/) GSE131928. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Construction of the risk score model

The expression levels of five PLK family genes were extracted from the CGGA database. Univariate Cox proportional hazards regression was applied to assess the association between each gene and overall survival, and the corresponding regression coefficients were obtained. Based on these coefficients, a risk score model was established using the following formula: Risk Score = (Expression_PLK1 × Coef_PLK1) + … + (Expression_PLK5 × Coef_PLK5). Patients were then divided into high- and low-risk groups according to the median value of the calculated risk score.

Model validation and nomogram construction

A multivariate Cox regression model was established to evaluate the independent prognostic significance of the PLK-based risk score. A nomogram was constructed to predict patient survival using the “rms” package (version 8.0.0). The model’s performance was assessed using calibration curves and time-dependent receiver operating characteristic (ROC) curves [1-, 3-, and 5-year area under the curve (AUC)]. Time-dependent ROC curves and AUC values were generated using the “timeROC” package (version 0.4). Age, tumor grade, radiotherapy, and chemotherapy status were included in the multivariate analysis. Calibration curves were constructed to evaluate the agreement between predicted and observed survival, indicating the model’s calibration performance.

Functional enrichment and gene set enrichment analysis

Differential expression analysis was performed using the “limma” package with thresholds of P<0.05 and |log fold change (FC)| >0.65. Patients were grouped by PLK2 expression level, and differentially expressed genes (DEGs) were visualized using heatmaps (“pheatmap”, version 1.0.13). Gene Ontology (GO) enrichment (biological process, molecular function, and cellular component) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for DEGs highly expressed in each group using the “clusterProfiler” package (version 4.16.0).

Cell culture

Human glioma cell lines (U251MG, catalog no. TCHu58 and LN229, catalog no. TCHu244) were obtained from the Chinese Academy of Sciences (Shanghai, China) and cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM) (Gibco, Waltham, USA, Cat# 11965092) supplemented with 10% fetal bovine serum (FBS) (Gibco, Cat# 10099141C) at 37 ℃ in a humidified incubator with 5% CO₂. For routine culture, frozen cells were rapidly thawed in a 37 ℃ water bath, centrifuged, resuspended in complete medium, and transferred to culture dishes. Cells were passaged at approximately 80% confluence using 0.25% trypsin (Gibco, Cat# 25200072) and maintained at a 1:3 ratio. For cryopreservation, cells were resuspended in freezing medium containing 10% dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St. Louis, USA, Cat# D2650) and 10% FBS in DMEM, gradually cooled to −80 ℃, and stored in liquid nitrogen for long-term use.

Cell Counting Kit-8 (CCK-8) assay

U251MG cells were seeded into 96-well plates (Corning, Corning, USA, Cat# 3599) at 100 µL/well and incubated at 37 ℃ with 5% CO₂ for 24 h to allow attachment. Cells were then treated with PLK2-specific inhibitor ON1231320 (MedChemExpress, Monmouth Junction, USA, HY-100789) at concentrations of 0, 100, 200, and 300 nM for the indicated durations. At each time point, 10 μL of CCK-8 solution (Dojindo, Kumamoto, Japan, CK04-11) was added to each well and incubated for 1 h. Absorbance at 450 nm was measured using a microplate reader, and cell viability was calculated accordingly. All experiments were performed in triplicate and independently repeated at least three times.

Colony formation assay

U251MG and LN229 cells were seeded in 6-well plates (Corning, Cat# 3516) at 500 cells/well and cultured for 24 h. After cell attachment, ON1231320 was added at 0, 50, 100, or 200 nM, and cells were maintained for 2–3 weeks with regular medium changes. Colonies were fixed with 4% paraformaldehyde (Solarbio, Beijing, China, P1110) and stained with 0.1% crystal violet (Solarbio, G1063). Colonies containing more than 50 cells were counted and imaged. All experiments were performed in triplicate and independently repeated at least three times.

Wound healing assay

Cells were seeded in 6-well plates marked with 5 parallel lines on the reverse side. After reaching confluence, a wound was created using a 20 µL pipette tip. Cells were treated with ON1231320 in medium and incubated for 24 hours. Images were captured to measure wound closure. All experiments were performed in triplicate and independently repeated at least three times.

Cell cycle analysis by flow cytometry

To evaluate the effect of PLK2 inhibition on cell cycle distribution, U251MG and LN229 glioma cells were treated with 100 nM ON1231320 or vehicle control for 24 hours. Cells were then harvested, washed with PBS, and fixed in 70% ethanol at −20 ℃ overnight. After fixation, cells were washed, resuspended in PI/RNase staining buffer (BD Biosciences, San Jose, USA, Cat# 550825), and incubated in the dark for 30 minutes at room temperature. DNA content was analyzed using a CytoFLEX S flow cytometer (Beckman Coulter, Brea, USA), and data were processed with FlowJo software (version 10.8.1). Cell cycle phase distributions (G0G1, S, G2/M) were quantified, and experiments were independently repeated at least three times.

Statistical analysis

Statistical analyses were conducted using SPSS 22.0 and R software (version 4.4.1). Visualizations were generated using “ggplot2” (version 3.4.4). Student’s t-test was used for comparing gene expression between groups. Chi-squared tests were used for categorical variables, and one-way analysis of variance (ANOVA) for multi-group comparisons. Kaplan-Meier and log-rank tests were used for survival analysis. Univariate and multivariate Cox regression analyses identified independent prognostic factors. A P value <0.05 was considered statistically significant.

Results

Association between PLK family gene expression and tumor grade and molecular pathological features in glioma patients

To investigate the relationship between PLK family gene expression and glioma malignancy, we analyzed their expression profiles across tumor grades and molecular subtypes. As shown in Figure 1A, glioma patients were stratified into World Health Organization (WHO) grade II, III, and IV groups. The mRNA expression levels of PLK1, PLK2, PLK3, and PLK4 progressively increased with tumor grade, whereas PLK5 expression decreased, suggesting that PLK1–4 may be associated with tumor progression and poor prognosis, while PLK5 may be linked to more favorable outcomes. Consistently, Figure 1B presents immunohistochemical data from the HPA database, which showed higher protein expression levels of PLK1–4 in high-grade glioma tissues. In contrast, PLK5 protein levels were lower in high-grade samples, aligning with transcriptomic trends. In terms of molecular features (Figure 1C), PLK1–4 were more highly expressed in isocitrate dehydrogenase (IDH)-wildtype and 1p/19q non-codeleted gliomas, which are typically associated with more aggressive phenotypes. Conversely, PLK5 expression was significantly higher in IDH-mutant and 1p/19q codeleted tumors, indicating its potential link to favorable molecular subtypes. Among the five genes, only PLK2 and PLK3 showed significant expression differences between MGMT promoter methylated and unmethylated groups.

Figure 1 Relationship between PLK family gene expression and glioma grade and molecular characteristics. (A) mRNA expression levels of PLK family genes across glioma grades in the CGGA database. (B) Protein expression levels of PLK family members in glioma tissues of different grades from the HPA database (immunohistochemical staining, DAB chromogen, hematoxylin counterstain; scale bar =100 um). Images were obtained from the HPA database: PLK1, https://www.proteinatlas.org/ENSG00000166851-PLK1/pathology/glioma; PLK2, https://www.proteinatlas.org/ENSG00000145632-PLK2/pathology/glioma; PLK3, https://www.proteinatlas.org/ENSG00000173846-PLK3/pathology/glioma; PLK4, https://www.proteinatlas.org/ENSG00000142731-PLK4/pathology/glioma; PLK5, https://www.proteinatlas.org/ENSG00000185988-PLK5/pathology/glioma. (C) Correlation of PLK family gene expression with IDH mutation status, MGMT promoter methylation status, and 1p/19q codeletion status. ANOVA, analysis of variance; CGGA, Chinese Glioma Genome Atlas; DAB, 3,3'-diaminobenzidine; HPA, Human Protein Atlas; IDH, isocitrate dehydrogenase; MGMT, O6-methylguanine-DNA methyltransferase; mRNA, messenger RNA; PLK, polo-like kinase; WHO, World Health Organization.

Single-cell transcriptomic analysis reveals distinct expression patterns of PLK family genes in tumor cell subpopulations

To further investigate the intratumoral heterogeneity of PLK family gene expression, we analyzed a publicly available single-cell RNA-sequencing dataset from glioma (GSE131928). Tumor cells in glioma have been previously classified into five major transcriptional subtypes based on lineage resemblance: neural progenitor-like cells (NPCs), mesenchymal-like cells (MES), oligodendrocyte progenitor-like cells (OPCs), astrocyte-like cells (ACs), and cycling cells (CYCLING), each exhibiting distinct biological functions and states of differentiation.

As shown in Figure 2A, unsupervised clustering of single-cell transcriptomes revealed multiple cell populations within the dataset. Tumor cells were identified and extracted for further analysis (Figure 2B), and subsequently categorized into four major malignant subpopulations based on their transcriptional profiles.

Figure 2 Single-cell analysis of PLK family gene expression in glioma. (A) UMAP plot showing clustering of cell populations within the selected glioma single-cell dataset. (B) Identification and extraction of tumor cells, further divided into four transcriptionally distinct malignant subpopulations. (C-G) UMAP feature plots illustrating the expression patterns of PLK1, PLK2, PLK3, PLK4, and PLK5 across tumor cell subpopulations. (H) Heatmap displaying the differential expression levels of PLK family genes across the four malignant subgroups. AC, astrocyte-like cell; CYCLING, cycling cell; MES, mesenchymal-like cell; NPC, neural progenitor-like cell; OPC, oligodendrocyte progenitorlike cell; PLK, polo-like kinase; UMAP, Uniform Manifold Approximation and Projection.

UMAP feature plots of individual PLK genes (Figure 2C-2G) demonstrated that PLK2 and PLK3 were the most highly expressed among the five family members, with PLK2 showing particularly strong expression in the NPC-like subpopulation, which is associated with higher malignancy and proliferative potential. In contrast, PLK1 and PLK4 were enriched in the CYCLING subgroup, suggesting their involvement in cell cycle regulation. These observations were further supported by the heatmap (Figure 2H), which showed distinct PLK gene expression patterns across tumor cell subtypes, highlighting the potential functional divergence of PLK family members within the glioma cellular hierarchy.

PLK family gene expression is associated with prognosis in glioma patients

To evaluate the prognostic significance of PLK family members in glioma, we first conducted Kaplan-Meier survival analyses based on the mRNA expression levels of PLK1–PLK5 in the CGGA cohort. As shown in Figure 3A-3E, high expression of PLK1, PLK2, PLK3, and PLK4 was significantly associated with worse overall survival, whereas high expression of PLK5 was correlated with favorable prognosis. These findings are consistent with the expression trends observed across tumor grades, further suggesting that PLK1–4 may serve as markers of glioma aggressiveness, while PLK5 may be associated with less malignant disease. In line with these results, Figure 3F presents a forest plot of univariate Cox regression analyses for the five genes, all of which showed statistically significant hazard ratios (P<0.05). These data support the strong prognostic value of individual PLK family members in glioma.

Figure 3 Prognostic significance of PLK family genes in glioma. (A-E) Kaplan-Meier survival curves showing the association between mRNA expression levels of PLK1, PLK2, PLK3, PLK4, and PLK5 and overall survival in glioma patients (CGGA cohort). (F) Forest plot summarizing the univariate Cox regression analysis results for the five PLK genes. (G) Relationship between the constructed PLK-based risk score and glioma grade, overall survival, and key molecular markers. (H) Kaplan-Meier survival analysis of high-risk versus low-risk groups stratified by the PLK-based risk score, alongside a heatmap displaying gene expression patterns of PLK1–5 across the two groups. CGGA, Chinese Glioma Genome Atlas; HR, hazard ratio; IDH, isocitrate dehydrogenase; mRNA, messenger RNA; NA, not applicable; OS, overall survival; PLK, polo-like kinase; PRS, Polygenic Risk Score; WHO, World Health Organization.

Construction of the prognostic signature related to PLK genes

To construct a prognostic signature, univariate Cox regression coefficients were used to calculate a risk score for each patient using the following formula: Risk Score = (PLK1 × 0.71975) + (PLK2 × 0.19720) + (PLK3 × 0.74315) + (PLK4 × 0.75210) + (PLK5 × −0.70269). Patients were then stratified into high-risk and low-risk groups based on the median risk score.

To explore the clinical relevance of the PLK-based risk signature, we analyzed its association with clinical and molecular features. As shown in Figure 3G, a heatmap integrating the risk score with WHO grade, survival status, IDH mutation, and 1p/19q codeletion status revealed that higher risk scores correlated with higher tumor grade, poorer survival, IDH wildtype status, and absence of 1p/19q codeletion—features generally associated with aggressive glioma. In Figure 3H, patients were ranked by risk score and categorized into high- and low-risk groups. The high-risk group exhibited shorter overall survival and a higher number of deaths. Expression profiles of the five PLK genes across the risk spectrum showed that PLK1–4 expression increased with rising risk scores, while PLK5 expression was enriched in the low-risk group, reinforcing its potential role as a favorable prognostic marker.

The risk score of the PLK-based gene signature is an independent prognostic indicator

To determine whether the PLK-based gene signature serves as an independent prognostic factor, we performed multivariate Cox regression analysis incorporating clinical variables such as WHO grade, radiotherapy, and chemotherapy. As shown in Figure 4A, the risk score remained significantly associated with overall survival (P<0.001), even after adjusting for tumor grade and treatment status, indicating that it is an independent and robust prognostic indicator in glioma patients. WHO grade, radiotherapy, and chemotherapy were also independently associated with survival outcomes, confirming their clinical relevance. To evaluate the predictive power of the risk score, time-dependent ROC curves were generated for 1-, 3-, and 5-year overall survival in the CGGA cohort (Figure 4B). The AUC values were 0.73, 0.80, and 0.80, respectively, demonstrating that the PLK-based signature provides reliable survival prediction across multiple time points.

Figure 4 Prognostic significance of the 11-gene signature. (A) Forest plot of multivariate Cox regression analysis. (B) 1-, 3-, and 5-year survival ROC curves in CGGA cohort. (C) The nomogram prediction of glioma patients for 1-, 3-, and 5-year OS combining the signature with clinic pathological features. (D) Calibration curve for 1-, 3-, 5-year OS. AUC, area under the curve; CI, confidence interval; CGGA, Chinese Glioma Genome Atlas; HR, hazard ratio; OS, overall survival; ROC, receiver operating characteristic; WHO, World Health Organization.

A nomogram integrating the risk score with clinical parameters was constructed to provide an individualized survival prediction tool for glioma patients (Figure 4C). This visual model allows clinicians to estimate 1-, 3-, and 5-year survival probabilities by summing the point values corresponding to each variable. To assess the accuracy of this predictive model, calibration curves were plotted for 1-, 3-, and 5-year overall survival (Figure 4D). The predicted survival probabilities showed excellent concordance with the observed outcomes, as the calibration curves were closely aligned with the ideal 45-degree line, indicating good calibration and strong predictive accuracy of the nomogram.

Functional analysis of PLK2 associated differentially expressed genes

Given the consistently elevated expression of PLK2 in high-grade gliomas and its strong association with poor prognosis, we further investigated the functional implications of PLK2 dysregulation. DEGs were identified between PLK2 high- and low-expression groups in the CGGA dataset using the “limma” package in R (P<0.05, |log₂FC| >0.65), yielding 277 DEGs in the high-expression group and 224 DEGs in the low-expression group (Figure 5A,5B).

Figure 5 Functional enrichment analysis of PLK2-associated differentially expressed genes. (A) Volcano plot showing differentially expressed genes between high and low PLK2 expression groups. (B) Heatmap displaying expression patterns of representative DEGs between the two groups. (C,D) GO and KEGG pathway enrichment analyses of DEGs from the high and low PLK2 expression groups, respectively. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

GO and KEGG enrichment analyses were performed on the DEGs from each group to explore biological functions associated with PLK2 expression (Figure 5C,5D). In the PLK2 high-expression group, enriched terms were mainly related to extracellular matrix organization, including “collagen-containing extracellular matrix”, “basement membrane”, and “focal adhesion”, suggesting a role in tumor invasion and stromal remodeling. Conversely, genes enriched in the PLK2 low-expression group were predominantly involved in neurodevelopmental processes, such as “gliogenesis”, “regulation of neurogenesis”, and “oligodendrocyte differentiation”, indicating that lower PLK2 expression may preserve more differentiated or less malignant cellular states.

Targeted inhibition of PLK2 suppresses proliferation and migration of GBM cells

To evaluate the effect of PLK2 inhibition on GBM cell proliferation, we treated U251MG cells with the PLK2-specific inhibitor ON1231320 at concentrations of 0, 100, 200, and 300 nM. Cell viability was assessed at 12, 24, and 36 h using the CCK-8 assay. As shown in Figure 6A, ON1231320 inhibited cell proliferation in a dose- and time-dependent manner. Increasing concentrations and longer exposure times led to a progressive reduction in cell viability, indicating that PLK2 plays a role in promoting glioma cell growth. To further validate the anti-proliferative effect of PLK2 inhibition, colony formation assays were performed using U251MG and LN229 cells. Cells were cultured in media containing 0, 50, 100, or 200 nM ON1231320 for 2–3 weeks. As shown in Figure 6B,6C, both the number and size of colonies decreased with increasing inhibitor concentrations, suggesting that PLK2 inhibition significantly impairs the proliferative and clonogenic capacity of GBM cells.

Figure 6 The PLK2-specific inhibitor ON1231320 suppresses glioblastoma cell proliferation. (A) CCK-8 assay showing that ON1231320 inhibits the proliferation of U251MG cells in a time- and dose-dependent manner. (B,C) Colony formation assays demonstrating that ON1231320 reduces cell proliferation and clonogenic capacity in U251MG and LN229 cell lines, evaluated by macroscopic observation (B) and microscopic colony counting (C). Crystal violet staining, observed under an inverted microscope at ×100 magnification. CCK-8, Cell Counting Kit-8; PLK, polo-like kinase.

We next assessed the effect of ON1231320 on GBM cell migration using wound healing assays. U251MG and LN229 cells were treated with ON1231320 at concentrations of 0, 100, and 200 nM for 24 hours. As shown in Figure 7A, cells treated with 200 nM ON1231320 exhibited the largest remaining wound area, indicating reduced migratory ability. Quantification of migrated cells (Figure 7B) revealed a dose-dependent decrease in migration, further supporting the conclusion that PLK2 contributes to glioma cell motility.

Figure 7 The PLK2-specific inhibitor suppresses migration and induces G2/M arrest in glioblastoma cell lines. (A) Wound healing assay showing reduced migration of U251MG and LN229 cells following 24-hour treatment with ON1231320 at indicated concentrations, observed under an inverted microscope at ×100 magnification. (B) Quantification of migrated cells under microscopy, showing a dose-dependent inhibitory effect. (C) Flow cytometry histograms of DNA content in U251MG and LN229 cells after treatment with 100 nM ON1231320 for 24 hours, showing increased G2/M populations. (D) Pie charts summarizing the average proportions of cells in G1, S, and G2 phases based on triplicate experiments. Treatment with ON1231320 led to significant G2/M phase accumulation in both cell lines, indicating G2/M cell cycle arrest. **, P<0.01; ***, P<0.001. PLK, polo-like kinase.

To investigate whether the antiproliferative effects of PLK2 inhibition were associated with cell cycle alterations, we performed flow cytometry analysis following treatment with 100 nM ON1231320 for 24 hours. As shown in Figure 7C, PLK2 inhibition resulted in a marked accumulation of cells in the G2/M phase in both U251MG and LN229 cell lines. Quantification of cell cycle distributions (Figure 7D) showed that the proportion of G2-phase cells increased from 16.6% to 29.9% in U251MG cells, and from 14.3% to 34.8% in LN229 cells, accompanied by a corresponding decrease in G1-phase populations. These results suggest that PLK2 inhibition induces G2/M cell cycle arrest, thereby contributing to reduced glioma cell proliferation.

Discussion

Gliomas are the most common primary tumors of the central nervous system and encompass a spectrum of malignancies ranging from low-grade gliomas to GBM, the most aggressive form (25,26). Despite advances in surgical resection, radiotherapy, and chemotherapy, the overall prognosis for glioma patients remains poor, particularly in high-grade subtypes. The pathogenesis of gliomas involves complex genetic and epigenetic alterations, including mutations, DNA methylation, and histone modifications. Recent studies have identified several key signaling pathways involved in glioma development and progression, such as PI3K/AKT/mTOR, Notch, and Hedgehog, leading to the development of targeted inhibitors, including rapamycin, everolimus, and temsirolimus (27,28). Additionally, chemotherapeutic agents like temozolomide, which interferes with DNA replication and repair, and bevacizumab, an anti-VEGF antibody that inhibits angiogenesis, have shown clinical efficacy (29-31). Nevertheless, treatment responses vary significantly among patients, underscoring the need for better molecular markers to support prognosis and guide individualized therapy.

Dysregulation of the cell cycle is a hallmark of cancer, including GBM (8,32). Targeting cell cycle regulators such as CDK4/6 has shown promise in breast cancer and is being explored in GBM (33,34). PLK family members, particularly PLK1, are also critical cell cycle regulators (35). PLK1 is overexpressed in various malignancies and has been associated with poor prognosis and resistance to therapy (36). The PLK1 inhibitor Volasertib, which induces G2/M arrest and apoptosis, has been approved in Europe for relapsed acute myeloid leukemia (20). PLK3 and PLK4 have also been implicated in glioma progression (37-39). In contrast, the biological role of PLK2 in GBM remains largely unexplored.

In this study, we first investigated the expression patterns and clinical relevance of the PLK gene family in glioma. Transcriptomic analysis of the CGGA cohort revealed that PLK1–4 expression levels increased with tumor grade, while PLK5 exhibited the opposite trend. In addition, PLK1–4 were more highly expressed in molecular subtypes associated with poor prognosis, including IDH-wildtype and 1p/19q non-codeleted gliomas, whereas PLK5 was enriched in IDH-mutant and 1p/19q codeleted gliomas. Univariate Cox regression analysis confirmed that all five PLK genes were significantly associated with patient survival, suggesting their collective prognostic value. Building on these findings, we developed a PLK-based prognostic risk model using The Cancer Genome Atlas (TCGA) dataset and validated its performance in the CGGA cohort. The model demonstrated strong predictive accuracy for 1-, 3-, and 5-year survival (AUC =0.73, 0.80, and 0.80, respectively). Multivariate Cox analysis confirmed the risk score as an independent prognostic factor. A nomogram incorporating clinical features and the risk score enabled individualized survival prediction, and calibration curves showed high agreement between predicted and observed outcomes. These results support the clinical utility of the PLK-based signature in risk stratification and treatment planning. However, some clinical variables (e.g., extent of resection, performance status) were not available, which could have improved model performance. Future research should focus on validating this model in independent cohorts and exploring its integration into clinical workflows.

Among the PLK family members, PLK2 attracted our attention due to its consistent upregulation in high-grade gliomas and its association with poor clinical outcomes. High PLK2 expression was correlated with older age, IDH-wildtype status, and 1p/19q non-codeletion—features commonly linked to aggressive glioma phenotypes. Survival analysis across TCGA and CGGA cohorts confirmed that PLK2 overexpression was associated with significantly worse overall survival. To investigate the functional implications of PLK2 in glioma, we performed differential gene expression and GO enrichment analyses. Genes upregulated in the PLK2 high-expression group were primarily involved in extracellular matrix organization, collagen binding, and integrin-mediated signaling, indicating a possible role in promoting invasion and tumor progression. In contrast, genes associated with low PLK2 expression were enriched in neurodevelopmental processes, such as gliogenesis, neurogenesis, and oligodendrocyte differentiation, suggesting that PLK2 overexpression may suppress cellular differentiation and maintain a proliferative, undifferentiated state.

These predictions were validated through in vitro experiments. Treatment of U251MG and LN229 glioma cell lines with the PLK2-specific inhibitor ON1231320 significantly inhibited cell proliferation, as shown by CCK-8 and colony formation assays. Additionally, wound healing assays demonstrated that ON1231320 treatment impaired glioma cell migration. However, the wound healing results should be interpreted with caution, as the concentrations of ON1231320 used may have cytotoxic effects. Thus, reduced migration could be partially due to impaired cell viability rather than specific inhibition of motility. These findings confirm that PLK2 promotes both proliferative and migratory capacity in glioma cells and may serve as a viable therapeutic target. Flow cytometry further revealed that PLK2 inhibition led to G2/M phase arrest in both U251MG and LN229 cells, supporting its role in regulating cell cycle progression in glioma. Given the reported role of PLK2 in mitotic spindle formation, its inhibition may interfere with proper mitotic progression, ultimately leading to G2/M accumulation.

While this study provides meaningful insights into the role of PLK2 in glioma, some limitations should be noted. Functional assays were conducted in two commonly used glioma cell lines with a selective PLK2 inhibitor, which may not fully capture the biological heterogeneity of glioma. In addition, although differential expression and enrichment analyses suggest potential downstream effects of PLK2, further mechanistic investigations and in vivo studies will be helpful to strengthen the biological relevance of these findings (40). Finally, recent evidence suggests that the function of PLK2 may vary across tumor types and contexts, and future work is needed to explore its role in glioma more comprehensively.

Conclusions

In conclusion, our study offers a comprehensive overview of the PLK gene family in gliomas, underscoring PLK2 as a potential driver of malignancy. The PLK-based prognostic model demonstrated strong predictive value and clinical applicability. These findings lay a solid foundation for the further development of PLK2-targeted therapies and highlight its relevance in future translational glioma research.

Acknowledgments

We appreciate the patients who have participated in CGGA.

Footnote

Reporting Checklist: The authors have completed the TRIPOD and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1078/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1078/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1078/prf

Funding: This research was funded by Outstanding Young Talents of the Capital Medical University (No. B2101), Clinical and Translational Medicine Research Foundation of Chinese Academy of Medical Sciences (No. 2022-I2M-C&T-B-115).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1078/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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