CKMT2 functions as a novel oncogenic gene in osteosarcoma, revealed by bioinformatics and experimental approaches

Introduction

Osteosarcoma (OS) is the most common primary malignant bone tumor, predominantly affecting children and adolescents (1). It is a tumor of mesenchymal origin, with an annual incidence rate of four to five cases per million, characterized by the production of osteoid and originating from mesenchymal stem cells (2,3). OS typically develops in the metaphysis of long bones, such as the distal femur, proximal tibia, proximal femur, and proximal humerus (4). Approximately 60% of OS cases occur in individuals aged 10–20 years, making it the second leading cause of cancer-related death in this age group (5). The bimodal age distribution is one unique characteristic of OS. The first peak occurs during adolescence, particularly between 10 and 14 years of age, and is closely related to the rapid skeletal growth observed during puberty. A second peak is observed in older individuals (over 65 years old), in whom OS may be associated with pre-existing bone disorders such as Paget’s disease and bone infarction-related malignant transformation (3,6). In approximately 15–20% of patients, metastases are detected at the time of diagnosis, with the lungs being the most frequently involved site, accounting for nearly 85% of cases, followed by skeletal involvement (7,8). Despite being a rare disease, OS is associated with significant disability and mortality rates.

The standard treatment for OS includes neoadjuvant chemotherapy, surgical resection, and consolidation chemotherapy, which has demonstrated significant success in patients with localized disease (9). This multimodal approach has improved the disease-free survival rate to over 60% in localized OS cases; however, the survival rate remains below 30% for patients with metastatic disease (5). The genetic complexity and chromosomal instability of OS pose significant challenges to molecular research (10). Previous studies have identified multiple genetic and epigenetic abnormalities involved in OS pathogenesis. Among them, mutations or dysregulation of key tumor suppressor genes such as TP53 and RB1 are frequently observed and are closely associated with genomic instability and uncontrolled cell proliferation. In addition, alterations in SETD2 and abnormal activity of DNA methyltransferases (DNMTs), which are involved in epigenetic regulation, have also been implicated in OS progression and metastasis. These molecular abnormalities contribute to the biological heterogeneity and aggressive behavior of OS, highlighting the need to identify novel molecular mechanisms and therapeutic targets (11-13). Therefore, a detailed molecular understanding of the mechanisms underlying osteosarcomagenesis is critical for improving prognosis and therapeutic outcomes in these patients.

Creatine kinase mitochondrial 2 (CKMT2), also known as sarcomeric mitochondrial creatine kinase (sMtCK), is a tissue-specific enzyme exclusively expressed in skeletal muscles (14). Loss of CKMT2 function disrupts adenosine triphosphate (ATP) production and exacerbates ischemia-reperfusion injury (15). Enhancing CKMT2 expression has been shown to protect against ischemia-reperfusion damage by delaying mitochondrial permeability transition pore (mPTP) opening, thereby reducing mitochondrial damage and infarct size. CKMT2 levels have been reported to correlate with infarct size and left ventricular function, suggesting its potential as a marker of myocardial injury severity (15,16).

In addition to its role in myocardial function, CKMT2 overexpression has been observed in various malignancies, where it has been identified as a tumor-associated marker (17,18). It has also been proposed as a potential predictor of immunotherapy efficacy in certain cancers (19). CKMT2 regulates glucose metabolism by enhancing the Warburg effect through interactions with lactate dehydrogenase B (LDHB), thereby driving colorectal cancer growth and progression (14). Furthermore, CKMT2 has been implicated as a potential key regulator in the development of OS (20). Another study has suggested that CKMT2 may influence hypoxia and the immune microenvironment in OS patients (21). However, the precise role and molecular mechanisms of CKMT2 in OS remain poorly understood and require further investigation.

In this study, we analyzed data from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases and identified CKMT2 as a gene associated with OS, which correlates with poor prognosis in patients. We validated CKMT2 expression in OS cell lines, explored its oncogenic role, and investigated its impact on the biological behaviors of OS cells through in vitro experiments. In addition, we preliminarily found that CKMT2 is related to pyruvate metabolism. In this study, we aimed to investigate the clinical significance and biological role of CKMT2 in OS using data from the GEO and TARGET databases. We further sought to evaluate the expression of CKMT2 in OS cell lines and to explore its effects on the malignant biological behaviors of OS cells through in vitro experiments. In addition, we aimed to preliminarily investigate the relationship between CKMT2 and pyruvate metabolism in OS. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0726/rc).

Methods

Data sources

Bioinformatics approaches were used to evaluate the clinical relevance and potential biological pathways associated with CKMT2. We collected data from the GEO database, specifically the GSE33382 datasets, as well as the TARGET OS tissue dataset, which includes data from 86 OS tissue samples. All data were standardized using R software. The analysis of CKMT2 gene expression was based on the GSE33382 datasets, while survival data were obtained from the TARGET OS dataset. OS patients were classified into high-risk and low-risk groups based on the median expression of CKMT2. To evaluate survival differences between these two groups, Kaplan-Meier (K-M) analysis along with the log-rank test was performed. Additionally, receiver operating characteristic (ROC) curve analysis was conducted using the R “survivalROC” package, and the area under the curve (AUC) values were calculated to assess the specificity and sensitivity of CKMT2 as a prognostic biomarker. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Univariate and multivariate Cox proportional hazards regression analyses

To identify independent prognostic factors associated with overall survival in OS, univariate and multivariate Cox regression analyses were performed to exclude clinical characteristics with minimal prognostic value for overall survival. Variables such as gender, race, disease diagnosis, tumor site, and CKMT2 expression levels were assessed using the R package.

Nomogram construction for prognostic prediction

To enhance individualized survival prediction and assess the clinical application value of CKMT2, we constructed a nomogram to visualize the correlation between prognostic factors including disease diagnosis and CKMT2 expression and predict the 1-, 3-, and 5-year survival probabilities of OS patients. The nomogram was constructed using the R “rms” package, assigning points to each parameter based on its significance. The total points were calculated by summing the points assigned to all factors, allowing for survival prediction.

Gene Set Enrichment Analysis (GSEA)

To explore the potential molecular mechanisms and biological pathways associated with CKMT2 in OS, GSEA was performed using expression data from the TARGET dataset. This analysis aimed to identify signaling pathways potentially involved in CKMT2-mediated OS progression.

Protein-protein interaction (PPI) network and chemical interactions via Comparative Toxicogenomics Database (CTD)

To identify CKMT2-related interacting proteins and potential regulatory networks involved in OS progression, a PPI network was constructed using the STRING database (https://string-db.org/). Additionally, chemicals associated with CKMT2 were identified using the CTD (https://ctdbase.org/).

Estimation of immune cell types and immune composition in the model genes

To investigate the relationship between CKMT2 expression and the tumor immune microenvironment in OS, tumor immune cell infiltration levels were analyzed based on the median expression of CKMT2. The OS samples were divided into high- and low-risk groups. Tumor immune cell infiltration levels were calculated using the “CIBERSORT” algorithm to evaluate the infiltration levels of 22 immune cell types in these two groups (22).

Cell lines

Human OS cell lines MG63, SAOS-2, 143B, and U2OS, along with the human osteoblast cell line (hFOB1.19), were purchased from Wuhan Pricella Biotechnology (China) and used for subsequent experiments.

Western blot

Western blot analysis was performed to determine whether CKMT2 regulates glycolysis-related proteins involved in metabolic reprogramming in OS cells. Total cellular proteins were extracted using radioimmunoprecipitation assay (RIPA) buffer (Servicebio Technology, Wuhan, China). Phenylmethanesulfonyl fluoride (PMSF, Servicebio Technology) was added to the lysate buffer at a 1:100 ratio. Western blot analysis was performed following standard protocols (23). Primary antibodies against CKMT2 (1:2,000, 13207-1-AP, Proteintech, Wuhan, China), PKM2 (1:4,000, 15822-1-AP, Proteintech), LDHA (1:5,000, 19987-1-AP, Proteintech) and GAPDH (1:100,000, 60004-1-Ig, Proteintech) were used, along with secondary HRP-conjugated antibodies: Goat Anti-Rabbit IgG (H + L) (1:10,000, SA00001-2, Proteintech) and Goat Anti-Mouse IgG (H + L) (1:10,000, SA00001-1, Proteintech).

Cell transfection

To investigate the functional role of CKMT2 in OS cells, CKMT2 expression was silenced using specific small interfering RNA (siRNA) purchased from GENE CREATE Technology (Wuhan, China). MG63 cells were seeded in 6-well plates, and transfection was carried out using Lipofectamine 3000 reagent (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s instructions.

Cell viability assay

To evaluate the effect of CKMT2 on OS cell proliferation and viability, Cell Counting Kit-8 (CCK-8) assays were performed. The cell suspension was diluted to 2×10⁴ cells/mL and evenly inoculated into 96-well plates, with 100 µL per well. After cell adhesion, 10 µL of CCK-8 (C0038, Beyotime Technology, Shanghai, China) was added to each well at 0, 24, 48, and 72 hours. After incubation at 37 °C for 1 hour, the absorbance of the solution at 450 nm was measured using a microplate reader (Thermo Fisher Scientific).

Flow cytometry for cell cycle assay

To determine whether CKMT2 affects OS cell proliferation through regulation of cell cycle progression, cell cycle distribution was analyzed using a cell cycle analysis kit (C1052, Beyotime Technology). MG63 cells were seeded in 6-well plates and subsequently transfected. After collection, cells were processed according to the manufacturer’s instructions.

5-ethynyl-2'-deoxyuridine (EdU) assay

To further validate the effect of CKMT2 on DNA synthesis and proliferative activity in OS cells, EdU assays were performed using the EdU kit with Alexa Fluor 555 (C0075, Beyotime Technology). MG63 cells were seeded in 24-well plates and then transfected. After fixation with 4% paraformaldehyde, cells were processed according to the manufacturer’s instructions.

Wound-healing assay

To assess the role of CKMT2 in OS cell migratory capacity, wound-healing assays were conducted. Briefly, cells were inoculated into 6-well plates and cultured to a density of 90–100%. A sterile 200 µL pipette tip was used to create a wound across the cell layer. After washing the cells with phosphate-buffered saline (PBS) three times, the floating cells were removed, and 2% fetal bovine serum (FBS) medium was added. The wound area was observed under an inverted microscope (Leica, Germany) at 0 and 24 h, and images were captured. The wound healing process was analyzed using ImageJ software.

Transwell invasion assay

To evaluate whether CKMT2 influences the invasive ability of OS cells, Transwell invasion assays were performed. Matrigel (C0372, Beyotime Technology) was applied to the bottom of the upper chamber. MG63 cells (5×10⁴) were seeded in the upper chamber with serum-free medium, and 500 µL of 10% FBS complete medium was added to the lower chamber. After incubation for 48 hours, the Transwell chamber was washed with PBS, fixed with 4% paraformaldehyde for 30 minutes, and stained with 0.1% crystal violet for 15 minutes. The invading cells were observed under an inverted microscope (Leica, Germany) and counted using ImageJ software.

Pyruvate and L-lactate assays

To verify whether CKMT2 modulates glycolytic metabolism in OS cells, we detected intracellular pyruvate and L-lactate levels. The concentrations of pyruvate and L-lactate in OS cells were measured using commercially available assay kits—pyruvate assay kit (S0299) and L-lactate assay kit (S0208) from Beyotime Biotechnology (Shanghai, China). All procedures were performed strictly according to the manufacturer’s instructions.

Statistical analysis

Statistical analysis was conducted using GraphPad Prism 9.0. Data are presented as mean ± standard deviation (SD). Each experiment was repeated at least three times. Differences between the two groups were assessed using a two-tailed Student’s t-test. A P value of less than 0.05 was considered statistically significant.

Results

CKMT2 is highly expressed in OS and serves as an independent prognostic indicator

First, we analyzed GEO dataset GSE33382 and observed that CKMT2 expression was significantly elevated in OS tissues compared to normal tissues (Figure 1A). To further characterize the expression profile of CKMT2 in OS, we conducted a heatmap analysis to compare its expression level with those of 19 representative oncogenic genes using the GEO dataset (Figure S1). Additionally, we examined CKMT2 expression levels in several OS cell lines, including MG63, Saos2, 143B, and U2OS. Among these, CKMT2 expression was highest in the MG63 cell line (Figure 1B). Therefore, MG63 was selected for subsequent experiments. Furthermore, CKMT2 expression was found to be significantly associated with overall survival based on K-M survival analysis using the TARGET OS dataset. Patients in the high CKMT2 expression group had a markedly worse prognosis compared to those in the low CKMT2 expression group (Figure 1C). To evaluate the diagnostic value of CKMT2, ROC curves were constructed. The results demonstrated that CKMT2 could partially predict the prognosis of OS patients (Figure 1D). Subsequently, univariate (Figure 1E) and multivariate Cox (Figure 1F) regression analyses were performed to assess whether CKMT2 expression could serve as an independent factor associated with overall survival in OS. Our findings revealed that CKMT2 expression, along with Disease_diagnosis, was significantly correlated with the overall survival of OS patients. To further enhance prognostic prediction, we developed a nomogram integrating Disease_diagnosis and CKMT2 expression to estimate 1-, 3-, and 5-year survival probabilities. In the nomogram, each factor (Disease_diagnosis and CKMT2 expression) was assigned a specific score based on the point scale, and the total score was calculated by summing the scores of all factors. This total score was then used to estimate survival rates at 1-, 3-, and 5-year intervals, providing an intuitive and practical method to predict patient outcomes (Figure 1G). In summary, our results identified CKMT2 as an independent prognostic indicator for OS, with potential utility for improving survival prediction models.

Figure 1 CKMT2 is highly expressed in osteosarcoma and serves as an independent prognostic indicator. (A) CKMT2 expression in osteosarcoma based on the GSE33382 dataset. (B) CKMT2 protein expression in osteosarcoma cell lines. (C) Kaplan-Meier survival analysis based on CKMT2 expression using the TARGET dataset. (D) ROC curve evaluating the predictive value of CKMT2. Univariate (E) and multivariate (F) Cox regression analyses of overall survival. (G) Nomogram combining clinical variables and CKMT2 expression for survival prediction. AUC, area under the curve; CI, confidence interval; HR, hazard ratio; ROC, receiver operating characteristic; TARGET, Therapeutically Applicable Research to Generate Effective Treatments.

Knockdown of CKMT2 inhibits proliferation, migration, and invasion of OS cells

To investigate the effects of CKMT2 on OS cells, we used siRNA to knock down CKMT2 expression. The knockdown efficiency was validated by Western blot, which showed that siRNA targeting site 2 exhibited the highest knockdown efficiency (Figure 2A). Therefore, subsequent experiments were conducted using this siRNA. First, the CCK-8 assay revealed a significant reduction in OS cell proliferation following CKMT2 knockdown (Figure 2B). Flow cytometry analysis further demonstrated that CKMT2 inhibition caused cell cycle arrest at the G1 phase, accompanied by a reduction in the proportion of cells in the S phase (Figure 2C). Similarly, EdU assays indicated decreased cell proliferation after CKMT2 silencing (Figure 2D). Next, we performed wound-healing assays and observed that cell migration was significantly impaired following CKMT2 knockdown (Figure 2E). Consistently, Transwell invasion assays revealed a marked reduction in the invasive capability of OS cells with reduced CKMT2 expression (Figure 2F). In summary, these results collectively indicate that CKMT2 knockdown effectively suppresses the proliferation, migration, and invasion of OS cells.

Figure 2 Knockdown of CKMT2 inhibits proliferation, migration, and invasion of osteosarcoma cells. (A) Validation of CKMT2 knockdown efficiency by Western blot. (B) CCK-8 assay for cell proliferation. (C) Cell cycle analysis by flow cytometry. (D) EdU assay for cell proliferation. (E) Wound healing assay for cell migration. (F) Transwell assay for cell invasion (crystal violet stained). Scale bar, 100 µm. *, compared with NC group, P<0.05. G1, S, and G2 represent the proportions of cells in the G1, S, and G2 phases of the cell cycle. CCK-8, Cell Counting Kit-8; Con, untreated control; EdU, 5-ethynyl-2'-deoxyuridine; NC, negative control; OD, optical density; siCKMT2, small interfering RNA targeting CKMT2.

CKMT2 regulates pyruvate metabolism in OS

To explore the potential signaling pathways through which CKMT2 exerts its effects, we performed GSEA to identify the underlying mechanisms and pathways regulated by CKMT2 in OS. The results revealed that CKMT2 is most closely associated with alanine, aspartate, and glutamate metabolism; arginine and proline metabolism; pyruvate metabolism; and tryptophan metabolism (Figure 3A). Among these enriched pathways, pyruvate metabolism is the most directly related to glycolysis, as pyruvate is the final product of this pathway and a precursor for lactate generation via LDHA (24). Considering the close relationship between CKMT2 and tumor cell glycolysis and energy metabolism, we chose to focus on the pyruvate metabolism pathway. We first found that knocking down CKMT2 significantly decreased the protein expression levels of key glycolytic enzymes PKM2 and LDHA (Figure 3B,3C). Furthermore, by measuring pyruvate and L-lactate levels, we observed a significant reduction in their contents following CKMT2 silencing (Figure 3D,3E).

Figure 3 CKMT2 regulates pyruvate metabolism in osteosarcoma. (A) GSEA showing enrichment of metabolic pathways associated with CKMT2. (B,C) Protein levels of PKM2 and LDHA by Western blot. (D,E) Measurements of pyruvate and L-lactate levels in osteosarcoma cells. *, P<0.05. GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; NC, negative control; siCKMT2, small interfering RNA targeting CKMT2.

CKMT2-associated genes, protein interactions, and potential drug candidates in OS

In addition, we investigated the relationship between CKMT2 and associated genes using the TARGET database. The analysis identified the five most positively correlated genes as CARNS1, HSPB7, IGSF11, MB, and MYOM1, while the five most negatively correlated genes were CMTM3, CTNNBIP1, PAGE5, SCARF2, and UBE2E2 (Figure 4A). PPI network analysis further demonstrated that CKMT2 directly interacts with several co-expressed genes (Figure 4B). These findings suggest that CKMT2 may contribute to the development and prognosis of OS through its interactions with these co-expressed genes. Finally, using the CTD, we identified multiple chemicals that exhibit strong associations with CKMT2 (Figure 4C). This provides new insights into potential drug interventions targeting CKMT2 for the treatment of OS.

Figure 4 CKMT2-associated genes, protein interactions, and potential drug candidates in osteosarcoma. (A) Correlation analysis of the top positively and negatively associated genes based on the TARGET dataset. (B) PPI network of CKMT2 and related genes. (C) Chemical-gene interaction analysis based on the CDT. CDT, Comparative Toxicogenomics Database; PPI, protein-protein interaction; TARGET, Therapeutically Applicable Research to Generate Effective Treatments.

Associations between CKMT2 gene expression and immunity in OS

Increasing evidence suggests that immune cell infiltration plays a crucial role in the development and progression of OS. To further investigate the relationship between CKMT2 expression and immune cell infiltration, we used the CIBERSORT algorithm to assess the infiltration levels of 22 types of immune cells (Figure 5A). The results revealed that resting mast cells (MCs) may play a significant role in OS progression. To visualize these findings, a violin plot was constructed, further supporting the above results (Figure 5B). Correlation analysis showed that CKMT2 expression was significantly associated with the infiltration levels of resting MCs (Figure 5C,5D). These findings collectively indicate that CKMT2 expression is closely related to immune cell infiltration in OS.

Figure 5 Associations between CKMT2 gene expression and immunity in osteosarcoma. (A) Immune cell composition analysis using CIBERSORT. (B) Violin plot showing differences in immune cell infiltration. (C) Correlation analysis between CKMT2 expression and various immune cell types. The P value marked as highlight represents the statistically significant result for resting mast cells. (D) Detailed correlation analysis between CKMT2 expression and resting mast cells.

Discussion

OS, the most prevalent primary solid malignancy of the bones, is associated with a high mortality rate (25). The limited success of current treatments can be partially attributed to an incomplete understanding of OS pathophysiology (26). In recent years, the use of public databases such as GEO and TARGET has increased, helping to identify potential biomarkers in OS (27). Mitochondrial metabolism has emerged as a key target for cancer therapy, as abnormalities in mitochondrial energy metabolism and the associated key genes play a significant role in the development of OS. For instance, studies have shown that aberrant elevation of LETM1 disrupts mitochondrial function and energy metabolism, promoting lung metastasis in OS (28). Additionally, research has identified two key genes related to mitochondrial energy metabolism pathways (KCNJ5 and PFKFB2) that can effectively predict the prognosis of OS (29).

CKMT2, a member of the creatine kinase isozyme family, is tightly coupled to ATP output through adenine nucleotide transport proteins or carriers, thus playing a crucial role in ATP synthesis and respiratory chain activity (19). CKMT2 activity is associated with oxidative capacity, as it increases the availability of adenosine diphosphate (ADP) to the respiratory chain complex V, which regulates mitochondrial membrane potential (Δψm) and reactive oxygen species (ROS) formation (30,31). Overexpression of CKMT2 has been reported in various malignant tumors, including OS, where it functions as a key regulator (20,21). However, the precise role of CKMT2 in OS remains poorly understood. Therefore, in this study, we mined public data on OS to reveal the prognostic significance and immunological roles of CKMT2, further investigating its functional impact on OS cells and providing potential therapeutic targets for personalized treatment.

In this study, we analyzed the OS-related datasets GSE33382 and TARGET, and identified CKMT2 as a gene of interest. We found that CKMT2 was highly expressed in OS tissues and cell lines, as confirmed by Western blot analysis. Its high expression was significantly correlated with poor overall survival. Univariate and multivariate Cox regression analyses indicated that CKMT2 expression is an independent prognostic factor for overall survival. To predict the 1-, 3-, and 5-year survival probabilities for OS patients, we developed a nomogram that combines clinical variables with CKMT2 expression, demonstrating good performance. Additionally, we performed CKMT2 knockdown in the OS cell line MG63 to evaluate its functional impact. CCK-8 assays showed a reduction in cell proliferation with lower levels of CKMT2. Flow cytometry analysis revealed that CKMT2 inhibition resulted in cell cycle arrest in the G1 phase, inhibiting the G1-S phase transition. Moreover, EdU assays demonstrated that CKMT2 suppression inhibited OS cell proliferation. Wound healing and Transwell assays further revealed that the migration and invasion capabilities of OS cells were significantly reduced following CKMT2 knockdown. These results indicate that CKMT2 plays a significant role in the proliferation, migration, and invasion of OS cells.

To explore the potential mechanisms underlying CKMT2’s role in OS, GSEA revealed that CKMT2 was notably linked to several metabolic pathways, including alanine, aspartate, and glutamate metabolism; arginine and proline metabolism; pyruvate metabolism; and tryptophan metabolism, all of which have been closely associated with OS (32-35). Our subsequent experiments further demonstrated that CKMT2 is closely involved in glycolysis and pyruvate metabolism. Additionally, using the TARGET database, we identified genes most positively and negatively correlated with CKMT2 and performed PPI network analysis to predict potential interacting genes. These identified genes could provide valuable insights into the specific mechanisms of CKMT2 in OS. Lastly, using the CTD, we analyzed various compounds that might target CKMT2, offering potential therapeutic avenues for OS treatment.

To investigate the relationship between CKMT2 gene expression and immunity in OS, we analyzed tumor immune cell infiltration levels using the CIBERSORT algorithm. Patients were classified into high- and low-risk groups based on the median expression of CKMT2. We found a significant association between CKMT2 expression and resting MC infiltration levels. MCs, among the top five infiltrating immune cells in OS, can be classified into resting and activated types (36). Resting MCs can influence tumor growth by releasing inflammatory mediators (37). Heymann et al. demonstrated that OS cells could promote the viability and activity of MCs, which, in turn, produce receptor activator of NF-κB ligand, a key molecular triad involved in regulating bone remodeling (38). Similarly, Inagaki et al. suggested that MCs could serve as potential biomarkers for osteolysis (39). These findings suggest that elevated CKMT2 expression may be associated with resting MCs.

Accumulating evidence suggests that MCs exert important modulatory effects on the tumor immune microenvironment. Previous studies have shown that tumor-associated MCs secrete immunosuppressive cytokines and pro-angiogenic mediators, thereby promoting tumor growth, metastasis, and immune escape. MCs can also interact with cytotoxic T cells and natural killer cells through direct cell-cell interactions and inflammatory mediator release, thereby influencing antitumor immune responses. In addition, MC-derived interleukin-6 (IL-6), interleukin-10 (IL-10), tumor necrosis factor-alpha (TNF-α), and C-C motif chemokine ligand 2 (CCL2) can recruit macrophages and promote their polarization toward the immunosuppressive M2 phenotype while suppressing M1-mediated antitumor immunity. Collectively, these findings suggest that MCs may contribute to the establishment of an immunosuppressive microenvironment in OS (40-42). While this study primarily focused on in vitro experiments, future research should explore the interactions between OS cells and MCs in greater detail.

In conclusion, CKMT2 shows promise as a potential prognostic biomarker for OS. However, certain limitations in this study must be addressed. We combined public databases and experimental approaches to validate CKMT2 expression and its functional role in OS cells. Nevertheless, in terms of mechanistic exploration, we primarily relied on GSEA, public databases, and PPI network analysis to identify potential pathways and associated genes. We only conducted preliminary investigations suggesting that CKMT2 may exert its effects through the pyruvate metabolism pathway. Additionally, our study is based entirely on in vitro experiments, and there is a lack of direct in vivo evidence to validate the role of CKMT2 in OS tumorigenesis. To address this, we plan to establish a xenograft mouse model to further elucidate CKMT2’s role in tumor formation. These are important directions for future research.

Conclusions

Finally, in this study, we screened for novel oncogenic and prognostic biomarkers in OS using integrated bioinformatics analyses and experimental validation. CKMT2 expression was found to be significantly elevated in OS tissues and cell lines and was correlated with poor patient prognosis. Functional experiments showed that CKMT2 promotes the proliferation, migration, and invasion of OS cells. Mechanistically, CKMT2 may promote OS progression by regulating pyruvate metabolism and glycolytic activity. Furthermore, CKMT2 expression was correlated with immune cell infiltration, particularly resting MCs, suggesting its potential involvement in the tumor immune microenvironment. Overall, these findings suggest that CKMT2 may serve as a promising prognostic biomarker and therapeutic target in OS. However, its precise biological roles and therapeutic potential require further in vivo and mechanistic investigations.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0726/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0726/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0726/prf

Funding: This work was supported by an Institutional Research Project of Nantong Sixth People’s Hospital (No. ntly202403).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-0726/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. The 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/.

References

1. Liu J, Yi C, Gong D, et al. Construction of a 5-Gene super-enhancer-related signature for osteosarcoma prognosis and the regulatory role of TNFRSF11B in osteosarcoma. Transl Oncol 2024;47:102047. [Crossref] [PubMed]
2. Urlić I, Jovičić MŠ, Ostojić K, et al. Cellular and Genetic Background of Osteosarcoma. Curr Issues Mol Biol 2023;45:4344-58. [Crossref] [PubMed]
3. Lu YY, Lu W, Zheng J, et al. High APEX1 Expression Facilitates Osteosarcoma Cell Proliferation. Asian Pac J Cancer Prev 2025;26:453-63. [Crossref] [PubMed]
4. Lai J, Kong W, Fu Q, et al. PSMD14 is a novel prognostic marker and therapeutic target in osteosarcoma. Diagn Pathol 2024;19:79. [Crossref] [PubMed]
5. Beird HC, Bielack SS, Flanagan AM, et al. Osteosarcoma. Nat Rev Dis Primers 2022;8:77. Erratum in: Nat Rev Dis Primers 2022;8:82. [Crossref] [PubMed]
6. Young E, Kelly B, Cain JE. Targeting developmental vulnerabilities in childhood sarcomas. Cancer Metastasis Rev 2025;44:72. [Crossref] [PubMed]
7. Jia S, Gong B, Chen H, et al. Advances in Nano-Drug Delivery Systems for Osteosarcoma: From Targeting Strategies to Combating Lung Metastasis. Int J Nanomedicine 2026;21:586319. [Crossref] [PubMed]
8. Shrestha P, Shrestha R, Zhou Y, et al. STAT3 inhibition in combination with CD47 blockade inhibits osteosarcoma lung metastasis. Front Immunol 2025;16:1608375. [Crossref] [PubMed]
9. Nie JH, Wan CY, Li H, et al. Personalized prediction of chemotherapy efficacy in osteosarcoma through patient-derived organoids: correlation with survival and tumor proliferation potential. J Exp Clin Cancer Res 2025;45:16. [Crossref] [PubMed]
10. Wu CC, Livingston JA. Genomics and the Immune Landscape of Osteosarcoma. Adv Exp Med Biol 2020;1258:21-36. [Crossref] [PubMed]
11. El Motassime A, Vitiello R, Comodo RM, et al. Osteosarcoma: A Comprehensive Morphological and Molecular Review with Prognostic Implications. Biology (Basel) 2025;14:1407. [Crossref] [PubMed]
12. Chen X, Tian B, Wang Y, et al. Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine Int J Mol Med 2025;55:92. (Review). [Crossref] [PubMed]
13. Katsianou MA, Andreou D, Korkolopoulou P, et al. Epigenetic Modifications in Osteosarcoma: Mechanisms and Therapeutic Strategies. Life (Basel) 2025;15:1202. [Crossref] [PubMed]
14. Cai S, Xia Q, Duan D, et al. Creatine kinase mitochondrial 2 promotes the growth and progression of colorectal cancer via enhancing Warburg effect through lactate dehydrogenase B. PeerJ 2024;12:e17672. [Crossref] [PubMed]
15. Whittington HJ, Ostrowski PJ, McAndrew DJ, et al. Over-expression of mitochondrial creatine kinase in the murine heart improves functional recovery and protects against injury following ischaemia-reperfusion. Cardiovasc Res 2018;114:858-69. [Crossref] [PubMed]
16. Lang A, Oehler D, Benkhoff M, et al. Mitochondrial Creatine Kinase 2 (Ckmt2) as a Plasma-Based Biomarker for Evaluating Reperfusion Injury in Acute Myocardial Infarction. Biomedicines 2024;12:2368. [Crossref] [PubMed]
17. Pratt R, Vallis LM, Lim CW, et al. Mitochondrial creatine kinase in cancer patients. Pathology 1987;19:162-5. [Crossref] [PubMed]
18. Kanemitsu F, Kawanishi I, Mizushima J, et al. Mitochondrial creatine kinase as a tumor-associated marker. Clin Chim Acta 1984;138:175-83. [Crossref] [PubMed]
19. Lin W, Zhou J, Ma Y, et al. Prognostic value of mitochondrial CKMT2 in Pan-cancer and its tumor immune correlation analysis. Sci Rep 2024;14:342. [Crossref] [PubMed]
20. Wang H, Tang M, Ou L, et al. Biological analysis of cancer specific microRNAs on function modeling in osteosarcoma. Sci Rep 2017;7:5382. [Crossref] [PubMed]
21. Zhang W, Lyu P, Andreev D, et al. Hypoxia-immune-related microenvironment prognostic signature for osteosarcoma. Front Cell Dev Biol 2022;10:974851. [Crossref] [PubMed]
22. Zhang Y, Ye X, Xu J, et al. Identification and functional characterization of glycosyltransferase-related biomarkers for tuberculosis diagnosis. AMB Express 2025;15:56. [Crossref] [PubMed]
23. Wu W, Cheng Z, Nan Y, et al. L-selectin Promotes Migration, Invasion and Inflammatory Response of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis via NF-kB Signaling Pathway. Inflammation 2025;48:2960-72. [Crossref] [PubMed]
24. Prochownik EV, Wang H. The Metabolic Fates of Pyruvate in Normal and Neoplastic Cells. Cells 2021;10:762. [Crossref] [PubMed]
25. Liu S, Liu C, Wang Y, et al. The role of programmed cell death in osteosarcoma: From pathogenesis to therapy. Cancer Med 2024;13:e7303. [Crossref] [PubMed]
26. Rossi M, Pellegrino C, Rydzyk MM, et al. Chalcones induce apoptosis, autophagy and reduce spreading in osteosarcoma 3D models. Biomed Pharmacother 2024;179:117284. [Crossref] [PubMed]
27. Niu J, Yan T, Guo W, et al. Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy. Front Oncol 2020;10:1628. [Crossref] [PubMed]
28. Shi Y, Kang Q, Zhou H, et al. Aberrant LETM1 elevation dysregulates mitochondrial functions and energy metabolism and promotes lung metastasis in osteosarcoma. Genes Dis 2024;11:100988. [Crossref] [PubMed]
29. Yang S, Liu L, Liu X, et al. The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma. Medicine (Baltimore) 2023;102:e36046. [Crossref] [PubMed]
30. Perry CG, Kane DA, Lin CT, et al. Inhibiting myosin-ATPase reveals a dynamic range of mitochondrial respiratory control in skeletal muscle. Biochem J 2011;437:215-22. [Crossref] [PubMed]
31. Rizo-Roca D, Guimarães DSPSF, Pendergrast LA, et al. Decreased mitochondrial creatine kinase 2 impairs skeletal muscle mitochondrial function independently of insulin in type 2 diabetes. Sci Transl Med 2024;16:eado3022. [Crossref] [PubMed]
32. Zhong Z, Mao S, Lin H, et al. Alteration of intracellular metabolome in osteosarcoma stem cells revealed by liquid chromatography-tandem mass spectrometry. Talanta 2019;204:6-12. [Crossref] [PubMed]
33. Pu F, Liu J, Jing D, et al. LncCCAT1 interaction protein PKM2 upregulates SREBP2 phosphorylation to promote osteosarcoma tumorigenesis by enhancing the Warburg effect and lipogenesis. Int J Oncol 2022;60:44. [Crossref] [PubMed]
34. Hu XK, Rao SS, Tan YJ, et al. Fructose-coated Angstrom silver inhibits osteosarcoma growth and metastasis via promoting ROS-dependent apoptosis through the alteration of glucose metabolism by inhibiting PDK. Theranostics 2020;10:7710-29. [Crossref] [PubMed]
35. Xiang D, Han X, Li J, et al. Combination of IDO inhibitors and platinum(IV) prodrugs reverses low immune responses to enhance cancer chemotherapy and immunotherapy for osteosarcoma. Mater Today Bio 2023;20:100675. [Crossref] [PubMed]
36. Zhu T, Han J, Yang L, et al. Immune Microenvironment in Osteosarcoma: Components, Therapeutic Strategies and Clinical Applications. Front Immunol 2022;13:907550. [Crossref] [PubMed]
37. Yang C, Zhang Y, Liu Y, et al. Study on the molecular mechanism of UBA52 and BARD1 regulating hepatocellular carcinoma through the PI3 K/AKT signaling pathway. Discov Oncol 2025;16:840. [Crossref] [PubMed]
38. Heymann MF, Lézot F, Heymann D. The contribution of immune infiltrates and the local microenvironment in the pathogenesis of osteosarcoma. Cell Immunol 2019;343:103711. [Crossref] [PubMed]
39. Inagaki Y, Hookway E, Williams KA, et al. Dendritic and mast cell involvement in the inflammatory response to primary malignant bone tumours. Clin Sarcoma Res 2016;6:13. [Crossref] [PubMed]
40. Shu F, Yu J, Liu Y, et al. Mast cells: key players in digestive system tumors and their interactions with immune cells. Cell Death Discov 2025;11:8. [Crossref] [PubMed]
41. Ligan C, Ma XH, Zhao SL, et al. The regulatory role and mechanism of mast cells in tumor microenvironment. Am J Cancer Res 2024;14:1-15. [Crossref] [PubMed]
42. Baran J, Sobiepanek A, Mazurkiewicz-Pisarek A, et al. Mast Cells as a Target-A Comprehensive Review of Recent Therapeutic Approaches. Cells 2023;12:1187. [Crossref] [PubMed]