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. 2026 Jun 17;17:1244. doi: 10.1007/s12672-026-05385-3

Development and validation of a novel prognostic and for osteosarcoma patients utilizing multiple organelle related genes

Bing Sun 1,2,#, Jieyang Zhu 2,#, Yi Jiang 2, Sihui Chen 2, Tao Zhang 2,✉
PMCID: PMC13507021  PMID: 42307876

Abstract

Organelles have been reported to be closely associated with tumor development and progression, but their role in osteosarcoma (OS) remains largely unexplored. Organelle related genes (ORGs) associated with OS prognosis were identified using Cox regression analysis. A prognostic model was subsequently constructed through multivariate Cox regression analysis and validated using an independent dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. In addition, immune infiltration analysis, enrichment analysis and drug sensitivity evaluation were performed. Finally, in vitro experiments were conducted to validate the potential roles of ORGs in OS. We identified 3 ORGs (ACSS2, CLTCL1, and PLD3) that were significantly associated with OS prognosis. A novel 3 ORG signature was established, which effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes. This signature served as an independent prognostic factor. The areas under the receiver operating characteristic (ROC) curve for the 1-, 4-, and 7-year survival rates were 0.66, 0.74, and 0.83, respectively. These findings were further validated using the independent GSE21257 dataset, where the corresponding ROC curve values for the 1-, 4-, and 7-year survival rates were 0.71, 0.80, and 0.68, respectively. Drug sensitivity analysis revealed differential responses to 4 drugs between the risk groups, with the 3 ORGs (ACSS2, CLTCL1 and PLD3) showing positive correlations with 2 drugs (BI_2536, Dactinomycin). Additionally, functional experiments confirmed the role of ACSS2 in OS cell behavior. This novel ORG signature not only provides a valuable tool for patient stratification but also offers insights into the biological processes driving OS progression and potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-05385-3.

Keywords: Osteosarcoma, Prognosis, Immune, Organelle, Biomarker

Introduction

Osteosarcoma (OS) is the most common type of malignant bone tumor and arises from stromal cell lineages [1]. OS is more prevalent in adolescents, with typical symptoms and signs including local pain, swelling, and restricted joint movement [2–3]. Currently, standard treatment for OS generally involves preoperative chemotherapy, followed by surgical resection of the affected area [4]. However, owing to the rapid growth of the tumor, some patients are already at an advanced stage at the time of diagnosis [5]. Therefore, there is need to identify novel prognostic indicators and therapeutic targets in clinical practice to improve treatment outcomes for patients with OS.

Organelle-related genes (ORGs) have been shown to have practical value in cancer diagnosis and treatment [6]. Increasing evidence suggests that the function of the Golgi apparatus (GA) is closely associated with cancer development [7–8]. In addition, the GA can enhance the secretion of immune factors, promote the formation of an immunosuppressive tumor microenvironment, and facilitate metastasis and tumor progression [9]. Tumor suppressor genes and oncogenes can directly or indirectly regulate mitochondrial function, leading to mitochondrial dysfunction [10]. Cancer cells often exhibit alterations in their redox state and metabolism, which are closely linked to mitochondria, as they serve as the primary sites of energy metabolism and reactive oxygen species production. Severe mitochondrial dysfunction may lead to cell death, whereas mild mitochondrial dysfunction can increase redox imbalance and mitochondrial reactive oxygen species production, thereby promoting cancer cell invasion and proliferation [11].

ORGs have been shown to have practical value in cancer diagnosis and treatment [12]. However, their role in OS remains unclear. Therefore, this study first screened for ORGs significantly associated with prognosis in OS patients using univariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) method. A prognostic signature was then constructed based on these identified ORGs, followed by analyses of their biological functions, associations with the immune microenvironment, and implications for drug sensitivity. Overall, our findings aim to provide insights that may improve therapeutic strategies and patient management in OS.

Materials and methods

OS dataset acquisition and processing

Two independent gene expression profiles of OS, TARGET_OS and GSE21257, containing 138 OS samples from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database (https://ocg.cancer.gov/programs/target). Among them, TARGET_OS dataset (85 OS samples) was used as the training set, GSE21257 dataset (53 OS samples) was used as the validation set for subsequent analysis [13]. The R 4.4.1 tool is used to evaluate gene expression levels. 2030 mitochondria-related genes were collected from MitoCarta 3.0 [14], 61 lysosomal-related genes were retrieved from the GSEA dataset (https://www.gseamsigdb.org/gsea/msigdb/human/geneset/LYSOSOME.htmland) [15], We extracted 1653 golgi apparatus from the GSEA database (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) [16], resulting in a total of 3744 ORGs. 176 duplicates were removed, and ultimately 3568 ORGs were used for our analysis.

Identification of prognostic significant ORGs related to OS patients

On the TARGET_OS dataset and GSE21257 dataset, the optimal cutoff values for gene expression levels were calculated using the surv_cutpoint function (surviminer package in R software), which categorized samples into high-expression and low-expression groups based on the identified cutoff. LASSO and univariate Cox regression analysis was then performed to assess the association between each ORGs and overall survival. Ten-fold crossover is used to verify the ideal value of the penalty parameter, and Kaplan-Meier (KM) curves were generated to visualize survival differences between groups. Log-rank tests were conducted to determine statistical significance (p < 0.05) [17].

Construction and validation of a prognostic risk stratification model for OS patients

Multivariate Cox regression was used to analyze each selected ORG’s coefficients (TARGET_OS dataset). The expression levels of these genes and their coefficients were then used to construct a prognostic risk score model for OS patients. Risk score=q1*ORGExp1 + q2*ORGExp2 + qi*ORGExpi, where q represents the coefficient of the multivariate COX regression model and ORGExp represents the expression level of the ORG. Patients were stratified into high-risk and low-risk subgroups based on the median risk score. The prognostic value of the prognostic risk stratification model was validated on GSE21257 dataset [18–19].

Enrichment analysis of biological pathways

Based on TARGET_OS dataset, Genes closely associated with the risk score (correlation coefficient |R| > 0.4 and P < 0.05) were identified for further analysis. The clusterProfiler R package was utilized to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and Gene Set Enrichment Analysis (GSEA). Thresholds for significance were set at a normalized P-value < 0.05 and FDR < 0.2 to identify enriched biological processes and pathways related to the selected genes [20].

Differential analysis of tumor immune microenvironment

The infiltration of 22 immune cell types in OS from the TARGET_OS dataset was analyzed using the CIBERSORT R package, which calculated the relative abundance of infiltrating immune cells from gene expression profiles. The Wilcoxon rank-sum test was used to compare immune cell infiltration between high-risk and low-risk patients (p < 0.05). Spearman correlation analysis was then performed to assess relationships between the ORGs and the abundance of infiltrating immune cells, and the results were visualized using the ggplot2 and pheatmap packages [21].

Analysis of drug sensitivity differences in OS patients with different risk stratification

Based on TARGET_OS dataset, the oncoPredict R package was utilized to evaluate the drug sensitivity of OS to 198 different agents. The analysis focused on identifying drugs whose IC50 values differed significantly between the high-risk and low-risk groups [22].

3 ORGs expression profiling in OS cells

The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) database was utilized to examine the expression profile of 3 ORGs (ACSS2, CLTCL1, and PLD3) across OS cells (expressed in normalized transcripts per million (nTPM) values) [3].

Construction of ACSS2 overexpression OS cell model

The Saos-2 cells were obtained from the cell bank of The Affiliated Hospital of Jiaxing University. Saos-2 cells were randomly allocated to two experimental groups: (1) a negative control group (Saos-2 + NC) transfected with the empty vector and (2) an experimental group (Saos-2 + ACSS2-OE) transfected with the ACSS2 overexpression construct. Transfection efficiency was subsequently confirmed by quantitative PCR analysis. Each group had three replicates. p < 0.05 was considered significant [23]. *p < 0.05, **p < 0.01, ***p < 0.001.

Cell cycle experiments

Saos-2 cells were transferred into pre-labeled centrifuge tubes and centrifuged at 1,000 rpm for 5 min at 4 °C, after which the supernatant was discarded. The cell pellet was resuspended in 3 mL of pre-cooled PBS and centrifuged again for 5 min, and the supernatant was discarded. Pre-cooled 75% ethanol was then added, and the cells were fixed overnight at 4 °C. The fixed cells were washed three times with pre-cooled PBS, with each wash followed by centrifugation at 1,000 rpm for 5 min at 4 °C and removal of the supernatant. Finally, propidium iodide staining solution was added, and the cells were stained for 30 min at 37 °C in the dark before flow cytometric analysis. Each group had three replicates. p < 0.05 was considered significant [24]. *p < 0.05, **p < 0.01, ***p < 0.001.

Apoptosis experiments

Cell suspensions containing 1 × 106 Saos-2 cells were harvested at each time point and washed twice with ice-cold PBS. The cells were resuspended in 1 mL of 1× Binding Buffer to a final concentration of 1 × 106 cells/mL for flow cytometric analysis. Within 60 min of assessment, 5 µL of FITC-conjugated Annexin V was added to each tube; the mixture was then gently mixed and incubated for 10 min at room temperature in the dark. Subsequently, 5 µL of propidium iodide was added, followed by an additional 5 min incubation under the same conditions. Each group had three replicates. p < 0.05 was considered significant [24]. *p < 0.05, **p < 0.01, ***p < 0.001.

Results

Identification of prognostic significant ORGs related to OS patients

Based on TARGET_OS dataset and GSE21257 dataset, a total of 6 ORGs were significantly associated with prognosis in OS at P < 0.05. LASSO analysis (Based on TARGET_OS dataset) was used to further refine our selection, identifying 3 ORGs (ACSS2, CLTCL1 and PLD3) with the highest predictive value for survival (Figures S1). KM analyses showed that low expression of each of these 3 genes was consistently associated with poor prognosis in OS patients (Fig. 1A, B).

Fig. 1.

Fig. 1

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Survival analysis for 3 ORGs. A The KM curves for the 3 ORGs (ACSS2, CLTCL1 and PLD3) in TARGET_OS dataset; B The KM curves for the 3 ORGs (ACSS2, CLTCL1 and PLD3) in GSE21257 dataset

Construction and validation of a prognostic risk stratification model for OS patients

A prognostic risk stratification model was developed based on 3 ORGs. Risk score=-0.2438044×ACSS2 -0.3436737×CLTCL1 -0.2495299×PLD3, which effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes (Fig. 2A). Multivariate Cox regression analysis identified the risk score as an independent prognostic factor for OS patients (Fig. 2B). The areas under the receiver operating characteristic (ROC) curve for the 1-, 4-, and 7-year survival rates were 0.66, 0.74, and 0.83, respectively (Fig. 3A). These findings were further validated using the independent GSE21257 dataset (Figure S2), where the corresponding ROC curve values for the 1-, 4-, and 7-year survival rates were 0.71, 0.80, and 0.68, respectively (Fig. 3B).

Fig. 2.

Fig. 2

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Construction and validation of a prognostic risk stratification model for OS patients. A Kaplan Meier curves show significant differences in prognosis among OS patients with different risk stratification; B The risk score was identified as an independent risk factor for the prognosis of OS patients (P < 0.05)

Fig. 3.

Fig. 3

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The risk score predictions for 1-year, 4-year and 7-year mortality rates. For the 1-, 4-, and 7-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.66, 0.74, and 0.83, respectively in TARGET_OS dataset (A). For the 1-, 4-, and 7-year survival rates, the obtained areas under the receiver operating characteristic curve values were 0.71, 0.80, and 0.68, respectively in GSE21257 dataset (B)

Enrichment analysis of biological pathways

A total of 2183 genes that were significantly negatively correlated with the prognostic risk score were included in the biological pathway enrichment analysis. The results of biological pathway analysis mainly enriched in protein localization to mitochondrion, protein targeting to mitochondrion, immune effector process, B cell receptor signaling pathway, Neutrophil extracellular trap formation (Fig. 4A-D).

Fig. 4.

Fig. 4

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Identification of the prognostic model-related biological pathways. A GO functional analysis; B The GSEA-GO functional analysis; C KEGG functional analysis; D The GSEA-KEGG functional analysis

Differential analysis of tumor immune microenvironment and drug sensitivity

The box diagram shows a significant difference in immune cell infiltration between low-risk and high-risk patients (Fig. 5A). In addition, high-risk patients have higher tumor purity (Fig. 5B). 3 ORGs (ACSS2, CLTCL1 and PLD3) are positively correlated with Neutrophils infiltration and negatively correlated with B cell infiltration. (Fig. 5C, D). Significant differences in drug sensitivity were observed between the high-risk and low-risk groups across 4 drugs (BI_2536, Bortezomib, Dactinomycin, MG_132) (Fig. 6A). 3 ORGs (ACSS2, CLTCL1 and PLD3) were positively correlated with 2 drugs (BI_2536, Dactinomycin) (Fig. 6B).

Fig. 5.

Fig. 5

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The relationship between tumor immune microenvironment. A The box diagram shows a significant difference in immune cell infiltration between low-risk and high-risk patients; B In addition, high-risk patients have higher tumor purity; C, D 3 ORGs (ACSS2, CLTCL1 and PLD3)

Fig. 6.

Fig. 6

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Drug sensitivity analysis. A There are significant differences in 4 drugs (BI_2536, Bortezomib, Dactinomycin, MG_132) among different risk groups; B 3 ORGs (ACSS2, CLTCL1 and PLD3) are associated with 2 drugs (BI_2536, Dactinomycin) are positively correlated

ORGs expression profiling in OS cells and establishment of a Saos-2 cell model with ACSS2 overexpression

Based on HPA database, in commonly used OS cells (143B, Saos-2, and U2OS), 3 ORGs (ACSS2, CLTCL1 and PLD3) were observed to have relatively high expression levels in the Saos-2 cells (Figure S3). qPCR analysis confirmed successful overexpression of ACSS2 in the Saos-2 + ACSS2-OE group (Figure S4).

Cell cycle experiments and apoptosis experiments

Cell cycle profiling demonstrated that ACSS2 overexpression in Saos-2 cells (Saos-2 + ACSS2-OE) led to a significant arrest in the G1 phase compared to control cells (Saos-2 + NC) (Figs. 7A). Apoptosis analysis revealed a significantly higher rate of apoptosis in the Saos-2 + ACSS2-OE group relative to the Saos-2 + NC control group (Figs. 7B).

Fig. 7.

Fig. 7

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Cell cycle experiments and Apoptosis experiments. Cell cycle profiling demonstrated that ACSS2 overexpression in Saos-2 cells (Saos-2 + ACSS2-OE) led to a significant arrest in the G1 phase compared to control cells (Saos-2 + NC) (A). Apoptosis analysis revealed a significantly higher rate of apoptosis in the Saos-2 + ACSS2-OE group relative to the Saos-2 + NC control group (B). *p < 0.05, **p < 0.01, ***p < 0.001

Discussion

Currently, advances in medical technology have improved the prognosis of patients with OS. However, most patients still face challenges of drug resistance and recurrence during the treatment process [25]. Identifying novel therapeutic targets and prognostic markers is therefore crucial for improving patient outcomes and survival rates [26]. As is well established, ORGs significantly influence the development and progression of various cancers [7], but their role in OS has not been fully explored. In this study, we systematically evaluated the role of ORGs in OS and identified three ORGs with prognostic and potential therapeutic value. Subsequently, a novel prognostic signature for OS patients was developed. This prognostic model provide a theoretical basis for improving the clinical diagnosis and treatment of OS.

The 3 ORGs (ACSS2, CLTCL1 and PLD3) were negatively correlated with B-cell infiltration. The anti-tumor immune response mediated by tumor infiltrating B cells and plasma cells is mainly initiated through the secretion of tumor specific antibodies by plasma cells, which contribute to antibody dependent cytotoxicity and facilitate the phagocytic clearance of tumor cells by immune effector cells [27]. In addition, B cells can directly or indirectly participate in antigen presentation and exhibit anti-tumor functions, including the production of antibodies against tumor associated antigens, enhancement of phagocytosis, and antigen presentation to CD4 + T cells [28]. B cells can activate T cells through antigen presentation, contributing to the formation of an “immune hot” tumor microenvironment and enhancing the activity of immune cells such as CD8 + T cells. For example, in breast cancer, lung cancer, and other malignancies, higher B cell infiltration has been associated with better prognosis [29–30]. KM analyses demonstrated that low expression of these 3 ORGs (ACSS2, CLTCL1 and PLD3) was consistently associated with poor prognosis in OS patients. Therefore, 3 ORGs (ACSS2, CLTCL1 and PLD3) may affect the prognosis of patients by regulating immune infiltration of B cells, however, the specific mechanism needs further in-depth research.

The tumor microenvironment (TME) is composed of the extracellular matrix and various cell types, including immune and non-immune cells. It plays a critical role in cancer progression, drug resistance, and the effectiveness of immunotherapy [31–32]. Our findings showed that, compared with low-risk score patients, high-risk score patients exhibited lower immune scores. A low immune score suggests reduced immune cell activity within the TME, which may limit the ability of immune checkpoint inhibitors (such as PD-1/PD-L1 inhibitors) to effectively activate the immune system. As a result, these patients may have a reduced response rate to immunotherapy and poorer therapeutic outcomes [33], which may partly explain the worse prognosis observed in the high-risk group. Therefore, for patients with low immune scores in the high-risk group, combination chemotherapy, targeted therapy, or immunomodulatory approaches may be required to reduce tumor burden, remodel the TME, and enhance sensitivity to immunotherapy. Meanwhile, our study found that patients in the high-risk group exhibited higher tumor purity, reflecting the dynamic interaction between tumor cells and the TME. High tumor purity is often associated with reduced immune cell infiltration, limited efficacy of immunotherapy, and consequently poorer patient prognosis [34]. Our risk score was also associated with the B cell receptor signaling pathway. B-cell immune infiltration can induce apoptosis of target cells through the B cell receptor signaling pathway [35]. Our results further indicate that changes in ACSS2 expression are associated with B cell immune infiltration and can regulate apoptosis in OS cells. These findings suggest that targeting pathways related to B cell immune infiltration may help regulate tumor behavior and promote tumor cell apoptosis. Since a key goal of tumor immunotherapy is to induce tumor cell apoptosis [36], this study may provide a theoretical basis for improving therapeutic strategies in OS. However, further research is needed to clarify the underlying mechanism.

Increasing evidence suggests that resistance to standard chemotherapy remains a major challenge in OS treatment [37]. Specifically, significant differences in drug sensitivity were observed between the high-risk and low-risk groups across four drugs (Bortezomib, MG_132, Dactinomycin, BI_2536). Studies have reported that Bortezomib can inhibit OS cell growth and induce apoptosis in both in vitro and in vivo experiments [38]. MG_132 is considered a potential therapeutic agent for OS by inducing autophagy and regulating protein homeostasis [37]. In addition, the combination of Dactinomycin, bleomycin and cyclophosphamide has been shown to enhance therapeutic effects against OS cells [39]. In animal models, intravenous injection of BI_2536 can significantly inhibits the growth of OS xenografts and reduces tumor volume [40]. These findings indicate substantial molecular heterogeneity between OS risk subgroups (high-risk and low-risk), which may help guide the development of personalized treatment strategies. However, although these four drugs show potential therapeutic value in OS, further validation in prospective cohorts is still required.

A prognostic risk stratification model was constructed based on 3 ORGs (ACSS2, CLTCL1 and PLD3). In TARGET_OS dataset, the areas under the ROC curve for the 1-, 4-, and 7-year survival rates were 0.66, 0.74, and 0.83, respectively. These findings were further validated using the independent GSE21257 dataset, where the corresponding ROC curve values for the 1-, 4-, and 7-year survival rates were 0.71, 0.80, and 0.68, respectively. Therefore, our risk scoring model can provide good auxiliary treatment and auxiliary diagnosis for clinical practice. In current clinical practice, the assessment of malignancy and prognosis in OS mainly relies on surgical staging systems and the histological classification of bone and soft tissue tumors [41]. In addition, increased serum levels of alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) are typically associated with poor prognosis [42]. Although these indicators may reflect underlyinggenetic characteristics of OS, they are insufficient to address tumor heterogeneity. Our prognostic model demonstrated good accuracy in supporting survival prediction and provides a robust molecular based prognostic model for patients with OS. Further validation in prospective cohorts represents the next direction of our research.

The prognostic value of organelle-related gene signatures has been confirmed in various cancers. For example, in lung adenocarcinoma, 14 organelle-related genes were used to construct a prognostic model, the pathways associated with organelle related features are mainly enriched in the complement and coagulation cascade pathways [8]. In hepatocellular carcinoma, pathways related to organelle related features are mainly enriched in organic acid metabolism processes and cell cycle [43]. In multiple myeloma, pathways associated with organelle related features are mainly enriched in the cell cycle [14]. However, in OS, the pathways related to organelle related features in our study are mainly enriched in immune pathways (immune effector process, B cell receptor signaling pathway), reflecting their correlation with the immune microenvironment of OS. Compared with other cancers such as lung adenocarcinoma, hepatocellular carcinoma, and multiple myeloma, the organelle-related features of OS focus more on bone tissue-microenvironment adaptation, three ORGs (ACSS2, CLTCL1, and PLD3) were also associated with the immune microenvironment, and further cell experiments revealed the correlation between ORG (ACSS2) and OS cell function (apoptosis and cell cycle), providing a theoretical basis for the precise diagnosis and treatment of OS.

To date, published studies have shown that the gene and protein expression of 2 ORGs (ACSS2 and CLTCL1) are significantly downregulated in OS cells [1, 26]. Meanwhile, PLD3 has been incorporated into multiple OS prognostic models [44–45]. Although the present study preliminarily explored the potential prognostic value of three ORGs in OS using public database data. We further investigated the function of ACSS2 in OS cells through in vitro experiments. However, one limitation of this study is that the functions of the other two ORGs (PLD3 and CLTCL1) in OS cells were not examined experimentally; this will be addressed in our future work. Meanwhile, our findings are based on the TARGET and GEO databases, prospective clinical validation is required to confirm the utility of this prognostic signature. Second, animal experimentation is needed to elucidate the precise mechanisms by which these genes influence OS progression and treatment response.

Conclusion

This study provides the first comprehensive analysis of the prognostic significance of ORGs in OS patients, identifying three ORGs (ACSS2, CLTCL1 and PLD3) with prognostic value. a novel prognostic signature for OS patients was developed based three ORGs (ACSS2, CLTCL1 and PLD3), the areas under the ROC curve for the 1-, 4-, and 7-year survival rates were 0.66, 0.74, and 0.83, respectively. These findings were further validated using the independent OS dataset, where the corresponding ROC curve values for the 1-, 4-, and 7-year survival rates were 0.71, 0.80, and 0.68, respectively. In OS patients, this prognostic feature independently predicts risk. This novel ORGs signature provides a valuable tool for patient stratification and offer a new therapeutic approach for OS.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1. (322KB, docx)

Acknowledgements

Not applicable.

Author contributions

Bing Sun, Jieyang Zhu: Designed this research; Bing Sun, Yi Jiang: Analyzed and interpreted the data; Tao Zhang: Contributed reagents, materials, analysis tools or data; Bing Sun, Jieyang Zhu, Yi Jiang, Sihui Chen, Tao Zhang: Wrote the paper. All authors reviewed the manuscript.

Funding

This study was funded by The Key Departments of Jiaxing, China (Grant Agreement 2023-ZC-012).

Data availability

GSE21257 dataset was downloaded from the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). TARGET_OS dataset was downloaded from the TARGET database (https://ocg.cancer.gov/programs/target).

Declarations

Ethics approval and consent to participate

Not applicable. This study did not involve human participants, clinical samples, or clinical trials. Publicly available data from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database and Human Protein Atlas (HPA) database were used in accordance with their respective data access policies. The existing cells in the cell bank were used. Not applicable. No individual patient data, identifiable images, or personal details are included in this manuscript that would require specific patient consent for publication.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Bing Sun and Jieyang Zhu have contributed equally to this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (322KB, docx)

Data Availability Statement

GSE21257 dataset was downloaded from the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). TARGET_OS dataset was downloaded from the TARGET database (https://ocg.cancer.gov/programs/target).

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