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ONCOLOGY / CLINICAL RESEARCH
Costimulatory molecule-related gene-based prognostic signature and immune characteristics in osteosarcoma
1
Hand Surgery/Wound Reconstruction Surgery, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People’s Hospital, Lishui, Zhejiang, China
Submission date: 2026-01-08
Acceptance date: 2026-02-15
Online publication date: 2026-05-01
Corresponding author
Yonghui Shen
Hand Surgery/Wound Reconstruction Surgery Lishui Hospital of Wenzhou Medical University The First Affiliated Hospital of Lishui University Lishui People’s Hospital Lishui, Zhejiang, China
Hand Surgery/Wound Reconstruction Surgery Lishui Hospital of Wenzhou Medical University The First Affiliated Hospital of Lishui University Lishui People’s Hospital Lishui, Zhejiang, China
Zengyong Hu
Hand Surgery/Wound Reconstruction Surgery Lishui Hospital of Wenzhou Medical University The First Affiliated Hospital of Lishui University Lishui People’s Hospital Lishui, Zhejiang, China
Hand Surgery/Wound Reconstruction Surgery Lishui Hospital of Wenzhou Medical University The First Affiliated Hospital of Lishui University Lishui People’s Hospital Lishui, Zhejiang, China
Article (PDF)
Supplementary files
References (61)
Costimulatory molecule-related - Supplementary Figures.pdf
Costimulatory molecule-related - Supplementary Table SI.xlsx
Costimulatory molecule-related - Supplementary Table SII.xlsx
Costimulatory molecule-related - Supplementary Table SIII.xlsx
Costimulatory molecule-related - Supplementary Table SIV.xlsx
KEYWORDS
TOPICS
ABSTRACT
Introduction:
Osteosarcoma (OSA) is a primary malignant bone tumor with a poor prognosis. Costimulatory molecule-related genes (CMRGs) are crucial for T cell-mediated antitumor immunity, yet their prognostic and therapeutic value in OSA remains unclear.
Material and methods:
Non-negative matrix factorization (NMF) clustering was used to define CMRG-based subtypes. Prognostic signature genes were selected using univariate Cox, LASSO, and multivariate regression analyses. The predictive performance of the model was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Immune infiltration and immune checkpoint differences were evaluated via ssGSEA. Drug sensitivity was predicted using CellMiner, and gene expression was validated by qRT-PCR in OSA cells.
Results:
This study identified five characteristic genes related to the prognosis of OSA and constructed a prognostic model. The evaluation results indicated that the model has favorable predictive performance, with calibration curves and DCA indicating good agreement between predicted and observed survival probabilities. Furthermore, immunotherapy response analysis revealed that patients with lower risk scores had more favorable immune responses and lower rates of disease metastasis. Notably, DMAPT, Obatoclax, 8-Chloro-adenosine, and Palbociclib showed a significant positive correlation with MYC. Additionally, qRT-PCR results indicated that EPYC, SCD1, and AOC3 were significantly overexpressed in OSA cells.
Conclusions:
This study integrates CMRG-based subtyping, risk modeling, immune profiling, and experimental validation in OSA. The identified biomarkers may help inform personalized treatment and survival risk stratification in OSA patients.
Osteosarcoma (OSA) is a primary malignant bone tumor with a poor prognosis. Costimulatory molecule-related genes (CMRGs) are crucial for T cell-mediated antitumor immunity, yet their prognostic and therapeutic value in OSA remains unclear.
Material and methods:
Non-negative matrix factorization (NMF) clustering was used to define CMRG-based subtypes. Prognostic signature genes were selected using univariate Cox, LASSO, and multivariate regression analyses. The predictive performance of the model was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Immune infiltration and immune checkpoint differences were evaluated via ssGSEA. Drug sensitivity was predicted using CellMiner, and gene expression was validated by qRT-PCR in OSA cells.
Results:
This study identified five characteristic genes related to the prognosis of OSA and constructed a prognostic model. The evaluation results indicated that the model has favorable predictive performance, with calibration curves and DCA indicating good agreement between predicted and observed survival probabilities. Furthermore, immunotherapy response analysis revealed that patients with lower risk scores had more favorable immune responses and lower rates of disease metastasis. Notably, DMAPT, Obatoclax, 8-Chloro-adenosine, and Palbociclib showed a significant positive correlation with MYC. Additionally, qRT-PCR results indicated that EPYC, SCD1, and AOC3 were significantly overexpressed in OSA cells.
Conclusions:
This study integrates CMRG-based subtyping, risk modeling, immune profiling, and experimental validation in OSA. The identified biomarkers may help inform personalized treatment and survival risk stratification in OSA patients.
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