Wang Yaling, Hsu Peichun, Hu Haiyan, Lin Feng, Wei Xiaokang
Department of Oncology, Shanghai Eighth People's Hospital, Shanghai, China.
Department of Orthopedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
BMC Cancer. 2025 Mar 12;25(1):445. doi: 10.1186/s12885-024-13278-3.
Osteosarcoma is a rare tumor with poor clinical outcomes. New therapeutic targets are urgently needed. Previous research indicates that genes abnormally expressed in osteosarcoma are significantly involved in the arachidonic acid (AA) metabolic pathway. However, the role of arachidonic acid metabolism-related genes (AAMRGs) in osteosarcoma prognosis remains unknown.
Osteosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were classified into high-score and low-score groups based on AAMRGs scores obtained through ssGSEA analysis. The intersecting genes were identified from weighted gene co-expression network analysis (WGCNA), DEGs (osteosarcoma vs. normal) and DE-AAMRGs (high- vs. low-score). An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. Model performance was evaluated using Kaplan-Meier survival and receiver operating characteristic (ROC) curve analysis. In vitro experiments of the AA related biomarkers was validated.
Our study constructed an AAMRGs prognostic signature (CD36, CLDN11, STOM, EPYC, PANX3). K-M analysis indicated that patients in the low-risk group showed superior overall survival to high-risk group (p<0.05). ROC curves showed that all AUC values in the prognostic model exceeded 0.76. By ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and estimate score. Correlation analysis showed the strongest positive correlation between STOM and natural killer cells, and the highest negative association between PANX3 and central memory CD8 T cells. An AAMRGs prognostic signature was constructed for osteosarcoma prognosis.
The study suggested that a high level of AAMRGs might serve as a biomarker for poor prognosis in osteosarcoma and offers a potential explanation for the role of cyclooxygenase inhibitors in cancer. The five biomarkers (CD36, CLDN11, EPYC, PANX3, and STOM) were screened to construct an AAMRGs risk model with prognostic value, providing a new reference for the prognosis and treatment of osteosarcoma.
骨肉瘤是一种临床预后较差的罕见肿瘤。迫切需要新的治疗靶点。先前的研究表明，在骨肉瘤中异常表达的基因显著参与花生四烯酸（AA）代谢途径。然而，花生四烯酸代谢相关基因（AAMRGs）在骨肉瘤预后中的作用仍不清楚。
根据通过单样本基因集富集分析（ssGSEA）获得的AAMRGs评分，将来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的骨肉瘤样本分为高分和低分两组。从加权基因共表达网络分析（WGCNA）、差异表达基因（骨肉瘤与正常组织）和差异表达AAMRGs（高分与低分）中鉴定出交集基因。通过Cox回归和LASSO算法建立了五个AAMRGs的AA代谢预测模型。使用Kaplan-Meier生存分析和受试者工作特征（ROC）曲线分析评估模型性能。对AA相关生物标志物进行了体外实验验证。
我们的研究构建了一个AAMRGs预后特征（CD36、CLDN11、STOM、EPYC、PANX3）。K-M分析表明，低风险组患者的总生存期优于高风险组（p<0.05）。ROC曲线显示，预后模型中的所有AUC值均超过0.76。通过ESTIMATE算法，我们发现高风险组患者的免疫评分、基质评分和估计评分较低。相关性分析显示，STOM与自然杀伤细胞之间的正相关性最强，PANX3与中枢记忆CD8 T细胞之间的负相关性最高。构建了一个用于骨肉瘤预后的AAMRGs预后特征。
该研究表明，高水平的AAMRGs可能作为骨肉瘤预后不良的生物标志物，并为环氧化酶抑制剂在癌症中的作用提供了潜在解释。筛选出五个生物标志物（CD36、CLDN11、EPYC、PANX3和STOM）构建了具有预后价值的AAMRGs风险模型，为骨肉瘤的预后和治疗提供了新的参考。
