Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.