Comprehensive analysis of fatty acid and lactate metabolism-related genes for prognosis value, immune infiltration, and therapy in osteosarcoma patients

Osteosarcoma is the most frequent bone tumor. Notwithstanding that significant medical progress has been achieved in recent years, the 5-year overall survival of osteosarcoma patients is inferior. Regulation of fatty acids and lactate plays an essential role in cancer metabolism. Therefore, our study aimed to comprehensively assess the fatty acid and lactate metabolism pattern and construct a fatty acid and lactate metabolism-related risk score system to predict prognosis in osteosarcoma patients. Clinical data and RNA expression data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We used the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic risk score model. Relationships between the risk score model and age, gender, tumor microenvironment characteristics, and drug sensitivity were also explored by correlation analysis. We determined the expression levels of prognostic genes in osteosarcoma cells via Western blotting. We developed an unknown fatty acid and lactate metabolism-related risk score system based on three fatty acid and lactate metabolism-related genes (SLC7A7, MYC, and ACSS2). Survival analysis showed that osteosarcoma patients in the low-risk group were likely to have a better survival time than those in the high-risk group. The area under the curve (AUC) value shows that our risk score model performs well in predicting prognosis. Elevated fatty acids and lactate risk scores weaken immune function and the environment of the body, which causes osteosarcoma patients' poor survival outcomes. In general, the constructed fatty acid and lactate metabolism-related risk score model can offer essential insights into subsequent mechanisms in available research. In addition, our study may provide rational treatment strategies for clinicians based on immune correlation analysis and drug sensitivity in the future.