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.
Osteosarcoma is a common malignant bone tumor in children and adolescents. The overall survival of osteosarcoma patients is remarkably poor. Herein, we sought to establish a reliable risk prognostic model to predict the prognosis of osteosarcoma patients. Patients ’ RNA expression and corresponding clinical data were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus databases. A consensus clustering was conducted to uncover novel molecular subgroups based on 200 hypoxia-linked genes. A hypoxia-risk models were established by Cox regression analysis coupled with LASSO regression. Functional enrichment analysis, including Gene Ontology annotation and KEGG pathway analysis, were conducted to determine the associated mechanisms. Moreover, we explored relationships between the risk scores and age, gender, tumor microenvironment, and drug sensitivity by correlation analysis. We identified two molecular subgroups with significantly different survival rates and developed a risk model based on 12 genes. Survival analysis indicated that the high-risk osteosarcoma patients likely have a poor prognosis. The area under the curve (AUC) value showed the validity of our risk scoring model, and the nomogram indicates the model’s reliability. High-risk patients had lower Tfh cell infiltration and a lower stromal score. We determined the abnormal expression of three prognostic genes in osteosarcoma cells. Sunitinib can promote osteosarcoma cell apoptosis with down-regulation of KCNJ3 expression. In summary, the constructed hypoxia-related risk score model can assist clinicians during clinical practice for osteosarcoma prognosis management. Immune and drug sensitivity analysis can provide essential insights into subsequent mechanisms. KCNJ3 may be a valuable prognostic marker for osteosarcoma development.
Background The prognosis of patients with metastatic osteosarcoma is very poor, with long-term survival in < 20%. Transforming growth factor-β (TGF-β) playing an important role in the metastasis and prognosis of osteosarcoma patients. The aim of our study was to develop a TGF-β signaling pathway-based model to predict prognosis and immune status in osteosarcoma patients. Methods TGF-β related genes were comprehensively collected from several databases. The training cohort was downloaded from the TARGET database, while the validation cohort was extracted from GEO database. We identified differentially expressed TGF-β related genes between osteosarcoma patients with or without metastatic. We conducted cox regression to identify prognostic genes and developed the TGF-β based risk score. Then, we constructed a nomogram based on TGF-β based risk score, and conducted enrichment analysis to evaluate related genes and immune microenvironment. We used western blotting to detect the expression of genes. Statistical analysis was conducted by using R software and GraphPad Prism. Results We found four TGF-β related genes (MAPK1, MYC, PML and SLC2A8) which were associated with prognosis. Then we built a TGF-β based risk score for prognosis prediction and validated the good performance of this risk score system. We also developed a nomogram based on this risk score. In addition, we found cancer immunity was quilt different in low- and high-risk groups. Conclusion A novel risk score based on TGF-β was developed and can be used to predict prognosis time for osteosarcoma patients. Our research may also open an avenue for future studies of the relationships between TGF-β related genes and immunity.
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