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

Abstract

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.

Keywords: fatty acid metabolism; immunity; lactate metabolism; metastasis; osteosarcoma; prognosis.

Figure 1

Expressions of the 18 fatty acid and lactate metabolism–related genes and the interactions among them. (A) Heat map (blue: low expression level; red: high expression level) of the fatty acid and lactate metabolism–related genes between the non-metastatic (nonmetastatic, brilliant blue) and the metastatic tissues (metastatic, red). P-values were showed as *P < 0.05; **P < 0.01. (B) The correlation network of these genes (Cutoff = 0.1; red line: positive correlation; blue line: negative correlation).

Figure 2

Tumor classification based on the fatty acid and lactate metabolism–related DEGs. (A) Eighty-four OS patients were grouped into three clusters according to the consensus clustering matrix (k = 3). (B) Kaplan–Meier OS curves for the three clusters.

Figure 3

Construction of risk signature in the TARGET cohort. (A) Univariate Cox regression analysis of overall survival for each fatty acid and lactate metabolism–related gene, and three genes with P < 0.05. (B) Cross-validation for tuning the parameter selection in the LASSO regression. (C) LASSO regression of the three overall survival-related genes. (D) Distribution of patients based on the risk score. (E) The survival status of low-risk and high-risk population. (F) PCA plot for osteosarcoma patients based on the risk score. (G) The t-SNE analysis based on the risk score. (H) Kaplan–Meier curves for the overall survival of patients in the high- and low-risk groups. (I) ROC curves demonstrated the predictive efficiency of the risk score.

Figure 4

Validation of the risk model in the GEO cohort. (A) Distribution of patients in the GEO cohort based on the median risk score in the TARGET cohort. (B) The survival status of low-risk and high-risk population. (C) PCA plot for osteosarcoma patients. (D) The t-SNE analysis based on the risk score. (E) Kaplan–Meier curves for comparison of the overall survival between low- and high-risk groups; (F) Time-dependent ROC curves for osteosarcoma patients.

Figure 5

The expression levels of fatty acid and lactate metabolism related genes between osteosarcoma cell lines and osteoblasts. (A) Western blotting analysis of the expressions of SLC7A7, MYC, and ACSS2 proteins in hFOB, U20S, and 143B groups. GAPDH serves as an internal standard. The gels have been run under the same experimental conditions. (B) A histogram of the OD values of SLC7A7, MYC, and ACSS2 in each group (n = 3 per group). The obtained data are represented as M ± SE. Significance: **p < 0.01 versus hFOB group.

Figure 6

Construction and calibration of nomogram. (A) Nomogram integrating risk score and clinical characteristics. (B) Calibration of the nomogram at 1-, 3-, and 5-year survival in the TARGET cohort.

Figure 7

Independence detection of the constructed risk prediction model. (A) Univariate analysis for the TARGET cohort (gender: age, metastatic). (B) Multivariate analysis for the TARGET cohort. (C) Heat map (blue: low expression; red: high expression) for the connections between clinicopathologic features and the risk groups. **p < 0.01.

Figure 8

Functional analysis based on the DEGs between the two-risk groups in the TARGET cohort. (A) Bubble graph for GO enrichment (the bigger bubble means the more genes enriched, and the increasing depth of red means the differences were more obvious; q-value: the adjusted p-value). (B) Barplot graph for KEGG pathways (the longer bar means the more genes enriched, and the increasing depth of red means the differences were more obvious).

Figure 9

Immune status between different risk groups and the association between risk score and tumor microenvironment. (A) Comparison of the enrichment scores of 16 types of immune cells between low- (blue box) and high-risk (red box) group in the TARGET cohort. *p < 0.05, **p < 0.01, and ***p < 0.001; (B) Comparison of the enrichment scores of 13 types of immune functions between low- (blue box) and high-risk (red box) group in the TARGET cohort. *p < 0.05, **p < 0.01, and ***p < 0.001; (C) Comparison of the enrichment scores of 16 types of immune cells between low- (blue box) and high-risk (red box) group in the GEO cohort. *p < 0.05, **p < 0.01, and ***p < 0.001; (D) Comparison of the enrichment scores of 13 types of immune functions between low- (blue box) and high-risk (red box) group in the GEO cohort. *p < 0.05, **p < 0.01, and ***p < 0.001.

Figure 10

Estimate analysis for osteosarcoma patients. (A) The relationship between risk score and immune score in the TARGET cohort. (B) The relationship between risk score and stromal score in TARGET cohort. (C) The relationship between risk score and immune score in the GEO cohort. (D) The relationship between risk score and stromal score in the GEO cohort.

Figure 11

Scatter plot of relationship between prognostic gene expression and drug sensitivity. The top 16 correlation analyses are shown based on the p-value.