Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

Background Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored. Methods RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments. Results This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages. Conclusion The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells. Supplementary Information The online version contains supplementary material available at 10.1186/s12929-024-00999-7.


Figure S1 .
Figure S1.The gene expression data distribution of the TARGET (A) and GEO (B) databases.

Figure S3 .
Figure S3.The efficiency of ST3GAL4 overexpression plasmid in this study.

Figure S4 .
Figure S4.The efficiency of ST3GAL4 siRNAs in this study.

Figure S5 .
Figure S5.Differences of the overall immune infiltration (A) and 28 immune cells infiltration (B) between high and low metabolism groups.

Figure S6 .
Figure S6.The correlation between various immune features in osteosarcoma.

Figure S7 .
Figure S7.Differences of the expression of immune checkpoints (A) and core biological pathway activity (B) between high and low metabolism groups.

Figure S8 .
Figure S8.PPI network and hub genes in the carbohydrate metabolic pathway.A PPI network of carbohydrate metabolic pathway genes according to the STRING database.B The top 10 hub genes of carbohydrate metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S9 .
Figure S9.PPI network and hub genes in the energy metabolic pathway.A PPI network of energy metabolic pathway genes according to the STRING database.B The top 10 hub genes of energy metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S10 .
Figure S10.PPI network and hub genes in the lipid metabolic pathway.A PPI network of lipid metabolic pathway genes according to the STRING database.B The top 10 hub genes of lipid metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S11 .
Figure S11.PPI network and hub genes in the amino acid metabolic pathway.A PPI network of amino acid metabolic pathway genes according to the STRING database.B The top 10 hub genes of amino acid metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S12 .
Figure S12.PPI network and hub genes in the nucleotide metabolic pathway.A PPI network of nucleotide metabolic pathway genes according to the STRING database.B The top 10 hub genes of nucleotide metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S13 .
Figure S13.PPI network and hub genes in the TCA cycle metabolic pathway.A PPI network of TCA cycle metabolic pathway genes according to the STRING database.B The top 10 hub genes of TCA cycle metabolic pathway genes.C Correlations among hub genes in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.D Univariate Cox regression analysis of overall survival for hub genes.

Figure S14 .
Figure S14.Metabolic pathway-related clusters based on seven metabolic super-

Figure S15 .
Figure S15.LASSO regression of 114 prognosis-related MRGs.A LASSO coefficient profiles of 25 prognostic MRGs.B Ten-time cross-validation for tuning parameter selection in the LASSO model.

Figure S16 .
Figure S16.Correlations among 17 MRGs of the risk model in the TARGET cohort.Red representing negative correlations and blue representing positive correlations.Blank represents a correlation P-value > 0.05.

Figure S17 .
Figure S17.The heatmap of 28 immune cells between high and low risk groups.

Figure S18 .
Figure S18.Correlations between risk score and overall immune infiltration (A), 28 immune cells infiltration (B), and the expression of immune checkpoints (C) and differences of core biological pathway activity between high and low risk score (D).

Figure S19 .
Figure S19.Feature plots and violin plots for the core MRGs in the risk model.A,B Feature plots and violin plots of MRGs with positive coefficients in the risk model.C,D Feature plots and violin plots of MRGs with negative coefficients in the risk model.The color legend shows the normalized expression levels of the genes.

Figure S20 .
Figure S20.The relationships between ST3GAL4 and CAF score, TIDE score, and dysfunction and exclusion of CTLs.

Figure S22 .
Figure S22.The prognosis and immunological features of other members in the

Figure S23 .
Figure S23.Correlations of the expression of ST3GAL4 with metabolic superpathways (A) and differences of metabolic super-pathways activity between high-ST3GAL4 and low-ST3GAL4 groups (B).

Figure S24 .
Figure S24.The GSEA results of ST3GAL4 (A) and other core MRGs (B) based on KEGG gene set.