Establishment and evaluation of a prognosis model of soft tissue sarcoma based on lactic acid metabolism gene
Objective To establish a risk model for lactate metabolism-related genes in soft tissue sarcomas,and to explore their correlation with tumor mutational burden,immune cells,immune related functions and immune checkpoints in the tumor microenvironment.Methods The differentially expressed genes associated with lactate metabolism in soft tissue sarcoma were obtained after intersecting the differentially expressed genes extracted by TCGA and GTEx database with lactate metabolism-related gene sets from the Msigdb database.The pathway enrichment analysis was carried out by gene ontology and Kyoto Encyclopedia of Genes and Genomes.By the univariate Cox regression and least absolute shrinkage and selection operator to construct a risk score model,and divided the soft tissue sarcomas samples into high-risk and low-risk groups.Finally,the differences of the tumor mutational burden and immunity in the tumor microenvironment between the two groups were analyzed.Results A total of 29 differentially expressed genes associated with lactate metabolism were screened,and the results of pathways enrichment analysis showed that they were mainly related to metabolic processes.By constructing a predictive risk score model,soft tissue sarcoma samples were divided into high-risk and low-risk groups.Tumor mutational burden analysis showed that the first mutated gene was tumor protein p53 in all soft tissue sarcoma samples.Missense mutation was the most common type of somatic mutations,and the frequency of mutation was higher in the high-risk group than that in the low-risk group.The analysis of tumor microenvironment indicated that the infiltration of M0 and M2 macrophage was more in the high-risk group,while the low-risk group had a higher percentage of infiltrating CD8+T cells and monocytes.The immune related functional response and immune checkpoints expression was higher in the low-risk group.Conclusion The lactate metabolism scoring model can better evaluate the prognosis of patients with soft tissue sarcoma and reflect the state of tumor microenvironment.
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