A New Signature of Sarcoma Based on the Tumor Microenvironment Benefits Prognostic Prediction

Sarcomas are a group of malignant tumors derived from mesenchymal tissues that display complex and variable pathological types. The impact of the immune properties of the tumor microenvironment (TME) on the prognosis, treatment, and management of sarcomas has attracted attention, requiring the exploration of sensitive and accurate signatures. In this study, The Cancer Genome Atlas (TCGA) database was searched to screen for an RNA sequencing dataset, retrieving 263 sarcoma and 2 normal samples with survival data. Genes associated with immune regulation in sarcomas were retrieved from the Tumor Immune Estimation Resource database to estimate tumor purity and immune cell infiltration levels. The samples were then divided into the immune-high and immune-low groups. Then, we screened for differentially expressed genes (DEGs) between the two groups. The intersection between immune-related genes and DEGs was then determined. Univariate Cox and least absolute shrinkage and selection operator analyses were used to select ideal genes for prognostic prediction and subsequent construction of a risk signature. A survival analysis was performed to reveal the dissimilarity in survival between the high- and low-score groups. Finally, a nomogram was generated to verify the accuracy and reliability of the signature. Through Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression (ESTIMATE) analysis, high ESTIMATE, and low tumor purity were significantly associated with a favorable prognosis. Moreover, a total of 5259 DEGs were retrieved, the majority of which were downregulated. In total, 590 immune-associated genes overlapped with the DEGs, among which nine hub genes were identified. Finally, two candidate genes, ACVR2B and NFYA, were identified, based on which a risk signature was constructed. The risk signature constructed in this study is accurate and reliable for the prognostic prediction and phenotyping of sarcomas.

Automated summary

In this study, a risk signature for prognostic prediction and phenotyping of sarcomas was constructed based on RNA sequencing data. The immune properties of the tumor microenvironment were explored, and differential gene expression analysis was performed. Nine hub genes were identified, and two candidate genes were used to construct the risk signature. The signature was found to be accurate and reliable for predicting prognosis.