Identification and comprehensive analysis of an immune-related gene prognostic model for indicating tumor immune microenvironment features in soft tissue sarcoma.

Background: the STS is a rare type of tumor, and although its treatment has improved greatly in recent decades, the treatment of STS and the development of new drugs remains a major challenge. A new identification of prognostic biomarkers that reflect the biological heterogeneity of STS could therefore lead to better interventions for STS patients. In recent years, there has been a growing interest in the investigation of the impact of immune-related genes on cancer prognosis. Methods: based on RNA-seq data obtained from TCGA-STS and GTEx patients, differential expression analysis, consensus clustering, enrichment analysis, tumor microenvironment assessment, risk model construction and other data analysis were performed. Last but not least, CALR, a central regulator inSTS, demonstrated oncogenic properties through overexpression/knockdown assays, supported by qRT-PCR and immunofluorescence data. Results: we constructed a prognostic model containing 8 IRGs for predicting STS prognosis by using the LASSO regression. Furthermore, the samples were categorized as either high-risk or low-risk based on the risk score computed by the model. Additionally, we compared the tumor microenvironment of STS samples using the ESTIMATE and CIBERSORT algorithms. Last, our experimental results proved that CALR was up-regulated in sarcoma cells compared to in normal cell. Conclusions: conclusively, IRGPM is a promising immune-related prognostic biomarker. As a prognostic indicator of immunotherapy, IRGPM might also help differentiate molecular and immune characteristics in STS.