LAUSR

Simple Summary Soft tissue sarcomas are uncommon and diverse solid tumors with high risks that have a poor prognosis. Tumor microenvironment (TME) and hypoxia play critical roles in tumor development. Therefore, we aimed to determine whether linking hypoxia-related parameters to TME cells could improve prognosis and treatment outcomes. The Hypoxia-TME classifier was first proposed by us using TCGA-SARC court (n = 258) and fusion data from GSE63157 and GSE30929 (n = 225). This classifier is capable of correctly classifying patients based on their prognosis and immune type. In addition, immunotherapy and chemotherapy programs were provided in a more specific manner. Several key genes were identified for future research as a result of the classification results. Abstract Soft tissue sarcoma (STS) is one of the rarest but most aggressive cancer. It is important to note that intratumoral hypoxia and tumor microenvironment (TME) infiltration play a significant role in the growth and therapeutic resistance of STS. The goal of this study was therefore to determine whether linking hypoxia-related parameters to TME cells could provide a more accurate prediction of prognosis and therapeutic response. An analysis of 109 hypoxia-related genes and 64 TME cells was conducted in STS. Hypoxia-TME classifier was constructed based on 6 hypoxia prognostic genes and 8 TME cells. As a result, we evaluated the prognosis, tumor, and immune characteristics, as well as the effectiveness of therapies in Hypoxia-TME-defined subgroups. The Lowplus group showed a better prognosis and therapeutic response than any other subgroup. It is possible to unravel these differences based on immune-related molecules and somatic mutations in tumors. Further validation of Hypoxia-TME was done in an additional cohort of 225 STS patients. Additionally, we identified five key genes through differential analysis and RT-qPCR, namely, ACSM5, WNT7B, CA9, MMP13, and RAC3, which could be targeted for therapy. As a whole, the Hypoxia-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic outcome, providing new approaches to therapy strategizing for patients.