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The prognoses of sarcomas are poor and the responses of them to systemic therapies are limited and controversial. Thus, there is an urgent need to stratify the risk factors and identify the patients who may benefit from systemic therapies. Here, we developed a reliable, complement-based gene signature to predict the prognosis of sarcoma patients. Survival-related complement genes were identified by univariate Cox analyses and were used to build a gene signature, which was further selected using the least absolute shrinkage and selection operator model, and determined using a stepwise Cox proportional hazards regression model. The whole sarcoma cohort of TCGA was randomly divided into a training set and a test set. The signature was constructed using the training set and validated subsequently in the test set, the whole TCGA sarcoma cohort, and another two independent cohorts from the TARGET and GEO databases, respectively. Furthermore, the prognostic value of the signature was also validated in an independent cohort from our center. This model effectively predicted prognoses across the training set, different validation cohorts, and different clinical subgroups. Next, immune cell infiltration analysis, GO and KEGG analysis, and gene set enrichment analysis were performed to explore possible underlying mechanisms of this signature. Moreover, this signature may predict the response to immunotherapy. Collectively, the current complement-related gene signature can predict overall survival and possible immunotherapy response of sarcoma patients; it may serve as a powerful prognostic tool to further optimize clinical treatment and prognosis management for sarcoma patients.

A Complement-Related Gene Signature for Predicting Overall Survival and Immunotherapy Efficacy in Sarcoma Patients