m6A-related metabolism molecular classification with distinct prognosis and immunotherapy response in soft tissue sarcoma

Abstract

N6-methyladenosine (m6A) methylation, one of the most crucial RNA modifications, has been proven to play a key role that affect prognosis of soft tissue sarcoma (STS). However, m6A methylation potential role in STS metabolic processes remains unknown. We comprehensively estimated the m6A metabolic molecular subtypes and corresponding survival, immunity, genomic and stemness characteristics based on 568 STS samples and m6A related metabolic pathways. Then, to quantify the m6A metabolic subtypes, machine learning algorithms were used to develop the m6A-metabolic Scores of individual patients. Finally, two distinct m6A metabolic subtypes (Cluster A and Cluster B) among the STS patients were identified. Compared to Cluster B subtype, the Cluster A subtype was mainly characterized by better survival advantages, activated anti-tumor immune microenvironment, lower gene mutation frequency and higher anti-PD-1 immunotherapy response rates. We also found that the m6A-metabolic Scores could accurately predict the molecular subtype of STS, prognosis, the abundance of immune cell infiltration, tumor metastasis status, sensitivity to chemotherapeutics and immunotherapy response. In general, this study revealed that m6A-regulated tumor metabolism processes played a key role in terms of prognosis of STS, tumor progression, and immune microenvironment. The identification of metabolic molecular subtypes and the construction of m6A-metabolic Score will help to more effectively guide immunotherapy, metabolic therapy and chemotherapy in STS.

Keywords: cancer metabolism; immunotherapy; m6A modification; machine learning; molecular classification; tumor microenvironment.