Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer

Abstract

High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15–25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignant type in all gynecologic malignancies. Metabolic reprogramming in tumor microenvironment (TME), especially fatty acid metabolism, has been identified to play a crucial role in cancer prognosis. Yet, the underlying mechanism of fatty acid metabolism on ovarian cancer progression is severely understudied. Recently, studies have demonstrated the role of fatty acid metabolism reprogramming in immune cells, but their roles on cancer cell metastasis and cancer immunotherapy response are poorly characterized. Here, we reported that the fatty acid–related genes are aberrantly varied between ovarian cancer and normal samples. Using samples in publicly databases and bio-informatic analyses with fatty acid–related genes, we disentangled that cancer cases can be classified into high- and low-risk groups related with prognosis. Furthermore, the nomogram model was constructed to predict the overall survival. Additionally, we reported that different immune cells infiltration was presented between groups, and immunotherapy response differed in two groups. Results showed that our signature may have good prediction value on immunotherapy efficacy, especially for anti–PD-1 and anti–CTLA-4. Our study systematically marked the critical association between cancer immunity in TME and fatty acid metabolism, and bridged immune phenotype and metabolism programming in tumors, thereby constructed the metabolic-related prognostic model and help to understand the underlying mechanism of immunotherapy response.