Metabolism related gene signature predicts prognosis and indicates tumor immune infiltration in ovarian cancer

Energy metabolism plays a crucial role in supporting cancer cell growth and driving tumor progression. Our objective was to create a unique gene signature based on metabolic genes that could accurately predict the prognosis of patients with ovarian cancer (OC). We accessed microarray data of patients with OC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients from the TCGA dataset were divided into training and internal validation sets, maintaining a ratio of 3:1. Based on Least absolute shrinkage and selection operator Cox regression analysis, twenty-nine metabolism-related genes were identified for the development of the metabolic signature. Patients in the training set were successfully divided into low-and high-risk groups with a significantly different prognosis (Hazard Ratio (HR): 2.76, 95% Confidence Interval (CI): 2.12–3.59, p < 0.001). The prognostic value of this signature was confirmed in the internal (HR: 3.06, 95% CI: 1.80–5.17, p < 0.001) and external validation sets (HR: 2.17, 95% CI: 1.57–2.99, p < 0.001). The time-dependent receiver operating characteristic (ROC) at the 5-year interval demonstrated that the prognostic accuracy of this metabolic signature (Area under curve (AUC) = 0.723) was superior to that of any other clinicopathological features, including the Federation of Gynecology and Obstetrics stage (AUC = 0.509), grade (AUC = 0.536), and debulking status (AUC = 0.637). Further immune cell infiltration analysis showed that low-risk patients had a higher enrichment of immune-activating cells. In conclusion, a novel metabolic signature with good performance was established in this study. This prognostic model could aid in the identification of high-risk patients who require aggressive follow-up and therapeutic strategies.

Ovarian cancer; TCGA; GEO; Metabolism; Prognosis

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