Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer

Purpose: Tumor metabolism has been a novel driver of personalized cancer medicine, with aggressive efforts to regulate the metabolic system to prolong their life. The aim of this study is to explore the prognostic value of metabolism in ovarian serous cystadenocarcinoma (OSC), which is the most common subtype of ovarian cancer, accounts for 75-80% of reported cases. Patients and methods: we integrated the expression profiles of metabolism-related genes (MRGs) in survival in 379 OSC patients based on The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight key prognostic MRGs, which were related with overall survival (OS) significantly in OSC. The eight genes represented important clinical significance and prognostic value in OSC. Then a prognostic index was constructed. Results: A total of 701 differentially expressed metabolism-related genes (MRGs) were identified in OSC patients based on TCGA database. Functional enrichment analyses hinted that metabolism may act in a significant role in the development and progression of OSC. Random walking with restart (RWR) algorithm, Univariate cox and lasso regression analysis indicated a prognostic signature based on MRGs (ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately in prognostic predictions. Conclusion: This study provides a latent prognostic feature for predicting the prognosis of OSC patients and the molecular mechanism of OSC metabolism.