Comprehensive Analysis of Purine-Metabolism-Related Gene Signature for Predicting Ovarian Cancer Prognosis, Immune Landscape, and Potential Treatment Options

Abstract

Purine metabolism is an important branch of metabolic reprogramming and has received increasing attention in cancer research. Ovarian cancer is an extremely dangerous gynecologic malignancy for which there are no adequate tools to predict prognostic risk. Here, we identified a prognostic signature consisting of nine genes related to purine metabolism, including ACSM1, CACNA1C, EPHA4, TPM3, PDIA4, JUNB, EXOSC4, TRPM2, and CXCL9. The risk groups defined by the signature are able to distinguish the prognostic risk and the immune landscape of patients. In particular, the risk scores offer promising personalized drug options. By combining risk scores with clinical characteristics, we have created a more detailed composite nomogram that allows for a more complete and individualized prediction of prognosis. In addition, we demonstrated metabolic differences between platinum-resistant and platinum-sensitive ovarian cancer cells. In summary, we have performed the first comprehensive analysis of genes related to purine metabolism in ovarian cancer patients and created a feasible prognostic signature that will aid in risk prediction and support personalized medicine.

Multi-Omics Analysis of NCI-60 Cell Line Data Reveals Novel Metabolic Processes Linked with Resistance to Alkylating Anti-Cancer Agents

“…Our data also showed a decrease in glutamine, which is one of the substrates that contribute to the de novo biosynthetic pathway of purine metabolism [29], which may indicate a reliance on increased glutamine consumption to support these changes in nucleotide metabolism. Notably, several other studies have shown that purine metabolism is a metabolic vulnerability of cancer cells and that this pathway is implicated in therapy resistance in a number of cancer types [37][38][39][40][41][42][43][44][45]. The current study shows that purine metabolism is a differentiator for resistant cancer cells in an entire drug class, and that changes in certain aspects of this pathway (e.g., increases in xanthine-and hypoxanthine-related reactions) are the most robust differentiators.…”

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

“…Increasing evidence suggests that multigene-based gene signatures are promising prognosis prediction models for OC [3][4][5][6][7][8][9][10]. However, despite having had several multigene signatures established for OC, their prognostic accuracy remains weak.…”