4.7 Article

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

Journal

JOURNAL OF PERSONALIZED MEDICINE
Volume 13, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/jpm13050776

Keywords

purine metabolism; ovarian cancer; personalized medicine; bioinformatical analysis; drug sensitivity; extensive non-targeted metabolomics

Ask authors/readers for more resources

Purine metabolism is an important branch of metabolic reprogramming in cancer research. In this study, a prognostic signature consisting of nine genes related to purine metabolism was identified in ovarian cancer. The signature can distinguish prognostic risk and immune landscape of patients, and provide personalized drug options. A composite nomogram combining risk scores with clinical characteristics was created for more accurate and individualized prediction of prognosis.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available