Back

Comprehensive analysis of metabolic reprogramming-related gene signaturesmetabolic reprogramming related gene signature for predicting ovarian cancer prognosis, immune landscape, and potential treatment options

Han, R.; Guo, E.; Li, Z.; Zhou, H.; Liu, B.; Wang, Y.; Hou, X.; Zheng, F.; Xu, Y.; Yu, J.

2025-09-01 bioinformatics
10.1101/2025.08.28.672868 bioRxiv
Show abstract

Ovarian cancer (OV) is the most lethal gynecologic malignancy. Metabolic reprogramming is a distinctive feature of cancer and is associated with tumorigenesis and progression. It could be a potential therapeutic target for cancer treatments and a biomarker for assessing cancer prognosis. In this study, we identified metabolic reprogramming-related differentially expressed genes (MRRDEGs) in OV through differential gene expression analysis and conducted a comprehensive characterization of these MRRDEGs. Based on the MRRDEGs, we constructed an effective prognostic risk model including five model genes for OV. The risk score was a valid independent prognostic factor that could more accurately predict the survival of OV patients. It could classify OV patients into distinct risk groups with significant differences in survival. We observed significant differences between risk groups in biological pathway activity, immune cell infiltration patterns, and immunotherapy responses. Specifically, the low-risk group demonstrated superior immunotherapy response compared to the high-risk group. These findings significantly advance our understanding of the relationship between metabolic reprogramming and OV pathogenesis, progression, prognosis, and immunotherapy response, laying a foundation for developing novel biomarkers and therapeutic targets in the future and providing an important reference for the formulation of precision medicine strategies.

Matching journals

The top 13 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.