Construction and validation of a prognostic model for overall survival time of patients with ovarian cancer by metabolism-related genes
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View abstract on PubMed
Summary
This summary is machine-generated.A new prognostic signature of 21 metabolism-related genes (MRGs) accurately predicts outcomes in ovarian cancer patients. This signature aids in understanding tumor behavior and guides potential therapeutic strategies.
Area Of Science
- Oncology
- Metabolomics
- Bioinformatics
Background
- Ovarian cancer presents high morbidity and mortality.
- Tumor cell metabolic reprogramming significantly influences cancer progression.
Purpose Of The Study
- To establish and validate a prognostic signature based on metabolism-related genes (MRGs) for ovarian cancer.
- To explore the clinical utility of this signature in various subgroups and its association with immune infiltration and drug sensitivity.
Main Methods
- LASSO-Cox regression analysis to identify prognostic MRGs.
- Functional enrichment analysis and gene expression profiling.
- Assessment of immune cell infiltration and drug susceptibility associated with the MRG signature.
Main Results
- A robust prognostic signature comprising 21 MRGs was developed and validated.
- These MRGs showed differential expression in ovarian tumors and were linked to lipid metabolism and molecular binding.
- The signature effectively predicted overall survival across clinical subgroups and correlated with immune cell infiltration and drug resistance.
Conclusions
- The metabolism-related prognostic signature is a stable and accurate predictor of patient outcomes in ovarian cancer.
- This signature holds potential for guiding clinical treatment decisions and developing targeted therapies.
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