Somatic mutation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma
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View abstract on PubMed
Summary
This summary is machine-generated.A new 5-gene panel, including MUC16, ATM, NOTCH3, KMT2A, and CTNNA1, effectively predicts prognosis and platinum response in advanced ovarian clear cell carcinoma (OCCC), enabling precise medicine strategies.
Area Of Science
- Oncology
- Genomics
- Machine Learning
Background
- Ovarian clear cell carcinoma (OCCC) is a distinct epithelial ovarian cancer subtype.
- Advanced OCCC cases exhibit a poor patient prognosis.
- There is a need for risk stratification to guide precision medicine in OCCC.
Purpose Of The Study
- To identify prognostic gene signatures for advanced OCCC.
- To develop a gene panel for risk stratification in OCCC.
- To evaluate the predictive accuracy of the gene panel against established clinical factors.
Main Methods
- Conducted next-generation sequencing (NGS) on 44 advanced OCCC patients (FIGO stage II-IV).
- Employed machine learning algorithms (XGBoost, RSF, Cox regression) to identify prognostic genes.
- Constructed and validated a 5-gene panel for risk stratification and compared its efficacy with FIGO stage and residual disease using ROC and decision curve analyses.
Main Results
- Identified MUC16, ATM, NOTCH3, KMT2A, and CTNNA1 as key prognostic mutated genes.
- The 5-gene panel demonstrated superior predictive capability for prognosis and platinum response in both internal and external cohorts compared to FIGO stage and residual disease.
Conclusions
- Mutations in MUC16, ATM, NOTCH3, KMT2A, and CTNNA1 are linked to poor prognosis in advanced OCCC.
- The developed 5-gene risk stratification panel shows significant predictive power for prognosis and platinum response.
- This gene panel holds potential as a novel biomarker for precision medicine in OCCC.
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