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Assessment of lncRNA biomarkers based on NETs for prognosis and therapeutic response in ovarian cancer

  • 0Department of Gynecology and Obstetrics, the Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Scientific Reports +

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April 15, 2025

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April 15, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies six neutrophil extracellular trap (NETs)-related long non-coding RNAs (lncRNAs) that can predict ovarian cancer (OC) prognosis and chemotherapy response, offering new clinical decision-making tools.

Area Of Science

  • Oncology
  • Immunology
  • Molecular Biology

Background

  • Ovarian cancer (OC) has high mortality and lacks reliable prognostic factors.
  • Neutrophil extracellular traps (NETs) are increasingly implicated in cancer progression, including OC.

Purpose Of The Study

  • To develop a prognostic model for OC using NETs-related biomarkers.
  • To assess the model's ability to guide clinical decisions and predict chemotherapy sensitivity.

Main Methods

  • Developed a prognostic model using six NETs-associated lncRNAs via LASSO regression.
  • Employed univariate/multivariate Cox regression, Kaplan-Meier, ROC, GO, KEGG, and GSEA analyses.
  • Validated the model using in vitro/in vivo experiments and clinical data.

Main Results

  • A six-NETs-related lncRNA model (GAS5, GBP1P1, LINC00702, LINC01933, LINC02362, ZNF687-AS1) accurately predicted OC prognosis.
  • The model demonstrated distinct immune function and checkpoint expression profiles between risk groups.
  • Predicted differential chemotherapy sensitivity, with higher drug resistance in the low-risk group.

Conclusions

  • The developed NETs-lncRNA model serves as a robust prognostic and predictive tool for ovarian cancer.
  • This model can aid in personalized treatment strategies and clinical decision-making for OC patients.

Keywords:

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