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Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer

  • 0Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Cancer Cell International +

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February 3, 2024

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February 3, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies a two-gene signature (AKT2 and DAPK1) related to anoikis for predicting ovarian cancer prognosis. The signature aids in assessing survival and guiding precision medicine decisions for ovarian cancer patients.

Area Of Science

  • Oncology
  • Molecular Biology
  • Genetics

Background

  • Ovarian cancer (OV) is a lethal gynecological malignancy with high recurrence rates.
  • Anoikis, a form of programmed cell death, is crucial in cancer progression, but its prognostic role in OV remains understudied.
  • Understanding prognostic biomarkers is vital for improving patient outcomes in OV.

Purpose Of The Study

  • To identify anoikis-related genes (ARGs) as prognostic markers for ovarian cancer.
  • To develop a prognostic signature and a nomogram model for predicting overall survival in OV patients.
  • To explore the relationship between the prognostic signature, tumor immune microenvironment, and therapeutic responses.

Main Methods

  • Differential gene expression analysis and LASSO-Cox regression to identify prognostic ARGs from TCGA-OV data.
  • Kaplan-Meier survival analysis and time-dependent ROC curves to validate the prognostic signature.
  • Nomogram construction and validation using TCGA-OV and ICGC-OV cohorts.
  • CIBERSORT algorithm to analyze tumor immune landscape and immune checkpoint molecule expression.
  • Immunohistochemistry (IHC) to validate DAPK1 expression in patient samples.

Main Results

  • A two-gene signature (AKT2 and DAPK1) was identified with significant prognostic value in OV (p < 0.05).
  • The developed nomogram demonstrated reliable prediction of 1-, 3-, and 5-year overall survival in both training and validation cohorts (p < 0.001 and p = 0.030, respectively).
  • High-risk group showed altered immune cell infiltration (e.g., resting Myeloid Dendritic Cells) and elevated immune checkpoint molecules (CD274, PDCD1LG2).
  • High-risk patients exhibited increased sensitivity to immunotherapy but decreased sensitivity to cisplatin and bleomycin.
  • Aberrant DAPK1 upregulation correlated with poor prognosis in an independent patient cohort.

Conclusions

  • The anoikis-related gene signature (AKT2 and DAPK1) serves as a promising prognostic tool for ovarian cancer.
  • This signature can predict patient survival and potential response to different therapies, aiding personalized treatment strategies.
  • The findings support the integration of anoikis-related biomarkers into precision medicine approaches for ovarian cancer management.

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