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Related Experiment Video

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A Novel Partial EMT-Associated Transcriptomic Signature for Prognostic Stratification in Ovarian Cancer.

Chia-Chia Chao1, Cheng-Yao Lin2,3,4, Po-Chun Chen5,6,7

  • 1Department of Respiratory Therapy, Fu Jen Catholic University, New Taipei City, Taiwan.

Oncology Research
|May 1, 2026
PubMed
Summary

A new six-gene partial epithelial-mesenchymal transition (p-EMT) signature improves prognostic accuracy in ovarian cancer. This transcriptomic signature aids in stratifying patients and predicting overall survival for better clinical decisions.

Keywords:
Partial epithelial-mesenchymal transitionovarian cancerpartial epithelial–mesenchymal transition (p-EMT) signatureprognosistranscriptomic risk score

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Area of Science:

  • Oncology
  • Genomics
  • Molecular Biology

Background:

  • Partial epithelial-mesenchymal transition (p-EMT) is linked to metastasis and poor outcomes in various cancers.
  • The prognostic role of p-EMT in ovarian cancer requires further elucidation.

Purpose of the Study:

  • To develop and validate an ovarian cancer-specific transcriptomic signature based on p-EMT genes.
  • To assess if this signature enhances prognostic stratification and overall survival prediction.

Main Methods:

  • A pan-cancer p-EMT gene set was curated.
  • A six-gene signature was developed using LASSO regression on TCGA-OV data.
  • The signature was integrated into a nomogram and validated on external cohorts (GSE140082, GSE165808).

Main Results:

  • The six-gene p-EMT signature (ADAM9, ANXA8L1, FSTL3, RABAC1, TPM4, TWIST1) significantly correlated with overall survival (OS).
  • It stratified patients into high- and low-risk groups (HR=1.74, p<0.001).
  • The nomogram incorporating the signature, age, and FIGO stage showed strong predictive performance (e.g., 3-year AUC=0.700 in TCGA-OV).

Conclusions:

  • The developed six-gene p-EMT transcriptomic signature holds significant prognostic value in ovarian cancer.
  • This signature can potentially improve individualized risk stratification and support clinical decision-making.