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Background <p>To develop Deubiquitinase-Associated Signatures (DAS) to predict the prognosis of gynecological cancer patients.</p> Methods <p>Using a Cox–Lasso regression model, we have developed deubiquitinase-associated signatures for Cervical, Ovarian, and Uterine cancers. Developed DAS were validated in TCGA and GEO datasets. Survival analysis was carried out to know the effect of factors Like menopausal stage and grade on DAS. The Survival prediction accuracy of DAS was analyzed using ROC curves. Immune infiltration scores of 22 immune subtypes were explored using the&#xa0;CIBERSORT package in risk groups classified by DAS. Further, to target the unfavorable deubiquitinases (DUBs), compounds were identified using CMap database.</p> Results <p>Three DAS were developed for Cervical, Ovarian, and Uterine cancer types. DAS was able to predict Survival and classify patients into two groups in TCGA and GEO datasets. DAS is an independent predictor of Survival irrespective of tumor grade and menopausal stage. DAS, along with the clinical features, improves the accuracy of predictions. CIBERSORT analysis has shown that immune cell infiltration is associated with risk groups divided by DAS. Using CMap, 52 compounds were identified to target unfavorable DUBs.</p> Conclusion <p>DAS is a good predictor of survival, and targeting unfavorable DUBs may reduce tumor progression in gynecological cancers.</p>

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Deubiquitinases as prognostic biomarker and potential drug target for gynecological cancers

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Mavika Kondapally,
  • Anubha Dey,
  • Abhiram Matta,
  • Harshitha Golagana,
  • Shashi Kiran,
  • Manjari Kiran

摘要

Background

To develop Deubiquitinase-Associated Signatures (DAS) to predict the prognosis of gynecological cancer patients.

Methods

Using a Cox–Lasso regression model, we have developed deubiquitinase-associated signatures for Cervical, Ovarian, and Uterine cancers. Developed DAS were validated in TCGA and GEO datasets. Survival analysis was carried out to know the effect of factors Like menopausal stage and grade on DAS. The Survival prediction accuracy of DAS was analyzed using ROC curves. Immune infiltration scores of 22 immune subtypes were explored using the CIBERSORT package in risk groups classified by DAS. Further, to target the unfavorable deubiquitinases (DUBs), compounds were identified using CMap database.

Results

Three DAS were developed for Cervical, Ovarian, and Uterine cancer types. DAS was able to predict Survival and classify patients into two groups in TCGA and GEO datasets. DAS is an independent predictor of Survival irrespective of tumor grade and menopausal stage. DAS, along with the clinical features, improves the accuracy of predictions. CIBERSORT analysis has shown that immune cell infiltration is associated with risk groups divided by DAS. Using CMap, 52 compounds were identified to target unfavorable DUBs.

Conclusion

DAS is a good predictor of survival, and targeting unfavorable DUBs may reduce tumor progression in gynecological cancers.