Evidence map›Paper›PMID 41201663›Full record

ArticleInternational journal of clinical oncology2026

Deubiquitinases as prognostic biomarker and potential drug target for gynecological cancers.

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

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Article in International journal of clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Mavika KondapallyDepartment of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.ORCID http://orcid.org/0009-0004-4738-984X
Anubha DeyDepartment of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.ORCID http://orcid.org/0000-0001-5934-2732
Abhiram MattaDepartment of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.ORCID http://orcid.org/0009-0006-6044-0544
Harshitha GolaganaDepartment of Biochemistry, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.ORCID http://orcid.org/0009-0004-0936-6460
Shashi Kiran *Department of Biochemistry, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India.ORCID http://orcid.org/0000-0003-2088-8273
Manjari Kiran *Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Gachibowli, Hyderabad, 500046, India. manjari.hcu@uohyd.ac.in.ORCID http://orcid.org/0000-0003-0153-7072

Funding

ICMR- National Institute of Virology IIRPIG-2024-01-03223University of Hyderabad UoH-IoE-RC2-21-012University of Hyderabad UoH-IoE-RC2-21-020
6 · The paper itself

Abstract

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

methodsUsing 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.

resultsThree 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.

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

Indexed as

Biomarkers, TumorDeubiquitinating EnzymesGenital Neoplasms, FemaleFemaleHumansMiddle AgedPrognosisBiomarkers, TumorDeubiquitinating EnzymesCervical CancerDeubiquitinasesOvarian CancerUterine cancer

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.