Deubiquitinases as prognostic biomarkers and potential drug targets for gynecological cancers.

ObjectiveTo develop Deubiquitinase-Associated Signatures (DAS) to predict the prognosis of gynecological cancer patients. MethodUsing 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 can decrease tumor progression in gynecological cancers. SynopsisO_LIDeubiquitinases (DUBs) are associated with cancer progression, limited studies on gynecological cancers. C_LIO_LIWe used cox-lasso regression to develop a DUB-Associated prognostic Signature (DAS). C_LIO_LIDAS stratifies patients into high-low-risk and improves survival prediction accuracy. C_LIO_LISmall molecules were identified that can target poor prognostic DUBs. C_LI