Transglutaminase 2 exacerbates ovarian cancer survival by directly inactivating GSK3β

Ho Lee1,2, Joon Hee Kang1,2, Hyun Jung Kim3

  • 1Division of Cancer Biology, National Cancer Center, Goyang, Gyeonggi, Republic of Korea.

Cell Death & Disease
|February 2, 2026
PubMed

Insights

Transglutaminase 2 (TGase 2) drives ovarian cancer metastasis and drug resistance by promoting epithelial-mesenchymal transition (EMT). Inhibiting TGase 2 interaction with GSK3β offers a novel therapeutic strategy to overcome treatment challenges.

Area of Science:

  • Oncology
  • Molecular Biology
  • Biochemistry

Background:

  • Ovarian cancer frequently recurs with metastasis despite initial chemotherapy response.
  • Drug-resistant ovarian cancer cells show elevated transglutaminase 2 (TGase 2) levels, linked to epithelial-mesenchymal transition (EMT) and chemotherapy evasion.
  • TGase 2 is crucial for maintaining the mesenchymal phenotype, but its mechanism in promoting EMT is unclear.

Purpose of the Study:

  • To elucidate the mechanism by which TGase 2 promotes EMT in ovarian cancer.
  • To identify TGase 2 as a therapeutic target for overcoming drug resistance and metastasis.

Main Methods:

  • Investigated the interaction between TGase 2 and glycogen synthase kinase-3β (GSK3β).
  • Utilized domain mapping to identify the interaction site between TGase 2 and GSK3β.
  • Assessed the effect of pharmacological inhibition of the TGase 2-GSK3β interaction in a xenograft model.

Main Results:

  • TGase 2 directly binds to GSK3β, promoting β-catenin stabilization and EMT.
  • The N-terminus of TGase 2 interacts with the mid-region of GSK3β, leading to GSK3β autophagic degradation.
  • Pharmacological disruption of this interaction with streptonigrin, combined with chemotherapy, improved survival in ovarian cancer xenografts.

Conclusions:

  • TGase 2 is a key regulator of EMT, metastasis, and drug resistance in ovarian cancer.
  • Targeting the TGase 2-GSK3β interaction presents a promising therapeutic strategy for advanced ovarian cancer.

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
765
X-Inactivation01:58

X-Inactivation

The human X chromosome contains over ten times the number of genes as in the Y chromosome. Since males have only one X chromosome, and females have two, one might expect females to produce twice as many of the proteins, with undesirable results.
41.8K
Activation and Inactivation of G Proteins01:22

Activation and Inactivation of G Proteins

Heterotrimeric G proteins are guanine nucleotide-binding proteins. As the name suggests, heterotrimeric G proteins are composed of three subunits: alpha, beta, and gamma. They remain GDP-bound or GTP-bound inside the cells and switch between inactive/active states. The Gα subunit possesses the nucleotide-binding pocket that binds guanine nucleotides and switches between GDP or GTP-bound states. In contrast, the Gꞵ and Gγ subunits are always bound together with high...
11.5K
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
718
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
430
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
789