Abstract
Relevance: Endometrial cancer (EC) is clinically heterogeneous, and reliance solely on clinicopathological parameters may be insufficient for accurate risk stratification. Integration of TCGA-derived molecular classification into ESGO/ESTRO/ESP algorithms supports the transition toward personalized, risk-adapted management.The study aimed to characterize the molecular profiling of EC, its classification framework and current molecular and immunohistochemical approaches used to identify EC biological subtypes.Materials and Methods: A structured literature review of contemporary EC molecular profiling models and clinically applicable typing methods was performed.Results: According to GLOBOCAN 2022, 420,368 new uterine corpus cancer cases and 97,723 deaths were recorded worldwide (ASR: 8.4 and 1.7 per 100,000 for incidence and mortality, respectively). In Kazakhstan, EC ranks 4th among female cancers (1,291 new cases; ~381 deaths; 5-year prevalence ~5,051). Key components of molecular stratification include IHC assessment of MMR proteins (MLH1, MSH2, MSH6, PMS2) and p53 status, as well as POLE exonuclease-domain mutation testing, enabling classification into four TCGA groups: POLE-ultramutated, MMR-deficient (MSI-H), NSMP, and p53-abnormal.Conclusion: Molecular profiling and surrogate IHC algorithms refine EC risk assessment and inform clinical decision-making, facilitating a shift from uniform treatment to personalized, risk-adapted strategies supported by standardized diagnostic workflows.