Abstract
PURPOSE
Molecular classifications have advanced prognostic stratification in endometrial carcinoma (EC), but they do not explicitly account for cause-specific mortality (CSM) and host-clinical context. We aimed to derive a clinically accessible classification that links demographic/clinicopathologic features, CSM, and molecular landscapes and to test whether it refines molecular-based survival stratification.
METHODS
We conducted latent class analysis (LCA) on 46,772 cases of primary EC from the SEER program to identify latent characteristic lineages. The resulting LCA scheme was mapped to The Cancer Genome Atlas (TCGA) data. Cumulative incidence functions and Fine-Gray competing risks regression were used to estimate CSM. The Fisher test was employed to evaluate its association with TCGA subtypes. Transcriptomic differential expression analysis and pathway enrichment analysis were performed where data were accessible. Likelihood ratio tests assessed treatment-LCA interactions.
RESULTS
Five clinically interpretable classes emerged. Class 4 had significantly increased subdistribution hazard for CSM (1.34,
P
= .031); classes 3/5 trended higher. Classes were associated with TCGA subtypes (
P
CONCLUSION
LCA of routine clinical variables identifies subgroups susceptible to CSM and complements TCGA molecular classification, improving survival stratification.