image: (A) Workflow of network perturbation analysis using gene expression data from EC and normal tissues. Our approach leverages the principle that physiological gene regulatory networks maintain homeostatic stability, whereas pathological states induce systematic disruptions in these interaction patterns. The analytical pipeline comprised four sequential stages. Initially, we generated gene expression rankings for each sample in both EC and normal sample cohorts, constructing sample-specific rank matrices. We then mapped these rankings onto a reference interaction network derived from the Reactome database, computing differential rankings between interacting gene pairs to generate delta rank matrices. To quantify disease-specific perturbations, we established a baseline interaction profile from healthy controls and calculated deviation scores for each gene pair in EC samples. This yielded an interaction perturbation matrix that captured network-level alterations associated with EC pathophysiology. (B-C) Perturbation patterns showing clear separation between tumor and normal samples. (D) Functional enrichment of 694 perturbed genes in therapeutic resistance pathways. (E) Clustering evaluation showing optimal separation at k=3. (F) Consensus clustering heatmap. (G-H) CDF curves and PAC analysis confirming three-subtype stability. (I) PCA showing spatial segregation of three patient groups (C1, C2, C3). (J) Kaplan-Meier curves showing C1 with poorest prognosis, C2 and C3 with favorable outcomes. (K) Validation of subtypes across four cohorts using NTP algorithm with signature genes.
Credit: Hengrui Liu, University of Cambridge; Hao Chi, University of Hawaii at Manoa; Jingyuan Ning, Chinese Academy of Medical Sciences
A new study published in iMetaMed introduces a systems-level approach to classifying endometrial cancer that could transform treatment decisions for patients with this disease.
Using network perturbation analysis—a method that examines how gene regulatory networks are disrupted in tumors compared to normal tissue—researchers analyzed transcriptomic data from 783 endometrial cancer patients across four independent cohorts. This approach identified three molecularly distinct subtypes with markedly different clinical outcomes.
The most aggressive subtype, designated C1, showed the poorest prognosis and was characterized by loss of hormone receptor signaling, an immune-excluded tumor microenvironment, and elevated TP53 mutations. Critically, drug sensitivity analysis revealed that C1 tumors are specifically sensitive to microtubule-targeting agents (such as paclitaxel) and proteasome inhibitors (such as bortezomib), while showing resistance to DNA-damaging agents.
"This classification system provides a practical framework for personalized treatment strategies," the researchers noted. "Molecular profiling at diagnosis could rapidly identify patients requiring intensified treatment with effective agents whilst avoiding resistant therapies."
The findings suggest that stratifying patients based on network-level molecular disruptions, rather than individual gene expression alone, may offer more clinically actionable insights for treatment selection in endometrial cancer.
Method of Research
Data/statistical analysis
Article Title
Network perturbation analysis identifies three molecular subtypes with distinct clinical outcomes and therapeutic vulnerabilities in endometrial cancer
Article Publication Date
15-Dec-2025
COI Statement
The Authors declare no conflicts of interest regarding this study. All authors have agreed to publish this manuscript.