Article

Beyond one-size-fits-all: single-cell transcriptomic signatures predict drug efficacy and reveal responder subgroups in endometriosis

2025-12-12

Abstract excerpt

Endometriosis affects ∼10% of reproductive-age women, yet targeted non-hormonal therapies remain unavailable, and treatment response is highly variable. Here, we apply a single-cell framework to resolve therapeutic heterogeneity at a resolution previously unattained in drug development efforts. Using scRNA-seq profiles from eutopic and ectopic tissues, combined with a machine learning-based drug response model, we...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 06a196a9-a9f4-56c4-8cd0-825fedb48f30
DOI: 10.64898/2025.12.09.693218

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Beyond one-size-fits-all: single-cell transcriptomic signatures predict drug efficacy and reveal responder subgroups in endometriosisDOI 10.64898/2025.12.09.693218Read

Single-cell transcriptome profiles the heterogeneity of tumor cells and microenvironments for different pathological endometrial cancer and identifies specific sensitive drugsSemantic proximity Read

Multidimensional Single-Cell Transcriptomic Profiling of Uterine Leiomyosarcomas Reveals Distinct Tumor States with Inferred Therapeutic VulnerabilitiesSemantic proximity Read

Predicting drug response from single-cell expression profiles of tumoursSemantic proximity Read

Integration of Single Cell and Bulk RNA-Sequencing Reveals Key Genes and Immune Cell Infiltration to Construct a Predictive Model and Identify Drug Targets in EndometriosisSemantic proximity Read

Tracing Endometriosis: Coupling deeply phenotyped, single-cell based Endometrial Differences and AI for disease pathology and predictionSemantic proximity Read

Single-Cell Transcriptomic Profiling and Machine Learning Integration Unveil Stromal Cell Heterogeneity in Endometriosis.Semantic proximity Read

Single-Cell Profiling Reveals Altered Endometrial Cellular Features Across the Menstrual Cycle in Endometriosis PatientsSemantic proximity Read

Integrated analysis of single-cell and bulk transcriptomic data reveals altered cellular composition and predictive cell types in endometriosisSemantic proximity Read

Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell levelSemantic proximity Read

Single‐cell transcriptomic atlas of different endometriosis indicating that an interaction between endometriosis‐associated mesothelial cells (EAMCs) and ectopic stromal cells may influence progesterone resistanceSemantic proximity Read

An integrated single-cell reference atlas of the human endometriumSemantic proximity Read

Mapping Human Uterine Disorders Through Single-Cell TranscriptomicsSemantic proximity Read