Hypothesis-Generating Single-Cell Analysis of the Endometrial Cancer Immune Microenvironment With Complementary Multiomics Evidence.
- 1Chengdu Women's and Children's Central Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China uestc.edu.cn.
Abstract
Background
Endometrial cancer (EC) has a heterogeneous immune microenvironment, and response-associated biology remains incompletely characterized. We used the retained EC single-cell dataset to describe immune-cell states and complemented it with TCGA-UCEC, independent gut microbiome cohorts, and RL95-2 proof-of-concept experiments. All responder versus nonresponder scRNA-seq comparisons are descriptive and hypothesis generating because only one sample was available per clinical response group.Methods
Single-cell RNA sequencing (scRNA-seq) data from EC patients undergoing anti-PD-1 immunotherapy (GEO: GSE251923; one responder, GSM7990051, and one nonresponder, GSM7990052; 17,546 cells retained after quality control) were analyzed using Scanpy, Leiden clustering, and over-representation analysis with GO biological process, KEGG, and MSigDB Hallmark gene sets using Enrichr with Benjamini-Hochberg correction. Bulk RNA-seq from TCGA-UCEC (554 tumor and 35 normal samples) was used for complementary transcriptomic analysis. Gut microbiome 16S rRNA datasets (SRA: PRJNA897842 and PRJNA833670) were analyzed as independent cohorts, and RL95-2 qRT-PCR experiments were interpreted as proof-of-concept.Results
Ten major cell populations were identified in the EC immune microenvironment. In this single responder/nonresponder sample pair, the nonresponder sample showed patterns consistent with higher M2-associated macrophage marker expression, increased T-cell checkpoint/exhaustion marker expression, and lower cytotoxic activity scores. These observations are descriptive and cannot establish patient-level significance. TCGA-UCEC analyses and the independent microbiome and RL95-2 experiments provide complementary, hypothesis-generating evidence; no unverified classifier-performance estimate is reported.Conclusions
This study provides a hypothesis-generating description of immune patterns in one EC responder and one nonresponder sample, complemented by TCGA-UCEC, independent microbiome datasets, and RL95-2 proof-of-concept experiments. The findings nominate candidate immune, hormonal, and microbiome-associated pathways for further investigation but do not establish patient-level response differences, causality, or clinically validated biomarkers.
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BibTeX
@article{ouyang2026hypothesisgenerating,
title = {{Hypothesis-Generating Single-Cell Analysis of the Endometrial Cancer Immune Microenvironment With Complementary Multiomics Evidence.}},
author = {Xiaolan Ouyang and Xiaoli Guo and Xiaoxi Yang and Juan Wen},
journal = {International journal of genomics},
year = {2026},
month = oct,
doi = {10.1155/ijog/5125648},
url = {https://europepmc.org/article/MED/42894744},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hypothesis-Generating Single-Cell Analysis of the Endometrial Cancer Immune Microenvironment With Complementary Multiomics Evidence.
AU - Xiaolan Ouyang
AU - Xiaoli Guo
AU - Xiaoxi Yang
AU - Juan Wen
PY - 2026
DA - 2026/10/18
JO - International journal of genomics
DO - 10.1155/ijog/5125648
UR - https://europepmc.org/article/MED/42894744
AB - Background Endometrial cancer (EC) has a heterogeneous immune microenvironment, and response-associated biology remains incompletely characterized. We used the retained EC single-cell dataset to describe immune-cell states and complemented it with TCGA-UCEC, independent gut microbiome cohorts, and RL95-2 proof-of-concept experiments. All responder versus nonresponder scRNA-seq comparisons are descriptive and hypothesis generating because only one sample was available per clinical response group.Methods Single-cell RNA sequencing (scRNA-seq) data from EC patients undergoing anti-PD-1 immunotherapy (GEO: GSE251923; one responder, GSM7990051, and one nonresponder, GSM7990052; 17,546 cells retained after quality control) were analyzed using Scanpy, Leiden clustering, and over-representation analysis with GO biological process, KEGG, and MSigDB Hallmark gene sets using Enrichr with Benjamini-Hochberg correction. Bulk RNA-seq from TCGA-UCEC (554 tumor and 35 normal samples) was used for complementary transcriptomic analysis. Gut microbiome 16S rRNA datasets (SRA: PRJNA897842 and PRJNA833670) were analyzed as independent cohorts, and RL95-2 qRT-PCR experiments were interpreted as proof-of-concept.Results Ten major cell populations were identified in the EC immune microenvironment. In this single responder/nonresponder sample pair, the nonresponder sample showed patterns consistent with higher M2-associated macrophage marker expression, increased T-cell checkpoint/exhaustion marker expression, and lower cytotoxic activity scores. These observations are descriptive and cannot establish patient-level significance. TCGA-UCEC analyses and the independent microbiome and RL95-2 experiments provide complementary, hypothesis-generating evidence; no unverified classifier-performance estimate is reported.Conclusions This study provides a hypothesis-generating description of immune patterns in one EC responder and one nonresponder sample, complemented by TCGA-UCEC, independent microbiome datasets, and RL95-2 proof-of-concept experiments. The findings nominate candidate immune, hormonal, and microbiome-associated pathways for further investigation but do not establish patient-level response differences, causality, or clinically validated biomarkers.
SN - 2314-436X
SN - 2314-4378
ER -