Related Experiment Video
Updated: Oct 20, 2026
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
Published on: March 25, 2020
Hypothesis-Generating Single-Cell Analysis of the Endometrial Cancer Immune Microenvironment With Complementary
Xiaolan Ouyang1, Xiaoli Guo1, Xiaoxi Yang1
1Chengdu Women's and Children's Central Hospital School of Medicine University of Electronic Science and Technology of China Chengdu China uestc.edu.cn.
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.
