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01 · ABSTRACT

Abstract

Background: Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. The antibodies targeting the programmed death 1 (PD-1) pathway (anti-PD therapy) have transformed cancer treatment, including EC. However, only a fraction of EC patients respond to anti-PD therapy. The underlying mechanism leading to failure in other patients is less understood. Methods: We performed single-cell RNA sequencing data analysis of an anti-PD1 responder and non-responder EC cases. Also, we analyzed spatial transcriptomics data of an endometrial adenocarcinoma case to determine cell colocalization. The TCGA UCEC dataset was utilized to investigate the relationship between identified gene signatures and clinical outcomes. The analysis was done using the R software. Results: The cells were classified into ten clusters with different pathways enriched between the anti-PD1 therapy responder and non-responder. The differentially expressed genes in these pathways accurately predicted the anti-PD1 therapy response in the single-cell RNA-seq data. Cell-cell communication revealed high signaling between the epithelial cells, mesangial cells, and regulatory T cells (Tregs). We discovered syndecan-4 (SDC4) as the Treg receptor for midkine (MDK/MK) associated with anti-PD1 therapy tolerance in EC. The epithelial cells were the major source of MDK, and they colocalized with the Tregs. Finally, the TCGA UCEC dataset analysis indicated that high expression levels of SDC4 correlated with poor overall survival in patients. Conclusion: The single-cell RNA-seq analysis performed herein revealed MDK-SDC4 signaling as the driver inducing the immune suppression phenotype in the anti-PD1 therapy non-responder. These findings suggest that blocking the MDK-SDC4 signal might help to overcome the anti-PD1 therapy tolerance in EC. These results show novel potential immunotherapeutic targets in EC.
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02 · OJS METADATA

Keywords

endometrial canceranti-PD1 tolerancesingle-cell RNA sequencingmidkinesyndecan-4
03 · PUBLICATION RECORD

Article details

JournalMedical Research Archives
IssueVol 14 No 2 (2026): Vol.14, Issue 2, February 2026
SectionResearch Articles
Published28 February 2026
DOI10.18103/mra.v14i2.7302
ISSN2375-1924
04 · RIGHTS & REUSE

Rights & reuse

This article is published under a Creative Commons Attribution License (CC BY 3.0) and may be shared or distributed by anyone as long as attribution is given to the journal.

Authors & affiliations

SB

Stefan Bossmann

The University of Kansas Medical Center, Department of Cancer Biology

MN

Muturi Njoka

The University of Kansas Medical Center, Department of Cancer Biology, Kansas City, KS 66061, USA

YL

Yanming Li

The University of Kansas Medical Center, Department of Biostatistics and Data Science

Medical Research Archives

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