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Immune Surveillance by NK Cells and Phagocytes01:25

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Immune surveillance is an integral part of the innate immune system, involving the continuous monitoring of peripheral tissues to detect and respond to pathogens, infected cells, or cancerous cells. This surveillance is conducted primarily by natural killer (NK) cells and phagocytes, which employ distinct but complementary mechanisms to identify and eliminate threats.
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The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...
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Single-Cell Transcriptomics Reveals Biomarkers for NK Cell Dysfunction in Endometriosis-Associated Immune

Wangshu Li1, Kexin Zhu1, Bowen Xu1

  • 1Department of Key Laboratory of Pediatric and Female Malignant Tumors, Dalian Women and Children's Medical Group, Dalian, China.

Mediators of Inflammation
|February 5, 2026
PubMed
Summary

Natural killer (NK) cell dysfunction is central to endometriosis (EM) pathogenesis. Key genes granulysin (GNLY), perforin 1 (PRF1), and ENTPD1 show diagnostic potential for EM.

Keywords:
biomarkersendometriosisimmune dysregulationnatural killer cellssingle-cell RNA sequencing

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Area of Science:

  • Reproductive Immunology
  • Molecular Biology
  • Genomics

Background:

  • Endometriosis (EM) is linked to immune system dysregulation.
  • Natural killer (NK) cell dysfunction is a key factor in EM immune evasion and lesion growth.

Purpose of the Study:

  • To investigate the role of immune cell dysfunction in EM pathogenesis.
  • To identify molecular biomarkers for EM diagnosis and potential therapeutic targets.

Main Methods:

  • Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq integration.
  • Machine learning algorithms (LASSO, SVM-RFE) for feature selection.
  • Validation via qPCR, wound healing, and Transwell assays.

Main Results:

  • scRNA-seq identified 11 cell clusters; 20 differentially expressed genes (DEGs) were prioritized.
  • Machine learning identified GNLY, PRF1, and ENTPD1 as key diagnostic genes for EM.
  • ENTPD1 enhances endometrial stromal cell migration and may modulate NK cell function via adenosine signaling.

Conclusions:

  • NK cell dysfunction plays a critical role in EM.
  • GNLY, PRF1, and ENTPD1 are potential diagnostic biomarkers for EM.
  • ENTPD1 has dual functions promoting EM lesion formation and offers a therapeutic target.