Single-Cell RNA Sequencing of PBMCs Identified Junction Plakoglobin (JUP) as Stratification Biomarker for Endometriosis
Bibliographic record
Abstract
This study aimed to identify unique characteristics in the peripheral blood mononuclear cells (PBMCs) of endometriosis patients and develop a non-invasive early diagnostic tool. Using single-cell RNA sequencing (scRNA-seq), we constructed the first single-cell atlas of PBMCs from endometriosis patients based on 107,964 cells and 25,847 genes. Within CD16+ monocytes, we discovered JUP as a dysregulated gene. To assess its diagnostic potential, we measured peritoneal fluid (PF) and serum JUP levels in a large cohort of 199 patients including 20 women with ovarian cancer (OC). JUP was barely detectable in PF but was significantly elevated in the serum of patients with endometriosis and OC, with levels 1.33 and 2.34 times higher than controls, respectively. Additionally, JUP was found in conditioned culture media of CD14+/CD16+ monocytes aligning with our scRNA-seq data. Serum JUP levels correlated with endometriosis severity and endometrioma presence but were unaffected by dysmenorrhea, menstrual cycle, or adenomyosis. When combined with CA125 (cancer antigen 125) JUP enhanced the specificity of endometriosis diagnosis from 89.13% (CA125 measured alone) to 100%. While sensitivity remains a challenge at 19%, our results suggest that JUP’s potential to enhance diagnostic accuracy warrants additional investigation. Furthermore, employing serum JUP as a stratification marker unlocked the potential to identify additional endometriosis-related genes, offering novel insights into disease pathogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".