Role of Transcription Factor, LIM Homeobox 9 (LHX9) in Inflammatory Response by PGE2 and Thrombin in SERPINA1‐Silencing Endometrial Stromal Cells
Bibliographic record
Abstract
Endometriosis is hypothesized to result from retrograde menstruation where cell debris including endometrial stromal cells (ESCs) travel through the fallopian tubes. This chronic inflammatory disease is characterized by inflammatory and fibrotic endometrial tissue. We have previously observed reduced expression of the anti-inflammatory factor SERPINA1 in endometriosis-like lesions in a mouse model implanted with human ESCs. Additionally, pro-inflammatory factors present in peritoneal hemorrhage exacerbated inflammation in these grafts, partly through prostaglandin (PG) E2 and thrombin. However, it remains unclear whether the reduction of SERPINA1, in combination with PGE2 and thrombin, synergistically influences the expression of inflammatory factors in endometriosis lesions and the underlying mechanisms. We analyzed RNA sequencing data from ESCs treated with SERPINA1 siRNA and PGE2/thrombin, comparing them to data sets derived from ESCs subjected to either SERPINA1 knockdown or PGE2/thrombin treatment. Comparative analysis identified 49 transcripts that were upregulated under both conditions and enriched for transcription regulatory genes, including SNAI1, HDAC5, PBX1, SOX4, EPAS1, LHX9, and MAFK. Silencing SNAI1, HDAC5, SOX4, EPAS1, or LHX9 suppressed IL6, CXCL8, and IL1B expression, which had been upregulated by SERPINA1 siRNA and PGE2/thrombin. Among these genes, LHX9 expression was significantly elevated in ectopic lesions, predominantly localized to stromal and glandular epithelial cells, with more pronounced expression during the secretory phase. LHX9 levels were also increased in endometriotic lesions compared to the normal endometrium. In conclusion, reduced SERPINA1 expression in ectopic ESCs, combined with PGE2/thrombin, induces inflammatory cytokine expression linked to LHX9. Pharmacological targeting of LHX9 may present a promising therapeutic strategy for mitigating chronic inflammation in endometriotic lesions.
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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 | 0.000 |
| 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 | 0.000 | 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".