Molecular landscape of pelvic organ prolapse provides insights into disease etiology and clues towards putative novel treatments
Bibliographic record
Abstract
Abstract Background Pelvic organ prolapse (POP) represents a major health care burden in women but its underlying pathophysiological mechanisms have not been elucidated. Objective To integrate the results from a large scale exome chip study with published genetic and expression data into a molecular landscape of POP. Design, setting, and participants The exome chip study was conducted in 526 women with POP and 960 healthy controls. To corroborate the findings, we analysed differential gene expression data from 12 POP patients. Vaginal fibroblasts from 4 women with POP were used to test the effect of the anti-diabetic drug metformin. Outcome measurements and statistical analysis The exome chip study used a case-control design to identify single nucleotide variants (SNVs) associated with POP after Bonferroni correction. The molecular landscape was built using the UniProt and PubMed databases to identify functional interactions between the POP candidate genes/proteins. We performed enrichment and upstream regulator analyses of the differentially expressed genes. The effect of metformin in fibroblasts was assessed using one-sample t-test. Results and limitations We found significant association between POP and SNVs in 54 genes. The proteins encoded by 26 of these genes fit into a molecular landscape, together with 37 other POP candidate molecules and two POP-implicated microRNAs. This landscape is located in and around epithelial cells and fibroblasts of the urogenital tract and harbors four interacting biological processes - epithelial-mesenchymal transition, immune response, modulation of the extracellular matrix, and fibroblast function - that are regulated by sex hormones and TGFB1. Based on the landscape, we predicted and showed that metformin alters gene expression in fibroblasts of POP patients in a beneficial direction. The main limitation of our study is that we have no independent replication of the exome chip results. Conclusions The integrated molecular landscape of POP that we built provides insights into the biological processes underlying the disease and clues towards novel treatments. Patient summary We reported the first exome chip study of POP and combined the genes identified in this study with other data from the literature to build a ‘molecular landscape’ of POP. This landscape will advance our understanding of the disease and may lead to novel treatments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".