PLP1 may serve as a potential diagnostic biomarker of uterine fibroids.

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Methods: The gene expression and genome-wide DNA methylation profiles were obtained from Gene Expression Omnibus database (GEO). GSE45189, GSE31699, and GSE593 datasets were included. GEO2R and Venn diagrams were used to analyze the differentially expressed genes (DEGs) and extract the hub genes. Gene Ontology (GO) analysis was performed by the online tool Database for Annotation, Visualization, and Integrated Discovery (DAVID). The mRNA and protein expression of hub genes were validated by RT-qPCR, western blot, and immunohistochemistry. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value. Results: We detected 22 DEGs between UFs and normal myometrium, which were enriched in cell maturation, apoptotic process, hypoxia, protein binding, and cytoplasm for cell composition. By finding the intersection of the data between differentially expressed mRNA and DNA methylation profiles, 3 hub genes were identified, including transmembrane 4 L six family member 1 (TM4SF1), TNF superfamily member 10 (TNFSF10), and proteolipid protein 1 (PLP1). PLP1 was validated to be up-regulated significantly in UFs both at mRNA and protein levels. The area under the ROC curve (AUC) of PLP1 was 0.956, with a sensitivity of 79.2% and a specificity of 100%. Conclusion: Overall, our results indicate that PLP1 may be a potential diagnostic biomarker for uterine fibroids.", "claims": [{"id": "claim-method", "statement": "All differential genes were identified using the online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/). Benjamin-Hochberg was applied for the control of false discovery rate (FDR), and p < 0.05 was utilized as the database’s cut-off criteria.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36386836", "locator": "sections[4] \"2.2 Identification of DEGs between uterine fibroids and normal myometrium\" paragraph 2", "quote": "All differential genes were identified using the online analysis tool GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/). 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