Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis.

Autor:	Li Y; Department of Gynecology, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China., Chen H; Department of Rehabilitation Medical Center, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China., Zhang H; Department of Rehabilitation Medical Center, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China., Lin Z; Hydrogen Medical Research Center, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China., Song L; Department of Gynecology, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China., Zhao C; Department of Orthopedics, The Affiliated Taian City Central Hospital of Qingdao University, Tai'an, Shandong, China.; Medical Integration and Practice Center, Shandong University School of Medicine, Shandong University, Jinan, Shandong, China.
Jazyk:	angličtina
Zdroj:	Frontiers in endocrinology [Front Endocrinol (Lausanne)] 2024 Nov 21; Vol. 15, pp. 1373011. Date of Electronic Publication: 2024 Nov 21 (Print Publication: 2024).
DOI:	10.3389/fendo.2024.1373011
Abstrakt:	Background: Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis. Methods: Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers. Results: A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1 , CD36 , MICB , and PRDX6 . Predictive nomograms were generated based on these biomarkers. ANXA1 , CD36 , and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin. Conclusion: This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Li, Chen, Zhang, Lin, Song and Zhao.)
Databáze:	MEDLINE
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