Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome

被引:0
|
作者
Luo, Yujia [1 ]
Zhou, Yuanyuan [2 ]
Jiang, Hanyue [3 ]
Zhu, Qiongjun [4 ]
Lv, Qingbo [4 ]
Zhang, Xuandong [1 ]
Gu, Rui [1 ]
Yan, Bingqian [1 ]
Wei, Li [1 ]
Zhu, Yuhang [5 ]
Jiang, Zhou [1 ]
机构
[1] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept NICU, Hangzhou, Peoples R China
[2] Zhejiang Univ, Womens Hosp, Sch Med, Dept Reprod Endocrinol, Hangzhou, Peoples R China
[3] Wenzhou Med Univ, Wenzhou, Peoples R China
[4] Zhejiang Univ, Sir Run Run Shaw Hosp, Sch Med, Dept Cardiol, Hangzhou, Peoples R China
[5] Zhejiang Univ, Sch Med, Womens Hosp, Hangzhou 310006, Zhejiang, Peoples R China
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
Polycystic ovary syndrome; Atherosclerosis; Bioinformatics analysis; Machine learning; Immune infiltration; INTIMA-MEDIA THICKNESS; ADIPOSE-TISSUE; INFLAMMATION; PREDICTION; AUTOPHAGY; CELLS;
D O I
10.1038/s41598-024-69065-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.
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页数:13
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