Evidence map›Paper›PMID 39107365›Full record

ArticleScientific reports2024

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

Yujia Luo, Yuanyuan Zhou, Hanyue Jiang, Qiongjun Zhu, Qingbo Lv, Xuandong Zhang, Rui Gu, Bingqian Yan, Li Wei, Yuhang Zhu and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yujia Luo *Department of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Yuanyuan Zhou *Department of Reproductive Endocrinology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Hanyue JiangWenzhou Medical University, Wenzhou, China.
Qiongjun ZhuDepartment of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Qingbo LvDepartment of Cardiology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Xuandong ZhangDepartment of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Rui GuDepartment of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Bingqian YanDepartment of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Li WeiDepartment of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Yuhang ZhuWomen's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China.
Zhou JiangDepartment of NICU, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. 5200013@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AtherosclerosisPolycystic Ovary SyndromeAnimalsBiomarkersFemaleGene Expression ProfilingGene Regulatory NetworksGranulosa CellsHumansMachine LearningMiceRAW 264.7 CellsBiomarkersAtherosclerosisBioinformatics analysisImmune infiltrationMachine learningPolycystic ovary syndrome

Identifiers

PMID39107365
PMCPMC11303752

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.