Evidence map›Paper›PMID 38454353›Full record

ArticleBMC cardiovascular disorders2024

Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach.

Fanli Bu, Xiao Qin, Tiantian Wang, Na Li, Man Zheng, Zixuan Wu, Kai Ma

Open access · goldAbstract read
In one paragraph

Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

7 authors at 4 institutions in 1 country.

Fanli Bu *Dongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China.
Xiao Qin *Dongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China.
Tiantian WangDongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China.
Na LiDongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China.
Man ZhengDongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China.
Zixuan WuGuangzhou University of Chinese Medicine, Guangzhou, China.
Kai MaDongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, People's Republic of China. 2847328341@qq.com.
Shandong Provincial Hospital · CNDongyang People's Hospital · CNGuangzhou University of Chinese Medicine · CNWenzhou Medical University · CN

Funding

Shandong Medical and Health Science and Technology Development Program (2019WS040)
6 · The paper itself

Abstract

backgroundThis study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS.

methodsA set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets.

resultsThis investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results.

conclusionIn conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

Indexed as

AtherosclerosisCoronary Artery DiseaseBiomarkersComputational BiologyHumansMachine LearningNucleotidesBiomarkersNucleotidesAtherosclerosis (AS)BioinformaticsLasso regressionPyrimidine Metabolism Genes (PyMGs)SVM-RFE

Identifiers

PMID38454353
PMCPMC10921789
OpenAlexW4392549850

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.