Evidence map›Paper›PMID 35295271›Full record

ArticleFrontiers in cardiovascular medicine2022

Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification.

Tao Xiong, Shen Han, Lei Pu, Tian-Chen Zhang, Xu Zhan, Tao Fu, Ying-Hai Dai, Ya-Xiong Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

11 citing papers in PubMed, 14 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Tao XiongCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Shen HanCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Lei PuCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Tian-Chen ZhangCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Xu ZhanDepartment of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Tao FuCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Ying-Hai DaiCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Ya-Xiong LiCardiovascular Surgery, Yan'an Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Yan'an Hospital Affiliated To Kunming Medical University · CNKunming Medical University · CNChongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: The purpose of this study was to identify potential diagnostic markers for aortic valve calcification (AVC) and to investigate the function of immune cell infiltration in this disease. Methods: The AVC data sets were obtained from the Gene Expression Omnibus. The identification of differentially expressed genes (DEGs) and the performance of functional correlation analysis were carried out using the R software. To explore hub genes related to AVC, a protein-protein interaction network was created. Diagnostic markers for AVC were then screened and verified using the least absolute shrinkage and selection operator, logistic regression, support vector machine-recursive feature elimination algorithms, and hub genes. The infiltration of immune cells into AVC tissues was evaluated using CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. Finally, the Connectivity Map database was used to forecast the candidate small molecule drugs that might be used as prospective medications to treat AVC. Results: A total of 337 DEGs were screened. The DEGs that were discovered were mostly related with atherosclerosis and arteriosclerotic cardiovascular disease, according to the analyses. Gene sets involved in the chemokine signaling pathway and cytokine-cytokine receptor interaction were differently active in AVC compared with control. As the diagnostic marker for AVC, fibronectin 1 (FN1) (area the curve = 0.958) was discovered. Immune cell infiltration analysis revealed that the AVC process may be mediated by naïve B cells, memory B cells, plasma cells, activated natural killer cells, monocytes, and macrophages M0. Additionally, FN1 expression was associated with memory B cells, M0 macrophages, activated mast cells, resting mast cells, monocytes, and activated natural killer cells. AVC may be reversed with the use of yohimbic acid, the most promising small molecule discovered so far. Conclusion: FN1 can be used as a diagnostic marker for AVC. It has been shown that immune cell infiltration is important in the onset and progression of AVC, which may benefit in the improvement of AVC diagnosis and treatment.

Indexed as

aortic valve calcificationbiomarkerCIBERSORTdiagnosticimmune infiltrationsmall molecule drugs

Identifiers

PMID35295271
PMCPMC8918776
OpenAlexW4214682286

What OpenQuestion holds

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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.