AimThe 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. MethodsThe 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. ResultsA 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 naive 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. ConclusionFN1 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.
基金：
the Chinese
National Natural Science Foundation (No. 82060093), the
Science and Technology Department of Yunnan Province
(No. 202101AY070001-211 and No. 2019FE001-268), and
Yunnan Province’s Key Laboratory of Cardiovascular Disease
(No. 2018DG008).
第一作者机构：[1]Kunming Med Univ, Yanan Affiliated Hosp, Cardiovasc Surg, Kunming, Yunnan, Peoples R China
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推荐引用方式(GB/T 7714)：
Xiong Tao,Han Shen,Pu Lei,et al.Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification[J].FRONTIERS IN CARDIOVASCULAR MEDICINE.2022,9:doi:10.3389/fcvm.2022.832591.
APA：
Xiong, Tao,Han, Shen,Pu, Lei,Zhang, Tian-Chen,Zhan, Xu...&Li, Ya-Xiong.(2022).Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification.FRONTIERS IN CARDIOVASCULAR MEDICINE,9,
MLA：
Xiong, Tao,et al."Bioinformatics and Machine Learning Methods to Identify FN1 as a Novel Biomarker of Aortic Valve Calcification".FRONTIERS IN CARDIOVASCULAR MEDICINE 9.(2022)
