Identification And Validation Of Biomarkers For Calcific Aortic Valve Disease Based On Bioinformatics Analysis And Construction And Validation Of Clinical Prediction Model

Background Calcific aortic valve disease(CAVD)is one of the most common cardiac valve diseases worldwide,including aortic valve calcification(AVC)and calcific aortic valve stenosis(CAVS).CAVS is characterized by progressive fibrocalcific remodeling and thickening of the aortic valve leaflets,which,over many years,leads to severe obstruction of the cardiac outflow.Surgery or transcatheter aortic valve replacement(TAVR)is the only effective treatment option for severe CAVS.However,surgery is associated with significant trauma and limitations,including increased complications due to anticoagulation and the need for reoperation due to the limited lifespan of artificial valves.Furthermore,TAVR is currently limited to patients with high surgical risk.Currently,there is no alternative medical treatment to reverse the progression of CAVD,except for managing complications(such as antibiotics for infection prevention and vasodilators for acute decompensation).Therefore,it is crucial to identify new biomarkers or develop prediction models for CAVD to achieve rapid diagnosis and accurate identification,thereby improving the clinical diagnostic level and personalized treatment of CAVD.CAVD may have a close association with lipid metabolism.This study aims to screen for fatty acid metabolism-related biomarkers in aortic valve calcification and investigate the role of immune cell infiltration in aortic valve calcification.Additionally,it aims to explore the risk factors of CAVD,construct a predictive model for the disease,and provide reliable theoretical references for the prevention,diagnosis,and prediction of CAVD.Methods1.In the first part of the study,AVC-related datasets from the Gene Expression Omnibus(GEO)database were utilized.Differential expression analysis and weighted gene co-expression analysis were conducted using R language.Differential co-expressed genes were identified using Venn diagrams.Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify functions closely associated with aortic valve calcification.Fat metabolism-related genes were obtained from the Molecular Signatures Database,and after removing duplicates,three machine learning algorithms—least absolute shrinkage and selection operator regression analysis,support vector machine recursive feature elimination,and random forest—were employed to identify biomarkers related to fat metabolism in aortic valve calcification.Immunocyte infiltration analysis of normal and AVC tissues was performed using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts algorithm,and the correlation between biomarkers and immunocytes was calculated.Finally,single-gene gene set enrichment analysis was conducted to predict HIBCH-related pathways.2.In the second part of the study,a retrospective analysis was conducted on patients with CAVS who underwent surgical treatment at the Affiliated Hospital of Qingdao University between 2016 and 2020,along with patients who underwent physical examination during the same period.Based on the 2020 American College of Cardiology/American Heart Association guidelines for the management of valvular heart disease,the patients were divided into the CAVS group and the normal group.A total of 548 patients were included in this study,with106 patients in the CAVS group and 442 patients in the normal group.The participants were randomly assigned to a training group and a validation group in a ratio of 7:3.In the training set,the LASSO algorithm was used for feature dimension reduction and selection of the best clinical features.The independent predictors of patients with CAVS were determined by univariate and multivariate logistic regression,and nomogram was constructed.Univariate and multivariate logistic regression analyses were performed to determine the independent predictive factors for CAVS patients and construct nomogram.Calibration curves,ROC curves,and DCA were used to evaluate the models in the training and validation sets.Results1.This part of the study involved screening the differentially expressed genes in the dataset and identified 2,416 DEGs and one co-expression module.A total of 1473 differentially coexpressed genes were acquired.GO and KEGG enrichment analyses demonstrated that differentially co-expressed genes were closely related to fatty acid metabolism.LASSO regression analysis,SVM-REF,and random forest revealed that 3-hydroxyisobutyryl-Co A hydrolase was a biomarker of fatty acid metabolism-related genes in AVC.Significant high levels of memory B cells were found in AVC than normal samples,while activated natural killer cells were significantly low in AVC than normal samples.A significantly positive relevance was observed between HIBCH and activated NK cells,Regulatory T cells,monocytes,na(?)ve B cells,activated dendritic cells,resting memory CD4 T cells,resting NK cells,and CD8 T cells.A significantly negative relevance was observed between HIBCH and resting dendritic cells,activated mast cells,activated memory CD4 T cells,memory B cells,neutrophils,gamma delta T cells,M0 macrophages,and plasma cells.The single-gene GSEA results suggest that HIBCH may work through the inhibition of multiple immune-related pathways.2.In this section of the study,a multivariate logistic regression analysis was conducted to screen for 11 independent predictive factors: history of hypertension,history of carotid atherosclerosis,age,diastolic blood pressure,C-reactive protein,direct bilirubin,alkaline phosphatase,low-density lipoprotein,lipoprotein(a),uric acid,and cystatin C.A nomogram was constructed using the above indicators,and the results showed good model calibration.In the training set,the model exhibited good discrimination and accuracy(AUC = 0.981),with a sensitivity of 91.89% and specificity of 95.48%.The nomogram performed well in the validation set(AUC = 0.955,95% CI: 0.925-0.985),with a sensitivity of 93.75% and specificity of 84.09%.Additionally,DCA demonstrated that the nomogram had high clinical practicability.Conclusions1.HIBCH can serve as a potential biomarker and therapeutic target for aortic valve calcification,and fatty acid metabolism may play an important role in the progression of aortic valve calcification.Additionally,HIBCH is closely associated with immune cell infiltration in aortic valve calcification.The screening of key genes may provide a new research direction for disease intervention strategies.2.This section of the study screened risk factors for CAVS.These indicators were used to construct a risk prediction model for CAVS.The model demonstrated good effectiveness,and DCA indicated its high clinical utility,providing convenience for healthcare professionals and potential risk individuals in assessing CAVS risk.