Vascular calcification (VC) is an actively regulated dynamic process characterizing by abnormal deposition of calcium phosphate mineral in the extracellular matrix and in cells of the arterial wall. Significant advances have been made in comprehending the ferroptosis linked to VC, yet the precise molecular mechanism is still not fully understood. Interpretability and explainability of machine learning models are crucial for incorporating them into decision-making processes. We used the Shapley additive explanation (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods in this study to interpret and explain a random forest model in order to discover the significant attributes.

This paper employed the GEO tools to get a VC dataset. The DEGs were discovered using the EdgeR package in R to identify potential ferroptosis-associated hub genes that could be used for VC diagnosis. We used qRT-PCR and western blotting techniques to confirm the DEGs associated with ferroptosis that were discovered in the microarray data. Finally, we suggest two innovative strategies, using SHAP and LIME, to enhance interpretation. We evaluated the explanatory outcomes of the SHAP scheme with other approaches using GEO datasets.

We uncovered 49 ferroptosis DEGs in VC, including 31 upregulated and 18 downregulated genes. The outputs obtained from the GSEA and the study of the KEGG using WebGestalt revealed that the differentially expressed genes (DEGs) related to ferroptosis are found to be involved in six paths, one of which was the Ferroptosis signaling pathway. SHAP and LIME interpretation aligned well with the interpretations provided by the current methodologies. We demonstrated the significance of TP63 and GPX2 as crucial predictive factors for VC using of suggested methodologies. Lastly, we examined the three genes identified by two machine learning models in vitro and observed that the mRNA and protein profile levels of FTH1 exhibited an elevated level and the levels of SLC3A2 and SLC7A11 exhibited a reduced level in the β -GP-treated class in comparison to the normal class. The nomogram and 5 potential hub genes exhibited excellent predictive performance, with AUC values ranging from 0.724 to 0.969.

Our investigation found three ferroptosis-associated potential hub genes by comprehensive exploration (FTH1, SLC3A2, and SLC7A11). In addition, we created a nomogram for VC diagnosis utilising bioinformatics and machine learning approaches (SHAP and LIME). Our methods are effective for analyzing machine learning models and may reveal the fundamental connections among variables and outputs.