Identification of Lipid Metabolism-Related Gene Markers
https://papers.ssrn.com/sol3/papers.cfm ... id=4974603
Abstract
Background: Multiple sclerosis (MS) is an autoimmune inflammatory disorder. This study aimed to identify lipid metabolism-related gene markers and construct a diagnostic model for MS.
Methods: Differentially expressed lipid metabolism-related genes were identified and performed functional enrichment analysis. Least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were employed to screen hub genes. The predictive power of hub genes was evaluated using receiver operating characteristic (ROC) curves. We developed an artificial neural network (ANN) model and validated its performance. Immune cell infiltration analysis, Gene set enrichment analysis, and ceRNA network construction were performed.
Results: FA2H, INSIG1, EHHADH, and SPHK1 were identified as gene markers. Their areas under the ROC curve (AUCs) were 0.653, 0.796, 0.694, and 0.696, respectively. The ANN model exhibited good performance, with AUCs of 0.923 in the training set and 0.771 in the test set. Gamma.delta.T.cell, Natural.killer.T.cell, Plasmacytoid.dendritic.cell, Regulatory.T.cell, and Type.1.T.helper.cell were highly expressed in the MS group. A ceRNA network showed a complex regulatory interplay.
Conclusion: Our study emphasized the crucial role of lipid metabolism in MS, identifing FA2H, INSIG1, EHHADH, and SPHK1 as gene markers. The ANN model exhibited good performance on both the training and testing sets.
Lipid Metabolism-Related Gene Markers
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