Bioinformatics-Based Identification of Platelet-Related Genes and Analysis of Immune Infiltration Landscape in Endometriosis
Bioinformatics identified six platelet-related hub genes (CD40, CSRP1, FLNA, C1GALT1C1, EIF2AK1, PTMA) that were highly diagnostic for endometriosis and correlated with immune cell infiltration differences.
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This study used two endometriosis gene expression datasets (GSE7305 and GSE51981) to identify differentially expressed genes, intersect them with platelet-related genes to screen platelet-related DEGs, and then performed functional enrichment, hub-gene selection via two machine learning algorithms, and ROC-based diagnostic evaluation. It reports six platelet-related hub genes (CD40, CSRP1, FLNA, C1GALT1C1, EIF2AK1, and PTMA) with high diagnostic specificity for endometriosis, alongside higher infiltration of most immune cell types in endometriosis compared with controls, including activated B cells, MDSCs, macrophages, mast cells, monocytes, NK cells, neutrophils, T follicular helper cells, and Th1 cells, with lower activated CD4 T cells and immature dendritic cells. Correlation analyses were used to connect hub gene expression with immune infiltration patterns. The paper’s major limitation is that it is based on bioinformatics inference from public transcriptomic datasets rather than independent experimental validation. This paper is centrally about endometriosis — it identifies platelet-related hub genes and links them to the immune infiltration landscape in endometriosis.
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