Revealing the diagnostic value and immune infiltration of senescence-related genes in endometriosis: a combined single-cell and machine learning analysis - Zou, Lian; Meng, Lou; Xu, Yan; Wang, Kana; Zhang, Jiawen (Frontiers in Pharmacology, 2023)
Endometriosis stands as one of the most prevailing recurrent maladies. It is linked with manifestations encompassing pelvic discomfort, dysmenorrhea, and reproductive insufficiency.Moreover, it holds the potential to undergo malignant transformation, exerting a profound impact on a woman's quality of life. However, a precise and non-invasive diagnostic technique for this condition is currently absent. By leveraging microarray datasets, executing differential gene analysis, conducting WGCNA, and deploying machine learning algorithms including random forest, support vector machine, and LASSO analysis, an exhaustive exploration of senescence-related genes (SRGs) is undertaken. This meticulous investigation culminates in the identification of a cluster of genes comprising BAK1, LMNA, and FLT1, thereby substantiating their candidacy as potential discerning biomarkers. These biomarkers are subsequently employed to construct an artificial neural network classifier model alongside a graphical representation in the form of a Nomogram. A comprehensive scrutiny, encompassing profiling of immune cell infiltration and single-cell analysis, underscores the viability of this gene assemblage as promising therapeutic targets for ameliorating endometriosis.Furthermore, the integration of these biomarkers augments diagnostic precision, promising an enhanced diagnostic journey for future endometriosis patients within clinical settings.