Abstract

Endometriosis (EMT) is characterized by a chronic inflammatory disorder in the female reproductive system, posing significant challenges to global women’s health. Necrosis by Sodium Overload (NESCO) is a novel immunogenic programmed cell death (PCD) pattern that may potentially inhibit natural killer (NK) cell activation by increasing cytotoxicity and the inflammatory response in the EMT microenvironment. By integrating three bulk datasets to compare endometrium tissues between endometriosis patients and normal controls and the NESCO gene list from a public database, we identified NK- and NESCO (NN)-associated hub genes via integrative bioinformatic analyses utilizing Limma, WGCNA, CIBERSORT and machine learning frameworks. The diagnostic performance of NN-associated hub genes was evaluated across the three aforementioned datasets and two independent validation sets. Furthermore, their molecular and immune features were estimated at the bulk and single-cell transcriptomic levels. In addition, endometriosis patients were classified into two novel molecular subgroups based on consensus clustering of NN. Finally, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and molecular docking were used to identify compounds in Chinese traditional medicine (CTM) that can target NN-associated hub genes for endometriosis treatment.FABP4andSLC2A1can be considered NN-associated hub genes that are involved in EMT pathogenesis, and natural compounds including the CTM GuiZhiFuLingWan (GZFLW) can be considered therapeutic agents for EMT treatment as they targetFABP4andSLC2A1. Our study is the first to reveal the diagnostic and druggable roles of NESCO and NK cells, the corresponding molecular and immune features of NN-associated hub genes, and the therapeutic potential of GZFLW.