Shared genes of polycystic ovary syndrome and sedentary behavior as a novel immune landscape biomarker for endometrial cancer.

Endometrial cancer (EC) is associated with significant risk factors such as polycystic ovarian syndrome (PCOS) and sedentary behavior. In our study, we aim to employ machine learning algorithms to investigate the potential molecular processes that underlie their interaction and explore their respective roles in the diagnosis and immunotherapy of EC. The GEO database provides access to microarray data, which was utilized in this study to identify gene expression modules associated with PCOS and sedentary behavior, using weighted gene expression network analysis (WGCNA). Cluego software was then employed to investigate the energy enrichment of shared pathways in both PCOS and sedentary individuals, and differential gene analysis was used to confirm another two databases. The miRNAs-mRNAs controlled network was constructed to verify the pathway. The immune-related factors of the shared pathway in EC were then analyzed. Finally, to validate our findings, we conducted cell experiments using EC cell lines (AN3CA, KLE, Ishikawa, RL95-2, and HEC-1A). We found that increased intracellular aromatic compound anabolism is a common feature of both PCOS and sedentary individuals. We then developed a disease pathway model that was based on the common genetic characteristics of PCOS and sedentary behavior. We utilized pathway typing in EC samples and found a significant survival difference between the two subgroups, with the upregulated expression type exhibiting an immune-hot phenotype. Finally, the experimental results confirmed the expression of the hub gene (NAA15) in EC. The findings of our study suggest that genes related to the intracellular aromatic compound metabolic pathway can be used for immunotherapy of EC.