Unveiling the impact of estrogen exposure on ovarian cancer: a comprehensive risk model and immune landscape analysis

Abstract

This study examines the impact of estrogenic compounds like bisphenol A (BPA), estradiol (E2), and zearalenone (ZEA) on human ovarian cancer, focusing on constructing a risk model, conducting gene set variation analysis (GSVA), and evaluating immune infiltration. Differential gene expression analysis identified 980 shared differentially expressed genes (DEGs) in human ovarian cells exposed to BPA, E2, and ZEA, indicating disruptions in ribosome biogenesis and RNA processing. Using the cancer genome atlas ovarian cancer (TCGA-OV) dataset, a least absolute shrinkage and selection operator (LASSO)-based risk model was developed incorporating prognostic genes 4-hydroxyphenylpyruvate dioxygenase like (HPDL), Thy-1 cell surface antigen (THY1), and peptidase inhibitor 3 (PI3). This model effectively stratified ovarian cancer patients into high-risk and low-risk categories, showing significant differences in overall survival, disease-specific survival, and progression-free survival. GSVA analysis linked HPDL expression to pathways related to the cell cycle, DNA damage, and repair, while THY1 and PI3 were associated with apoptosis, hypoxia, and proliferation pathways. Immune infiltration analysis revealed distinct immune cell profiles for high and low-expression groups of HPDL, THY1, and PI3, indicating their influence on the tumor microenvironment. The findings demonstrate that estrogenic compounds significantly alter gene expression and oncogenic pathways in ovarian cancer. The risk model integrating HPDL, THY1, and PI3 offers a strong prognostic tool, with GSVA and immune infiltration analyses providing insights into the interplay between these genes and the tumor microenvironment, suggesting potential targets for personalized therapies.