Integrating network toxicology, machine learning, and single-cell sequencing to reveal the FASN-mediated role of phenolic endocrine disruptors in water in promoting prostate cancer

by Xinyao Zhu, Qilong Wu, Yuqi Li, Zhiyu Liu, Yang Zeng, Zhiqiang Zeng, Yubo Zhou, Lunhong Zou, Xiaochun Wu, Dan Zhao, Qingfu Deng, Tao Zhou Background Phenolic endocrine-disrupting chemicals (EDCs) like nonylphenol (NP) and octylphenol (OP) are widespread water pollutants. Their estrogen-like properties are suspected contributors to prostate cancer, but their precise molecular mechanisms remain unclear.

Methods We employed a multidimensional framework to investigate this link. Potential NP/OP targets were predicted using SwissTargetPrediction, SEA, and CTD databases and cross-referenced with prostate cancer-associated genes from GeneCards and OMIM.

Differential expression analysis of the GSE46602 dataset (36 tumor vs. 14 benign samples) identified candidate genes, which were refined to core genes using Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms.

Their diagnostic power was evaluated via an Artificial Neural Network (ANN) model and validated in The Cancer Genome Atlas (TCGA) cohort. Single-cell RNA sequencing data from six prostate cancer samples (GSE137829) were analyzed to reveal cell-type-specific expression patterns.

Molecular docking and molecular dynamics (MD) simulations assessed binding stability between pollutants and target proteins.