Weighted Gene Co-Expression Network Analysis and Machine Learning Reveal that USP1 Drives Lipid Metabolism and Macrophage Polarization in Cervical Cancer Cells.

Cervical cancer is a leading preventable cause of cancer morbidity and mortality globally. Previous studies have indicated that dysregulation of the ubiquitin-proteasome system participates in lipid metabolism and cervical cancer progression. However, the role and mechanism of deubiquitinase ubiquitin-specific protease 1 (USP1) in cervical cancer are still unclear. The cervical cancer transcriptome data from the GSE90738 database were downloaded from the Gene Expression Omnibus dataset. Differential expression gene (DEG) analysis, weighted gene coexpression network analysis (WGCNA), GeneCards database, and ubibrowser2.0 database were employed to identify potential ubiquitin-related targets involved in lipid metabolism during cervical cancer progression. Then, these intersected targets were subjected to cross-validation using three machine learning algorithms-Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), ultimately identifying one hub gene. GSE90738 and GEPIA databases were used to analyze USP1 expression in cervical cancer patients. The relationship between USP1 and overall survival or progress-free survival of cervical cancer patients was analyzed. USP1 mRNA level was detected by real-time quantitative polymerase chain reaction (RT-qPCR). USP1, FASN, and GPX4 protein levels were determined using western blot assay. Cell proliferation was detected using 5-ethynyl-2'-deoxyuridine (EdU) and colony formation assays. Lipid accumulation (Oil Red O/Nile Red), lipid-ROS, and ferrous iron levels were measured using special kits. A co-culture model of THP1 macrophages and cervical cancer cells was conducted to investigate the impacts of tumor cell-derived USP1 on THP1 macrophage polarization. The effect of USP1 on tumorigenesis was examined using a xenograft tumor model in vivo. A total of 19 potential signature genes were identified by DEG analysis, WGCNA, GeneCards database, and ubibrowser2.0 database. Through the three machine learning algorithms of LASSO, RF, and SVM-RFE, one hub gene (USP1) was identified with diagnostic potential. Furthermore, USP1 was upregulated in cervical cancer, and its silencing could repress cervical cancer cell proliferation, lipid metabolism, and promote ferroptosis. Meanwhile, USP1 silencing could hinder M2 polarization of TAMs by downregulating TGF-β1 and IL-10. Besides, USP1 deficiency suppressed tumor growth in vivo. Through bioinformatics analysis and experiments, this study discovered that USP1 knockdown inhibited cervical cancer growth and TAM M2 polarization, providing a promising therapeutic target for cervical cancer treatment.