Identification of Stemness-Related Genes for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma by Integrated Bioinformatics Analysis - Guo, Hongjun; Wang, Siqiao; Ju, Min; Yan, Penghui; Sun, Wenhuizi; Li, Zhenyu; Wu, Siyu; Lin, Ruoyi; Xian, Shuyuan; Yang, Daoke; Wang, Jun; Huang, Zongqiang (Frontiers in Cell and Developmental Biology, 2021)
Background Invasion and metastasis of cervical cancer are the main factors affecting the prognosis of patients with Cervical Squamous Cell Carcinoma (CESC). Therefore, it is of vital importance to find novel biomarkers that are associated with CESC invasion and metastasis, which will aid in the amelioration of individualized therapeutic methods for advanced patients. Methods The gene expression profiles of 10 metastatic and 116 non-metastatic samples were downloaded from The Cancer Genome Atlas (TCGA), where differentially expressed genes (DEGs) were and the weighted gene correlation network analysis (WGCNA) was employed to identify the stemness related genes (SRGs). Univariate and multivariate regression analyses were used to identify the most significant prognostic key genes. Differential expression analysis of transcription factor (TF) and Gene Set Variation Analysis (GSVA) were used respectively to identify the potential upstream regulation of TFs and downstream signaling pathways. Co-expression analysis among significantly enriched TFs, key SRGs, and signaling pathways were performed to construct metastasis-specific regulation network in CESC. Connectivity Map (CMap) analysis was performed to identify bioactive small molecules which might be potential inhibitors for the network. Additionally, direct regulatory patterns of key genes were validated by ChIP-seq and ATAC-seq data. Results DEGs in yellow module acquired via WGCNA were defined as key genes which were most significantly related to mRNAsi. A multivariate Cox regression model was constructed and then utilized to explore the prognostic value of key genes by risk score. The area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.842. There was an obvious co expression pattern between the transcription factor NR5A2 and the key gene VIM (R = 0.843, p < 0.001), while VIM was also significantly co-expressed with hallmark epithelial mesenchymal transition signaling pathway (R = 0.318, p < 0.001). Naringenin was selected as the potential bioactive small molecule inhibitor for metastatic CESC based on CMap analysis. Conclusions VIM positively regulated by NR5A2 affected epithelial mesenchymal transition signaling pathways in metastatic CESC, and naringenin was the inhibitor for the treatment of metastatic CESC via suppressing cancer stemness. This hypothetical signaling axis may provide potential biomarkers and therapeutic targets for metastatic CESC.