Identification of Stemness-Related Genes for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma by Integrated Bioinformatics Analysis

Abstract

BackgroundInvasion 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.MethodsThe 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 defined. 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 utilized to explore the potential upstream regulation of TFs and downstream signaling pathways, respectively. Co-expression analysis was performed among significantly enriched TFs, key SRGs, and signaling pathways to construct a 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.ResultsDEGs 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 SRGs by risk score. Area under curve (AUC) of the receiver operating characteristic (ROC) curve was 0.842. There was an obvious co expression pattern between the TF NR5A2 and the key gene VIM (R = 0.843, p < 0.001), while VIM was also significantly co-expressed with hallmark epithelial mesenchymal transition (EMT) 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.ConclusionsVIM positively regulated by NR5A2 affected EMT signaling pathways in metastatic CESC, and naringenin was the inhibitor for the treatment of metastatic CESC via suppressing cancer stemness. This hypothetical signaling axis and potential inhibitors provide biomarkers and novel therapeutic targets for metastatic CESC.

Identifying the EMT-related signature to stratify prognosis and evaluate the tumor microenvironment in lung adenocarcinoma

“…As a measure of inflammation, the cytolytic activity (CYT) score was computed as the geometric mean of the RPKM expression of granzyme A (GZMA) and perforin-1 (PRF1) mRNA expression levels in the tumor tissues (Wakiyama et al, 2018). We also computed mRNA stemness index (mRNAsi) using the "TCGAbiolinks" R package based on the mRNA levels obtained from one-class logistic regression machine learning (OCLR) algorithm (Guo et al, 2021), where a higher mRNAsi represents a greater tumor dedifferentiation and higher cancer stem cell levels.…”

Characterization of the Molecular Alterations Induced by the Prolonged Exposure of Normal Colon Mucosa and Colon Cancer Cells to Low-Dose Bisphenol A

“…NR5A2 is over-expressed in pancreatic cancer and promotes EMT [ 37 ]. NR5A2 and the key gene vimentin ( VIM ) were significantly co-expressed in cervical squamous cell carcinoma, and VIM was also significantly co-expressed with EMT signaling pathway genes [ 38 ]. Vacuole membrane protein 1 ( VMP1 ) is an endoplasmic reticulum-resident and multi-spanning membrane protein.…”

Comprehensive Analysis and Validation Reveal Potential MYCN Regulatory Biomarkers Associated with Prognosis of Neuroblastoma

“…Bioinformatics analysis technology has been increasingly used to nd new therapeutic targets and diagnostic markers for various cancers in recent years [31][32][33]. Compared with a single cohort study, a multi-cohort study has a lower false-positive rate and false-negative rate [34].…”