Construction of Metastasis-Specific Regulation Network in Ovarian Cancer Based on Prognostic Stemness-Related Signatures

WE aimed to reveal the correlation between ovarian cancer (OV) metastasis and cancer stemness in OV. RNA-seq data and clinical information of 591 OV samples (551 without metastasis and 40 with metastasis) were obtained from TCGA. The edgeR method was used to determine differentially expressed genes (DEGs) and transcription factors (DETFs). Then, mRNA expression-based stemness index was calculated using one-class logistic regression (OCLR). Weighted gene co-expression network analysis (WGCNA) was used to define stemness-related genes (SRGs). Univariate and multivariate Cox proportional hazard regression were conducted to identify the prognostic SRGs (PSRGs). PSRGs, DETFs, and 50 hallmark pathways quantified by gene set variation analysis (GSVA) were integrated into Pearson co-expression analysis. Significant co-expression interactions were utilized to construct an OV metastasis-specific regulation network. Cell communication analysis was carried out based on single cell RNA sequencing data to explore the molecular regulation mechanism of OV. Eventually, assay for targeting accessible-chromatin with high throughout sequencing (ATAC), chromatin immunoprecipitation sequencing (ChIP-seq) validation, and multiple data sets were used to validate the expression levels and prognostic values of key stemness-related signatures. Moreover, connectivity map (CMap) was used to identify potential inhibitors of stemness-related signatures. Based on edgeR, WGCNA, and Cox proportional hazard regression, 22 PSRGs were defined to construct a prognostic prediction model for metastatic OV. In the metastasis-specific regulation network, key TF-PSRS interaction pair was NR4A1-EGR3 (correlation coefficient = 0.81, p < 0.05, positive), and key PSRG-hallmark pathway interaction pair was EGR3-TNF alpha signaling via NF kappa B (correlation coefficient = 0.44, p < 0.05, positive), which were validated in multi-omics databases. Thioridazine was postulated to be the most significant compound in treatment of OV metastasis. PSRGs played critical roles in OV metastasis. Specifically, EGR3 was the most significant PSRG, which was positively regulated by DETF NR4A1, inducing metastasis via TNF alpha signaling.

Automated summary

This study aimed to investigate the correlation between ovarian cancer metastasis and cancer stemness. Through analyzing RNA-seq data and clinical information from TCGA, differentially expressed genes (DEGs) and transcription factors (DETFs) were identified. Stemness-related genes (SRGs) and prognostic SRGs (PSRGs) were defined and integrated into a metastasis-specific regulation network. Single cell RNA sequencing data and validation experiments were used to explore the molecular regulation mechanism and validate the key stemness-related signatures. A prognostic prediction model for metastatic ovarian cancer was constructed and potential inhibitors of stemness-related signatures were identified.