Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets - Zhang, Yongjian; Huang, Wei; Chen, Dejia; Zhao, Yue; Sun, Fusheng; Wang, Zhiqiang; Lou, Ge (Frontiers in Genetics, 2022)
Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at single cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and an bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructed a 13-gene risk model, named RMGS (Recurrence Marker Gene Signature). Based on the RMGS score, the samples were stratified into high-risk and low-risk groups, and these two groups displayed significant survival difference in two independent validation cohorts including The Cancer Genome Atlas (TCGA). And RMGS score remained significantly independent in multivariate analysis after adjusting for clinical factors, including tumor grade and stage. Furthermore, there existed closely associations between RMGS score and immune characterizations, including checkpoint inhibition, EMT signature and T cell infiltration. Finally, the associations between RMGS score and molecular subtypes revealed that samples with mesenchymal subtype displayed higher RMGS score. In the meanwhile, the genomics characterization from these two risk groups were also identified. In conclusion, a recurrence related RMGS model we identified could provide a new understanding of ovarian cancer prognosis at the single cell level and offer reference for therapy decision for patient treatment.