Background: High-grade serous ovarian cancer (HGS) is the most aggressive form of ovarian cancer due to its rapid spread, insidious onset, and early dissemination throughout the abdominal cavity. However, the molecular pathogenesis of HGS remains unclear. This study aimed to identify key pathogenic genes and explore the underlying molecular mechanisms of HGS using bioinformatics analysis and biological experiments. Methods: Two datasets were downloaded from the Gene Expression Omnibus databases to find differentially expressed genes (DEGs) between HGS and normal tissue samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were applied to investigate the primary functions of the DEGs. The protein-protein interaction network of the DEGs was constructed, and the interactions of various genes were ranked. Results: Topoisomerase IIα (TOP2A) was identified as the hub gene associated with survival and mutation. Gene Set Enrichment Analysis and Gene Set Variation Analysis were conducted to predict the potential biological functions of TOP2A. Furthermore, the TOP2A expression level was significantly up-regulated in HGS cell lines, SKOV3 and HEY. Moreover, the proliferation, migration, and invasion capacities of SKOV3 and HEY cells were strongly suppressed after TOP2A knockdown. In addition, the levels of phosphorylated Smad2 and Smad3, the key members of the transforming growth factor-β (TGF-β)/Smad pathway that regulate HGS tumorigenesis, strongly decreased after knockdown of TOP2A. Conclusions: This study identified that TOP2A was up-regulated in HGS, and it accelerated HGS progression via the TGF-β/Smad pathway. The findings provided a blueprint for TOP2A serving as a therapeutic target and a treatment response prediction biomarker for HGS.

Ovarian cancer (OC) is one of the most lethal gynecologic malignancies. Most patients die of metastasis due to a lack of other treatments aimed at improving the prognosis of OC patients. In the present study, we use multiple methods to identify prognostic S1 as the dominant subtype in OC, possessing the most ligand–receptor pairs with other cell types. Based on markers of S1, the consensus clustering algorithm is used to explore the clinical treatment subtype in OC. As a result, we identify two clusters associated with distinct survival and drug response. Notably, IFI6 contributes to the cluster classification and seems to be a vital gene in OC carcinogenesis. Functional enrichment analysis demonstrates that its functions involve G2M and cisplatin resistance, and downregulation of IFI6 suppresses proliferation capabilities and significantly potentiates cisplatin-induced apoptosis of OC cells in vitro. To explore possible mechanisms of IFI6 influencing OC proliferation and cisplatin resistance, GSEA is conducted and shows that IFI6 is positively correlated with the NF-κB pathway, which is validated by RT-qPCR. Significantly, we develop a prognostic model including IFI6, RiskScore, which is an independent prognostic factor and presents encouraging prognostic values. Our findings provide novel insights into elucidating the biology of OC based on single-cell RNA-sequencing. Moreover, this approach is potentially helpful for personalized anti-cancer strategies and predicting outcomes in the setting of OC.