Abstract 183P
Background
Ovarian cancer (OC) is a highly aggressive malignancy with limited treatment options. Recent studies have focused on understanding the role of cancer-associated fibroblasts (CAFs) in OC progression.
Methods
Machine learning algorithms were utilized to analyze large-scale bulk transcriptomic datasets and identify FGF7 as a potential oncogenic factor. Expression levels of FGF7 were compared between CAFs, OC tissues, normal fibroblasts (NFs), and non-cancerous tissues. Various experimental techniques, including single-cell transcriptome analysis and in vitro experiments, were employed to investigate the interaction between FGF7 and OC cells, as well as the downstream signaling pathways involved.
Results
FGF7 expression was significantly elevated in CAFs and OC tissues compared to NFs and non-cancerous tissues, respectively. Higher FGF7 levels were associated with advanced tumor stage, vascular invasion, and poor prognosis. Experimental results demonstrated that CAFs-derived FGF7 enhanced OC cell proliferation, migration, and invasion. Mechanistic investigations revealed that FGF7 inhibited the degradation of hypoxia-inducible factor 1 alpha (HIF-1α) under normoxia, leading to the activation of EMT-related transcription factors and down-regulation of epithelial markers.
Conclusions
This study suggests that targeting the FGF7/HIF-1α/EMT axis may provide therapeutic opportunities for intervening in OC progression. Inhibition of FGF7 or HIF-1α signaling may be potential strategies to consider in future therapeutic interventions for OC.
Legal entity responsible for the study
The author.
Funding
National Natural Science Foundation of China.
Disclosure
The author has declared no conflicts of interest.
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