Signal transducers and activators of transcription (STAT) transcription factors, a family of genes encoding transcription factors, have been linked to the development of numerous types of tumors. However, there is a relative paucity of a comprehensive investigation of the expression and functional analysis of STATs in ovarian cancer (OV).
Gene expression profile interaction analysis (GEPI2A), Metascape, The Cancer Genome Atlas (TCGA), Kaplan-Meier Plotter, Linkedomics, and CancerSEA databases were used for expression analysis and functional enrichment of STATs in ovarian cancer patients. We screened potential predictive genes and evaluated their prognostic value by constructing the minor absolute shrinkage and selection operator (LASSO) Cox proportional risk regression model. We explored STAT5A expression and its effects on cell invasion using ovarian cancer cells and a tissue microarray.
The expression level of STAT1 was higher, but that of STAT2-6 was lower in cancerous ovarian tissues compared to normal tissues, which were closely associated with the clinicopathological features. Low STAT1, high STAT4, and 6 mRNA levels indicated high overall survival. STAT1, 3, 4, and 5A were collectively constructed as prognostic risk models. STAT3, and 5A, up-regulating in the high-risk group, were regarded as risk genes. In subsequent validation, OV patients with a low level of P-STAT5A but not low STAT5A had a longer survival time (
It is suggested that STAT1, STAT4, and STAT6 may be potential targets for the proper treatment of ovarian cancer. STAT5A and P-STAT5A, biomarkers identified in ovarian cancer, may offer new perspectives for predicting prognosis and assessing therapeutic effects.