Integrated Bioinformatical Analysis for Identification the invasion-related Genes in ovarian cancer

Abstract Ovarian cancer (OC) is the most prevalent cancer among females and its prognosis is closely related to the tumor microenvironment and the progression of the disease. This study aimed to explore the relationship between invasion-related genes (IRGs) and the prognosis of OC. Using the Cancer Genome Atlas (TCGA) database, 65 IRGs were identified and a risk model was constructed based on 4 of these IRGs. This model was able to differentiate OC patients into two risk groups with significant differences in prognosis, immune cell infiltration, and phenotypes. The model was validated using multiple methods and datasets, and the expressions of the four hub genes were confirmed using qPCR and immunohistochemistry staining assays. The results of this study suggest that the risk model could play a significant role in predicting the prognosis of OC. The findings also contribute to a better understanding of OC invasion and could assist in the development of more personalized and precise therapeutic strategies.