Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

Abstract

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (<i>SNRPA1</i>, <i>CCL19</i>, <i>CXCL11</i>, <i>CDC5L</i>, <i>APCDD1</i>, <i>LPAR2</i>, <i>PI3</i>, <i>PLEKHF1</i>, <i>CCDC80</i>, <i>CPXM1</i> and <i>CTAG2</i>) for Fédération Internationale de Gynécologie et d'Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.