Construction and validation of an anoikis-related gene prognostic signature in ovarian cancer

Ovarian cancer (OC) is identified as one of the most aggressive malignant diseases that imperil women health. Anoikis has been proved to be significantly correlated with the aggressiveness of dislodged cancer cells and poor prognosis of cancer patients. Currently, few researches focus on the prognostic role of anoikis-related genes (AIRGs) on the OC cases. Here, we firstly and systematically analyzed the predictive ability of AIRGS on the prognosis of patients with OC. The genomics data and clinicopathologic information of OC patients were retrieved from TCGA project. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) method were executed to construct an anoikis–related risk score (AIRS) indicator based on 11 AIRGs. ICGC-OV and GSE26712 were used as independent validation datasets. A nomogram integrated AIRS and pathologic parameters, survival analysis and time-dependent receiver operating characteristic (ROC) curves were utilized to assess the predictive performance of this signature. Successfully, OC patients were divided into two sets based on the optimum cut-off value of risk scores. High risk category evidently suffers from more unfavorable survival outcomes. What is more, AIRS was closely connected with tumor microenvironment (TME) characteristics, mutant landscape, m6A modification and clinical implications. Interestingly, we validated EML2 as a crucial risk gene in OC based on GSEA, single-cell analysis and immunohistochemical staining results. Collectively, AIRS was validated as an independent prognostic indicator with good predictive validity in OC. It can function as a robust clinical parameter which effectively stratifies OC patients and promotes the development of precise medicine.