Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune‐related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17‐IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17‐IRGP signature noticeably split patients into high‐ and low‐risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll‐like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17‐IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.
Background: Ovarian cancer (OV) is the most common gynecological cancer and is a major cause of cancer-related death among women worldwide. Immunotherapy has recently been of great interest, and it has been proven to be an effective treatment strategy. For this reason, the work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. Methods: The Gene ExpressionOmnibus (GEO) and the Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients, whose samples were divided into training, validation, and testing sets. The InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, were used to build an IRGP signature. The functional enrichment analysis was performed to investigate the biological activities of IRGPs. Then, the correlation between risk scores and clinicopathological parameters was further analyzed. Finally, we compared our signature and four existing signatures for OV. Results: We first identified a 17-IRGP signature significantly associated with survival based on LASSO regression model. The average area under the curve (AUC) values of the training, validation, and all
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