Abstract
Ovarian cancer (OV) has become the most lethal gynecological cancer. However, its treatment methods and staging system are far from ideal. In the present study, taking the advantage of large-scale public cohorts, we extracted a list of immune-related prognostic genes that differentially expressed in tumor and normal ovarian tissues. Importantly, an individualized immune-related gene based prognostic model (IPM) for OV patients were developed. Furthermore, we validated our IPM in Gene Expression Omnibus (GEO) repository and compared the immune landscape and pathways between high-risk and low-risk groups. The results of our study can serve as an important model to identify the immune subset of patients and has potential for use in immune therapeutic selection and patient management.
Highlights
With patients often diagnosing at an advanced stage, Ovarian Cancer (OV) has become the most lethal gynecological cancer [1]
As the the Cancer Genome Atlas (TCGA) database provided our access to the clinical characteristics and gene expression profiles of 388 ovarian cancer patients, we could confirm the performance of immune score in OV
We determined the ability of the immune score to forecast the infiltration of immune cells in OV by utilizing the recently reported CIBERSORT, which could evaluate the fraction of 22 kinds of tumor infiltrating cells (TIICs)
Summary
With patients often diagnosing at an advanced stage, Ovarian Cancer (OV) has become the most lethal gynecological cancer [1]. Patients with OV may have no symptoms or mild symptoms until the cancer is in its advanced stages [1], which responds poorly to treatment. According to the International Federation of Gynecology and Obstetrics (FIGO) staging system, treatments for OV patients usually include debulking surgery and adjuvant or neoadjuvant chemotherapy. Gene expression of biomarkers in tumor tissues has been proved to be reliably related to clinical outcome [3, 4]. In the context of additional clinical therapy, it is vital to identify the subcategory of patients with poor survival outcomes and higher mortality. It is of primary importance to recognize a more comprehensive prognostic signature that includes the biological context. Extensive databases of the biological characteristics and accessibility of all-encompassing public cohorts with data on their gene expression have been established
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