Abstract
With the highest case-fatality rate among women, the molecular pathological alterations of ovarian cancer (OV) are complex, depending on the diversity of genomic alterations. Increasing evidence supports that immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV using multiple methods to capture genomic signatures regulating immune events to identify reliable predictions of different outcomes. A dataset of 309 ovarian serous cystadenocarcinoma patients with overall survival >90 days from The Cancer Genome Atlas (TCGA) was analyzed. Multiple estimations and clustering methods identified and verified two immune clusters with component differences. Functional analyses pointed out immune-related alterations underlying internal genomic variables potentially. After extracting immune genes from a public database, the LASSO Cox regression model with 10-fold cross-validation was used for selecting genes associated with overall survival rate significantly, and a risk score model was then constructed. Kaplan–Meier survival and Cox regression analyses among cohorts were performed systematically to evaluate prognostic efficiency among the risk score model and other clinical pathological parameters, establishing a predictive ability independently. Furthermore, this risk score model was compared among identified signatures in previous studies and applied to two external cohorts, showing better prediction performance and generalization ability, and also validated as robust in association with immune cell infiltration in bulk tissues. Besides, a transcription factor regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gyneco-oncologists with predictive values for the prognosis and treatment management of patients with OV.
Highlights
Ovarian cancer (OV) is the second leading cause of gynecological cancer and has the highest casefatality rate among women, with 21,750 new cases and 13,940 deaths predicted for 2020 in the United States (Siegel et al, 2020)
Using TCGA dataset in an unbiased manner, we systematically evaluated the tumor microenvironment (TME) in OV by multiple approaches to investigate immune activity and proposed prognostic analyses based on bulk immune genes
No significant difference in overall survival time was observed between these two different clusters, suggesting that genomic alterations may play dominant roles in affecting the functionality of immune cells via modification of TME and, the immunotherapy response (Abdalla et al, 2014)
Summary
Ovarian cancer (OV) is the second leading cause of gynecological cancer and has the highest casefatality rate among women, with 21,750 new cases and 13,940 deaths predicted for 2020 in the United States (Siegel et al, 2020). The functions of non-cancer cells such as stromal or immune cells and non-cellular components in a tumor microenvironment (TME) and their interactions are still poorly understood even though plenty of studies and clinical trials have been conducted for the purpose of improved survival rate and reduced chemotherapy resistance. Other studies of the TME during tumor development reveal multi-omics prognostic biomarkers that may be used for imaging or liquid biopsy analysis, both important to select the most suitable therapies and stratification of patients, including OV (Abadjian et al, 2017; Wu et al, 2017; Willumsen et al, 2018; Guo et al, 2019; Jiang et al, 2020). The lack of successful treatment leads us to measure comprehensive genomic and epigenomic alterations that affect outcomes and constitute therapeutic targets, and further research studies are still needed urgently
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