Abstract
BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.
Highlights
Ovarian cancer (OC) is one of the most common gynecological tumors worldwide (Lheureux et al, 2019)
The Single sample gene-set enrichment analysis (ssGSEA) analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics
The five-immune-related-gene-signature prognostic model (CCL18, CXCL13, human leukocyte antigen (HLA)-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival
Summary
Ovarian cancer (OC) is one of the most common gynecological tumors worldwide (Lheureux et al, 2019). More than half of late-stage OC patients treated by conventional oncology would not respond very well, which means that the current standardized protocols (surgery and chemotherapy) are not enough to combat this (Corrado et al, 2019). It is necessary to identify the patient subset with worse survival who will need additional clinical therapy; for example, new targeted treatments, including poly(adenosine diphosphate-ribose) polymerase inhibitors, antiangiogenic drugs, and immune checkpoint inhibitors, potentially affect the improvement of survival (Elsherif et al, 2019). It is necessary to establish new biomarkers that are related to cancer prognosis and survival, and a complete biological database benefits the construction of a more common prognostic signature for OC. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC)
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