Dataset Information

Construction of a new tumor immunity-related signature to assess and classify the prognostic risk of ovarian cancer.

ABSTRACT: Ovarian cancer is associated with a high mortality rate. In this study, we established a new immune-related signature that can stratify ovarian cancer patients. First, we obtained immune-related genes through IMMUPORT, and DEGs (Differential Expression Genes) by analyzing the GSE26712 dataset. The APP (Antigen Processing and Presentation) and DEG signatures were established using univariate and multivariate Cox models. Kaplan-Meier analysis revealed the signatures' prognostic value in training and validation cohorts (HR: 0.379 VS. 0.450; 0.333 VS. 0.327). Nomogram analysis was used to assess the signatures' ability to predict the 30-month prognosis, which was evaluated using the calibration curve and time-dependent ROC curve (30-month AUC: 0.665 VS. 0.743). Time-dependent ROC, Decision Curve Analysis (DCA) and Integrated discrimination improvement (IDI) was used to compare the new model to previously published gene signatures. 30-month AUC composite variable (0.736) was higher than 9-gene signature (0.657), and composite variable had a larger net benefit and a higher IDI (+2.436%) relative to the 9-gene signature. Tumor immune infiltration and tumor microenvironment scores of the 2 groups separated by APP signature were compared. GSEA was used to identify enriched KEGG pathways. Conclusively, the proposed signature can stratify ovarian cancer patients by risk-score and guide clinical decisions.

SUBMITTER: Ding J

PROVIDER: S-EPMC7695433 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Construction of a new tumor immunity-related signature to assess and classify the prognostic risk of ovarian cancer.

Ding Jiashan J Zhang Qiaoling Q Chen Shichao S Huang Huikai H He Linsheng L

Aging 20201108 21

Ovarian cancer is associated with a high mortality rate. In this study, we established a new immune-related signature that can stratify ovarian cancer patients. First, we obtained immune-related genes through IMMUPORT, and DEGs (Differential Expression Genes) by analyzing the GSE26712 dataset. The APP (Antigen Processing and Presentation) and DEG signatures were established using univariate and multivariate Cox models. Kaplan-Meier analysis revealed the signatures' prognostic value in training a ...[more]

PMID: 33154188

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Project description: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.

Project description:BackgroundOvarian cancer (OC) is one of the most common and lethal malignant tumors worldwide and the prognosis of OC remains unsatisfactory. Transcription factors (TFs) are demonstrated to be associated with the clinical outcome of many types of cancers, yet their roles in the prognostic prediction and gene regulatory network in patients with OC need to be further investigated.MethodsTFs from GEO datasets were collected and analyzed. Differential expression analysis, WGCNA and Cox-LASSO regression model were used to identify the hub-TFs and a prognostic signature based on these TFs was constructed and validated. Moreover, tumor-infiltrating immune cells were analyzed, and a nomogram containing age, histology, FIGO_stage and TFs-based signature were established. Potential biological functions, pathways and the gene regulatory network of TFs in signature was also explored.ResultsIn this study, 6 TFs significantly associated with the prognosis of OC were identified. These TFs were used to build up a TFs-based signature for predicting the survival of patients with OC. Patients with OC in training and testing datasets were divided into high-risk and low-risk groups, according to the median value of risk scores determined by the signature. The two groups were further used to validate the performance of the signature, and the results showed the TFs-based signature had effective prediction ability. Immune infiltrating analysis was conducted and abundance of B cells naïve, T cells CD4 memory resting, Macrophages M2 and Mast cells activated were significantly higher in high-risk group. A nomogram based on the signature was established and illustrated good predictive efficiencies for 1, 2, and 3-year overall survival. Furthermore, the construction of the TFs-target gene regulatory network revealed the potential mechanisms of TFs in OC.ConclusionsTo our best knowledge, it is for the first time to develop a prognostic signature based on TFs in OC. The TFs-based signature is proven to be effective in predicting the survival of patients with OC. Our study may facilitate the clinical decision-making for patients with OC and help to elucidate the underlying mechanism of TFs in OC.

Dataset Information

Construction of a new tumor immunity-related signature to assess and classify the prognostic risk of ovarian cancer.

Publications

Construction of a new tumor immunity-related signature to assess and classify the prognostic risk of ovarian cancer.

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