Research Paper Volume 13, Issue 7 pp 10289—10311

A novel immune-related prognostic signature in epithelial ovarian carcinoma

Tong Su1, *, , Panpan Zhang1, *, , Fujun Zhao2, , Shu Zhang1, ,

  • 1 Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Gynecology Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
  • 2 Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
* Equal contribution

Received: September 21, 2020       Accepted: January 21, 2021       Published: April 4, 2021
How to Cite

Copyright: © 2021 Su et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The immune response is associated with the progression and prognosis of epithelial ovarian cancer (EOC). However, the roles of infiltrated immune cells and immune-related genes (IRGs) in EOC have not been reported comprehensively. In the current study, the differentially expressed genes (DEGs) were filtered based on the integrated gene expression data acquired from The University of California at Santa Cruz (UCSC) Genome Browser. Then, IRGs and transcriptional factors (TFs) were screened based on the ImmPort database and Cistrome database. A total of 501 differentially expressed IRGs, and 76 TFs were detected. A TF-mediated network was constructed by univariate Cox analysis to reveal the potential regulatory mechanisms of IRGs. Next, a nine immune-based prognostic risk model using nine IRGs (PI3, CXCL10, CXCL11, LCN6, CCL17, CCL25, MIF, CX3CR1, and CSPG5) was established. Based on the risk score worked out from the signature, the EOC patients could be classified into low-risk and high-risk groups. Furthermore, the immune landscapes, elevated by the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm and the Tumor Immune Estimation Resource (TIMER) database, effectuated different patterns in two groups. Thus, an immune-based prognostic risk model of EOC elucidates the immune status in the tumor microenvironment, and hence, could be used for prognosis.


EOC: epithelial ovarian cancer; IRGs: immune-related genes; DEGs: differentially expressed genes; UCSC: The University of California at Santa Cruz; TFs: transcriptional factors; CIBERSORT: Cell-type Identification By Estimating Relative Subsets of RNA Transcripts; TIMER: Tumor Immune Estimation Resource; TME: tumor microenvironment; TIICs: tumor-infiltrating immune cells; TCGA: The Cancer Genome Atlas; GTEx: Genotype Tissue Expression; FPKM: fragments per kilobase million; logFC: log fold change; FDR: false discovery rate; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene Ontology; BP: biological process; MF: molecular function; CC: cellular component; HPA: The Human Protein Atlas; ROC: receiver operating characteristic curve; KM: Kaplan–Meier; OS: overall survival; Tfh cells: T follicular helper cells; Tregs: regulatory T cells; NK cells: natural killer cells; FOXP3: forkhead box P3; AUC: area under the curve; PI3: peptidase inhibitor 3.