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Research Paper|Volume 12, Issue 12|pp 11398—11415

Immune profiling reveals prognostic genes in high-grade serous ovarian cancer

Yong Wu1,2, Lingfang Xia1,2, Ping Zhao3, Yu Deng4, Qinhao Guo1,2, Jun Zhu1,2, Xiaojun Chen1,2, Xingzhu Ju1,2, Xiaohua Wu1,2
  • 1Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
  • 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
  • 3Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
  • 4Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
* Equal contribution
Received: December 13, 2019Accepted: March 30, 2020Published: June 16, 2020
https://doi.org/10.18632/aging.103199

Copyright © 2020 Wu 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.

Abstract

High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease with diverse clinical outcomes, highlighting a need for prognostic biomarker identification. Here, we combined tumor microenvironment (TME) scores with HGSOC characteristics to identify immune-related prognostic genes through analysis of gene expression profiles and clinical patient data from The Cancer Genome Atlas and the International Cancer Genome Consortium public cohorts. We found that high TME scores (TMEscores) based on the fractions of immune cell types correlated with better overall survival. Furthermore, differential expression analysis revealed 329 differentially expressed genes between patients with high vs. low TMEscores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that these genes participated mainly in immune-related functions and, among them, 48 TME-related genes predicted overall survival in HGSOC. Seven of those genes were associated with prognosis in an independent HGSOC database. Finally, the two genes with the lowest p-values in the prognostic analysis (GBP1, ETV7) were verified through in vitro experiments. These findings reveal specific TME-related genes that could serve as effective prognostic biomarkers for HGSOC.