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Research Paper|Volume 12, Issue 6|pp 4879—4895

A nine-gene signature related to tumor microenvironment predicts overall survival with ovarian cancer

Qi Ding1,2, Shanshan Dong1,2, Ranran Wang1,2, Keqiang Zhang4, Hui Wang3, Xiao Zhou1,2, Jing Wang4, Kee Wong2, Ying Long1, Shuai Zhu1, Weigang Wang4, Huayi Ren1, Yong Zeng1,2
  • 1Translational Medicine Center, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
  • 2Engineering Technology Research Center for Diagnosis-Treatment and Application of Tumor Liquid Biopsy, Changsha, China
  • 3Key Laboratory of Radiation Oncology, Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
  • 4The Fifth Department of Gynecological Oncology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China
* Equal contribution
Received: October 1, 2019Accepted: March 2, 2020Published: March 24, 2020
https://doi.org/10.18632/aging.102914

Copyright © 2020 Ding 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

Mounting evidence suggests that immune cell infiltration within the tumor microenvironment (TME) is a crucial regulator of carcinogenesis and therapeutic efficacy in ovarian cancer (OC). In this study, 593 OC patients from TCGA were divided into high and low score groups based on their immune/stromal scores resulting from analysis utilizing the ESTIMATE algorithm. Differential expression analysis revealed 294 intersecting genes that influencing both the immune and stromal scores. Further Cox regression analysis identified 34 differentially expressed genes (DEGs) as prognostic-related genes. Finally, the nine-gene signature was derived from the prognostic-related genes using a Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression. This nine-gene signature could effectively distinguish the high-risk patients in the training (TCGA database) and validation (GSE17260) cohorts (all p < 0.01). A time-dependent receiver operating characteristic (ROC) analysis showed that the nine-gene signature had a reasonable predictive accuracy (AUC = 0.707, AUC =0.696) in both cohorts. In addition, this nine-gene signature is associated with immune infiltration in TME by Gene Set Variation Analysis (GSVA), and can be used to predict the survival of patients with OC.