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Research Paper|Volume 12, Issue 21|pp 21544—21558

TCGA and ESTIMATE data mining to identify potential prognostic biomarkers in HCC patients

Guolin He1, Shunjun Fu1, Yang Li1, Ting Li1, Purong Mei1, Lei Feng1, Lei Cai1, Yuan Cheng1, Chenjie Zhou1, Yujun Tang1, Wenbin Huang1, Haiyan Liu1, Bohong Cen2,3, Mingxin Pan1, Yi Gao1
  • 1Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
  • 2Department of Pharmacy, Zhujiang Hospital of Southern Medical University, Guangzhou 510282, Guangdong, China
  • 3Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, Guangdong, China
* Equal contribution
Received: April 1, 2020Accepted: August 8, 2020Published: November 11, 2020
https://doi.org/10.18632/aging.103943

Copyright: © 2020 He 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

Hepatocellular carcinoma (HCC) is an aggressive form of cancer characterized by a high recurrence rate following resection. Studies have implicated stromal and immune cells, which form part of the tumor microenvironment, as significant contributors to the poor prognoses of HCC patients. In the present study, we first downloaded gene expression datasets for HCC patients from The Cancer Genome Atlas database and categorized the patients into low and high stromal or immune score groups. By comparing those groups, we identified differentially expressed genes significantly associated with HCC prognosis. The Gene Ontology database was then used to perform functional enrichment analysis, and the STRING network database was used to construct protein-protein interaction networks. Our results show that most of the differentially expressed genes were involved in immune processes and responses and the plasma membrane. Those results were then validated using another a dataset from a HCC cohort in the Gene Expression Omnibus database and in 10 pairs of HCC tumor tissue and adjacent nontumor tissue. These findings enabled us to identify several tumor microenvironment-related genes that associate with HCC prognosis, and some those appear to have the potential to serve as HCC biomarkers.