Original Research ARTICLE
Identification of prognostic Stromal-Immune Score-Based Genes in Hepatocellular Carcinoma microenvironment
- 1Nanfang Hospital, Southern Medical University, China
- 2Guangdong Provincial Key Laboratory of Hepatic Diseases, Southern Medical University, China
- 3State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, China
- 4Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, China
- 5Department of Medical Quality Management, Nanfang Hospital, Southern Medical University, China
- 6Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, China
A growing amount of evidence has suggested the clinical importance of stromal and immune cells in the liver cancer microenvironment. However, reliable prognostic signatures based on assessments of stromal and immune components have not been well-established. This study aimed to identify stromal-immune score-based potential prognostic biomarkers for hepatocellular carcinoma (HCC). Stromal and immune scores were estimated from transcriptomic profiles of a liver cancer cohort from The Cancer Genome Atlas (TCGA) using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select prognostic genes. Favorable overall survivals and progression-free interval were found in patients with high stromal score and immune score, and 828 differentially expressed genes (DEGs) were identified. Functional enrichment analysis and protein‐protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. Matrix metallopeptidase 9 (MMP9) was identified as a prognostic Tumor microenvironment (TME)-associated gene by using LASSO and Tumor IMmune Estimation Resource (TIMER) algorithms and was found to be positively correlated with immunosuppressive molecules and drug response.
Keywords: liver cancer, Estimate, Bioinformatics analysis, biomarker, tumor-microenvironment
Received: 02 Nov 2020;
Accepted: 06 Jan 2021.
Copyright: © 2021 LIU, Yu, Liu, Zou, Zhou, Hu and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Guangchuang Yu, Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China, email@example.com
Prof. Li Liu, Guangdong Provincial Key Laboratory of Hepatic Diseases, Southern Medical University, Guangzhou, China, firstname.lastname@example.org