An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma
Growing evidence suggests that the superiority of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) could act as biomarkers for cancer prognosis. However, the prognostic marker for hepatocellular carcinoma with high accuracy and sensitivity is still lacking. In this research, a retrospective,...
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doaj-8137b5c6d7014d44bee47da94fb5d92d2020-11-25T04:00:15ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182020-01-01202010.1155/2020/88723298872329An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular CarcinomaRui Zhu0Wenna Guo1Xin-Jian Xu2Liucun Zhu3Department of Mathematics, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Zhengzhou University, Zhengzhou, Henan 450001, ChinaDepartment of Mathematics, Shanghai University, Shanghai 200444, ChinaSchool of Life Sciences, Shanghai University, Shanghai 200444, ChinaGrowing evidence suggests that the superiority of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) could act as biomarkers for cancer prognosis. However, the prognostic marker for hepatocellular carcinoma with high accuracy and sensitivity is still lacking. In this research, a retrospective, cohort-based study of genome-wide RNA-seq data of patients with hepatocellular carcinoma was carried out, and two protein-coding genes (GTPBP4, TREM-1) and one lncRNA (LINC00426) were sorted out to construct an integrative signature to predict the prognosis of patients. The results show that both the AUC and the C-index of this model perform well in TCGA validation dataset, cross-platform GEO validation dataset, and different subsets divided by gender, stage, and grade. The expression pattern and functional analysis show that all three genes contained in the model are associated with immune infiltration, cell proliferation, invasion, and metastasis, providing further confirmation of this model. In summary, the proposed model can effectively distinguish the high- and low-risk groups of hepatocellular carcinoma patients and is expected to shed light on the treatment of hepatocellular carcinoma and greatly improve the patients’ prognosis.http://dx.doi.org/10.1155/2020/8872329 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rui Zhu Wenna Guo Xin-Jian Xu Liucun Zhu |
spellingShingle |
Rui Zhu Wenna Guo Xin-Jian Xu Liucun Zhu An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma Computational and Mathematical Methods in Medicine |
author_facet |
Rui Zhu Wenna Guo Xin-Jian Xu Liucun Zhu |
author_sort |
Rui Zhu |
title |
An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma |
title_short |
An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma |
title_full |
An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma |
title_fullStr |
An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma |
title_full_unstemmed |
An Integrating Immune-Related Signature to Improve Prognosis of Hepatocellular Carcinoma |
title_sort |
integrating immune-related signature to improve prognosis of hepatocellular carcinoma |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2020-01-01 |
description |
Growing evidence suggests that the superiority of long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) could act as biomarkers for cancer prognosis. However, the prognostic marker for hepatocellular carcinoma with high accuracy and sensitivity is still lacking. In this research, a retrospective, cohort-based study of genome-wide RNA-seq data of patients with hepatocellular carcinoma was carried out, and two protein-coding genes (GTPBP4, TREM-1) and one lncRNA (LINC00426) were sorted out to construct an integrative signature to predict the prognosis of patients. The results show that both the AUC and the C-index of this model perform well in TCGA validation dataset, cross-platform GEO validation dataset, and different subsets divided by gender, stage, and grade. The expression pattern and functional analysis show that all three genes contained in the model are associated with immune infiltration, cell proliferation, invasion, and metastasis, providing further confirmation of this model. In summary, the proposed model can effectively distinguish the high- and low-risk groups of hepatocellular carcinoma patients and is expected to shed light on the treatment of hepatocellular carcinoma and greatly improve the patients’ prognosis. |
url |
http://dx.doi.org/10.1155/2020/8872329 |
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