Evidence map›Paper›PMID 32355535›Full record

ArticleAmerican journal of translational research2020

Combined signature of nine immune-related genes: a novel risk score for predicting prognosis in hepatocellular carcinoma.

Yunliang Tang, Zhenguo Zeng, Jiao Wang, Guoyong Li, Chao Huang, Xiaoyang Dong, Zhen Feng

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Article in American journal of translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yunliang TangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Zhenguo ZengDepartment of Critical Care Medicine, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Jiao WangDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Guoyong LiDepartment of General Surgery, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Chao HuangDepartment of General Surgery, The Second Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Xiaoyang DongDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.
Zhen FengDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University Nanchang, Jiangxi, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one of the most common internal malignancies worldwide and is associated with a poor prognosis. There is an urgent need to identify diagnostic and prognostic biomarkers of HCC pathogenesis and progression. Accordingly, in this study, we analyzed differentially expressed immune-related genes (IRGs) from 329 patients with HCC from The Cancer Genome Atlas datasets. Functional analysis revealed that the IRGs had potential effects on tumor immune processes, such as inflammatory responses and growth factor activity. In the training group, we constructed a nine-IRG formula to predict prognosis in patients with HCC. To validate the protein and mRNA levels of these IRGs, we used the Human Protein Atlas database and quantitative PCR analysis and found that most protein expression levels matched the corresponding mRNA expression levels. Furthermore, we also validated the prognostic value of the new risk model in another independent cohort (n = 277) from a Gene Expression Omnibus dataset (GSE14520). Our data suggested that there was a significant association between our risk model and patient prognosis. Stratification analysis showed that the nine-IRG signature was significantly associated with overall survival in men. Finally, the signature was found to be correlated with various clinicopathological features. Intriguingly, the prognostic index based on the IRGs reflected infiltration by several types of immune cells. In summary, our data provided evidence that the nine-IRG signature could serve as an independent biomarker to predict prognosis in patients with HCC.

Indexed as

gene expression omnibusHepatocellular carcinomaimmune-related genesprognostic signaturerisk scorethe cancer genome atlas

Identifiers

PMID32355535
PMCPMC7191166

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.