Evidence map›Paper›PMID 34868990›Full record

ArticleFrontiers in oncology2021

A New Risk Score Based on Eight Hepatocellular Carcinoma- Immune Gene Expression Can Predict the Prognosis of the Patients.

Dingde Ye, Yaping Liu, Guoqiang Li, Beicheng Sun, Jin Peng, Qingxiang Xu

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Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Dingde YeNanjing Drum Tower Hospital, Medicine School of Southeast University, Nanjing, China.
Yaping LiuSchool of Life Science and Technology, Southeast University, Nanjing, China.
Guoqiang LiDepartment of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China.
Beicheng SunDepartment of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China.
Jin PengDepartment of General Surgery, Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China.
Qingxiang XuNanjing Drum Tower Hospital, Medicine School of Southeast University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is one of the malignant tumors with high morbidity and mortality worldwide. Immunotherapy has emerged as an increasingly important cancer treatment modality. However, the potential relationship between immune genes and HCC still needs to be explored. The purpose of this study is to construct a new prognostic risk signature to predict the prognosis of HCC patients based on the expression of immune-related genes (IRGs) and explore its potential mechanism.

methodsWe analyzed the gene expression data of 332 HCC patient samples and 46 adjacent normal tissues samples (Solid Tissue Normal including cirrhotic tissue) in The Cancer Genome Atlas (TCGA) database and clinical characteristics. We analyzed the gene expression data, identified differentially expressed IRGs in HCC tissues, filtered IRGs with prognostic value to construct an IRG signature, and classified patients into high and low gene expression groups based on the expression of IRGs in their tumor tissues. We also investigated the potential molecular mechanisms of IRGs through a bioinformatics approach using Protein-Protein Interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis and Gene Ontology (GO) database analysis. Differentially expressed IRGs associated with significant clinical outcomes (SIRGs) were identified by univariate Cox regression analysis. An immune-related risk score model (IRRSM) was established based on Lasso Cox regression analysis and multivariate Cox regression analysis. Based on the IRRSM, the immune score of the patients was calculated, and the patients were divided into high-risk and low-risk patients according to the median score, and the differences in survival between the two groups were compared. Then, the correlation analysis between the IRRSM and clinical characteristics was performed, and the IRRSM was validated using the International Cancer Genome Consortium (ICGC) database.

resultsThe IRRSM was eventually constructed and confirmed to be an independent prognostic model for HCC patients. The IRRSM was shown to be positively correlated with the infiltration of four types of immune cells.

conclusionOur results showed that some SIRGs have potential value for predicting the prognosis and clinical outcomes of HCC patients. IRGs affect the prognosis of HCC patients by regulating the tumor immune microenvironment (TIME). This study provides a new insight for immune research and treatment strategies in HCC patients.

Indexed as

hepatocellular carcinomaimmune-related genesimmune-related risk scorenomogramprognosis

Identifiers

PMID34868990
PMCPMC8639602

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