Dataset Information

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients.

ABSTRACT:

Background

Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75-85?% of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA (The Cancer Genome Atlas) to acquire further insight into the role of lipid metabolism-related genes in HCC patient prognosis.

Methods

We analyzed the mRNA expression profiles of 424 HCC patients from the TCGA database. GSEA(Gene Set Enrichment Analysis) was performed to identify lipid metabolism-related gene sets associated with HCC. We performed univariate Cox regression and LASSO(least absolute shrinkage and selection operator) regression analyses to identify genes with prognostic value and develop a prognostic model, which was tested in a validation cohort. We performed Kaplan-Meier survival and ROC (receiver operating characteristic) analyses to evaluate the performance of the model.

Results

We identified three lipid metabolism-related genes (ME1, MED10, MED22) with prognostic value in HCC and used them to calculate a risk score for each HCC patient. High-risk HCC patients exhibited a significantly lower survival rate than low-risk patients. Multivariate Cox regression analysis revealed that the 3-gene signature was an independent prognostic factor in HCC. Furthermore, the signature provided a highly accurate prediction of HCC patient prognosis.

Conclusions

We identified three lipid-metabolism-related genes that are upregulated in HCC tissues and established a 3-gene signature-based risk model that can accurately predict HCC patient prognosis. Our findings support the strong links between lipid metabolism and HCC and may facilitate the development of new metabolism-targeted treatment approaches for HCC.

SUBMITTER: Wang W

PROVIDER: S-EPMC7881464 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients.

Wang Wenjie W Zhang Chen C Yu Qihong Q Zheng Xichuan X Yin Chuanzheng C Yan Xueke X Liu Gang G Song Zifang Z

BMC gastroenterology 20210212 1

<h4>Background</h4>Liver cancer is one of the most common malignancies worldwide. HCC (hepatocellular carcinoma) is the predominant pathological type of liver cancer, accounting for approximately 75-85 % of all liver cancers. Lipid metabolic reprogramming has emerged as an important feature of HCC. However, the influence of lipid metabolism-related gene expression in HCC patient prognosis remains unknown. In this study, we performed a comprehensive analysis of HCC gene expression data from TCGA ...[more]

PMID: 33579192

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Project description:Liver cancer is a public disease burden with an increasing incidence rate globally. Bile acid and bile salt's metabolic pathways participate in liver tumorigenesis and regulate the tumor microenvironment. However, there still remains a lack of systematic analysis of the genes related to bile acid and bile salt metabolic pathways in hepatocellular carcinoma (HCC). The mRNA expression data and clinical follow-up information of patients with HCC were obtained from public databases, including The Cancer Genome Atlas, Hepatocellular Carcinoma Database, Gene Expression Omnibus, and IMvigor210. The bile acid and bile salt metabolism-related genes were extracted from Molecular Signatures Database. Univariate Cox and logistic least absolute shrinkage and selection operator regression analyses were conducted to establish the risk model. Single sample gene set enrichment analysis, Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data, and Tumor Immune Dysfunction and Exclusion were adopted to analyze immune status. The efficiency of the risk model was tested using a decision tree and a nomogram. We determined two molecular subtypes based on bile acid and bile salt metabolism-related genes, with the prognosis of the S1 subtype being markedly superior to the S2 subtype. Next, we established a risk model based on the differentially expressed genes between the two molecular subtypes. The high-risk and low-risk groups showed significant differences in the biological pathways, immune score, immunotherapy response, and drug susceptibility. Our results demonstrated the good predictive performance of the risk model in immunotherapy datasets and established that it could be an essential factor affecting the prognosis of HCC. In conclusion, we identified two molecular subtypes based on bile acid and bile salt metabolism-related genes. The risk model established in our study could effectively predict the prognosis of patients with HCC and their immunotherapeutic response, which may contribute to targeted immunotherapy in HCC.

Project description:Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.

Dataset Information

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients.

Background

Methods

Results

Conclusions

Publications

Development of a novel lipid metabolism-based risk score model in hepatocellular carcinoma patients.

Similar Datasets

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