Dataset Information

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes.

ABSTRACT: Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer and ranks sixth in the incidence rate worldwide. The role of adenylate uridylate- (AU-) rich element genes (AREGs) in HCC remains unclear. HCC-related datasets were acquired from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Differentially expressed AREGs (DE-AREGs) between HCC samples and healthy controls were identified. The univariate Cox and LASSO analyses were performed to determine the prognostic genes. Furthermore, a signature and corresponding nomogram were configured for the clinical prediction of HCC. The potential signature-related biological significance was explored using functional and pathway enrichment analysis. Additionally, immune infiltration analysis was also performed. Finally, the expression of prognostic genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR). A total of 189 DE-AREGs between normal and HCC samples were identified, wherein CENPA, TXNRD1, RABIF, UGT2B15, and SERPINE1 were selected to generate an AREG-related signature. Moreover, the prognostic accuracy of the AREG-related signature was also confirmed. Functional analysis indicated that the high-risk score was related to various functions and pathways. Inflammation and immune-related analyses indicated that the difference of T cell and B cell receptor abundance, microvascular endothelial cells (MVE), lymphatic endothelial cells (lye), pericytes, stromal cells, and the six immune checkpoints was statistically significant between the different risk groups. Similarly, RT-qPCR outcomes of these signature genes were also significant. In conclusion, an inflammation-associated signature based on five DE-AREGs was constructed, which could act as a prognostic indicator of patients with HCC.

SUBMITTER: Song L

PROVIDER: S-EPMC10169245 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes.

Song Li L Su Xiangzheng X Lu Yao Y Hua Dongliang D Gao Ziren Z

Mediators of inflammation 20230502

Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer and ranks sixth in the incidence rate worldwide. The role of adenylate uridylate- (AU-) rich element genes (AREGs) in HCC remains unclear. HCC-related datasets were acquired from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Differentially expressed AREGs (DE-AREGs) between HCC samples and healthy controls were identified. The univariate Cox and LASSO analyses were performed to determine ...[more]

PMID: 37181805

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Project description:Background: Eosinophils, a type of white blood cell originating from the bone marrow, are widely believed to play a crucial role in inflammatory processes, including allergic reactions and parasitic infections. However, the relationship between eosinophils and liver cancer is not well understood. Methods: Tumor immune infiltration scores were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Key modules and hub genes associated with eosinophils were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Univariate and multivariate Cox analyses, along with LASSO regression, were used to identify prognostic genes and create a risk model. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to evaluate the immunotherapeutic significance of the eosinophil-associated gene risk score (ERS) model. Experiments such as flow cytometry, immunohistochemical analysis, real-time quantitative PCR (RT-qPCR), and Western blotting were used to determine gene expression levels and the status of eosinophil infiltration in tumors. Results: A risk trait model including 4 eosinophil-associated genes (RAMP3, G6PD, SSRP1, PLOD2) was developed by univariate Cox analysis and Lasso screening. Pathologic grading (p < 0.001) and model risk scores (p < 0.001) were found to be independent predictors of hepatocellular carcinoma (HCC) patient survival. Western blotting revealed higher levels of eosinophil peroxidase (EPX) in HCC tissues compared to adjacent normal tissues. Immunohistochemistry showed that eosinophils mainly infiltrated the connective tissue around HCC. The HCC samples showed low expression of RAMP3 and high expression of G6PD, SSRP1, and PLOD2, as detected by IHC and RT-qPCR analysis. The in vivo mouse experiments showed that IL-33 treatment induced the recruitment of eosinophils and reduced the number of intrahepatic tumor nodules. Conclusion: Overall, eosinophil infiltration in HCC is significantly correlated with patient survival. The risk assessment model based on eosinophil-related genes serves as a reliable clinical prognostic indicator and provides insights for precise treatment of HCC.

Dataset Information

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes.

Publications

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes.

Similar Datasets

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