Dataset Information

EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.

ABSTRACT: This study aimed to develop a model using Epstein-Barr virus (EBV)-associated hub genes in order to predict the prognosis of nasopharyngeal carcinoma (NPC). Differential expression analysis, univariate regression analysis, and machine learning were performed in three microarray datasets (GSE2371, GSE12452, and GSE102349) collected from the GEO database. Three hundred and sixty-six EBV-DEGs were identified, 25 of which were found to be significantly associated with NPC prognosis. These 25 genes were used to classify NPC into two subtypes, and six genes (C16orf54, CD27, CD53, CRIP1, RARRES3, and TBC1D10C) were found to be hub genes in NPC related to immune infiltration and cell cycle regulation. It was shown that these genes could be used to predict the prognosis of NPC, with functions related to tumor proliferation and immune infiltration, making them potential therapeutic targets. The findings of this study could aid in the development of screening and prognostic methods for NPC based on EBV-related features.

SUBMITTER: Ding T

PROVIDER: S-EPMC10537168 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.

Ding Tengteng T Zhang Yuanbin Y Ren Zhixuan Z Cong Ying Y Long Jingyi J Peng Manli M Faleti Oluwasijibomi Damola OD Yang Yinggui Y Li Xin X Lyu Xiaoming X

Viruses 20230912 9

This study aimed to develop a model using Epstein-Barr virus (EBV)-associated hub genes in order to predict the prognosis of nasopharyngeal carcinoma (NPC). Differential expression analysis, univariate regression analysis, and machine learning were performed in three microarray datasets (GSE2371, GSE12452, and GSE102349) collected from the GEO database. Three hundred and sixty-six EBV-DEGs were identified, 25 of which were found to be significantly associated with NPC prognosis. These 25 genes w ...[more]

PMID: 37766321

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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

EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.

Publications

EBV-Associated Hub Genes as Potential Biomarkers for Predicting the Prognosis of Nasopharyngeal Carcinoma.

Similar Datasets

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