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A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prog...

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Autores principales: Guo, Houtian, Lu, Fei, Lu, Rongqi, Huang, Meiqi, Li, Xuejing, Yuan, Jianhui, Wang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361245/
https://www.ncbi.nlm.nih.gov/pubmed/37484410
http://dx.doi.org/10.1016/j.heliyon.2023.e17054
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author Guo, Houtian
Lu, Fei
Lu, Rongqi
Huang, Meiqi
Li, Xuejing
Yuan, Jianhui
Wang, Feng
author_facet Guo, Houtian
Lu, Fei
Lu, Rongqi
Huang, Meiqi
Li, Xuejing
Yuan, Jianhui
Wang, Feng
author_sort Guo, Houtian
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prognosis of HCC was developed. METHODS: HCC driver genes were analyzed comprehensively to develop a better prognostic signature. The dataset of HCC patients included mRNA sequencing data and clinical information from the TCGA, the ICGC, and the Guangxi Medical University Cancer Hospital cohorts. First, LASSO was performed to develop a prognostic signature for differentially expressed driver genes in the TCGA cohort. Then, the robustness of the signature was assessed using survival and time-dependent ROC curves. Furthermore, independent predictors were determined using univariate and multivariate Cox regression analyses. Stepwise multi-Cox regression analysis was employed to identify significant variables for the construction of a nomogram that predicts survival rates. Functional analysis by Spearman correlation analysis, enrichment analysis (GO, KEGG, and GSEA), and immunoassay (ssGSEA and xCell) were performed. RESULT: A 4-driver gene signature (CLTC, DNMT3A, GMPS, and NRAS) was successfully constructed and showed excellent predictive efficiency in three cohorts. The nomogram indicated high predictive accuracy for the 1-, 3-, and 5-year prognoses of HCC patients, which included clinical information and risk score. Enrichment analysis revealed that driver genes were involved in regulating oncogenic processes, including the cell cycle and metabolic pathways, which were associated with the progression of HCC. ssGSEA and xCell showed differences in immune infiltration and the immune microenvironment between the two risk groups. CONCLUSION: The 4-driver gene signature is closely associated with the survival prediction of HCC and is expected to provide new insights into targeted therapy for HCC patients.
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spelling pubmed-103612452023-07-22 A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma Guo, Houtian Lu, Fei Lu, Rongqi Huang, Meiqi Li, Xuejing Yuan, Jianhui Wang, Feng Heliyon Research Article BACKGROUND: Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prognosis of HCC was developed. METHODS: HCC driver genes were analyzed comprehensively to develop a better prognostic signature. The dataset of HCC patients included mRNA sequencing data and clinical information from the TCGA, the ICGC, and the Guangxi Medical University Cancer Hospital cohorts. First, LASSO was performed to develop a prognostic signature for differentially expressed driver genes in the TCGA cohort. Then, the robustness of the signature was assessed using survival and time-dependent ROC curves. Furthermore, independent predictors were determined using univariate and multivariate Cox regression analyses. Stepwise multi-Cox regression analysis was employed to identify significant variables for the construction of a nomogram that predicts survival rates. Functional analysis by Spearman correlation analysis, enrichment analysis (GO, KEGG, and GSEA), and immunoassay (ssGSEA and xCell) were performed. RESULT: A 4-driver gene signature (CLTC, DNMT3A, GMPS, and NRAS) was successfully constructed and showed excellent predictive efficiency in three cohorts. The nomogram indicated high predictive accuracy for the 1-, 3-, and 5-year prognoses of HCC patients, which included clinical information and risk score. Enrichment analysis revealed that driver genes were involved in regulating oncogenic processes, including the cell cycle and metabolic pathways, which were associated with the progression of HCC. ssGSEA and xCell showed differences in immune infiltration and the immune microenvironment between the two risk groups. CONCLUSION: The 4-driver gene signature is closely associated with the survival prediction of HCC and is expected to provide new insights into targeted therapy for HCC patients. Elsevier 2023-06-07 /pmc/articles/PMC10361245/ /pubmed/37484410 http://dx.doi.org/10.1016/j.heliyon.2023.e17054 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Guo, Houtian
Lu, Fei
Lu, Rongqi
Huang, Meiqi
Li, Xuejing
Yuan, Jianhui
Wang, Feng
A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title_full A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title_fullStr A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title_full_unstemmed A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title_short A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
title_sort novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361245/
https://www.ncbi.nlm.nih.gov/pubmed/37484410
http://dx.doi.org/10.1016/j.heliyon.2023.e17054
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