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Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma
Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying specific molecular markers that can predict HCC prognosis is extremely important. MELK has been reported to play key roles in several types of human cance...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184526/ https://www.ncbi.nlm.nih.gov/pubmed/35693941 http://dx.doi.org/10.3389/fcell.2022.824938 |
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author | Liu, Yu Li, Rongkuan Wang, Xiaobo Xue, Zuguang Yang, Xiaozhou Tang, Bo |
author_facet | Liu, Yu Li, Rongkuan Wang, Xiaobo Xue, Zuguang Yang, Xiaozhou Tang, Bo |
author_sort | Liu, Yu |
collection | PubMed |
description | Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying specific molecular markers that can predict HCC prognosis is extremely important. MELK has been reported to play key roles in several types of human cancers and predict poor prognosis. This study was aimed to explore the impact of MELK on HCC. Methods: A pan-cancer analysis of MELK was conducted by The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data. The prognosis of MELK in various cancers was analyzed in GEPIA. Then, a ceRNA network of MELK was constructed based on the comprehensive consideration of the expression analysis, the correlation analysis, and the survival analysis by R software. The correlation of MELK and immune cell infiltration was analyzed by TIMER and CIBERSORT. Then, the overall survival of differentially expressed immune cells was conducted. The correlation of MELK and immune checkpoints expression was analyzed by GEPIA. Results: MELK was overexpressed in 14 types of human cancers, and its expression was significantly higher than that in both unmatched and paired normal samples in HCC. Higher MELK expression was correlated with poorer survival and advanced clinical stage, topography (T) stage, and histological grade. The univariate and multivariate Cox regression analyses showed that MELK was an independent risk factor for poor prognosis in HCC. Then, we constructed a ceRNA network consisting of MELK, miR-101-3p, and two lncRNAs (SNHG1 and SNHG6) after evaluating the expression and impact on prognosis in HCC of these RNAs. TIMER and CIBERSORT databases indicated that MELK was correlated with various immune cells including B cells, CD8(+) T cells, CD4(+) T cells, macrophage, neutrophil, and dendritic cells in HCC. Of them, B cells, CD4(+) T cells, macrophage, and neutrophil were related to the prognosis of HCC. In addition, MELK was significantly positively correlated with the immune checkpoint genes. Conclusions: MELK may be a novel potential biomarker for predicting prognosis and immunotherapy efficacy in patients with HCC. Our study may provide new molecular and therapeutic strategies for the treatment of HCC patients. |
format | Online Article Text |
id | pubmed-9184526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91845262022-06-11 Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma Liu, Yu Li, Rongkuan Wang, Xiaobo Xue, Zuguang Yang, Xiaozhou Tang, Bo Front Cell Dev Biol Cell and Developmental Biology Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying specific molecular markers that can predict HCC prognosis is extremely important. MELK has been reported to play key roles in several types of human cancers and predict poor prognosis. This study was aimed to explore the impact of MELK on HCC. Methods: A pan-cancer analysis of MELK was conducted by The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data. The prognosis of MELK in various cancers was analyzed in GEPIA. Then, a ceRNA network of MELK was constructed based on the comprehensive consideration of the expression analysis, the correlation analysis, and the survival analysis by R software. The correlation of MELK and immune cell infiltration was analyzed by TIMER and CIBERSORT. Then, the overall survival of differentially expressed immune cells was conducted. The correlation of MELK and immune checkpoints expression was analyzed by GEPIA. Results: MELK was overexpressed in 14 types of human cancers, and its expression was significantly higher than that in both unmatched and paired normal samples in HCC. Higher MELK expression was correlated with poorer survival and advanced clinical stage, topography (T) stage, and histological grade. The univariate and multivariate Cox regression analyses showed that MELK was an independent risk factor for poor prognosis in HCC. Then, we constructed a ceRNA network consisting of MELK, miR-101-3p, and two lncRNAs (SNHG1 and SNHG6) after evaluating the expression and impact on prognosis in HCC of these RNAs. TIMER and CIBERSORT databases indicated that MELK was correlated with various immune cells including B cells, CD8(+) T cells, CD4(+) T cells, macrophage, neutrophil, and dendritic cells in HCC. Of them, B cells, CD4(+) T cells, macrophage, and neutrophil were related to the prognosis of HCC. In addition, MELK was significantly positively correlated with the immune checkpoint genes. Conclusions: MELK may be a novel potential biomarker for predicting prognosis and immunotherapy efficacy in patients with HCC. Our study may provide new molecular and therapeutic strategies for the treatment of HCC patients. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9184526/ /pubmed/35693941 http://dx.doi.org/10.3389/fcell.2022.824938 Text en Copyright © 2022 Liu, Li, Wang, Xue, Yang and Tang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cell and Developmental Biology Liu, Yu Li, Rongkuan Wang, Xiaobo Xue, Zuguang Yang, Xiaozhou Tang, Bo Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title | Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title_full | Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title_fullStr | Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title_full_unstemmed | Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title_short | Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma |
title_sort | comprehensive analyses of melk-associated cerna networks reveal a potential biomarker for predicting poor prognosis and immunotherapy efficacy in hepatocellular carcinoma |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184526/ https://www.ncbi.nlm.nih.gov/pubmed/35693941 http://dx.doi.org/10.3389/fcell.2022.824938 |
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