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PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma

INTRODUCTION: PTEN often mutates in tumors, and its manipulation is suggested to be used in the development of preclinical tools in cancer research. This study aims to explore the biological impact of gene expression related to PTEN mutations and to develop a prognostic classification model based on...

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Autores principales: Cao, Lu, Ma, Xiaoqian, Zhang, Juan, Yang, Cejun, Rong, Pengfei, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361452/
https://www.ncbi.nlm.nih.gov/pubmed/37470852
http://dx.doi.org/10.1007/s12672-023-00743-x
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author Cao, Lu
Ma, Xiaoqian
Zhang, Juan
Yang, Cejun
Rong, Pengfei
Wang, Wei
author_facet Cao, Lu
Ma, Xiaoqian
Zhang, Juan
Yang, Cejun
Rong, Pengfei
Wang, Wei
author_sort Cao, Lu
collection PubMed
description INTRODUCTION: PTEN often mutates in tumors, and its manipulation is suggested to be used in the development of preclinical tools in cancer research. This study aims to explore the biological impact of gene expression related to PTEN mutations and to develop a prognostic classification model based on the heterogeneity of PTEN expression, and to explore its sensitivity as an indicator of prognosis and molecular and biologic features in hepatocellular carcinoma (HCC). MATERIAL AND METHODS: RNA-seq data and mutation data of the LIHC cohort sample downloaded from The Cancer Genome Atlas (TCGA). The HCC samples were grouped according to the mean expression of PTEN, and the tumor microenvironment (TME) was evaluated by ESTIMATE and ssGSEA. The prognostic classification model related to PTEN were constructed by COX and LASSO regression analysis of differentially expressed genes (DEGs) between PTEN-high and -low expressed group. RESULTS: The expression of PTEN was affected by copy number variation (CNV) and negatively correlated with immune score, IFNγ score and immune cell infiltration. 1281 DEGs were detected between PTEN-high and PTEN-low expressed group, 8 of the DEGs were finally filtered for developing a prognosis classification model. This model showed better prognostic value than other clinicopathological parameters, and the prediction accuracy of prognosis and ICB treatment for immunotherapy cohorts was better than that of TIDE model. CONCLUSIONS: This study demonstrated the effect of CNV on PTEN expression and the negative immune correlation of PTEN, and constructed a classification model related to the expression of PTEN, which was of guiding significance for evaluating prognostic results of HCC patients and ICB treatment response of cancer immunotherapy cohorts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00743-x.
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spelling pubmed-103614522023-07-22 PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma Cao, Lu Ma, Xiaoqian Zhang, Juan Yang, Cejun Rong, Pengfei Wang, Wei Discov Oncol Research INTRODUCTION: PTEN often mutates in tumors, and its manipulation is suggested to be used in the development of preclinical tools in cancer research. This study aims to explore the biological impact of gene expression related to PTEN mutations and to develop a prognostic classification model based on the heterogeneity of PTEN expression, and to explore its sensitivity as an indicator of prognosis and molecular and biologic features in hepatocellular carcinoma (HCC). MATERIAL AND METHODS: RNA-seq data and mutation data of the LIHC cohort sample downloaded from The Cancer Genome Atlas (TCGA). The HCC samples were grouped according to the mean expression of PTEN, and the tumor microenvironment (TME) was evaluated by ESTIMATE and ssGSEA. The prognostic classification model related to PTEN were constructed by COX and LASSO regression analysis of differentially expressed genes (DEGs) between PTEN-high and -low expressed group. RESULTS: The expression of PTEN was affected by copy number variation (CNV) and negatively correlated with immune score, IFNγ score and immune cell infiltration. 1281 DEGs were detected between PTEN-high and PTEN-low expressed group, 8 of the DEGs were finally filtered for developing a prognosis classification model. This model showed better prognostic value than other clinicopathological parameters, and the prediction accuracy of prognosis and ICB treatment for immunotherapy cohorts was better than that of TIDE model. CONCLUSIONS: This study demonstrated the effect of CNV on PTEN expression and the negative immune correlation of PTEN, and constructed a classification model related to the expression of PTEN, which was of guiding significance for evaluating prognostic results of HCC patients and ICB treatment response of cancer immunotherapy cohorts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00743-x. Springer US 2023-07-20 /pmc/articles/PMC10361452/ /pubmed/37470852 http://dx.doi.org/10.1007/s12672-023-00743-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Cao, Lu
Ma, Xiaoqian
Zhang, Juan
Yang, Cejun
Rong, Pengfei
Wang, Wei
PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title_full PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title_fullStr PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title_full_unstemmed PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title_short PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
title_sort pten-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361452/
https://www.ncbi.nlm.nih.gov/pubmed/37470852
http://dx.doi.org/10.1007/s12672-023-00743-x
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