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Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma

BACKGROUND: The induction of ferroptosis and pyroptosis has been highlighted as a novel approach to decide cancer cell fate. However, few studies have systematically explored the role of combining these two novel cell death modalities in hepatocellular carcinoma  (HCC). METHODS: Ferroptosis-related...

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Autores principales: Xu, Lijun, Zheng, Qing, Liu, Wenwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892773/
https://www.ncbi.nlm.nih.gov/pubmed/35236323
http://dx.doi.org/10.1186/s12885-022-09301-0
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author Xu, Lijun
Zheng, Qing
Liu, Wenwen
author_facet Xu, Lijun
Zheng, Qing
Liu, Wenwen
author_sort Xu, Lijun
collection PubMed
description BACKGROUND: The induction of ferroptosis and pyroptosis has been highlighted as a novel approach to decide cancer cell fate. However, few studies have systematically explored the role of combining these two novel cell death modalities in hepatocellular carcinoma  (HCC). METHODS: Ferroptosis-related genes (FRGs) and pyroptosis-related genes (PRGs) were retrieved and downloaded from FerrDb and GeneCards database, respectively. A prognostic classifier integrating with prognostic differentially expressed FRGs and PRGs was constructed by the least absolute shrinkage and selection operator (LASSO) algorithm in the TCGA-LIHC dataset and verified using the ICGC (LIRI-JP) dataset. RESULTS: A total of 194 differentially expressed FRGs and PRGs were identified and named as differentially expressed genes (DEGs) and, out of them 79 were found dramatically correlated with prognosis in HCC. Based on 13 key DEGs with prognostic value, a novel expression signature was constructed and used to stratify HCC patients into 2 groups. Kaplan–Meier analysis demonstrated that high-risk patients had a more dismal prognosis. Receiver operating characteristic curve (ROC) and multivariate Cox analysis confirmed its predictive power and independent characteristic. Immune profile analysis demonstrated that high-risk group had prominent upregulation of immunosuppressive cells, including macrophages, Th2_cells and Treg. The correlation analysis between this signature and immunosuppressive molecules, Immunophenoscore (IPS) and chemotherapeutic efficacy demonstrated that low-risk group had a higher  IC50 of cisplatin, mitomycin and doxorubicin and negatively related with CTLA4, HAVCR2, LAG3, PDCD1, TIGIT and ICIs treatment represented by CTLA4-/PD-1-, CTLA4 + /PD-1-, CTLA4-/PD-1 + . CONCLUSIONS: In this research, a novel expression signature was identified based on FRGs and PRGs in HCC, and this signature could be used to predict prognosis and select patients potentially benefiting from immunotherapies and chemotherapy.
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spelling pubmed-88927732022-03-10 Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma Xu, Lijun Zheng, Qing Liu, Wenwen BMC Cancer Research BACKGROUND: The induction of ferroptosis and pyroptosis has been highlighted as a novel approach to decide cancer cell fate. However, few studies have systematically explored the role of combining these two novel cell death modalities in hepatocellular carcinoma  (HCC). METHODS: Ferroptosis-related genes (FRGs) and pyroptosis-related genes (PRGs) were retrieved and downloaded from FerrDb and GeneCards database, respectively. A prognostic classifier integrating with prognostic differentially expressed FRGs and PRGs was constructed by the least absolute shrinkage and selection operator (LASSO) algorithm in the TCGA-LIHC dataset and verified using the ICGC (LIRI-JP) dataset. RESULTS: A total of 194 differentially expressed FRGs and PRGs were identified and named as differentially expressed genes (DEGs) and, out of them 79 were found dramatically correlated with prognosis in HCC. Based on 13 key DEGs with prognostic value, a novel expression signature was constructed and used to stratify HCC patients into 2 groups. Kaplan–Meier analysis demonstrated that high-risk patients had a more dismal prognosis. Receiver operating characteristic curve (ROC) and multivariate Cox analysis confirmed its predictive power and independent characteristic. Immune profile analysis demonstrated that high-risk group had prominent upregulation of immunosuppressive cells, including macrophages, Th2_cells and Treg. The correlation analysis between this signature and immunosuppressive molecules, Immunophenoscore (IPS) and chemotherapeutic efficacy demonstrated that low-risk group had a higher  IC50 of cisplatin, mitomycin and doxorubicin and negatively related with CTLA4, HAVCR2, LAG3, PDCD1, TIGIT and ICIs treatment represented by CTLA4-/PD-1-, CTLA4 + /PD-1-, CTLA4-/PD-1 + . CONCLUSIONS: In this research, a novel expression signature was identified based on FRGs and PRGs in HCC, and this signature could be used to predict prognosis and select patients potentially benefiting from immunotherapies and chemotherapy. BioMed Central 2022-03-02 /pmc/articles/PMC8892773/ /pubmed/35236323 http://dx.doi.org/10.1186/s12885-022-09301-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Xu, Lijun
Zheng, Qing
Liu, Wenwen
Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title_full Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title_fullStr Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title_full_unstemmed Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title_short Combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
title_sort combination of ferroptosis and pyroptosis to construct a prognostic classifier and predict immune landscape, chemotherapeutic efficacy and immunosuppressive molecules in hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892773/
https://www.ncbi.nlm.nih.gov/pubmed/35236323
http://dx.doi.org/10.1186/s12885-022-09301-0
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