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Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma

BACKGROUND: Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. METHODS: The pyroptosis gene e...

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Autores principales: Li, Ning, Ren, Shao-hua, Qin, Ya-fei, Shao, Bo, Qin, Hong, Wang, Zhaobo, Wang, Hong-da, Li, Guang-ming, Zhu, Yang-lin, Sun, Cheng-lu, Zhang, Jing-yi, Shi, Gang-gang, An, Xing-wei, Wang, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242783/
https://www.ncbi.nlm.nih.gov/pubmed/35782053
http://dx.doi.org/10.1155/2022/2680110
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author Li, Ning
Ren, Shao-hua
Qin, Ya-fei
Shao, Bo
Qin, Hong
Wang, Zhaobo
Wang, Hong-da
Li, Guang-ming
Zhu, Yang-lin
Sun, Cheng-lu
Zhang, Jing-yi
Shi, Gang-gang
An, Xing-wei
Wang, Hao
author_facet Li, Ning
Ren, Shao-hua
Qin, Ya-fei
Shao, Bo
Qin, Hong
Wang, Zhaobo
Wang, Hong-da
Li, Guang-ming
Zhu, Yang-lin
Sun, Cheng-lu
Zhang, Jing-yi
Shi, Gang-gang
An, Xing-wei
Wang, Hao
author_sort Li, Ning
collection PubMed
description BACKGROUND: Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. METHODS: The pyroptosis gene expression database of 54 candidates from The Cancer Genome Atlas (TCGA) were collected to discover the critical prognostic-related pyroptosis genes. A novel pyroptosis gene model was established to calculate the risk score. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) were used to verify its predictive ability. The International Cancer Genome Consortium (ICGC) data was collected as external validation data to verify the model's accuracy. We employed multiple bioinformatics tools and algorithms to evaluate the tumor immune microenvironment (TIME) and the response to immunotherapy. RESULTS: Our study found that most pyroptosis genes were expressed differently in normal and tumor tissues and that their expression was associated with the prognosis. Then, a precise four-pyroptosis gene model was generated. The one-year area under the curves (AUCs) among the training, internal, and external validation patients were 0.901, 0.727, and 0.671, respectively. An analysis of survival data revealed that individuals had a worse prognosis than patients with low risk. The analysis of TIME revealed that the low-risk group had more antitumor cells, fewer immunosuppressive cells, stronger immune function, less immune checkpoint gene expression, and better immunotherapy response than the high-risk group. Immunophenoscore (IPS) analysis also demonstrated that the low-risk score was related to superior immune checkpoint inhibitors therapy. CONCLUSION: A nomogram based on the four-pyroptosis gene signature was a novel tool to predict the effectiveness of immunotherapy for HCC. Therefore, individualized treatment targeting the pyroptosis genes may influence TIME and play an essential role in improving the prognosis in HCC patients.
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spelling pubmed-92427832022-06-30 Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma Li, Ning Ren, Shao-hua Qin, Ya-fei Shao, Bo Qin, Hong Wang, Zhaobo Wang, Hong-da Li, Guang-ming Zhu, Yang-lin Sun, Cheng-lu Zhang, Jing-yi Shi, Gang-gang An, Xing-wei Wang, Hao Biomed Res Int Research Article BACKGROUND: Immunotherapy has been considered as a promising cancer treatment for hepatocellular carcinoma (HCC). However, due to the particular immune environment of the liver, identifying patients who could benefit from immunotherapy is critical in clinical practice. METHODS: The pyroptosis gene expression database of 54 candidates from The Cancer Genome Atlas (TCGA) were collected to discover the critical prognostic-related pyroptosis genes. A novel pyroptosis gene model was established to calculate the risk score. Kaplan–Meier analysis and receiver operating characteristic curve (ROC) were used to verify its predictive ability. The International Cancer Genome Consortium (ICGC) data was collected as external validation data to verify the model's accuracy. We employed multiple bioinformatics tools and algorithms to evaluate the tumor immune microenvironment (TIME) and the response to immunotherapy. RESULTS: Our study found that most pyroptosis genes were expressed differently in normal and tumor tissues and that their expression was associated with the prognosis. Then, a precise four-pyroptosis gene model was generated. The one-year area under the curves (AUCs) among the training, internal, and external validation patients were 0.901, 0.727, and 0.671, respectively. An analysis of survival data revealed that individuals had a worse prognosis than patients with low risk. The analysis of TIME revealed that the low-risk group had more antitumor cells, fewer immunosuppressive cells, stronger immune function, less immune checkpoint gene expression, and better immunotherapy response than the high-risk group. Immunophenoscore (IPS) analysis also demonstrated that the low-risk score was related to superior immune checkpoint inhibitors therapy. CONCLUSION: A nomogram based on the four-pyroptosis gene signature was a novel tool to predict the effectiveness of immunotherapy for HCC. Therefore, individualized treatment targeting the pyroptosis genes may influence TIME and play an essential role in improving the prognosis in HCC patients. Hindawi 2022-06-22 /pmc/articles/PMC9242783/ /pubmed/35782053 http://dx.doi.org/10.1155/2022/2680110 Text en Copyright © 2022 Ning Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Ning
Ren, Shao-hua
Qin, Ya-fei
Shao, Bo
Qin, Hong
Wang, Zhaobo
Wang, Hong-da
Li, Guang-ming
Zhu, Yang-lin
Sun, Cheng-lu
Zhang, Jing-yi
Shi, Gang-gang
An, Xing-wei
Wang, Hao
Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title_full Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title_fullStr Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title_full_unstemmed Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title_short Four-Pyroptosis Gene-Based Nomogram as a Novel Strategy for Predicting the Effect of Immunotherapy in Hepatocellular Carcinoma
title_sort four-pyroptosis gene-based nomogram as a novel strategy for predicting the effect of immunotherapy in hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9242783/
https://www.ncbi.nlm.nih.gov/pubmed/35782053
http://dx.doi.org/10.1155/2022/2680110
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