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Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions

BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical...

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Autores principales: Zheng, Qiuxian, Yang, Qin, Zhou, Jiaming, Gu, Xinyu, Zhou, Haibo, Dong, Xuejun, Zhu, Haihong, Chen, Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243542/
https://www.ncbi.nlm.nih.gov/pubmed/34193146
http://dx.doi.org/10.1186/s12935-021-02033-4
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author Zheng, Qiuxian
Yang, Qin
Zhou, Jiaming
Gu, Xinyu
Zhou, Haibo
Dong, Xuejun
Zhu, Haihong
Chen, Zhi
author_facet Zheng, Qiuxian
Yang, Qin
Zhou, Jiaming
Gu, Xinyu
Zhou, Haibo
Dong, Xuejun
Zhu, Haihong
Chen, Zhi
author_sort Zheng, Qiuxian
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. METHODS: HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. RESULTS: Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. CONCLUSIONS: The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02033-4.
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spelling pubmed-82435422021-06-30 Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions Zheng, Qiuxian Yang, Qin Zhou, Jiaming Gu, Xinyu Zhou, Haibo Dong, Xuejun Zhu, Haihong Chen, Zhi Cancer Cell Int Primary Research BACKGROUND: Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. METHODS: HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. RESULTS: Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. CONCLUSIONS: The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02033-4. BioMed Central 2021-06-30 /pmc/articles/PMC8243542/ /pubmed/34193146 http://dx.doi.org/10.1186/s12935-021-02033-4 Text en © The Author(s) 2021 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 Primary Research
Zheng, Qiuxian
Yang, Qin
Zhou, Jiaming
Gu, Xinyu
Zhou, Haibo
Dong, Xuejun
Zhu, Haihong
Chen, Zhi
Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title_full Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title_fullStr Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title_full_unstemmed Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title_short Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
title_sort immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243542/
https://www.ncbi.nlm.nih.gov/pubmed/34193146
http://dx.doi.org/10.1186/s12935-021-02033-4
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