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Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma

BACKGROUND: Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear. METH...

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Autores principales: Wang, Jingrui, Ling, Sunbin, Ni, Jie, Wan, Yafeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185956/
https://www.ncbi.nlm.nih.gov/pubmed/35681134
http://dx.doi.org/10.1186/s12885-022-09662-6
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author Wang, Jingrui
Ling, Sunbin
Ni, Jie
Wan, Yafeng
author_facet Wang, Jingrui
Ling, Sunbin
Ni, Jie
Wan, Yafeng
author_sort Wang, Jingrui
collection PubMed
description BACKGROUND: Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear. METHODS: Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays. RESULTS: A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes. CONCLUSIONS: Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09662-6.
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spelling pubmed-91859562022-06-11 Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma Wang, Jingrui Ling, Sunbin Ni, Jie Wan, Yafeng BMC Cancer Research BACKGROUND: Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear. METHODS: Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays. RESULTS: A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes. CONCLUSIONS: Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09662-6. BioMed Central 2022-06-10 /pmc/articles/PMC9185956/ /pubmed/35681134 http://dx.doi.org/10.1186/s12885-022-09662-6 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
Wang, Jingrui
Ling, Sunbin
Ni, Jie
Wan, Yafeng
Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title_full Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title_fullStr Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title_full_unstemmed Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title_short Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
title_sort novel γδ t cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185956/
https://www.ncbi.nlm.nih.gov/pubmed/35681134
http://dx.doi.org/10.1186/s12885-022-09662-6
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