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Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma
We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Th...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164321/ https://www.ncbi.nlm.nih.gov/pubmed/37149620 http://dx.doi.org/10.1186/s12885-023-10850-1 |
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author | Chen, Xinyi Yang, Mu Wang, Lu Wang, Yuan Tu, Jingyao Zhou, Xiao Yuan, Xianglin |
author_facet | Chen, Xinyi Yang, Mu Wang, Lu Wang, Yuan Tu, Jingyao Zhou, Xiao Yuan, Xianglin |
author_sort | Chen, Xinyi |
collection | PubMed |
description | We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan–Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan–Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-10850-1. |
format | Online Article Text |
id | pubmed-10164321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101643212023-05-08 Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma Chen, Xinyi Yang, Mu Wang, Lu Wang, Yuan Tu, Jingyao Zhou, Xiao Yuan, Xianglin BMC Cancer Research We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan–Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan–Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-10850-1. BioMed Central 2023-05-06 /pmc/articles/PMC10164321/ /pubmed/37149620 http://dx.doi.org/10.1186/s12885-023-10850-1 Text en © The Author(s) 2023 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 Chen, Xinyi Yang, Mu Wang, Lu Wang, Yuan Tu, Jingyao Zhou, Xiao Yuan, Xianglin Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title | Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title_full | Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title_fullStr | Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title_full_unstemmed | Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title_short | Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma |
title_sort | identification and in vitro and in vivo validation of the key role of gsdme in pyroptosis-related genes signature in hepatocellular carcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164321/ https://www.ncbi.nlm.nih.gov/pubmed/37149620 http://dx.doi.org/10.1186/s12885-023-10850-1 |
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