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Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status
Purpose: Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related g...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880323/ https://www.ncbi.nlm.nih.gov/pubmed/33316777 http://dx.doi.org/10.18632/aging.202271 |
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author | Tian, Ruyue Hu, Jiexuan Ma, Xiao Liang, Lei Guo, Shuilong |
author_facet | Tian, Ruyue Hu, Jiexuan Ma, Xiao Liang, Lei Guo, Shuilong |
author_sort | Tian, Ruyue |
collection | PubMed |
description | Purpose: Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related genes in GC patients with varying MSI status. Results: The univariate and multivariate Cox regression analyses identified SEMA7A, NUDT6, SCGB3A1, NPR3, PTH1R, and SHC4 as signature genes, which were used to build the prognostic model for GC patients with microsatellite instability-low (MSI-L) and microsatellite stable (MSS). Whereas, for GC patients with microsatellite instability-high (MSI-H), prognostic model was established with three genes (SEMA6A, LTBP1, and BACH2), based on the univariate and multivariate Cox regression, and Kaplan-Meier survival analyses. Conclusion: The prognostic immune-related gene signature identified in this study may offer new targets for personalized treatment and immunotherapy for GC patients with MSI-H or MSI-L/MSS status. Methods: The Cancer Genome Atlas (TCGA) and ImmPort databases were used to extract expression data and to explore prognostic genes from the immune-related genes (IRGs), respectively. Univariate and multivariate Cox regression analysis were applied to identify IRGs correlated with patient prognosis. The regulatory network between prognostic IRGs and TFs were performed using R software. |
format | Online Article Text |
id | pubmed-7880323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-78803232021-02-22 Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status Tian, Ruyue Hu, Jiexuan Ma, Xiao Liang, Lei Guo, Shuilong Aging (Albany NY) Research Paper Purpose: Gastric cancer (GC) is one of the most common and fatal malignancies globally. While microsatellite instability (MSI) index has earlier been correlated with survival outcome in gastric cancer patients, the present study aims to construct a risk-stratification model based on immune-related genes in GC patients with varying MSI status. Results: The univariate and multivariate Cox regression analyses identified SEMA7A, NUDT6, SCGB3A1, NPR3, PTH1R, and SHC4 as signature genes, which were used to build the prognostic model for GC patients with microsatellite instability-low (MSI-L) and microsatellite stable (MSS). Whereas, for GC patients with microsatellite instability-high (MSI-H), prognostic model was established with three genes (SEMA6A, LTBP1, and BACH2), based on the univariate and multivariate Cox regression, and Kaplan-Meier survival analyses. Conclusion: The prognostic immune-related gene signature identified in this study may offer new targets for personalized treatment and immunotherapy for GC patients with MSI-H or MSI-L/MSS status. Methods: The Cancer Genome Atlas (TCGA) and ImmPort databases were used to extract expression data and to explore prognostic genes from the immune-related genes (IRGs), respectively. Univariate and multivariate Cox regression analysis were applied to identify IRGs correlated with patient prognosis. The regulatory network between prognostic IRGs and TFs were performed using R software. Impact Journals 2020-12-09 /pmc/articles/PMC7880323/ /pubmed/33316777 http://dx.doi.org/10.18632/aging.202271 Text en Copyright: © 2021 Tian et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Tian, Ruyue Hu, Jiexuan Ma, Xiao Liang, Lei Guo, Shuilong Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title | Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title_full | Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title_fullStr | Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title_full_unstemmed | Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title_short | Immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
title_sort | immune-related gene signature predicts overall survival of gastric cancer patients with varying microsatellite instability status |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7880323/ https://www.ncbi.nlm.nih.gov/pubmed/33316777 http://dx.doi.org/10.18632/aging.202271 |
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