Cargando…
Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer
BACKGROUND: Gastric cancer (GC) is the most commonly diagnosed malignancy worldwide. Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC. AIM: To construct an immune-related gene (IRG) signature for accurately predicting the prognosis...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443845/ https://www.ncbi.nlm.nih.gov/pubmed/32879664 http://dx.doi.org/10.4251/wjgo.v12.i8.857 |
_version_ | 1783573704895102976 |
---|---|
author | Qiu, Xiang-Ting Song, Yu-Cui Liu, Jian Wang, Zhen-Min Niu, Xing He, Jing |
author_facet | Qiu, Xiang-Ting Song, Yu-Cui Liu, Jian Wang, Zhen-Min Niu, Xing He, Jing |
author_sort | Qiu, Xiang-Ting |
collection | PubMed |
description | BACKGROUND: Gastric cancer (GC) is the most commonly diagnosed malignancy worldwide. Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC. AIM: To construct an immune-related gene (IRG) signature for accurately predicting the prognosis of patients with GC. METHODS: Differentially expressed genes (DEGs) between 375 gastric cancer tissues and 32 normal adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) GDC data portal. Then, differentially expressed IRGs from the ImmPort database were identified for GC. Cox univariate survival analysis was used to screen survival-related IRGs. Differentially expressed survival-related IRGs were considered as hub IRGs. Genetic mutations of hub IRGs were analyzed. Then, hub IRGs were selected to conduct a prognostic signature. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of the signature. The correlation of the signature with clinical features and tumor-infiltrating immune cells was analyzed. RESULTS: Among all DEGs, 70 hub IRGs were obtained for GC. The deletions and amplifications were the two most common types of genetic mutations of hub IRGs. A prognostic signature was identified, consisting of ten hub IRGs (including S100A12, DEFB126, KAL1, APOH, CGB5, GRP, GLP2R, LGR6, PTGER3, and CTLA4). This prognostic signature could accurately distinguish patients into high- and low- risk groups, and overall survival analysis showed that high risk patients had shortened survival time than low risk patients (P < 0.0001). The area under curve of the ROC of the signature was 0.761, suggesting that the prognostic signature had a high sensitivity and accuracy. Multivariate regression analysis demonstrated that the prognostic signature could become an independent prognostic predictor for GC after adjustment for other clinical features. Furthermore, we found that the prognostic signature was significantly correlated with macrophage infiltration. CONCLUSION: Our study proposed an immune-related prognostic signature for GC, which could help develop treatment strategies for patients with GC in the future. |
format | Online Article Text |
id | pubmed-7443845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-74438452020-09-01 Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer Qiu, Xiang-Ting Song, Yu-Cui Liu, Jian Wang, Zhen-Min Niu, Xing He, Jing World J Gastrointest Oncol Basic Study BACKGROUND: Gastric cancer (GC) is the most commonly diagnosed malignancy worldwide. Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC. AIM: To construct an immune-related gene (IRG) signature for accurately predicting the prognosis of patients with GC. METHODS: Differentially expressed genes (DEGs) between 375 gastric cancer tissues and 32 normal adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) GDC data portal. Then, differentially expressed IRGs from the ImmPort database were identified for GC. Cox univariate survival analysis was used to screen survival-related IRGs. Differentially expressed survival-related IRGs were considered as hub IRGs. Genetic mutations of hub IRGs were analyzed. Then, hub IRGs were selected to conduct a prognostic signature. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of the signature. The correlation of the signature with clinical features and tumor-infiltrating immune cells was analyzed. RESULTS: Among all DEGs, 70 hub IRGs were obtained for GC. The deletions and amplifications were the two most common types of genetic mutations of hub IRGs. A prognostic signature was identified, consisting of ten hub IRGs (including S100A12, DEFB126, KAL1, APOH, CGB5, GRP, GLP2R, LGR6, PTGER3, and CTLA4). This prognostic signature could accurately distinguish patients into high- and low- risk groups, and overall survival analysis showed that high risk patients had shortened survival time than low risk patients (P < 0.0001). The area under curve of the ROC of the signature was 0.761, suggesting that the prognostic signature had a high sensitivity and accuracy. Multivariate regression analysis demonstrated that the prognostic signature could become an independent prognostic predictor for GC after adjustment for other clinical features. Furthermore, we found that the prognostic signature was significantly correlated with macrophage infiltration. CONCLUSION: Our study proposed an immune-related prognostic signature for GC, which could help develop treatment strategies for patients with GC in the future. Baishideng Publishing Group Inc 2020-08-15 2020-08-15 /pmc/articles/PMC7443845/ /pubmed/32879664 http://dx.doi.org/10.4251/wjgo.v12.i8.857 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Basic Study Qiu, Xiang-Ting Song, Yu-Cui Liu, Jian Wang, Zhen-Min Niu, Xing He, Jing Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title | Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title_full | Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title_fullStr | Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title_full_unstemmed | Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title_short | Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
title_sort | identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer |
topic | Basic Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443845/ https://www.ncbi.nlm.nih.gov/pubmed/32879664 http://dx.doi.org/10.4251/wjgo.v12.i8.857 |
work_keys_str_mv | AT qiuxiangting identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer AT songyucui identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer AT liujian identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer AT wangzhenmin identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer AT niuxing identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer AT hejing identificationofanimmunerelatedgenebasedsignaturetopredictprognosisofpatientswithgastriccancer |