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Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we ai...

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Autores principales: Wang, Qian, Li, Xiangmei, Wang, Yahui, Qiu, Jiayue, Wu, Jiashuo, He, Yalan, Li, Ji, Kong, Qingfei, Han, Junwei, Jiang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843853/
https://www.ncbi.nlm.nih.gov/pubmed/35178071
http://dx.doi.org/10.3389/fgene.2021.801240
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author Wang, Qian
Li, Xiangmei
Wang, Yahui
Qiu, Jiayue
Wu, Jiashuo
He, Yalan
Li, Ji
Kong, Qingfei
Han, Junwei
Jiang, Ying
author_facet Wang, Qian
Li, Xiangmei
Wang, Yahui
Qiu, Jiayue
Wu, Jiashuo
He, Yalan
Li, Ji
Kong, Qingfei
Han, Junwei
Jiang, Ying
author_sort Wang, Qian
collection PubMed
description Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.
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spelling pubmed-88438532022-02-16 Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer Wang, Qian Li, Xiangmei Wang, Yahui Qiu, Jiayue Wu, Jiashuo He, Yalan Li, Ji Kong, Qingfei Han, Junwei Jiang, Ying Front Genet Genetics Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy. Frontiers Media S.A. 2022-02-01 /pmc/articles/PMC8843853/ /pubmed/35178071 http://dx.doi.org/10.3389/fgene.2021.801240 Text en Copyright © 2022 Wang, Li, Wang, Qiu, Wu, He, Li, Kong, Han and Jiang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wang, Qian
Li, Xiangmei
Wang, Yahui
Qiu, Jiayue
Wu, Jiashuo
He, Yalan
Li, Ji
Kong, Qingfei
Han, Junwei
Jiang, Ying
Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title_full Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title_fullStr Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title_full_unstemmed Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title_short Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer
title_sort development and validation of a three-gene prognostic signature based on tumor microenvironment for gastric cancer
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8843853/
https://www.ncbi.nlm.nih.gov/pubmed/35178071
http://dx.doi.org/10.3389/fgene.2021.801240
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