Cargando…

A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma

Background: Immune-related genes (IRGs) are critically involved in the tumor microenvironment (TME) of colon adenocarcinoma (COAD). Here, the study was mainly designed to establish a prognostic model of IRGs to predict the survival of COAD patients. Methods: The Cancer Genome Atlas (TCGA), Immunolog...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Yuan-Lin, Zhang, Yang, Guo, Yu-Chen, Yang, Zi-Hao, Xu, Yue-Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415395/
https://www.ncbi.nlm.nih.gov/pubmed/32788867
http://dx.doi.org/10.7150/ijms.45813
_version_ 1783569165407223808
author Sun, Yuan-Lin
Zhang, Yang
Guo, Yu-Chen
Yang, Zi-Hao
Xu, Yue-Chao
author_facet Sun, Yuan-Lin
Zhang, Yang
Guo, Yu-Chen
Yang, Zi-Hao
Xu, Yue-Chao
author_sort Sun, Yuan-Lin
collection PubMed
description Background: Immune-related genes (IRGs) are critically involved in the tumor microenvironment (TME) of colon adenocarcinoma (COAD). Here, the study was mainly designed to establish a prognostic model of IRGs to predict the survival of COAD patients. Methods: The Cancer Genome Atlas (TCGA), Immunology Database and Analysis Portal (ImmPort) database, and Cistrome database were utilized for extracting data regarding the expression of immune gene- and tumor-related transcription factors (TFs), aimed at the identification of differentially expressed genes (DEGs), differentially expressed IRGs (DEIRGs), and differentially expressed TFs (DETFs). Univariate Cox regression analysis was subsequently performed for the acquisition of prognosis-related IRGs, followed by establishment of TF regulatory network for uncovering the possible molecular regulatory association in COAD. Subsequently, multivariate Cox regression analysis was conducted to further determine the role of prognosis-related IRGs for prognostic prediction in COAD. Finally, the feasibility of a prognostic model with immunocytes was explored by immunocyte infiltration analysis. Results: A total of 2450 DEGs, 8 DETFs, and 79 DEIRGs were extracted from the corresponding databases. Univariate Cox regression analysis revealed 11 prognosis-related IRGs, followed by establishment of a regulatory network on prognosis-related IRGs at transcriptional levels. Functionally, IRG GLP2R was negatively modulated by TF MYH11, whereas IRG TDGF1 was positively modulated by TF TFAP2A. Multivariate Cox regression analysis was subsequently performed to establish a prognostic model on the basis of seven prognosis-related IRGs (GLP2R, ESM1, TDGF1, SLC10A2, INHBA, STC2, and CXCL1). Moreover, correlation analysis of immunocyte infiltration also revealed that the seven-IRG prognostic model was positively associated with five types of immunocytes (dendritic cell, macrophage, CD4 T cell, CD8 T cell, and neutrophil), which may directly reflect tumor immune state in COAD. Conclusions: Our present findings indicate that the prognostic model based on prognosis-related IRGs plays a crucial role in the clinical supervision and prognostic prediction of COAD patients at both molecular and cellular levels.
format Online
Article
Text
id pubmed-7415395
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Ivyspring International Publisher
record_format MEDLINE/PubMed
spelling pubmed-74153952020-08-11 A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma Sun, Yuan-Lin Zhang, Yang Guo, Yu-Chen Yang, Zi-Hao Xu, Yue-Chao Int J Med Sci Research Paper Background: Immune-related genes (IRGs) are critically involved in the tumor microenvironment (TME) of colon adenocarcinoma (COAD). Here, the study was mainly designed to establish a prognostic model of IRGs to predict the survival of COAD patients. Methods: The Cancer Genome Atlas (TCGA), Immunology Database and Analysis Portal (ImmPort) database, and Cistrome database were utilized for extracting data regarding the expression of immune gene- and tumor-related transcription factors (TFs), aimed at the identification of differentially expressed genes (DEGs), differentially expressed IRGs (DEIRGs), and differentially expressed TFs (DETFs). Univariate Cox regression analysis was subsequently performed for the acquisition of prognosis-related IRGs, followed by establishment of TF regulatory network for uncovering the possible molecular regulatory association in COAD. Subsequently, multivariate Cox regression analysis was conducted to further determine the role of prognosis-related IRGs for prognostic prediction in COAD. Finally, the feasibility of a prognostic model with immunocytes was explored by immunocyte infiltration analysis. Results: A total of 2450 DEGs, 8 DETFs, and 79 DEIRGs were extracted from the corresponding databases. Univariate Cox regression analysis revealed 11 prognosis-related IRGs, followed by establishment of a regulatory network on prognosis-related IRGs at transcriptional levels. Functionally, IRG GLP2R was negatively modulated by TF MYH11, whereas IRG TDGF1 was positively modulated by TF TFAP2A. Multivariate Cox regression analysis was subsequently performed to establish a prognostic model on the basis of seven prognosis-related IRGs (GLP2R, ESM1, TDGF1, SLC10A2, INHBA, STC2, and CXCL1). Moreover, correlation analysis of immunocyte infiltration also revealed that the seven-IRG prognostic model was positively associated with five types of immunocytes (dendritic cell, macrophage, CD4 T cell, CD8 T cell, and neutrophil), which may directly reflect tumor immune state in COAD. Conclusions: Our present findings indicate that the prognostic model based on prognosis-related IRGs plays a crucial role in the clinical supervision and prognostic prediction of COAD patients at both molecular and cellular levels. Ivyspring International Publisher 2020-07-19 /pmc/articles/PMC7415395/ /pubmed/32788867 http://dx.doi.org/10.7150/ijms.45813 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Sun, Yuan-Lin
Zhang, Yang
Guo, Yu-Chen
Yang, Zi-Hao
Xu, Yue-Chao
A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title_full A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title_fullStr A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title_full_unstemmed A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title_short A Prognostic Model Based on the Immune-related Genes in Colon Adenocarcinoma
title_sort prognostic model based on the immune-related genes in colon adenocarcinoma
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7415395/
https://www.ncbi.nlm.nih.gov/pubmed/32788867
http://dx.doi.org/10.7150/ijms.45813
work_keys_str_mv AT sunyuanlin aprognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT zhangyang aprognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT guoyuchen aprognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT yangzihao aprognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT xuyuechao aprognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT sunyuanlin prognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT zhangyang prognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT guoyuchen prognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT yangzihao prognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma
AT xuyuechao prognosticmodelbasedontheimmunerelatedgenesincolonadenocarcinoma