Cargando…

Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) w...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Linlin, Wang, Peng, Su, Mu, Yang, Lili, Wang, Qingbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159392/
https://www.ncbi.nlm.nih.gov/pubmed/35664306
http://dx.doi.org/10.3389/fgene.2022.880945
_version_ 1784719046365675520
author Jiang, Linlin
Wang, Peng
Su, Mu
Yang, Lili
Wang, Qingbo
author_facet Jiang, Linlin
Wang, Peng
Su, Mu
Yang, Lili
Wang, Qingbo
author_sort Jiang, Linlin
collection PubMed
description Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.
format Online
Article
Text
id pubmed-9159392
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-91593922022-06-02 Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma Jiang, Linlin Wang, Peng Su, Mu Yang, Lili Wang, Qingbo Front Genet Genetics Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk. Frontiers Media S.A. 2022-05-18 /pmc/articles/PMC9159392/ /pubmed/35664306 http://dx.doi.org/10.3389/fgene.2022.880945 Text en Copyright © 2022 Jiang, Wang, Su, Yang and Wang. 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
Jiang, Linlin
Wang, Peng
Su, Mu
Yang, Lili
Wang, Qingbo
Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title_full Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title_fullStr Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title_full_unstemmed Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title_short Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma
title_sort identification of mrna signature for predicting prognosis risk of rectal adenocarcinoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9159392/
https://www.ncbi.nlm.nih.gov/pubmed/35664306
http://dx.doi.org/10.3389/fgene.2022.880945
work_keys_str_mv AT jianglinlin identificationofmrnasignatureforpredictingprognosisriskofrectaladenocarcinoma
AT wangpeng identificationofmrnasignatureforpredictingprognosisriskofrectaladenocarcinoma
AT sumu identificationofmrnasignatureforpredictingprognosisriskofrectaladenocarcinoma
AT yanglili identificationofmrnasignatureforpredictingprognosisriskofrectaladenocarcinoma
AT wangqingbo identificationofmrnasignatureforpredictingprognosisriskofrectaladenocarcinoma