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Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories

Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis w...

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Autores principales: Xiang, Renshen, Fu, Jincheng, Ge, Yuhang, Ren, Jun, Song, Wei, Fu, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8710730/
https://www.ncbi.nlm.nih.gov/pubmed/34966734
http://dx.doi.org/10.3389/fcell.2021.705537
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author Xiang, Renshen
Fu, Jincheng
Ge, Yuhang
Ren, Jun
Song, Wei
Fu, Tao
author_facet Xiang, Renshen
Fu, Jincheng
Ge, Yuhang
Ren, Jun
Song, Wei
Fu, Tao
author_sort Xiang, Renshen
collection PubMed
description Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis was conducted on the GSE148345 dataset, and CDRG-based molecular subtypes were identified from the GSE39582 dataset. A CDRG-based prognostic RS signature was constructed using The Cancer Genome Atlas as the training set and GSE39582 as the validation set. Two subsets with distinct differentiation states, involving 40 hub CDRGs regulated by YY1 and EGR2, were identified by single-cell RNA sequencing data, of which subset I was related to hypoxia, metabolic disorders, and inflammation, and subset II was associated with immune responses and ferroptosis. The CDRG-based molecular subtypes could successfully predict the clinical outcomes of the patients, the tumor microenvironment status, the immune infiltration status, and the potential response to immunotherapy and chemotherapy. A nomogram integrating a five-CDRG-based RS signature and prognostic clinicopathological characteristics could successfully predict overall survival, with strong predictive performance and high accuracy. The study emphasizes the relevance of CC cell differentiation for predicting the prognosis and therapeutic response of patients to immunotherapy and chemotherapy and proposes a promising direction for CC treatment and clinical decision-making.
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spelling pubmed-87107302021-12-28 Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories Xiang, Renshen Fu, Jincheng Ge, Yuhang Ren, Jun Song, Wei Fu, Tao Front Cell Dev Biol Cell and Developmental Biology Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis was conducted on the GSE148345 dataset, and CDRG-based molecular subtypes were identified from the GSE39582 dataset. A CDRG-based prognostic RS signature was constructed using The Cancer Genome Atlas as the training set and GSE39582 as the validation set. Two subsets with distinct differentiation states, involving 40 hub CDRGs regulated by YY1 and EGR2, were identified by single-cell RNA sequencing data, of which subset I was related to hypoxia, metabolic disorders, and inflammation, and subset II was associated with immune responses and ferroptosis. The CDRG-based molecular subtypes could successfully predict the clinical outcomes of the patients, the tumor microenvironment status, the immune infiltration status, and the potential response to immunotherapy and chemotherapy. A nomogram integrating a five-CDRG-based RS signature and prognostic clinicopathological characteristics could successfully predict overall survival, with strong predictive performance and high accuracy. The study emphasizes the relevance of CC cell differentiation for predicting the prognosis and therapeutic response of patients to immunotherapy and chemotherapy and proposes a promising direction for CC treatment and clinical decision-making. Frontiers Media S.A. 2021-12-13 /pmc/articles/PMC8710730/ /pubmed/34966734 http://dx.doi.org/10.3389/fcell.2021.705537 Text en Copyright © 2021 Xiang, Fu, Ge, Ren, Song and Fu. 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 Cell and Developmental Biology
Xiang, Renshen
Fu, Jincheng
Ge, Yuhang
Ren, Jun
Song, Wei
Fu, Tao
Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title_full Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title_fullStr Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title_full_unstemmed Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title_short Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories
title_sort identification of subtypes and a prognostic gene signature in colon cancer using cell differentiation trajectories
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8710730/
https://www.ncbi.nlm.nih.gov/pubmed/34966734
http://dx.doi.org/10.3389/fcell.2021.705537
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