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A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape
As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354438/ https://www.ncbi.nlm.nih.gov/pubmed/37476379 http://dx.doi.org/10.3389/fonc.2023.1180723 |
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author | Dong, Lingjun Fu, Linhai Zhu, Ting Wu, Yuanlin Li, Zhupeng Ding, Jianyi Zhang, Jiandong Wang, Xiang Zhao, Junjun Yu, Guangmao |
author_facet | Dong, Lingjun Fu, Linhai Zhu, Ting Wu, Yuanlin Li, Zhupeng Ding, Jianyi Zhang, Jiandong Wang, Xiang Zhao, Junjun Yu, Guangmao |
author_sort | Dong, Lingjun |
collection | PubMed |
description | As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually. |
format | Online Article Text |
id | pubmed-10354438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103544382023-07-20 A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape Dong, Lingjun Fu, Linhai Zhu, Ting Wu, Yuanlin Li, Zhupeng Ding, Jianyi Zhang, Jiandong Wang, Xiang Zhao, Junjun Yu, Guangmao Front Oncol Oncology As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually. Frontiers Media S.A. 2023-07-05 /pmc/articles/PMC10354438/ /pubmed/37476379 http://dx.doi.org/10.3389/fonc.2023.1180723 Text en Copyright © 2023 Dong, Fu, Zhu, Wu, Li, Ding, Zhang, Wang, Zhao and Yu 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 | Oncology Dong, Lingjun Fu, Linhai Zhu, Ting Wu, Yuanlin Li, Zhupeng Ding, Jianyi Zhang, Jiandong Wang, Xiang Zhao, Junjun Yu, Guangmao A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title | A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title_full | A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title_fullStr | A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title_full_unstemmed | A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title_short | A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
title_sort | five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354438/ https://www.ncbi.nlm.nih.gov/pubmed/37476379 http://dx.doi.org/10.3389/fonc.2023.1180723 |
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