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Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis

OBJECTS: Colorectal cancer (CRC) is one of the most common cancers in the world. Approximately two-thirds of patients with CRC will develop colorectal cancer liver metastases (CRLM) at some point in time. In this study, we aimed to construct a prognostic model of CRLM and its competing endogenous RN...

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Autores principales: Zhang, Xuan, Wu, Tao, Zhou, Jinmei, Chen, Xiaoqiong, Dong, Chao, Guo, Zhangyou, Yang, Renfang, Liang, Rui, Feng, Qing, Hu, Ruixi, Li, Yunfeng, Ding, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169504/
https://www.ncbi.nlm.nih.gov/pubmed/37161577
http://dx.doi.org/10.1186/s12920-023-01523-w
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author Zhang, Xuan
Wu, Tao
Zhou, Jinmei
Chen, Xiaoqiong
Dong, Chao
Guo, Zhangyou
Yang, Renfang
Liang, Rui
Feng, Qing
Hu, Ruixi
Li, Yunfeng
Ding, Rong
author_facet Zhang, Xuan
Wu, Tao
Zhou, Jinmei
Chen, Xiaoqiong
Dong, Chao
Guo, Zhangyou
Yang, Renfang
Liang, Rui
Feng, Qing
Hu, Ruixi
Li, Yunfeng
Ding, Rong
author_sort Zhang, Xuan
collection PubMed
description OBJECTS: Colorectal cancer (CRC) is one of the most common cancers in the world. Approximately two-thirds of patients with CRC will develop colorectal cancer liver metastases (CRLM) at some point in time. In this study, we aimed to construct a prognostic model of CRLM and its competing endogenous RNA (ceRNA) network. METHODS: RNA-seq of CRC, CRLM and normal samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Limma was used to obtain differential expression genes (DEGs) between CRLM and CRC from sequencing data and GSE22834, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed, respectively. Univariate Cox regression analysis and lasso Cox regression models were performed to screen prognostic gene features and construct prognostic models. Functional enrichment, estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, single-sample gene set enrichment analysis, and ceRNA network construction were applied to explore potential mechanisms. RESULTS: An 8-gene prognostic model was constructed by screening 112 DEGs from TCGA and GSE22834. CRC patients in the TCGA and GSE29621 cohorts were stratified into either a high-risk group or a low-risk group. Patients with CRC in the high-risk group had a significantly poorer prognosis compared to in the low-risk group. The risk score was identified as an independent predictor of prognosis. Functional analysis revealed that the risk score was closly correlated with various immune cells and immune-related signaling pathways. And a prognostic gene-associated ceRNA network was constructed that obtained 3 prognosis gene, 14 microRNAs (miRNAs) and 7 long noncoding RNAs (lncRNAs). CONCLUSIONS: In conclusion, a prognostic model for CRLM identification was proposed, which could independently identify high-risk patients with low survival, suggesting a relationship between local immune status and prognosis of CRLM. Moreover, the key prognostic genes-related ceRNA network were established for the CRC investigation. Based on the differentially expressed genes between CRLM and CRC, the prognosis model of CRC patients was constructed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01523-w.
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spelling pubmed-101695042023-05-11 Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis Zhang, Xuan Wu, Tao Zhou, Jinmei Chen, Xiaoqiong Dong, Chao Guo, Zhangyou Yang, Renfang Liang, Rui Feng, Qing Hu, Ruixi Li, Yunfeng Ding, Rong BMC Med Genomics Research OBJECTS: Colorectal cancer (CRC) is one of the most common cancers in the world. Approximately two-thirds of patients with CRC will develop colorectal cancer liver metastases (CRLM) at some point in time. In this study, we aimed to construct a prognostic model of CRLM and its competing endogenous RNA (ceRNA) network. METHODS: RNA-seq of CRC, CRLM and normal samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Limma was used to obtain differential expression genes (DEGs) between CRLM and CRC from sequencing data and GSE22834, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed, respectively. Univariate Cox regression analysis and lasso Cox regression models were performed to screen prognostic gene features and construct prognostic models. Functional enrichment, estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, single-sample gene set enrichment analysis, and ceRNA network construction were applied to explore potential mechanisms. RESULTS: An 8-gene prognostic model was constructed by screening 112 DEGs from TCGA and GSE22834. CRC patients in the TCGA and GSE29621 cohorts were stratified into either a high-risk group or a low-risk group. Patients with CRC in the high-risk group had a significantly poorer prognosis compared to in the low-risk group. The risk score was identified as an independent predictor of prognosis. Functional analysis revealed that the risk score was closly correlated with various immune cells and immune-related signaling pathways. And a prognostic gene-associated ceRNA network was constructed that obtained 3 prognosis gene, 14 microRNAs (miRNAs) and 7 long noncoding RNAs (lncRNAs). CONCLUSIONS: In conclusion, a prognostic model for CRLM identification was proposed, which could independently identify high-risk patients with low survival, suggesting a relationship between local immune status and prognosis of CRLM. Moreover, the key prognostic genes-related ceRNA network were established for the CRC investigation. Based on the differentially expressed genes between CRLM and CRC, the prognosis model of CRC patients was constructed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01523-w. BioMed Central 2023-05-09 /pmc/articles/PMC10169504/ /pubmed/37161577 http://dx.doi.org/10.1186/s12920-023-01523-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Xuan
Wu, Tao
Zhou, Jinmei
Chen, Xiaoqiong
Dong, Chao
Guo, Zhangyou
Yang, Renfang
Liang, Rui
Feng, Qing
Hu, Ruixi
Li, Yunfeng
Ding, Rong
Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title_full Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title_fullStr Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title_full_unstemmed Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title_short Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis
title_sort establishment and verification of prognostic model and cerna network analysis for colorectal cancer liver metastasis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10169504/
https://www.ncbi.nlm.nih.gov/pubmed/37161577
http://dx.doi.org/10.1186/s12920-023-01523-w
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