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Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer
BACKGROUND: Metastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non‐metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833974/ https://www.ncbi.nlm.nih.gov/pubmed/36524971 http://dx.doi.org/10.1002/jcla.24800 |
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author | Li, Tong Yu, Qian Liu, Te Yang, Wenjing Chen, Wei Jin, Anli Wang, Hao Ding, Lin Zhang, Chunyan Pan, Baishen Wang, Beili Guo, Wei |
author_facet | Li, Tong Yu, Qian Liu, Te Yang, Wenjing Chen, Wei Jin, Anli Wang, Hao Ding, Lin Zhang, Chunyan Pan, Baishen Wang, Beili Guo, Wei |
author_sort | Li, Tong |
collection | PubMed |
description | BACKGROUND: Metastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non‐metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes of colorectal tumor metastasis by characterizing the immune status alteration. METHODS: CRC patient data were obtained from TCGA and GEO databases. We constructed a risk prognostic model by using Cox regression and the least absolute shrinkage and selection operator (LASSO) based on CRC metastasis‐related genes. We also obtained a nomogram to predict the prognosis of CRC patients. Finally, we explored the underlying mechanism of these metastasis‐related genes and CRC prognosis using immune infiltration analysis and experimental verification. RESULTS: According to our prognostic model, in TCGA, the area under the curve (AUC) values of the training and test sets were 0.72 and 0.76, respectively, and 0.68 for the GEO external data set. This suggested that the treatment and prognosis of patients could be effectively determined. At the same time, we found that the B and T cells in both tissues and peripheral blood of high MR‐risk score patients were mostly in immune static or inactivated states compared with those of low MR‐risk score patients. CONCLUSIONS: MR‐risk score has a direct correlation with CRC patient prognosis. It is useful for predicting the prognosis and patient immune status for these patients. |
format | Online Article Text |
id | pubmed-9833974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98339742023-01-13 Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer Li, Tong Yu, Qian Liu, Te Yang, Wenjing Chen, Wei Jin, Anli Wang, Hao Ding, Lin Zhang, Chunyan Pan, Baishen Wang, Beili Guo, Wei J Clin Lab Anal Research Articles BACKGROUND: Metastasis is the main cause of death of colorectal tumors, in our study a prognosis model was built by analyzing the differentially expressed genes between metastatic and non‐metastatic colorectal cancer (CRC). We used this feature to predict CRC patient prognosis and explore the causes of colorectal tumor metastasis by characterizing the immune status alteration. METHODS: CRC patient data were obtained from TCGA and GEO databases. We constructed a risk prognostic model by using Cox regression and the least absolute shrinkage and selection operator (LASSO) based on CRC metastasis‐related genes. We also obtained a nomogram to predict the prognosis of CRC patients. Finally, we explored the underlying mechanism of these metastasis‐related genes and CRC prognosis using immune infiltration analysis and experimental verification. RESULTS: According to our prognostic model, in TCGA, the area under the curve (AUC) values of the training and test sets were 0.72 and 0.76, respectively, and 0.68 for the GEO external data set. This suggested that the treatment and prognosis of patients could be effectively determined. At the same time, we found that the B and T cells in both tissues and peripheral blood of high MR‐risk score patients were mostly in immune static or inactivated states compared with those of low MR‐risk score patients. CONCLUSIONS: MR‐risk score has a direct correlation with CRC patient prognosis. It is useful for predicting the prognosis and patient immune status for these patients. John Wiley and Sons Inc. 2022-12-16 /pmc/articles/PMC9833974/ /pubmed/36524971 http://dx.doi.org/10.1002/jcla.24800 Text en © 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles Li, Tong Yu, Qian Liu, Te Yang, Wenjing Chen, Wei Jin, Anli Wang, Hao Ding, Lin Zhang, Chunyan Pan, Baishen Wang, Beili Guo, Wei Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title | Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title_full | Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title_fullStr | Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title_full_unstemmed | Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title_short | Development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
title_sort | development of 14‐gene signature prognostic model based on metastasis for colorectal cancer |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833974/ https://www.ncbi.nlm.nih.gov/pubmed/36524971 http://dx.doi.org/10.1002/jcla.24800 |
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