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A gene mutation-based risk model for prognostic prediction in liver metastases

BACKGROUND: Liver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver m...

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Autores principales: Yu, Bingran, Zhang, Ning, Feng, Yun, Xu, Weiqi, Zhang, Ti, Wang, Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463705/
https://www.ncbi.nlm.nih.gov/pubmed/37633919
http://dx.doi.org/10.1186/s12864-023-09595-9
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author Yu, Bingran
Zhang, Ning
Feng, Yun
Xu, Weiqi
Zhang, Ti
Wang, Lu
author_facet Yu, Bingran
Zhang, Ning
Feng, Yun
Xu, Weiqi
Zhang, Ti
Wang, Lu
author_sort Yu, Bingran
collection PubMed
description BACKGROUND: Liver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases. METHOD: We identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data. RESULTS: A gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM. CONCLUSIONS: The gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09595-9.
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spelling pubmed-104637052023-08-30 A gene mutation-based risk model for prognostic prediction in liver metastases Yu, Bingran Zhang, Ning Feng, Yun Xu, Weiqi Zhang, Ti Wang, Lu BMC Genomics Research BACKGROUND: Liver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases. METHOD: We identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data. RESULTS: A gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM. CONCLUSIONS: The gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09595-9. BioMed Central 2023-08-26 /pmc/articles/PMC10463705/ /pubmed/37633919 http://dx.doi.org/10.1186/s12864-023-09595-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Yu, Bingran
Zhang, Ning
Feng, Yun
Xu, Weiqi
Zhang, Ti
Wang, Lu
A gene mutation-based risk model for prognostic prediction in liver metastases
title A gene mutation-based risk model for prognostic prediction in liver metastases
title_full A gene mutation-based risk model for prognostic prediction in liver metastases
title_fullStr A gene mutation-based risk model for prognostic prediction in liver metastases
title_full_unstemmed A gene mutation-based risk model for prognostic prediction in liver metastases
title_short A gene mutation-based risk model for prognostic prediction in liver metastases
title_sort gene mutation-based risk model for prognostic prediction in liver metastases
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463705/
https://www.ncbi.nlm.nih.gov/pubmed/37633919
http://dx.doi.org/10.1186/s12864-023-09595-9
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