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Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas
Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integra...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518687/ https://www.ncbi.nlm.nih.gov/pubmed/37752948 http://dx.doi.org/10.1016/j.isci.2023.107678 |
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author | Vignoli, Alessia Miolo, Gianmaria Tenori, Leonardo Buonadonna, Angela Lombardi, Davide Steffan, Agostino Scalone, Simona Luchinat, Claudio Corona, Giuseppe |
author_facet | Vignoli, Alessia Miolo, Gianmaria Tenori, Leonardo Buonadonna, Angela Lombardi, Davide Steffan, Agostino Scalone, Simona Luchinat, Claudio Corona, Giuseppe |
author_sort | Vignoli, Alessia |
collection | PubMed |
description | Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance ((1)H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings. |
format | Online Article Text |
id | pubmed-10518687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-105186872023-09-26 Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas Vignoli, Alessia Miolo, Gianmaria Tenori, Leonardo Buonadonna, Angela Lombardi, Davide Steffan, Agostino Scalone, Simona Luchinat, Claudio Corona, Giuseppe iScience Article Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance ((1)H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings. Elsevier 2023-08-19 /pmc/articles/PMC10518687/ /pubmed/37752948 http://dx.doi.org/10.1016/j.isci.2023.107678 Text en © 2023. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Vignoli, Alessia Miolo, Gianmaria Tenori, Leonardo Buonadonna, Angela Lombardi, Davide Steffan, Agostino Scalone, Simona Luchinat, Claudio Corona, Giuseppe Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title | Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title_full | Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title_fullStr | Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title_full_unstemmed | Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title_short | Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
title_sort | novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518687/ https://www.ncbi.nlm.nih.gov/pubmed/37752948 http://dx.doi.org/10.1016/j.isci.2023.107678 |
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