Cargando…

From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management

High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions i...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Cun, Yu, Yang, Wang, Ge, Liu, Jingyang, Liu, Ruijuan, Liu, Lijuan, Yang, Xiaoxu, Li, Huayao, Gao, Chundi, Lu, Yi, Zhuang, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530334/
https://www.ncbi.nlm.nih.gov/pubmed/36204371
http://dx.doi.org/10.3389/fnut.2022.989989
_version_ 1784801659074904064
author Liu, Cun
Yu, Yang
Wang, Ge
Liu, Jingyang
Liu, Ruijuan
Liu, Lijuan
Yang, Xiaoxu
Li, Huayao
Gao, Chundi
Lu, Yi
Zhuang, Jing
author_facet Liu, Cun
Yu, Yang
Wang, Ge
Liu, Jingyang
Liu, Ruijuan
Liu, Lijuan
Yang, Xiaoxu
Li, Huayao
Gao, Chundi
Lu, Yi
Zhuang, Jing
author_sort Liu, Cun
collection PubMed
description High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.
format Online
Article
Text
id pubmed-9530334
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95303342022-10-05 From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management Liu, Cun Yu, Yang Wang, Ge Liu, Jingyang Liu, Ruijuan Liu, Lijuan Yang, Xiaoxu Li, Huayao Gao, Chundi Lu, Yi Zhuang, Jing Front Nutr Nutrition High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530334/ /pubmed/36204371 http://dx.doi.org/10.3389/fnut.2022.989989 Text en Copyright © 2022 Liu, Yu, Wang, Liu, Liu, Liu, Yang, Li, Gao, Lu and Zhuang. 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 Nutrition
Liu, Cun
Yu, Yang
Wang, Ge
Liu, Jingyang
Liu, Ruijuan
Liu, Lijuan
Yang, Xiaoxu
Li, Huayao
Gao, Chundi
Lu, Yi
Zhuang, Jing
From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title_full From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title_fullStr From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title_full_unstemmed From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title_short From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management
title_sort from tumor mutational burden to characteristic targets analysis: identifying the predictive biomarkers and natural product interventions in cancer management
topic Nutrition
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530334/
https://www.ncbi.nlm.nih.gov/pubmed/36204371
http://dx.doi.org/10.3389/fnut.2022.989989
work_keys_str_mv AT liucun fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT yuyang fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT wangge fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT liujingyang fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT liuruijuan fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT liulijuan fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT yangxiaoxu fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT lihuayao fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT gaochundi fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT luyi fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement
AT zhuangjing fromtumormutationalburdentocharacteristictargetsanalysisidentifyingthepredictivebiomarkersandnaturalproductinterventionsincancermanagement