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Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer
Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans‐nodules cross‐clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438979/ https://www.ncbi.nlm.nih.gov/pubmed/32898330 http://dx.doi.org/10.1002/ctm2.151 |
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author | Zhu, Zhenhua Zhang, Linlin Lv, Jiapei Liu, Xiaoxia Wang, Xiangdong |
author_facet | Zhu, Zhenhua Zhang, Linlin Lv, Jiapei Liu, Xiaoxia Wang, Xiangdong |
author_sort | Zhu, Zhenhua |
collection | PubMed |
description | Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans‐nodules cross‐clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We measured plasma lipidomic profiles of lung cancer patients and found that altered lipid panels and concentrations varied among lung cancer subtypes, genders, ages, stages, metastatic status, nutritional status, and clinical phenome severity. It was shown that phosphatidylethanolamine elements (36:2, 18:0/18:2, and 18:1/18:1) were SCLC specific, whereas lysophosphatidylcholine (20:1 and 22:0 sn‐position‐1) and phosphatidylcholine (19:0/19:0 and 19:0/21:2) were ADC specific. There were statistically more lipids declined in male, <60 ages, late stage, metastasis, or body mass index < 22 . Clinical trans‐omics analyses demonstrated that one phenome in lung cancer subtypes might be generated from multiple metabolic pathways and metabolites, whereas a metabolic pathway and metabolite could contribute to different phenomes among subtypes, although those needed to be furthermore confirmed by bigger studies including larger population of patients in multicenters. Thus, our data suggested that trans‐omic profiles between clinical phenomes and lipidomes might have the value to uncover the heterogeneity of lipid metabolism among lung cancer subtypes and to screen out phenome‐based lipid panels as subtype‐specific biomarkers. |
format | Online Article Text |
id | pubmed-7438979 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74389792020-08-21 Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer Zhu, Zhenhua Zhang, Linlin Lv, Jiapei Liu, Xiaoxia Wang, Xiangdong Clin Transl Med Research Articles Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans‐nodules cross‐clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We measured plasma lipidomic profiles of lung cancer patients and found that altered lipid panels and concentrations varied among lung cancer subtypes, genders, ages, stages, metastatic status, nutritional status, and clinical phenome severity. It was shown that phosphatidylethanolamine elements (36:2, 18:0/18:2, and 18:1/18:1) were SCLC specific, whereas lysophosphatidylcholine (20:1 and 22:0 sn‐position‐1) and phosphatidylcholine (19:0/19:0 and 19:0/21:2) were ADC specific. There were statistically more lipids declined in male, <60 ages, late stage, metastasis, or body mass index < 22 . Clinical trans‐omics analyses demonstrated that one phenome in lung cancer subtypes might be generated from multiple metabolic pathways and metabolites, whereas a metabolic pathway and metabolite could contribute to different phenomes among subtypes, although those needed to be furthermore confirmed by bigger studies including larger population of patients in multicenters. Thus, our data suggested that trans‐omic profiles between clinical phenomes and lipidomes might have the value to uncover the heterogeneity of lipid metabolism among lung cancer subtypes and to screen out phenome‐based lipid panels as subtype‐specific biomarkers. John Wiley and Sons Inc. 2020-08-20 /pmc/articles/PMC7438979/ /pubmed/32898330 http://dx.doi.org/10.1002/ctm2.151 Text en © 2020 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zhu, Zhenhua Zhang, Linlin Lv, Jiapei Liu, Xiaoxia Wang, Xiangdong Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title | Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title_full | Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title_fullStr | Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title_full_unstemmed | Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title_short | Trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
title_sort | trans‐omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438979/ https://www.ncbi.nlm.nih.gov/pubmed/32898330 http://dx.doi.org/10.1002/ctm2.151 |
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