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Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma
BACKGROUND: Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) ma...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969670/ https://www.ncbi.nlm.nih.gov/pubmed/36843111 http://dx.doi.org/10.1186/s12885-023-10658-z |
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author | Jamshidi, Neema Senthilvelan, Jayasuriya Dawson, David W. Donahue, Timothy R. Kuo, Michael D. |
author_facet | Jamshidi, Neema Senthilvelan, Jayasuriya Dawson, David W. Donahue, Timothy R. Kuo, Michael D. |
author_sort | Jamshidi, Neema |
collection | PubMed |
description | BACKGROUND: Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC. METHODS: In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast–enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses. RESULTS: Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05). CONCLUSIONS: A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-10658-z. |
format | Online Article Text |
id | pubmed-9969670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99696702023-02-28 Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma Jamshidi, Neema Senthilvelan, Jayasuriya Dawson, David W. Donahue, Timothy R. Kuo, Michael D. BMC Cancer Research BACKGROUND: Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC. METHODS: In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast–enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses. RESULTS: Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05). CONCLUSIONS: A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-023-10658-z. BioMed Central 2023-02-27 /pmc/articles/PMC9969670/ /pubmed/36843111 http://dx.doi.org/10.1186/s12885-023-10658-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Jamshidi, Neema Senthilvelan, Jayasuriya Dawson, David W. Donahue, Timothy R. Kuo, Michael D. Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title | Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title_full | Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title_fullStr | Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title_full_unstemmed | Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title_short | Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
title_sort | construction of a radiogenomic association map of pancreatic ductal adenocarcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9969670/ https://www.ncbi.nlm.nih.gov/pubmed/36843111 http://dx.doi.org/10.1186/s12885-023-10658-z |
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