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Systems biology analysis reveals new insights into invasive lung cancer

BACKGROUND: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop...

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Autores principales: Li, Dan, Yang, William, Arthur, Carolyn, Liu, Jun S., Cruz-Niera, Carolina, Yang, Mary Qu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293490/
https://www.ncbi.nlm.nih.gov/pubmed/30547817
http://dx.doi.org/10.1186/s12918-018-0637-z
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author Li, Dan
Yang, William
Arthur, Carolyn
Liu, Jun S.
Cruz-Niera, Carolina
Yang, Mary Qu
author_facet Li, Dan
Yang, William
Arthur, Carolyn
Liu, Jun S.
Cruz-Niera, Carolina
Yang, Mary Qu
author_sort Li, Dan
collection PubMed
description BACKGROUND: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development. RESULTS: In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth. CONCLUSIONS: Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0637-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-62934902018-12-17 Systems biology analysis reveals new insights into invasive lung cancer Li, Dan Yang, William Arthur, Carolyn Liu, Jun S. Cruz-Niera, Carolina Yang, Mary Qu BMC Syst Biol Research BACKGROUND: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development. RESULTS: In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth. CONCLUSIONS: Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12918-018-0637-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-14 /pmc/articles/PMC6293490/ /pubmed/30547817 http://dx.doi.org/10.1186/s12918-018-0637-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Li, Dan
Yang, William
Arthur, Carolyn
Liu, Jun S.
Cruz-Niera, Carolina
Yang, Mary Qu
Systems biology analysis reveals new insights into invasive lung cancer
title Systems biology analysis reveals new insights into invasive lung cancer
title_full Systems biology analysis reveals new insights into invasive lung cancer
title_fullStr Systems biology analysis reveals new insights into invasive lung cancer
title_full_unstemmed Systems biology analysis reveals new insights into invasive lung cancer
title_short Systems biology analysis reveals new insights into invasive lung cancer
title_sort systems biology analysis reveals new insights into invasive lung cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293490/
https://www.ncbi.nlm.nih.gov/pubmed/30547817
http://dx.doi.org/10.1186/s12918-018-0637-z
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