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Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma

BACKGROUND: Gastrointestinal adenocarcinoma (GIAD) has caused a serious disease burden globally. Targeted therapy for the transforming growth factor beta (TGF-β) signaling pathway is becoming a reality. However, the molecular characterization of TGF-β associated signatures in GIAD requires further e...

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Autores principales: Zhang, Qiaofeng, Liu, Furong, Qin, Lu, Liao, Zhibin, Song, Jia, Liang, Huifang, Chen, Xiaoping, Zhang, Zhanguo, Zhang, Bixiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274021/
https://www.ncbi.nlm.nih.gov/pubmed/34247571
http://dx.doi.org/10.1186/s12876-021-01869-4
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author Zhang, Qiaofeng
Liu, Furong
Qin, Lu
Liao, Zhibin
Song, Jia
Liang, Huifang
Chen, Xiaoping
Zhang, Zhanguo
Zhang, Bixiang
author_facet Zhang, Qiaofeng
Liu, Furong
Qin, Lu
Liao, Zhibin
Song, Jia
Liang, Huifang
Chen, Xiaoping
Zhang, Zhanguo
Zhang, Bixiang
author_sort Zhang, Qiaofeng
collection PubMed
description BACKGROUND: Gastrointestinal adenocarcinoma (GIAD) has caused a serious disease burden globally. Targeted therapy for the transforming growth factor beta (TGF-β) signaling pathway is becoming a reality. However, the molecular characterization of TGF-β associated signatures in GIAD requires further exploration. METHODS: Multi-omics data were collected from TCGA and GEO database. A pivotal unsupervised clustering for TGF-β level was performed by distinguish status of TGF-β associated genes. We analyzed differential mRNAs, miRNAs, proteins gene mutations and copy number variations in both clusters for comparison. Enrichment of pathways and gene sets were identified in each type of GIAD. Then we performed differential mRNA related drug response by collecting data from GDSC. At last, a summarized deep neural network for TGF-β status and GIADs was constracted. RESULTS: The TGF-β(high) group had a worse prognosis in overall GIAD patients, and had a worse prognosis trend in gastric cancer and colon cancer specifically. Signatures (including mRNA and proteins) of the TGF-β(high) group is highly correlated with EMT. According to miRNA analysis, miR-215-3p, miR-378a-5p, and miR-194-3p may block the effect of TGF-β. Further genomic analysis showed that TGF-β(low) group had more genomic changes in gastric cancer, such as TP53 mutation, EGFR amplification, and SMAD4 deletion. And drug response dataset revealed tumor-sensitive or tumor-resistant drugs corresponding to TGF-β associated mRNAs. Finally, the DNN model showed an excellent predictive effect in predicting TGF-β status in different GIAD datasets. CONCLUSIONS: We provide molecular signatures associated with different levels of TGF-β to deepen the understanding of the role of TGF-β in GIAD and provide potential drug possibilities for therapeutic targets in different levels of TGF-β in GIAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-021-01869-4.
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spelling pubmed-82740212021-07-13 Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma Zhang, Qiaofeng Liu, Furong Qin, Lu Liao, Zhibin Song, Jia Liang, Huifang Chen, Xiaoping Zhang, Zhanguo Zhang, Bixiang BMC Gastroenterol Research BACKGROUND: Gastrointestinal adenocarcinoma (GIAD) has caused a serious disease burden globally. Targeted therapy for the transforming growth factor beta (TGF-β) signaling pathway is becoming a reality. However, the molecular characterization of TGF-β associated signatures in GIAD requires further exploration. METHODS: Multi-omics data were collected from TCGA and GEO database. A pivotal unsupervised clustering for TGF-β level was performed by distinguish status of TGF-β associated genes. We analyzed differential mRNAs, miRNAs, proteins gene mutations and copy number variations in both clusters for comparison. Enrichment of pathways and gene sets were identified in each type of GIAD. Then we performed differential mRNA related drug response by collecting data from GDSC. At last, a summarized deep neural network for TGF-β status and GIADs was constracted. RESULTS: The TGF-β(high) group had a worse prognosis in overall GIAD patients, and had a worse prognosis trend in gastric cancer and colon cancer specifically. Signatures (including mRNA and proteins) of the TGF-β(high) group is highly correlated with EMT. According to miRNA analysis, miR-215-3p, miR-378a-5p, and miR-194-3p may block the effect of TGF-β. Further genomic analysis showed that TGF-β(low) group had more genomic changes in gastric cancer, such as TP53 mutation, EGFR amplification, and SMAD4 deletion. And drug response dataset revealed tumor-sensitive or tumor-resistant drugs corresponding to TGF-β associated mRNAs. Finally, the DNN model showed an excellent predictive effect in predicting TGF-β status in different GIAD datasets. CONCLUSIONS: We provide molecular signatures associated with different levels of TGF-β to deepen the understanding of the role of TGF-β in GIAD and provide potential drug possibilities for therapeutic targets in different levels of TGF-β in GIAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-021-01869-4. BioMed Central 2021-07-12 /pmc/articles/PMC8274021/ /pubmed/34247571 http://dx.doi.org/10.1186/s12876-021-01869-4 Text en © The Author(s) 2021 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
Zhang, Qiaofeng
Liu, Furong
Qin, Lu
Liao, Zhibin
Song, Jia
Liang, Huifang
Chen, Xiaoping
Zhang, Zhanguo
Zhang, Bixiang
Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title_full Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title_fullStr Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title_full_unstemmed Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title_short Characterization of TGFβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
title_sort characterization of tgfβ-associated molecular features and drug responses in gastrointestinal adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274021/
https://www.ncbi.nlm.nih.gov/pubmed/34247571
http://dx.doi.org/10.1186/s12876-021-01869-4
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