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Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses
INTRODUCTION: Ovarian cancer (OC) represents a serious disease with high mortality and lack of efficient predictive and prognostic biomarkers. ATP‐binding cassette (ABC) proteins constitute a large family dedicated to active transmembrane transport including transport of xenobiotics. MATERIALS AND M...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382717/ https://www.ncbi.nlm.nih.gov/pubmed/30672151 http://dx.doi.org/10.1002/cam4.1964 |
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author | Seborova, Karolina Vaclavikova, Radka Soucek, Pavel Elsnerova, Katerina Bartakova, Alena Cernaj, Petr Bouda, Jiri Rob, Lukas Hruda, Martin Dvorak, Pavel |
author_facet | Seborova, Karolina Vaclavikova, Radka Soucek, Pavel Elsnerova, Katerina Bartakova, Alena Cernaj, Petr Bouda, Jiri Rob, Lukas Hruda, Martin Dvorak, Pavel |
author_sort | Seborova, Karolina |
collection | PubMed |
description | INTRODUCTION: Ovarian cancer (OC) represents a serious disease with high mortality and lack of efficient predictive and prognostic biomarkers. ATP‐binding cassette (ABC) proteins constitute a large family dedicated to active transmembrane transport including transport of xenobiotics. MATERIALS AND METHODS: mRNA level was measured by quantitative RT‐PCR in tumor tissues from OC patients. Bioinformatics analyses were applied to two gene expression datasets (60 primary tumors and 29 peritoneal metastases). Two different approaches of expression data normalization were applied in parallel, and their results were compared. Data from publically available cancer datasets were checked to further validate our conclusions. RESULTS: The results showed significant connections between ABC gene expression profiles and time to progression (TTP), chemotherapy resistance, and metastatic progression in OC. Two consensus ABC gene profiles with clinical meaning were documented. (a) Downregulation of ABCC4, ABCC10, ABCD3, ABCE1, ABCF1, ABCF2, and ABCF3 was connected with the best sensitivity to chemotherapy and TTP. (b) Oppositely, downregulation of ABCB11 and upregulation of ABCB1 and ABCG2 were connected with the worst sensitivity to chemotherapy and TTP. Results from publicly available online databases supported our conclusions. CONCLUSION: This study stressed the connection between two well‐documented ABC genes and clinicopathological features—ABCB1 and ABCG2. Moreover, we showed a comparable connection also for several other ABC genes—ABCB11, ABCC4, ABCC10, ABCD3, ABCE1, ABCF1, ABCF2, and ABCF3. Our results add new clinically relevant information to this oncology field and can stimulate further exploration. |
format | Online Article Text |
id | pubmed-6382717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63827172019-03-01 Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses Seborova, Karolina Vaclavikova, Radka Soucek, Pavel Elsnerova, Katerina Bartakova, Alena Cernaj, Petr Bouda, Jiri Rob, Lukas Hruda, Martin Dvorak, Pavel Cancer Med Clinical Cancer Research INTRODUCTION: Ovarian cancer (OC) represents a serious disease with high mortality and lack of efficient predictive and prognostic biomarkers. ATP‐binding cassette (ABC) proteins constitute a large family dedicated to active transmembrane transport including transport of xenobiotics. MATERIALS AND METHODS: mRNA level was measured by quantitative RT‐PCR in tumor tissues from OC patients. Bioinformatics analyses were applied to two gene expression datasets (60 primary tumors and 29 peritoneal metastases). Two different approaches of expression data normalization were applied in parallel, and their results were compared. Data from publically available cancer datasets were checked to further validate our conclusions. RESULTS: The results showed significant connections between ABC gene expression profiles and time to progression (TTP), chemotherapy resistance, and metastatic progression in OC. Two consensus ABC gene profiles with clinical meaning were documented. (a) Downregulation of ABCC4, ABCC10, ABCD3, ABCE1, ABCF1, ABCF2, and ABCF3 was connected with the best sensitivity to chemotherapy and TTP. (b) Oppositely, downregulation of ABCB11 and upregulation of ABCB1 and ABCG2 were connected with the worst sensitivity to chemotherapy and TTP. Results from publicly available online databases supported our conclusions. CONCLUSION: This study stressed the connection between two well‐documented ABC genes and clinicopathological features—ABCB1 and ABCG2. Moreover, we showed a comparable connection also for several other ABC genes—ABCB11, ABCC4, ABCC10, ABCD3, ABCE1, ABCF1, ABCF2, and ABCF3. Our results add new clinically relevant information to this oncology field and can stimulate further exploration. John Wiley and Sons Inc. 2019-01-22 /pmc/articles/PMC6382717/ /pubmed/30672151 http://dx.doi.org/10.1002/cam4.1964 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. 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 | Clinical Cancer Research Seborova, Karolina Vaclavikova, Radka Soucek, Pavel Elsnerova, Katerina Bartakova, Alena Cernaj, Petr Bouda, Jiri Rob, Lukas Hruda, Martin Dvorak, Pavel Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title | Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title_full | Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title_fullStr | Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title_full_unstemmed | Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title_short | Association of ABC gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
title_sort | association of abc gene profiles with time to progression and resistance in ovarian cancer revealed by bioinformatics analyses |
topic | Clinical Cancer Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382717/ https://www.ncbi.nlm.nih.gov/pubmed/30672151 http://dx.doi.org/10.1002/cam4.1964 |
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