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KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer

BACKGROUND: In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, hi...

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Autores principales: De Donato, Marta, Babini, Gabriele, Mozzetti, Simona, Buttarelli, Marianna, Ciucci, Alessandra, Arduini, Gloria, De Rosa, Maria Cristina, Scambia, Giovanni, Gallo, Daniela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702713/
https://www.ncbi.nlm.nih.gov/pubmed/33250051
http://dx.doi.org/10.1186/s13046-020-01775-9
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author De Donato, Marta
Babini, Gabriele
Mozzetti, Simona
Buttarelli, Marianna
Ciucci, Alessandra
Arduini, Gloria
De Rosa, Maria Cristina
Scambia, Giovanni
Gallo, Daniela
author_facet De Donato, Marta
Babini, Gabriele
Mozzetti, Simona
Buttarelli, Marianna
Ciucci, Alessandra
Arduini, Gloria
De Rosa, Maria Cristina
Scambia, Giovanni
Gallo, Daniela
author_sort De Donato, Marta
collection PubMed
description BACKGROUND: In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way. METHODS: To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors. RESULTS: Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction. CONCLUSIONS: Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s13046-020-01775-9.
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spelling pubmed-77027132020-12-01 KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer De Donato, Marta Babini, Gabriele Mozzetti, Simona Buttarelli, Marianna Ciucci, Alessandra Arduini, Gloria De Rosa, Maria Cristina Scambia, Giovanni Gallo, Daniela J Exp Clin Cancer Res Research BACKGROUND: In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way. METHODS: To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors. RESULTS: Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction. CONCLUSIONS: Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s13046-020-01775-9. BioMed Central 2020-11-30 /pmc/articles/PMC7702713/ /pubmed/33250051 http://dx.doi.org/10.1186/s13046-020-01775-9 Text en © The Author(s) 2020 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/. 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 in a credit line to the data.
spellingShingle Research
De Donato, Marta
Babini, Gabriele
Mozzetti, Simona
Buttarelli, Marianna
Ciucci, Alessandra
Arduini, Gloria
De Rosa, Maria Cristina
Scambia, Giovanni
Gallo, Daniela
KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title_full KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title_fullStr KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title_full_unstemmed KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title_short KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
title_sort klf7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702713/
https://www.ncbi.nlm.nih.gov/pubmed/33250051
http://dx.doi.org/10.1186/s13046-020-01775-9
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