Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models
Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to de...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/13/3350 |
| _version_ | 1797592031405539328 |
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| author | Rachel Kerslake Birhanu Belay Suzana Panfilov Marcia Hall Ioannis Kyrou Harpal S. Randeva Jari Hyttinen Emmanouil Karteris Cristina Sisu |
| author_facet | Rachel Kerslake Birhanu Belay Suzana Panfilov Marcia Hall Ioannis Kyrou Harpal S. Randeva Jari Hyttinen Emmanouil Karteris Cristina Sisu |
| author_sort | Rachel Kerslake |
| collection | DOAJ |
| description | Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012–2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial–mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments. |
| first_indexed | 2024-03-11T01:46:42Z |
| format | Article |
| id | doaj.art-ffc8e1958a6d43e19f893fd259cef6fe |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T01:46:42Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-ffc8e1958a6d43e19f893fd259cef6fe2023-11-18T16:15:43ZengMDPI AGCancers2072-66942023-06-011513335010.3390/cancers15133350Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell ModelsRachel Kerslake0Birhanu Belay1Suzana Panfilov2Marcia Hall3Ioannis Kyrou4Harpal S. Randeva5Jari Hyttinen6Emmanouil Karteris7Cristina Sisu8Division of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UKComputational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, FinlandDivision of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UKDivision of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UKWarwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UKWarwickshire Institute for the Study of Diabetes, Endocrinology and Metabolism (WISDEM), University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UKComputational Biophysics and Imaging Group, The Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, FinlandDivision of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UKDivision of Biosciences, College of Health, Medicine and Life Sciences, Brunel University London, Uxbridge UB8 3PH, UKThree-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012–2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial–mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.https://www.mdpi.com/2072-6694/15/13/3350ovarian cancerhigh-grade serous ovarian cancer (HGSOC)monolayer2D3Dscaffold |
| spellingShingle | Rachel Kerslake Birhanu Belay Suzana Panfilov Marcia Hall Ioannis Kyrou Harpal S. Randeva Jari Hyttinen Emmanouil Karteris Cristina Sisu Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models Cancers ovarian cancer high-grade serous ovarian cancer (HGSOC) monolayer 2D 3D scaffold |
| title | Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models |
| title_full | Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models |
| title_fullStr | Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models |
| title_full_unstemmed | Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models |
| title_short | Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models |
| title_sort | transcriptional landscape of 3d vs 2d ovarian cancer cell models |
| topic | ovarian cancer high-grade serous ovarian cancer (HGSOC) monolayer 2D 3D scaffold |
| url | https://www.mdpi.com/2072-6694/15/13/3350 |
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