Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer
Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/13/9/1538 |
| _version_ | 1797488064357990400 |
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| author | Nathalie Schneider Ellen Reed Faddy Kamel Enrico Ferrari Mikhail Soloviev |
| author_facet | Nathalie Schneider Ellen Reed Faddy Kamel Enrico Ferrari Mikhail Soloviev |
| author_sort | Nathalie Schneider |
| collection | DOAJ |
| description | Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining. |
| first_indexed | 2024-03-09T23:56:47Z |
| format | Article |
| id | doaj.art-c2e5de02864f4d9798a4fb5a38ed243f |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-09T23:56:47Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-c2e5de02864f4d9798a4fb5a38ed243f2023-11-23T16:23:47ZengMDPI AGGenes2073-44252022-08-01139153810.3390/genes13091538Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast CancerNathalie Schneider0Ellen Reed1Faddy Kamel2Enrico Ferrari3Mikhail Soloviev4Department of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UKDepartment of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UKDepartment of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UKSchool of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UKDepartment of Biological Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UKEarly detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining.https://www.mdpi.com/2073-4425/13/9/1538transcription factorsbiological pathwaysgene expressionmicroarraystranscriptomicsmolecular biomarkers |
| spellingShingle | Nathalie Schneider Ellen Reed Faddy Kamel Enrico Ferrari Mikhail Soloviev Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer Genes transcription factors biological pathways gene expression microarrays transcriptomics molecular biomarkers |
| title | Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer |
| title_full | Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer |
| title_fullStr | Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer |
| title_full_unstemmed | Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer |
| title_short | Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer |
| title_sort | rational approach to finding genes encoding molecular biomarkers focus on breast cancer |
| topic | transcription factors biological pathways gene expression microarrays transcriptomics molecular biomarkers |
| url | https://www.mdpi.com/2073-4425/13/9/1538 |
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