Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers
Abstract Background In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enh...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-023-09571-3 |
| _version_ | 1797577991858946048 |
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| author | Emmanuelle Jacquet Florent Chuffart Anne-Laure Vitte Eleni Nika Mireille Mousseau Saadi Khochbin Sophie Rousseaux Ekaterina Bourova-Flin |
| author_facet | Emmanuelle Jacquet Florent Chuffart Anne-Laure Vitte Eleni Nika Mireille Mousseau Saadi Khochbin Sophie Rousseaux Ekaterina Bourova-Flin |
| author_sort | Emmanuelle Jacquet |
| collection | DOAJ |
| description | Abstract Background In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis. Results In this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression. Conclusions The GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse. |
| first_indexed | 2024-03-10T22:15:58Z |
| format | Article |
| id | doaj.art-fd2e9ffeb3304249be739b30ae6687f3 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-10T22:15:58Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-fd2e9ffeb3304249be739b30ae6687f32023-11-19T12:27:24ZengBMCBMC Genomics1471-21642023-08-0124111310.1186/s12864-023-09571-3Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancersEmmanuelle Jacquet0Florent Chuffart1Anne-Laure Vitte2Eleni Nika3Mireille Mousseau4Saadi Khochbin5Sophie Rousseaux6Ekaterina Bourova-Flin7Université Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesUniversité Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesUniversité Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesUniversité Grenoble Alpes, CHU Grenoble Alpes, Department of PathologyUniversité Grenoble Alpes, CHU Grenoble Alpes, Medical Oncology Unit, Cancer and Blood Diseases DepartmentUniversité Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesUniversité Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesUniversité Grenoble Alpes, INSERM U1209, CNRS UMR5309, EpiMed, Institute for Advanced BiosciencesAbstract Background In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis. Results In this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression. Conclusions The GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse.https://doi.org/10.1186/s12864-023-09571-3Cancer/testis antigensBreast cancerEctopic expressionSurvival analysisPrognosis biomarkers |
| spellingShingle | Emmanuelle Jacquet Florent Chuffart Anne-Laure Vitte Eleni Nika Mireille Mousseau Saadi Khochbin Sophie Rousseaux Ekaterina Bourova-Flin Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers BMC Genomics Cancer/testis antigens Breast cancer Ectopic expression Survival analysis Prognosis biomarkers |
| title | Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers |
| title_full | Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers |
| title_fullStr | Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers |
| title_full_unstemmed | Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers |
| title_short | Aberrant activation of five embryonic stem cell-specific genes robustly predicts a high risk of relapse in breast cancers |
| title_sort | aberrant activation of five embryonic stem cell specific genes robustly predicts a high risk of relapse in breast cancers |
| topic | Cancer/testis antigens Breast cancer Ectopic expression Survival analysis Prognosis biomarkers |
| url | https://doi.org/10.1186/s12864-023-09571-3 |
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