Article ; Online: A novel bioinformatic approach reveals cooperation between Cancer/Testis genes in basal-like breast tumors.
2024 Volume 43, Issue 18, Page(s) 1369–1385
Abstract: Breast cancer is the most prevalent type of cancer in women worldwide. Within breast tumors, the basal-like subtype has the worst prognosis, prompting the need for new tools to understand, detect, and treat these tumors. Certain germline-restricted genes ...
| Abstract | Breast cancer is the most prevalent type of cancer in women worldwide. Within breast tumors, the basal-like subtype has the worst prognosis, prompting the need for new tools to understand, detect, and treat these tumors. Certain germline-restricted genes show aberrant expression in tumors and are known as Cancer/Testis genes; their misexpression has diagnostic and therapeutic applications. Here we designed a new bioinformatic approach to examine Cancer/Testis gene misexpression in breast tumors. We identify several new markers in Luminal and HER-2 positive tumors, some of which predict response to chemotherapy. We then use machine learning to identify the two Cancer/Testis genes most associated with basal-like breast tumors: HORMAD1 and CT83. We show that these genes are expressed by tumor cells and not by the microenvironment, and that they are not expressed by normal breast progenitors; in other words, their activation occurs de novo. We find these genes are epigenetically repressed by DNA methylation, and that their activation upon DNA demethylation is irreversible, providing a memory of past epigenetic disturbances. Simultaneous expression of both genes in breast cells in vitro has a synergistic effect that increases stemness and activates a transcriptional profile also observed in double-positive tumors. Therefore, we reveal a functional cooperation between Cancer/Testis genes in basal breast tumors; these findings have consequences for the understanding, diagnosis, and therapy of the breast tumors with the worst outcomes. |
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| MeSH term(s) | Humans ; Breast Neoplasms/genetics ; Breast Neoplasms/pathology ; Female ; Computational Biology/methods ; Gene Expression Regulation, Neoplastic ; DNA Methylation ; Biomarkers, Tumor/genetics ; Biomarkers, Tumor/metabolism ; Cell Line, Tumor ; Male ; Epigenesis, Genetic | |||||
| Chemical Substances | Biomarkers, Tumor | |||||
| Language | English | |||||
| Publishing date | 2024-03-11 | |||||
| Publishing country | England | |||||
| Document type | Journal Article ; Research Support, Non-U.S. Gov't | |||||
| ZDB-ID | 639046-8 | |||||
| ISSN | 1476-5594 ; 0950-9232 | |||||
| ISSN (online) | 1476-5594 | |||||
| ISSN | 0950-9232 | |||||
| DOI | 10.1038/s41388-024-03002-7 | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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