在乳腺癌中，如同其他癌症，遗传和表观遗传失调可能导致一组通常沉默的组织特异性基因在错误的上下文中表达。这些基因在各种癌症中的激活赋予肿瘤细胞新的性质，并推动增强增殖和转移活性，导致存活预后不佳。在这项研究中，我们对来自睾丸、胎盘和胚胎干细胞的一组特异性组织基因进行了前所未有的系统性和无偏向的分析，这些基因在正常乳腺组织中不被表达，作为新的预后生物标志物的来源。为此，我们结合了严格的转录组数据分析的机器学习框架，并成功地创建了一个新的强大工具，在多个独立数据集中进行了验证，能够识别具有高复发风险的患者。这种无偏向的方法使我们能够确定了一个由DNMT3B、EXO1、MCM10、CENPF和CENPE这五个标志物构成的面板，这些标志物与乳腺癌的无病生存预后密切相关并且具有稳健和显著的关联。基于这些发现，我们创建了一个新的基因表达分类器（GEC），用于对患者进行分层。此外，借助鉴定的GEC，我们能够绘制出特别侵袭性肿瘤的分子图谱，这些肿瘤显示出男性生殖细胞的特征，并伴有与促转移和促增殖基因表达富集相关的特定代谢基因签名。GEC分类器能够可靠地在疾病的早期阶段识别出高复发风险的患者。特别是对于乳腺癌的luminal-A分子亚型，该亚型一般被认为具有良好的无病生存预后，我们特别推荐使用GEC工具来检测存在高复发风险的患者。© 2023. BioMed Central Ltd., Springer Nature的一部分。

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.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.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.© 2023. BioMed Central Ltd., part of Springer Nature.