Dugo Erica, Piva Francesco, Giulietti Matteo, Giannella Luca, Ciavattini Andrea
Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, Ancona, Italy.
Woman's Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, Ancona, Italy.
J Cell Mol Med. 2025 Aug;29(15):e70762. doi: 10.1111/jcmm.70762.
Endometrial cancer (EC) is the most common malignancy of the female reproductive tract; its prognosis is difficult to predict. Despite the technique of single-cell transcriptomic analysis (scRNA-seq) returning single-cell level expression data and promising to improve the accuracy of prognosis prediction, a tool that correlates transcriptomic signatures with survival is missing. To this aim, we have created SCENE, a database that collects information to correlate EC transcriptomic signatures with patient prognosis. We performed a review of the literature present in PubMed to collect transcriptomic signatures annotated with their characteristics, differential expression between healthy and sick patients, between patients with more and less favourable prognosis, and cellular pathways in which the genes are involved, as well as references to the original studies. The analysis of about 200 studies has allowed us to obtain 700 mRNA signatures, 60 microRNA (miRNA), and 150 long non-coding RNA (lncRNA), involved in 60 molecular pathways. Each signature is annotated with its specific prognostic outcome that it influences, such as overall survival (OS), progression-free survival (PFS), relapse-free survival (RFS), and disease-specific survival (DSS). The SCENE resource collects and annotates information that is widespread in the literature to facilitate the interpretation of transcriptomic data obtained with any technique in EC. In the case of scRNA-seq data, SCENE may reveal cells predisposed to develop therapy resistance and metastasis.
子宫内膜癌（EC）是女性生殖道最常见的恶性肿瘤，其预后难以预测。尽管单细胞转录组分析技术（scRNA-seq）能返回单细胞水平的表达数据，并有望提高预后预测的准确性，但目前仍缺少一种将转录组特征与生存情况相关联的工具。为此，我们创建了SCENE数据库，该数据库收集相关信息，以将EC转录组特征与患者预后相关联。我们对PubMed中发表的文献进行了综述，收集了带有其特征注释的转录组特征、健康与患病患者之间的差异表达、预后较好与较差患者之间的差异表达、基因所涉及的细胞通路，以及原始研究的参考文献。对约200项研究的分析使我们获得了700个mRNA特征、60个微小RNA（miRNA）和150个长链非编码RNA（lncRNA），它们涉及60条分子通路。每个特征都标注了其影响的特定预后结果，如总生存期（OS）、无进展生存期（PFS）、无复发生存期（RFS）和疾病特异性生存期（DSS）。SCENE资源收集并注释了文献中广泛存在的信息，以促进对通过任何技术在EC中获得的转录组数据的解读。对于scRNA-seq数据，SCENE可能揭示易产生治疗抗性和转移的细胞。
