Department of Science and Technology, University of Naples "Parthenope", Centro Direzionale, Isola C4, 80143, Naples, Italy.
Department of Computer Science, University of Milan, Via Celoria, 18, 20133, Milan, Italy.
BMC Bioinformatics. 2023 Mar 6;23(Suppl 6):569. doi: 10.1186/s12859-023-05167-6.
Recent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100[Formula: see text] conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer.
The aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade.
Here we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR's panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.
最近的研究表明，一类特殊的长非编码 RNA（lncRNA），即转录超保守区，是从特定的 DNA 区域（T-UCRs）转录而来的，在人类、小鼠和大鼠基因组中 100%保守。这是值得注意的，因为 lncRNA 通常是缺乏保守性的。尽管它们具有特殊性，但 T-UCRs 在许多疾病中，包括癌症，仍然研究得很少，但已知 T-UCRs 的失调与癌症以及人类神经、心血管和发育病理学有关。我们最近报道了 uc.8+ T-UCR 作为膀胱癌的潜在预后生物标志物。
本工作的目的是开发一种基于机器学习技术的方法，用于选择膀胱癌发病的预测性特征面板。为此，我们通过使用定制的表达微阵列分析了来自手术切除的正常和膀胱癌组织的 T-UCRs 的表达谱。对 24 例膀胱癌患者（低级别 12 例，高级别 12 例）的完整临床资料和 17 例正常膀胱上皮的对照样本进行了分析。在选择表达水平较高且具有统计学意义的 T-UCRs 后，我们采用了基于统计和机器学习的集成方法（即逻辑回归、随机森林、XGBoost 和 LASSO）来对最重要的诊断分子进行排序。我们确定了一个由 13 个选定的 T-UCRs 组成的特征面板，这些 T-UCRs 在癌症中表达谱发生改变，能够有效地将正常和膀胱癌患者样本区分开来。此外，使用这个特征面板，我们将膀胱癌患者分为四组，每组具有不同的生存程度。正如预期的那样，仅包含低级别膀胱癌患者的组比大多数高级别膀胱癌患者具有更好的总生存率。然而，特定的 T-UCR 失调特征可以识别出具有不同预后的膀胱癌患者的亚型，而与膀胱癌的分级无关。
在这里，我们通过机器学习应用程序展示了对膀胱癌（低级别和高级别）患者样本和正常膀胱上皮对照样本的分类结果。T-UCR 面板可用于学习可解释的人工智能模型，并为膀胱癌早期诊断开发稳健的决策支持系统，提供新患者的尿 T-UCRs 数据。与当前方法相比，使用该系统将导致采用非侵入性方法，减少患者的不适程序（如膀胱镜检查）。总体而言，这些结果提出了新的自动系统的可能性，这可能有助于膀胱癌患者基于 RNA 的预后和/或癌症治疗，并证明了人工智能在定义独立预后生物标志物面板方面的成功应用。
