背景:未知原发性癌(CUP)是转移性癌症的一个子集,其中癌细胞的原发性组织来源仍未被识别。CUP是全球第八大常见恶性肿瘤,占所有恶性肿瘤的5%。代表一种异常侵袭性的转移性癌症,中位生存期约为3~6个月.癌症发生的组织在我们对各种形式的细胞死亡的敏感性的理解中起着关键作用。因此,缺乏对起源组织(TOO)的了解,因此很难为CUP患者设计量身定制的有效治疗方法。开发快速和临床可实施的方法来识别主要部位的TOO对于治疗患有CUP的患者至关重要。非编码RNA可能具有起源鉴定的潜力,并且由于它们对化学降解的抗性,为临床实施提供了可靠的途径。
目的:本研究旨在研究microRNAs的潜力,非编码RNA的一个子集,作为通过数据驱动检测TOO的高度准确的生物标志物,转移性癌症的机器学习方法。
方法:我们使用了来自癌症基因组图谱数据集的microRNA表达数据,并评估了各种机器学习方法,从简单的分类器到深度学习方法。作为对我们分类器的测试,我们评估了来自序列阅读存档的194个原发性肿瘤样本的单独集合的准确性。我们使用排列特征重要性来确定潜在的microRNA生物标志物,并通过主成分分析和t分布随机邻居嵌入可视化来评估它们。
结果:我们的结果表明,可以设计强大的分类器来检测癌症基因组图谱数据集上转移样品的TOO,准确度高达97%(351/362),这可以在CUP的情况下使用。我们的研究结果表明,深度学习技术提高了预测准确性。我们从决策树的初始精度预测62.5%(226/362)发展到逻辑回归的93.2%(337/362),最终在转移样本上使用深度学习达到97%(351/362)的准确率。在“序列读取存档”验证集上,决策树的准确率较低,为41.2%(77/188),而深度学习实现了80.4%(151/188)的更高准确度。值得注意的是,我们的特征重要性分析显示了预测TOO是microRNA-10b的前3个最重要的特征,microRNA-205和microRNA-196b,这与以前的工作是一致的。
结论:我们的发现强调了使用机器学习技术来设计检测CUPTOO的准确测试的潜力。由于microRNAs通过细胞分泌的细胞外囊泡被携带到全身,由于它们存在于血浆中,因此它们可作为液体活检的关键生物标志物.我们的工作为基于microRNA的存在开发基于血液的癌症检测测试奠定了基础。
BACKGROUND: Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation.
OBJECTIVE: This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers.
METHODS: We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations.
RESULTS: Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work.
CONCLUSIONS: Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.