Cancer remains a major burden globally and the critical role of early diagnosis is self-evident. Although various miRNA-based signatures have been developed in past decades, clinical utilization is limited due to a lack of precise cutoff value. Here, we innovatively developed a signature based on pairwise expression of miRNAs (miRPs) for pan-cancer diagnosis using machine learning approach. We analyzed miRNA spectrum of 15832 patients, who were divided into training, validation, test, and external test sets, with 13 different cancers from 10 cohorts. Five different machine-learning (ML) algorithms (XGBoost, SVM, RandomForest, LASSO, and Logistic) were adopted for signature construction. The best ML algorithm and the optimal number of miRPs included were identified using area under the curve (AUC) and youden index in validation set. The AUC of the best model was compared to previously published 25 signatures. Overall, Random Forest approach including 31 miRPs (31-miRP) was developed, proving highly efficient in cancer diagnosis across different datasets and cancer types (AUC range: 0.980–1.000). Regarding diagnosis of cancers at early stage, 31-miRP also exhibited high capacities, with AUC ranging from 0.961 to 0.998. Moreover, 31-miRP exhibited advantages in differentiating cancers from normal tissues (AUC range: 0.976–0.998) as well as differentiating cancers from corresponding benign lesions. Encouragingly, comparing to previously published 25 different signatures, 31-miRP also demonstrated clear advantages. In conclusion, 31-miRP acts as a powerful model for cancer diagnosis, characterized by high specificity and sensitivity as well as a clear cutoff value, thereby holding potential as a reliable tool for cancer diagnosis at early stage.

中文翻译：

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独特的循环 microRNA 对特征可作为泛癌早期诊断的优质工具

癌症仍然是全球的主要负担，早期诊断的关键作用是不言而喻的。尽管在过去几十年中已经开发了各种基于 miRNA 的特征，但由于缺乏精确的截止值，临床应用受到限制。在这里，我们创新性地开发了一种基于 miRNA 成对表达 (miRP) 的特征，用于使用机器学习方法进行泛癌诊断。我们分析了 15832 名患者的 miRNA 谱，这些患者被分为训练组、验证组、测试组和外部测试组，涉及来自 10 个队列的 13 种不同癌症。采用五种不同的机器学习 (ML) 算法（XGBoost、SVM、RandomForest、LASSO 和 Logistic）来构建签名。使用验证集中的曲线下面积 (AUC) 和约登指数来确定最佳 ML 算法和包含的 miRP 的最佳数量。将最佳模型的 AUC 与之前发布的 25 个签名进行比较。总体而言，开发了包含 31 个 miRP (31-miRP) 的随机森林方法，证明在不同数据集和癌症类型的癌症诊断中非常有效（AUC 范围：0.980–1.000）。在早期癌症诊断方面，31-miRP也表现出很高的能力，AUC范围为0.961至0.998。此外，31-miRP在区分癌症与正常组织（AUC范围：0.976-0.998）以及区分癌症与相应良性病变方面表现出优势。令人鼓舞的是，与之前发布的 25 种不同的签名相比，31-miRP 也表现出了明显的优势。总之，31-miRP作为癌症诊断的强大模型，具有高特异性和敏感性以及明确的截止值，因此具有作为早期癌症诊断的可靠工具的潜力。