Department of Hepatobiliary Surgery, The First People's Hospital of Foshan, Foshan, Guangdong Province, China.
General Surgery Center, Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
Sci Rep. 2024 Apr 19;14(1):9016. doi: 10.1038/s41598-024-59480-y.
RNA modifications affect fundamental biological processes and diseases and are a research hotspot. Several micro-RNAs (miRNAs) exhibit genetic variant-targeted RNA modifications that can greatly alter their biofunctions and influence their effect on cancer. Therefore, the potential role of these miRNAs in cancer can be implicated in new prevention and treatment strategies. In this study, we determined whether RMvar-related miRNAs were closely associated with tumorigenesis and identified cancer-specific signatures based on these miRNAs with variants targeting RNA modifications using an optimized machine learning workflow. An effective machine learning workflow, combining least absolute shrinkage and selection operator analyses, recursive feature elimination, and nine types of machine learning algorithms, was used to screen candidate miRNAs from 504 serum RMvar-related miRNAs and construct a diagnostic signature for cancer detection based on 43,047 clinical samples (with an area under the curve value of 0.998, specificity of 93.1%, and sensitivity of 99.3% in the validation cohort). This signature demonstrated a satisfactory diagnostic performance for certain cancers and different conditions, including distinguishing early-stage tumors. Our study revealed the close relationship between RMvar-related miRNAs and tumors and proposed an effective cancer screening tool.
RNA 修饰影响基本的生物过程和疾病，是研究热点。一些 microRNA（miRNA）表现出针对遗传变异的靶向 RNA 修饰，这极大地改变了它们的生物功能，并影响它们对癌症的影响。因此，这些 miRNA 在癌症中的潜在作用可以被纳入新的预防和治疗策略。在这项研究中，我们确定了 RMvar 相关 miRNA 是否与肿瘤发生密切相关，并基于这些具有靶向 RNA 修饰变异的 miRNA，使用优化的机器学习工作流程，确定了癌症特异性特征。一种有效的机器学习工作流程，结合最小绝对收缩和选择算子分析、递归特征消除和九种机器学习算法，从 504 个血清 RMvar 相关 miRNA 中筛选候选 miRNA，并基于 43047 个临床样本构建癌症检测诊断签名（验证队列的曲线下面积值为 0.998，特异性为 93.1%，灵敏度为 99.3%）。该签名对某些癌症和不同条件（包括区分早期肿瘤）具有令人满意的诊断性能。我们的研究揭示了 RMvar 相关 miRNA 与肿瘤之间的密切关系，并提出了一种有效的癌症筛查工具。
