Ma Zhongling, Wang Rui, Yuan Ming, Wang Bo, Li Li, Zhao Tianfu, Zhao Xinhan
Department of Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Front Pharmacol. 2025 Jul 17;16:1640192. doi: 10.3389/fphar.2025.1640192. eCollection 2025.
Transfer RNA-derived small RNAs (tsRNAs) represent an emerging class of regulatory molecules with potential as cancer biomarkers. However, their diagnostic utility and regulatory mechanisms in breast cancer remain poorly characterized. This study integrates machine learning algorithms with traditional molecular biology approaches to identify tsRNA-based diagnostic signatures and their downstream targets.
We analyzed miRNA-seq data from 103 matched tumor-normal pairs from TCGA-BRCA as the discovery cohort and GSE117452 as validation. tsRNA profiles were extracted using a custom bioinformatics pipeline. Random forest algorithm was employed to develop a diagnostic model. Correlation analysis and RNAhybrid were used to identify tsRNA-mRNA regulatory relationships. Comprehensive multi-omics analyses including survival, immune infiltration, drug sensitivity, and pathway enrichment were performed for identified targets. Functional validation was conducted in breast cancer cell lines.
We identified 297 differentially expressed tsRNAs and developed a four-tsRNA signature (tRF-21-FSXMSL73E, tRF-20-XSXMSL73, tRF-23-FSXMSL730H, tRF-23-YJE76INB0J) achieving AUC of 0.98 in discovery and 0.82 in validation cohorts. tRF-21-FSXMSL73E showed strong correlation with FAM155B expression. Pan-cancer analysis revealed FAM155B overexpression in multiple malignancies with prognostic significance. FAM155B correlated with immune infiltration, drug resistance, and activation of oncogenic pathways. Functional studies confirmed FAM155B promotes breast cancer proliferation and migration.
Our machine learning approach successfully identified a robust tsRNA diagnostic signature and uncovered the tsRNA-FAM155B regulatory axis as a novel therapeutic target. This integrated methodology provides a framework for accelerating biomarker discovery by combining computational prediction with traditional validation, advancing precision medicine in breast cancer.
转运RNA衍生的小RNA（tsRNAs）是一类新兴的调控分子，具有作为癌症生物标志物的潜力。然而，它们在乳腺癌中的诊断效用和调控机制仍未得到充分表征。本研究将机器学习算法与传统分子生物学方法相结合，以识别基于tsRNA的诊断特征及其下游靶点。
我们分析了来自TCGA-BRCA的103对匹配的肿瘤-正常样本的miRNA测序数据作为发现队列，并以GSE117452作为验证队列。使用定制的生物信息学管道提取tsRNA谱。采用随机森林算法建立诊断模型。通过相关性分析和RNAhybrid来识别tsRNA- mRNA调控关系。对鉴定出的靶点进行了包括生存、免疫浸润、药物敏感性和通路富集在内的综合多组学分析。在乳腺癌细胞系中进行功能验证。
我们鉴定出297个差异表达的tsRNAs，并开发了一个四tsRNA特征（tRF-21-FSXMSL73E、tRF-20-XSXMSL73、tRF-23-FSXMSL730H、tRF-23-YJE76INB0J），在发现队列中的AUC为0.98，在验证队列中的AUC为0.82。tRF-21-FSXMSL73E与FAM155B表达呈强相关。泛癌分析显示FAM155B在多种恶性肿瘤中过表达并具有预后意义。FAM155B与免疫浸润、耐药性和致癌通路激活相关。功能研究证实FAM155B促进乳腺癌的增殖和迁移。
我们的机器学习方法成功识别出一个强大的tsRNA诊断特征，并揭示了tsRNA-FAM155B调控轴作为一个新的治疗靶点。这种综合方法通过将计算预测与传统验证相结合，为加速生物标志物发现提供了一个框架，推动了乳腺癌的精准医学发展。
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