DataSheet_1_Development of a tRNA-Derived Small RNA Prognostic Panel and Their Potential Functions in Osteosarcoma.csv

BackgroundTherapeutic outcomes of osteosarcoma treatment have not significantly improved in several decades. Therefore, strong prognostic biomarkers are urgently needed.
MethodsWe first extracted the tRNA-derived small RNA (tsRNA) expression profiles of osteosarcoma from the GEO database. Then, we performed a unique module analysis and use the LASSO-Cox model to select survival-associated tsRNAs. Model effectiveness was further verified using an independent validation dataset. Target genes with selected tsRNAs were predicted using RNAhybrid.
ResultsA LASSO-Cox model was established to select six prognostic tsRNA biomarkers: tRF-33-6SXMSL73VL4YDN, tRF-32-6SXMSL73VL4YK, tRF-32-M1M3WD8S746D2, tRF-35-RPM830MMUKLY5Z, tRF-33-K768WP9N1EWJDW, and tRF-32-MIF91SS2P46I3. We developed a prognostic panel for osteosarcoma patients concerning their overall survival by high-low risk. Patients with a low-risk profile had improved survival rates in training and validation dataset.
ConclusionsThe suggested prognostic panel can be utilized as a reliable biomarker to predict osteosarcoma patient survival rates.

背景：骨肉瘤（osteosarcoma）的治疗结局在数十年间未取得显著改善，因此临床上亟需高效可靠的预后生物标志物。
方法：本研究首先从GEO数据库中提取骨肉瘤的tRNA衍生小RNA（tRNA-derived small RNA, tsRNA）表达谱。随后开展独特模块分析，并采用LASSO-Cox模型筛选与生存相关的tsRNA。本研究通过独立验证数据集进一步验证了模型的预测效能，并利用RNAhybrid工具预测上述筛选得到的tsRNA的靶基因。
结果：本研究构建LASSO-Cox模型，筛选得到6个预后相关tsRNA生物标志物：tRF-33-6SXMSL73VL4YDN、tRF-32-6SXMSL73VL4YK、tRF-32-M1M3WD8S746D2、tRF-35-RPM830MMUKLY5Z、tRF-33-K768WP9N1EWJDW以及tRF-32-MIF91SS2P46I3。基于高低风险分层，本研究构建了骨肉瘤患者总生存期预后评估模型。在训练集与验证数据集当中，低风险组患者的生存率均显著更高。
结论：本研究提出的预后评估模型可作为可靠的生物标志物，用于预测骨肉瘤患者的总生存率。