Tang Zaixiang, Zeng Qinghua, Li Yan, Zhang Xinyan, Ma Jinlu, Suto Mark J, Xu Bo, Yi Nengjun
Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou 215123, China.
Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, 215123, China.
Oncotarget. 2017 Apr 18;8(16):27428-27439. doi: 10.18632/oncotarget.16194.
Adjuvant radiotherapy is an important clinical treatment option for the majority of sarcomas. The motivation of current study is to identify a gene signature and to predict radiosensitive patients who are most likely to benefit from radiotherapy. Using the public available data of soft tissue sarcoma from The Cancer Genome Atlas, we developed a cross-validation procedure for identifying a gene signature and predicting radiosensitive patients through. The result showed that the predicted radiosensitive patients who received radiotherapy had a significantly better survival with a reduced rate of new tumor event and disease progression. Strata analysis showed that the predicted radiosensitive patients had significantly better survival under radiotherapy independent of histologic types. A hierarchical cluster analysis was used to validate the gene signature, and the results showed the predicted sensitivity for each patient well matched the results from cluster analysis. Together, we demonstrate a radiosensitive molecular signature that can be potentially used for identifying radiosensitive patients with sarcoma.
辅助放疗是大多数肉瘤重要的临床治疗选择。本研究的目的是识别一个基因特征并预测最有可能从放疗中获益的放射敏感患者。利用来自癌症基因组图谱的软组织肉瘤公开可用数据,我们开发了一种交叉验证程序,用于识别基因特征并预测放射敏感患者。结果显示,接受放疗的预测放射敏感患者生存情况显著更好,新肿瘤事件和疾病进展发生率降低。分层分析表明,预测放射敏感患者在放疗情况下生存情况显著更好,与组织学类型无关。采用层次聚类分析来验证该基因特征,结果显示对每位患者的预测敏感性与聚类分析结果匹配良好。我们共同证明了一个放射敏感分子特征,其有可能用于识别肉瘤放射敏感患者。