New prognostic factors are needed to establish indications for haematopoietic stem cell transplantation (HSCT) in first complete remission (CR1) for T-cell lymphoblastic lymphoma (T-LBL) patients. We used microarray to compare T-LBL tissue samples (n = 75) and fetal thymus tissues (n = 20), and identified 35 differentially expressed miRNAs. Using 107 subjects as the training group, we developed a five-miRNA-based classifier to predict patient survival with LASSO Cox regression: lower risk was associated with better prognosis (disease-free survival (DFS): hazard ratio (HR) 4.548, 95% CI 2.433–8.499, p < 0.001; overall survival (OS): HR 5.030, 95% CI 2.407–10.513, p < 0.001). This classifier displayed good performance in the internal testing set (n = 106) and the independent external set (n = 304). High risk was associated with more favorable response to HSCT (DFS: HR 1.675, 95% CI 1.127–2.488, p = 0.011; OS: HR 1.602, 95% CI 1.055–2.433, p = 0.027). When combined with ECOG-PS and/or NOTCH1/FBXW7 status, this classifier had even better prognostic performance in patients receiving HSCT (DFS: HR 2.088, 95% CI 1.290–3.379, p = 0.003; OS: HR 1.996, 95% CI 1.203–3.311, p = 0.007). The five-miRNA classifier may be a useful prognostic biomarker for T-LBL adults, and could identify subjects who could benefit from HSCT.
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The key raw data have been uploaded onto the Research Data Deposit public platform (RDD), with the approval RDD number of RDDA2018000588. The microarray data have been deposited online under accession number GSE113749.
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We thank Junhang Luo (First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China) for manuscript consultation, and Wenjun He (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China) for supporting part of the data extraction and processing. This work was supported by grants from the National Natural Science Foundation of China (81672686, 81603137); Natural Science Foundation of Guangdong Province, China (2015A030313020); Sister Institution Net-work Fund of the MD Anderson Cancer Center; National Key R&D Program of China (2017YFC1309001, 2016YFC1302305).