| Autor: |
Xiao-Peng, Tian, Dan, Xie, Wei-Juan, Huang, Shu-Yun, Ma, Liang, Wang, Yan-Hui, Liu, Xi, Zhang, Hui-Qiang, Huang, Tong-Yu, Lin, Hui-Lan, Rao, Mei, Li, Fang, Liu, Fen, Zhang, Li-Ye, Zhong, Li, Liang, Xiao-Liang, Lan, Juan, Li, Bing, Liao, Zhi-Hua, Li, Qiong-Lan, Tang, Qiong, Liang, Chun-Kui, Shao, Qiong-Li, Zhai, Run-Fen, Cheng, Qi, Sun, Kun, Ru, Xia, Gu, Xi-Na, Lin, Kun, Yi, Yue-Rong, Shuang, Xiao-Dong, Chen, Wei, Dong, Wei, Sang, Cai, Sun, Hui, Liu, Zhi-Gang, Zhu, Jun, Rao, Qiao-Nan, Guo, Ying, Zhou, Xiang-Ling, Meng, Yong, Zhu, Chang-Lu, Hu, Yi-Rong, Jiang, Ying, Zhang, Hong-Yi, Gao, Wen-Jun, He, Zhong-Jun, Xia, Xue-Yi, Pan, Hai, Lan, Guo-Wei, Li, Lu, Liu, Hui-Zheng, Bao, Li-Yan, Song, Tie-Bang, Kang, Qing-Qing, Cai |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Leukemia. 34(9) |
| ISSN: |
1476-5551 |
| Popis: |
We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565-6.628; p 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857-5.339; p 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237-6.423; p 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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