Tiange Lu1,2,3, Lei Shi4, Guanggang Shi4, Yiqing Cai1,2,3, Shunfeng Hu1,2,3, Jiarui Liu1,2,3, Shuai Ren1,2,3, Xiangxiang Zhou5,6,7,8,9,10, Xin Wang11,12,13,14,15,16. 1. Department of Hematology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, No. 324, Jingwu Road, Jinan, 250021, Shandong, China. 2. Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China. 3. School of Medicine, Shandong University, Jinan, 250012, Shandong, China. 4. Department of Otolaryngology-Head and Neck Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China. 5. Department of Hematology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, No. 324, Jingwu Road, Jinan, 250021, Shandong, China. xiangxiangzhou@sdu.edu.cn. 6. Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China. xiangxiangzhou@sdu.edu.cn. 7. School of Medicine, Shandong University, Jinan, 250012, Shandong, China. xiangxiangzhou@sdu.edu.cn. 8. Shandong Provincial Engineering Research Center of Lymphoma, Jinan, 250021, Shandong, China. xiangxiangzhou@sdu.edu.cn. 9. Branch of National Clinical Research Center for Hematologic Diseases, Jinan, 250021, Shandong, China. xiangxiangzhou@sdu.edu.cn. 10. National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, 251006, China. xiangxiangzhou@sdu.edu.cn. 11. Department of Hematology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, No. 324, Jingwu Road, Jinan, 250021, Shandong, China. xinw007@126.com. 12. Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China. xinw007@126.com. 13. School of Medicine, Shandong University, Jinan, 250012, Shandong, China. xinw007@126.com. 14. Shandong Provincial Engineering Research Center of Lymphoma, Jinan, 250021, Shandong, China. xinw007@126.com. 15. Branch of National Clinical Research Center for Hematologic Diseases, Jinan, 250021, Shandong, China. xinw007@126.com. 16. National Clinical Research Center for Hematologic Diseases, The First Affiliated Hospital of Soochow University, Suzhou, 251006, China. xinw007@126.com.
Abstract
BACKGROUND: Mature T-cell lymphomas (MTCLs), a group of diseases with high aggressiveness and vulnerable prognosis, lack for the accurate prognostic stratification systems at present. Novel prognostic markers and models are urgently demanded. Aberrant lipid metabolism is closely related to the tumor progression but its prognostic significance in MTCLs remains unexplored. This study aims to investigate the relationship between dysregulated lipid metabolism and survival prognosis of MTCLs and establish a novel and well-performed prognostic scoring system for MTCL patients. METHODS: A total of 173 treatment-naive patients were enrolled in this study. Univariate and multivariate Cox regression analysis were performed to assess the prognostic significance of serum lipid profiles and screen out independent prognostic factors, which constituted a novel prognostic model for MTCLs. The performance of the novel model was assessed in the training and validation cohort, respectively, by examining its calibration, discrimination and clinical utility. RESULTS: Among the 173 included patients, 115 patients (01/2006-12/2016) constituted the training cohort and 58 patients (01/2017-06/2020) formed the validation cohort. Univariate analysis revealed declined total cholesterol (TC, P = 0.000), high-density lipoprotein cholesterol (HDL-C, P = 0.000) and increased triglycerides (TG, P = 0.000) correlated to inferior survival outcomes. Multivariate analysis revealed extranodal involved sites ≥ 2 (hazard ratio [HR]: 2.439; P = 0.036), β2-MG ≥ 3 mg/L (HR: 4.165; P = 0.003) and TC < 3.58 mmol/L (HR: 3.338; P = 0.000) were independent predictors. Subsequently, a novel prognostic model, EnBC score, was constructed with these three factors. Harrell's C-index of the model in the training and validation cohort was 0.840 (95% CI 0.810-0.870) and 0.882 (95% CI 0.822-0.942), respectively, with well-fitted calibration curves. The model divided patients into four risk groups with distinct OS [median OS: not available (NA) vs. NA vs. 14.0 vs. 4.0 months, P < 0.0001] and PFS (median PFS: 84.0 vs. 19.0 vs. 8.0 vs. 1.5 months, P < 0.0001). Time-dependent receiver operating characteristic curve and decision curve analysis further revealed that EnBC score provided higher diagnostic capacity and clinical benefit, compared with International Prognostic Index (IPI). CONCLUSION: Firstly, abnormal serum lipid metabolism was demonstrated significantly related to the survival of MTCL patients. Furthermore, a lipid-covered prognostic scoring system was established and performed well in stratifying patients with MTCLs.
BACKGROUND:Mature T-cell lymphomas (MTCLs), a group of diseases with high aggressiveness and vulnerable prognosis, lack for the accurate prognostic stratification systems at present. Novel prognostic markers and models are urgently demanded. Aberrant lipid metabolism is closely related to the tumor progression but its prognostic significance in MTCLs remains unexplored. This study aims to investigate the relationship between dysregulated lipid metabolism and survival prognosis of MTCLs and establish a novel and well-performed prognostic scoring system for MTCL patients. METHODS: A total of 173 treatment-naive patients were enrolled in this study. Univariate and multivariate Cox regression analysis were performed to assess the prognostic significance of serum lipid profiles and screen out independent prognostic factors, which constituted a novel prognostic model for MTCLs. The performance of the novel model was assessed in the training and validation cohort, respectively, by examining its calibration, discrimination and clinical utility. RESULTS: Among the 173 included patients, 115 patients (01/2006-12/2016) constituted the training cohort and 58 patients (01/2017-06/2020) formed the validation cohort. Univariate analysis revealed declined total cholesterol (TC, P = 0.000), high-density lipoprotein cholesterol (HDL-C, P = 0.000) and increased triglycerides (TG, P = 0.000) correlated to inferior survival outcomes. Multivariate analysis revealed extranodal involved sites ≥ 2 (hazard ratio [HR]: 2.439; P = 0.036), β2-MG ≥ 3 mg/L (HR: 4.165; P = 0.003) and TC < 3.58 mmol/L (HR: 3.338; P = 0.000) were independent predictors. Subsequently, a novel prognostic model, EnBC score, was constructed with these three factors. Harrell's C-index of the model in the training and validation cohort was 0.840 (95% CI 0.810-0.870) and 0.882 (95% CI 0.822-0.942), respectively, with well-fitted calibration curves. The model divided patients into four risk groups with distinct OS [median OS: not available (NA) vs. NA vs. 14.0 vs. 4.0 months, P < 0.0001] and PFS (median PFS: 84.0 vs. 19.0 vs. 8.0 vs. 1.5 months, P < 0.0001). Time-dependent receiver operating characteristic curve and decision curve analysis further revealed that EnBC score provided higher diagnostic capacity and clinical benefit, compared with International Prognostic Index (IPI). CONCLUSION: Firstly, abnormal serum lipid metabolism was demonstrated significantly related to the survival of MTCL patients. Furthermore, a lipid-covered prognostic scoring system was established and performed well in stratifying patients with MTCLs.
Authors: Steven H Swerdlow; Elias Campo; Stefano A Pileri; Nancy Lee Harris; Harald Stein; Reiner Siebert; Ranjana Advani; Michele Ghielmini; Gilles A Salles; Andrew D Zelenetz; Elaine S Jaffe Journal: Blood Date: 2016-03-15 Impact factor: 22.113