基于显著差异代谢物构建血液系统恶性肿瘤中枢神经系统浸润早期预测模型

doi:10.3969/j.issn.1673-8640.2026.04.013

摘要/Abstract

摘要：

目的采用代谢组学技术分析中枢神经系统白血病/淋巴瘤（CNSL）浸润后的显著差异代谢物，建立可用于早期预测CNSL的诊断模型，并进行初步验证。方法选取2018年1月—2021年9月海军军医大学第一附属医院血液内科经骨髓穿刺和活检确诊的恶性血液病伴CNSL患者25例（CNSL组）和非肿瘤患者29例（对照组），收集其脑脊液样本，采用超高效液相色谱-质谱（UPLC-MS）技术检测脑脊液代谢物，筛选2组显著差异性代谢物。采用Logistic回归分析评估CNSL的影响因素，并建立CNSL预测模型。采用受试者工作特征（ROC）曲线评价模型效能。另选取同期海军军医大学第一附属医院神经内科CNSL患者5例，收集其相关临床数据，用于验证模型的临床应用效果。结果共筛选出36种显著差异代谢物，涉及32条代谢通路；影响值居前2位的通路分别为精氨酸合成、谷氨酰胺和谷氨酸代谢，包含L-精氨酸、瓜氨酸、L-谷氨酰胺、L-谷氨酸和尿素5种关键代谢物，其中瓜氨酸、L-谷氨酰胺是CNSL的独立危险因素（P<0.05），基于这2种代谢物建立CNSL早期预测模型。ROC曲线分析结果显示，模型诊断CNSL的曲线下面积（AUC）为0.819，敏感性为72.0%，特异性为82.8%。临床验证结果显示，模型预测结果与实际观测结果一致性较好。结论基于显著差异代谢物构建的CNSL早期预测模型可以用于CNSL的早期诊断。

关键词: 代谢物, 血液系统恶性肿瘤, 中枢神经系统浸润, 脑脊液, 代谢组学

Abstract:

Objective To analyze the significantly differential metabolites after central nervous system leukemia/lymphoma（CNSL） infiltration based on metabolomics，establish a diagnostic model for early prediction of CNSL，and conduct preliminary validation. Methods From January 2018 to September 2021，25 patients with hematological malignancies accompanied by CNSL diagnosed by bone marrow puncture and biopsy at the Department of Hematology of the First Affiliated Hospital of Naval Medical University（CNSL group） and 29 non-tumor patients（control group） were enrolled. The brain cerebrospinal fluid samples were collected. The brain cerebrospinal fluid metabolites were determined using ultra-high performance liquid chromatography-mass spectrometry（UPLC-MS），and the significantly differential metabolites in the 2 groups were screened. Logistic regression analysis was used to evaluate the influence factors for CNSL，and a CNSL prediction model was established. Receiver operating characteristic（ROC） curve was used to evaluate the model efficacy. Totally，5 CNSL patients in the Department of Neurology during the same period were enrolled，and the relevant clinical data were collected for verifying the clinical application effect of the model. Results A total of 36 significantly differential metabolites were screened out，involving 32 metabolic pathways. The top 2 influencing pathways were arginine synthesis and glutamine and glutamete matabolism，including 5 key metabolites such as L-arginine，citrulline，L-glutamine，L-glutamate and urea. Citrulline and L-glutamine were independent rislc factors for to CNSL（P<0.05），and a CNSL diagnosing model was established based on these 2 metabolites. The areas under curves（AUC） of the model for diagnosing CNSL was 0.819，with a sensitivity of 72.0% and a specificity of 82.8%. The clinical verfication results showed that the model's prediction results were in good consistency with the actual observation results. Conclusions The CNSL prediction model established based on significantly differential metabolites can be used for the early diagnosis of CNSL.

Key words: Metabolite, Hematological malignancy, Central nervous system leukemia/lymphoma infiltration, Cerebrospinal fluid, Metabolomics

中图分类号:

R446.1

王勉, 王彬彬, 宋志强, 姚永华, 唐古生. 基于显著差异代谢物构建血液系统恶性肿瘤中枢神经系统浸润早期预测模型[J]. 检验医学, 2026, 41(4): 391-397.

WANG Mian, WANG Binbin, SONG Zhiqiang, YAO Yonghua, TANG Gusheng. An early prediction model for central nervous system leukemia/lymphoma infiltration of hematological malignancies based on significantly differential metabolomics[J]. Laboratory Medicine, 2026, 41(4): 391-397.

图/表 9

参考文献 24

[1]	BAILEY C, RICHARDSON L C, ALLEMANI C, et al. Adult leukemia survival trends in the United States by subtype:a population-based registry study of 370 994 patients diagnosed during 1995-2009[J]. Cancer, 2018, 124(19): 3856-3867.
[2]	MUNKER R, LABOPIN M, ESTEVE J, et al. Mixed phenotype acute leukemia:outcomes with allogeneic stem cell transplantation. A retrospective study from the Acute Leukemia Working Party of the EBMT[J]. Haematologica, 2017, 102(12):2134-2140.
[3]	ELLISON L F. Increasing survival from leukemia among adolescents and adults in Canada:a closer look[J]. Health Rep, 2016, 27(7):19-26.
[4]	JOHNSTON D L, ALONZO T A, GERBING R B, et al. Central nervous system disease in pediatric acute myeloid leukemia:a report from the Children's Oncology Group[J]. Pediatr Blood Cancer, 2017, 64(12):e26612.
[5]	CONSTANTINESCU C, BODOLEA C, PASCA S, et al. Clinical approach to the patient in critical state following immunotherapy and/or stem cell transplantation:guideline for the on-call physician[J]. J Clin Med, 2019, 8(6):884.
[6]	SANTAMBROGIO E, NICOLOSI M, VASSALLO F, et al. Aggressive non-Hodgkin lymphomas:risk factors and treatment of central nervous system recurrence[J]. Expert Rev Hematol, 2019, 12(9):787-796.
[7]	PUI C H, HOWARD S C. Current management and challenges of malignant disease in the CNS in paediatric leukaemia[J]. Lancet Oncol, 2008, 9(3):257-268.
[8]	KIM H K, SON S Y, OH J S, et al. Metabolic profiling during acute myeloid leukemia progression using paired clinical bone marrow serum samples[J]. Metabolites, 2021, 11(9):586.
[9]	FAME R M, LEHTINEN M K. Emergence and developmental roles of the cerebrospinal fluid system[J]. Dev Cell, 2020, 52(3):261-275.
[10]	BÁRCENAS-LÓPEZ D A, NÚÑEZ-ENRÍQUEZ J C, HIDALGO-MIRANDA A, et al. Transcriptome analysis identifies linc00152 as a biomarker of early relapse and mortality in acute lymphoblastic leukemia[J]. Genes(Basel), 2020, 11(3):302.
[11]	NIZIOŁ J, OSSOLIŃSKI K, TRIPET B P, et al. Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer[J]. Anal Bioanal Chem, 2020, 412(23):5827-5841.
[12]	NAVIAUX R K. Metabolic features of the cell danger response[J]. Mitochondrion, 2014, 16:7-17.
[13]	SEGAL M B. Extracellular and cerebrospinal fluids[J]. J Inherit Metab Dis, 1993, 16(4):617-638.
[14]	SUD M, FAHY E, COTTER D, et al. Metabolomics workbench:an international repository for metabolomics data and metadata,metabolite standards,protocols,tutorials and training,and analysis tools[J]. Nucleic Acids Res, 2016, 44(D1):D463-D470.
[15]	PAUL S, SHORT N J. Central nervous system involvement in adults with acute leukemia:diagnosis,prevention,and management[J]. Curr Oncol Rep, 2022, 24(4):427-436.
[16]	POPOV A, HENZE G, VERZHBITSKAYA T, et al. Absolute count of leukemic blasts in cerebrospinal fluid as detected by flow cytometry is a relevant prognostic factor in children with acute lymphoblastic leukemia[J]. J Cancer Res Clin Oncol, 2019, 145(5):1331-1339.
[17]	SHEN H, ZHAO Y, SHI Y, et al. The diagnostic and prognostic value of MRI in central nervous system involvement of acute myeloid leukemia:a retrospective cohort of 84 patients[J]. Hematology, 2020, 25(1):258-263.
[18]	苏梓健, 韦佩祺, 袁莉, 等. 缺血性脑卒中代谢组学发病机制及药物干预的研究进展[J]. 中风与神经疾病杂志, 2023, 40(6):568-572.
[19]	MILL J, LI L. Recent advances in understanding of Alzheimer's disease progression through mass spectrometry-based metabolomics[J]. Phenomics, 2022, 2(1):1-17.
[20]	WANG D H, FUJITA Y, DONO A, et al. The genomic alterations in glioblastoma influence the levels of CSF metabolites[J]. Acta Neuropathol Commun, 2024, 12(1):13.
[21]	WANG F X, CHEN K, HUANG F Q, et al. Cerebrospinal fluid-based metabolomics to characterize different types of brain tumors[J]. J Neurol, 2020, 267(4):984-993.
[22]	RASHKOVAN M, FERRANDO A. Metabolic dependencies and vulnerabilities in leukemia[J]. Genes Dev, 2019, 33(21-22):1460-1474.
[23]	CANTOR J R, SABATINI D M. Cancer cell metabolism:one hallmark,many faces[J]. Cancer Discov, 2012, 2(10):881-898.
[24]	DUCKER G S, RABINOWITZ J D. One-carbon metabolism in health and disease[J]. Cell Metab, 2017, 25(1):27-42.

组别	例数	白细胞计数/ （×106 L-1）	葡萄糖/（mmol·L-1）	蛋白质浓度/ （g·L-1）	流式细胞术检测的异常细胞百分比/%
CNSL组	25	11.00（2.00~45.00）	3.00（2.73~3.85）	0.44（0.31~0.67）	50.138（15.212~79.108）
对照组	29	1.00（0.00~2.00）	3.50（3.20~3.80）	0.29（0.25~0.33）	0
统计值		4.095	1.764	3.583	6.840
P值		<0.010	0.078	<0.010	<0.010

组别	例数	白细胞计数/ （×106 L-1）	葡萄糖/（mmol·L-1）	蛋白质浓度/ （g·L-1）	流式细胞术检测的异常细胞百分比/%
CNSL组	25	11.00（2.00~45.00）	3.00（2.73~3.85）	0.44（0.31~0.67）	50.138（15.212~79.108）
对照组	29	1.00（0.00~2.00）	3.50（3.20~3.80）	0.29（0.25~0.33）	0
统计值		4.095	1.764	3.583	6.840
P值		<0.010	0.078	<0.010	<0.010

代谢物	变化趋势^①	P值	VIP②值	倍数变化③
L-精氨酸	↑	<0.010 0	1.17	1.31
L-高胱氨酸	↓	<0.050 0	1.17	0.72
D-核糖	↓	<0.050 0	1.18	0.81
溶血磷脂胆碱（6：0/0：0）	↑	<0.010 0	1.21	1.94
二氢胸腺嘧啶	↑	<0.010 0	1.23	1.32
N（6）-甲基赖氨酸	↑	<0.010 0	1.22	2.07
多巴胺	↑	<0.010 0	1.29	1.25
2-甲基柠檬酸	↓	<0.010 0	1.36	0.60
生物素	↓	<0.010 0	1.38	0.69
瓜氨酸	↑	<0.010 0	1.39	1.93
α-酮异戊酸	↑	<0.010 0	1.40	1.43
羟基乙酸	↓	<0.010 0	1.40	0.75
黄嘌呤	↑	<0.010 0	1.44	1.80
L-组氨酸	↑	<0.010 0	1.51	2.62
甘油磷酰胆碱	↑	<0.010 0	1.59	1.48
L-谷氨酰胺	↑	<0.001 0	1.64	1.51
N-γ-谷氨酰胺	↑	<0.001 0	1.79	1.87
维生素B1	↓	<0.001 0	1.79	0.59
泛酸	↓	<0.000 1	1.79	0.63
N-乙酰神经氨酸	↓	<0.000 1	1.87	0.49
L-胱氨酸	↑	<0.001 0	2.01	3.85
腺苷	↓	<0.000 1	2.17	0.39
L-缬氨酸	↓	<0.010 0	1.38	0.57
L-左旋谷氨酸	↓	<0.001 0	1.62	0.61
4-吡哆酸	↓	<0.000 1	1.70	0.50
肌酸	↓	<0.050 0	1.05	0.28
甘露醇	↓	<0.050 0	1.13	0.28
尿素	↓	<0.050 0	1.15	0.21
α-生育酚	↓	<0.050 0	1.18	0.32
L-天冬酰胺	↑	<0.050 0	1.20	1.33
2-羟基丁酸	↑	<0.050 0	1.22	1.32
焦谷氨酸	↓	<0.010 0	1.22	0.22
L-色氨酸	↑	<0.050 0	1.32	2.01
次黄嘌呤	↓	<0.000 1	1.73	0.64
副黄嘌呤	↓	<0.000 1	1.81	0.48
L-乳酸	↓	<0.000 1	1.86	0.46

代谢物	变化趋势^①	P值	VIP②值	倍数变化③
L-精氨酸	↑	<0.010 0	1.17	1.31
L-高胱氨酸	↓	<0.050 0	1.17	0.72
D-核糖	↓	<0.050 0	1.18	0.81
溶血磷脂胆碱（6：0/0：0）	↑	<0.010 0	1.21	1.94
二氢胸腺嘧啶	↑	<0.010 0	1.23	1.32
N（6）-甲基赖氨酸	↑	<0.010 0	1.22	2.07
多巴胺	↑	<0.010 0	1.29	1.25
2-甲基柠檬酸	↓	<0.010 0	1.36	0.60
生物素	↓	<0.010 0	1.38	0.69
瓜氨酸	↑	<0.010 0	1.39	1.93
α-酮异戊酸	↑	<0.010 0	1.40	1.43
羟基乙酸	↓	<0.010 0	1.40	0.75
黄嘌呤	↑	<0.010 0	1.44	1.80
L-组氨酸	↑	<0.010 0	1.51	2.62
甘油磷酰胆碱	↑	<0.010 0	1.59	1.48
L-谷氨酰胺	↑	<0.001 0	1.64	1.51
N-γ-谷氨酰胺	↑	<0.001 0	1.79	1.87
维生素B1	↓	<0.001 0	1.79	0.59
泛酸	↓	<0.000 1	1.79	0.63
N-乙酰神经氨酸	↓	<0.000 1	1.87	0.49
L-胱氨酸	↑	<0.001 0	2.01	3.85
腺苷	↓	<0.000 1	2.17	0.39
L-缬氨酸	↓	<0.010 0	1.38	0.57
L-左旋谷氨酸	↓	<0.001 0	1.62	0.61
4-吡哆酸	↓	<0.000 1	1.70	0.50
肌酸	↓	<0.050 0	1.05	0.28
甘露醇	↓	<0.050 0	1.13	0.28
尿素	↓	<0.050 0	1.15	0.21
α-生育酚	↓	<0.050 0	1.18	0.32
L-天冬酰胺	↑	<0.050 0	1.20	1.33
2-羟基丁酸	↑	<0.050 0	1.22	1.32
焦谷氨酸	↓	<0.010 0	1.22	0.22
L-色氨酸	↑	<0.050 0	1.32	2.01
次黄嘌呤	↓	<0.000 1	1.73	0.64
副黄嘌呤	↓	<0.000 1	1.81	0.48
L-乳酸	↓	<0.000 1	1.86	0.46

通路名称	代谢物总数/个	命中数/个	P值	通路影响值
精氨酸合成	14	5	<0.000 1	0.42
D-谷氨酰胺和D-谷氨酸的代谢	6	2	<0.010 0	0.50
丙氨酸、天门冬氨酸和谷氨酸代谢	28	3	<0.050 0	0.31
组氨酸代谢	16	2	<0.050 0	0.22
精氨酸和脯氨酸的代谢	38	3	<0.010 0	0.16