An early prediction model for central nervous system leukemia/lymphoma infiltration of hematological malignancies based on significantly differential metabolomics

doi:10.3969/j.issn.1673-8640.2026.04.013

Abstract

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

CLC Number:

R446.1

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.

Figures/Tables 9

References 24

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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