Diagnostic and prognostic marker for children Langerhans cell histocytosis

Abstract

The invention discloses a group of diagnosis, typing and prognostic markers for children Langerhans cell histocytosis, and belongs to the technical field of disease diagnosis. In the invention, an overall metabolic profile of bone marrow aspirates (BMA) of 34 children LCH patients is analyzed, and the patients are divided into three subtypes: single system diseases (SS), multi-system diseases (MS-RO-) without dangerous organ tiredness and multi-system diseases (MS-RO +) with dangerous organ tiredness. The metabonomics characteristics of children diagnosed as LCH are described, the metabolic characteristics of a multi-system LCH (MS-RO +) group with dangerous organs being tired before and after treatment are comprehensively researched, and important metabolic markers for molecular diagnosis, subtype classification, treatment evaluation and prognosis are provided for children patients suffering from LCH, especially high-risk LCH.

Description

TECHNICAL FIELD

[0001] The present application relates to diagnostic, typing and prognostic markers of childhood Langerhans cell histiocytosis, belonging to the technical field of disease diagnosis. BACKGROUND

[0002] Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the accumulation of pathologic CD1a + and CD207 + cells in lesions, with a highly variable clinical presentation. LCH can occur in patients of all ages, but is predominantly seen in young children. The incidence of LCH in children under 15 years of age is 2.6-9.6 cases per million, with a median age at diagnosis of 3 years. Diagnosis relies on CD1a and CD207 staining of lesional tissue, and the severity of LCH is clinically assessed according to the severity of the disease, ranging from isolated single-system disease to fulminant multi-system involvement, with LCH children involving organs such as liver, spleen or bone marrow having a higher risk of death. Despite advances in LCH treatment, the relapse rate remains higher than 30%, leading to severe long-term neurological or endocrine complications, significantly affecting quality of life. Although some molecular features of LCH have been elucidated, including BRAF mutations (such as BRAF V600E ) and potential drivers of the mitogen-activated protein kinase (MAPK) pathway (MAP2K1, ARAF). Currently, treatment decisions rely only on clinical presentation, as no reliable molecular markers for risk stratification have been identified.

[0003] Metabolic reprogramming is a fundamental feature of cancer cells, playing a crucial role in promoting abnormal proliferation and promoting carcinogenesis. Metabolomics research has become a powerful tool for monitoring disease status and revealing the pathogenesis of various diseases. Metabolic pathway reprogramming has been extensively studied in the development and progression of cancer and resistance to various treatments. Advances in high-throughput metabolomics technology have enabled the quantitative and comprehensive identification of endogenous low-molecular-weight metabolites, which reflect the complex interactions between host factors, genetic influences and environmental exposures. BRAF is a known driver gene in LCH, and metabolic reprogramming has been reported in many diseases. However, the potential metabolic changes in the pathophysiology and treatment of LCH are still unclear. SUMMARY

[0004] In the present application, we analyzed the global metabolic profile in bone marrow aspirates (BMA) of 34 pediatric LCH patients, which were classified into three subtypes: single-system disease (SS), multi-system disease without risk organ involvement (MS-RO - ) and multi-system disease with risk organ involvement (MS-RO +). We described the metabolomic features of these clinical subtypes of children diagnosed with LCH and comprehensively investigated the metabolic features associated with high-risk LCH pre-treatment and post-treatment outcomes, aiming to provide important metabolic markers for diagnosis, subtype classification, treatment evaluation and prognosis of children with LCH. BRIEF DESCRIPTION OF DRAWINGS

[0005] Figure 1 : Principal component analysis of metabolites in LCH patients and healthy controls. (A) Experimental design, 34 samples were collected from children LCH patients at different treatment times. 10 healthy control individuals were analyzed as controls, ND: case pre-treatment, CR: remission group after treatment, Ctrl: control group. (B) The proportion of identified metabolites in each chemical category. (C) Volcano plot in cation mode. (D) Volcano plot in anion mode. (E) Orthogonal partial least squares discriminant analysis (OPLS-DA score) in cation mode. (F) Orthogonal partial least squares discriminant analysis (OPLS-DA score) in anion mode. The points in (E), (F) represent each biological replicate in LCH (green) and control (blue) groups. t[1] is the score of the sample on the first principal component, used to distinguish groups, to[1] is the score of the sample on the second principal component, to reflect the changes within the group. The ellipse represents the 95% confidence interval. (G) OPLS-DA permutation test in cation mode. (H) OPLS-DA permutation test in anion mode.

[0006] Figure 2 : Potential value of differential metabolites in the diagnosis of children LCH patients. (A) Hierarchical clustering heat map of different metabolites in cation and anion modes. “★” represents the most weight metabolites (p < 0.01). (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment bubble chart. The vertical axis represents the top 10 enriched signal channels, and the horizontal axis represents the enrichment factor. The darker the red in the circle, the smaller the p value in the enrichment analysis, indicating that the statistical difference of the pathway is more significant. C-D are the results of receiver operating characteristic (ROC) analysis for oleamide, Glu-Thr-Arg, inositol, N-acetyl-D-glucosamine 6-phosphate, D-mannose, hypoxanthine, xanthine and nicotinamide in LCH patients, respectively, to distinguish LCH patients and healthy controls, with an area under the ROC curve (AUC) of more than 90%.

[0007] Figure 3 : Analysis of metabolic differences between different subtypes of LCH patients. (A) Scatter plot of differential metabolites between SS and MS groups related to lipids and lipid-like molecules in LCH. (B) Hierarchical clustering heat map of differential metabolites between SS, MS-RO - and MS-RO + groups. (C) Hierarchical clustering heat map of differential metabolites between SS, MS-RO- and MS-RO + Significant differences in the concentration of 1-heptanoyl-sn-glycero-3-phosphocholine were observed between groups. (D) Hierarchical clustering heat map of different metabolites between TLS and IS patients. (E) Scatter plot showing the relative content levels of L-kynurenine and betaine aldehyde in TLS and IS groups, and ROC curves reflecting their different efficacy on LCH subtypes. * indicates p<0.05; ** indicates p<0.01; *** indicates p<0.001.

[0008] Figure 4 : LCH prognostic biomarker screening results. (A) Hierarchical clustering heat map of different metabolites between good prognosis group (GPG) and poor prognosis group (PPG). (B) Scatter plot of L-kynurenine, 2-arachidonoyl-1-stearoyl-ceramide-3-glycerophosphoethanolamine and 1-heptadecanoyl-ceramide-3-phosphocholine between good prognosis group (GPG) and poor prognosis group (PPG). (C) 5-year event-free survival (RFS) curves from 28 LCH patients, which were divided into high and low relative content groups according to the median content levels of L-kynurenine, 2-arachidonoyl-1-stearoyl-ceramide-3-glycerophosphoethanolamine and 1-heptadecanoyl-ceramide-3-phosphocholine. Statistical differences between curves were calculated by using the log-rank test. **, p<0.01.

[0009] Figure 5 : Multi-system LCH with risk organ involvement (MS-RO + ) group before treatment (ND) and after treatment remission (CR) metabolic characteristics and clinical significance. (A) OPLS-DA score in cation mode. (B) OPLS-DA results in anion mode. These points represent each biological replicate in the pre-treatment group (green) and the control group (blue). (C) OPLS-DA permutation test in cation mode (D) OPLS-DA permutation test in anion mode. (E) Hierarchical clustering heat map of LCH differential metabolites in (MS-RO + ) group before treatment and after treatment remission. (F) KEGG pathway analysis of differential metabolites in two samples. (G) Relative content levels of 1,2-distearyl-sn-glycero-3-phosphate-1-serine, 1-oleoyl-2-hydroxy-sn-glycero 3-phosphate (1'-rac glycerol), and dodecanoic acid were detected in LCH patient samples at different disease stages before treatment, and after treatment remission. ***, p<0.001; ns, not significant.

[0010] Figure 6 : Scatter plot of potential diagnostic metabolic biomarkers of LCH in children.

[0011] Figure 7: Standard curve graph of betaine aldehyde (A) and L-kynurenine (B).

[0012] Figure 8 : Concentration quantitative analysis graph of betaine aldehyde and L-kynurenine in children with LCH; wherein A is the concentration quantitative analysis graph of betaine aldehyde in children with LCH, and B is the concentration quantitative analysis graph of L-kynurenine in children with LCH.

[0013] Figure 9 : Targeted quantitative analysis of L-kynurenine on the event-free survival rate of patients with LCH in children.

[0014] Figure 10 : Quantitative analysis result graph of L-kynurenine in children with LCH in the good prognosis group (GPG) and the poor prognosis group (PPG). DETAILED DESCRIPTION

[0015] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in combination with specific embodiments.

[0016] 1 Patient and methods

[0017] 1.1 Patient population and clinical data collection

[0018] From January 2017 to June 2023, a total of 34 patients diagnosed with LCH were included in the Guangzhou Women and Children's Medical Center Affiliated to Guangzhou Medical University, of which 28 were newly diagnosed (ND) cases and 6 were complete remission (CR) cases. These patients were followed up to December 31, 2023. In addition, a control group consisting of 10 age- and gender-matched control individuals was established during the same period. Except for 1 case who died due to rapid disease progression and could not receive treatment, all patients received systemic treatment. The demographic characteristics of LCH patients are summarized in Table 1. The informed consent of the parents or guardians of the participants was obtained to participate in the research and related scientific investigations. This study was conducted after obtaining the approval of the Ethics Committee of Guangzhou Women and Children's Medical Center (Approval No.:

[2024] 024A01) and followed the principles of the "Helsinki Declaration".

[0019] Table 1 Demographic characteristics of LCH patients

[0020]

[0021]

[0022] 1.2 Preparation of metabolite extraction samples

[0023] 2-ml Vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) anticoagulant were used to collect individual bone marrow aspirate (BMA) samples, which were then centrifuged at 3000 x g for 15 min at 4 °C. The supernatant of the BMA samples was immediately isolated and stored at -80 °C until liquid chromatography-mass spectrometry (LC-MS) analysis. For metabolite extraction, 400 μL of extraction solvent of methanol / acetonitrile / H2O (2:2:1, v / v / v) was added to 100 μL of sample, followed by thorough vortexing. After mixing, the samples were incubated on ice for 20 min, and then centrifuged at 14000 x g for 20 min at 4 °C. The supernatant was then collected and dried in a vacuum centrifuge at 4 °C. For LC-MS analysis, the samples were re-dissolved in 100 μL of acetonitrile / water (1:1, v / v) solvent and transferred to LC vials.

[0024] 1.3 Liquid chromatography-mass spectrometry (LC-MS) analysis of metabolomics and data quality control

[0025] For non-targeted polar metabolite metabolomics analysis, the extracts were analyzed by electrospray ionization coupled with hydrophilic interaction chromatography using a quadrupole time-of-flight mass spectrometer (AB6600) at Shanghai Applied Protein Technology Co., Ltd. (Shanghai, China). The raw mass spectrometry data (wiff.scan files) were converted to MzXML files using ProteoWizard MSCconvert and then imported into XCMS software (https: / / xcmsonline.scripps.edu / J), retaining only variables with non-zero measurements exceeding 50% in at least one group. Metabolites were identified by comparing mass spectrometry / mass spectrometry (MS / MS) spectra with an in-house database established with existing standards. We used the Metabolomics Standards Initiative confidence levels to classify metabolite annotation confidence (level 1-4).

[0026] 1.4 Identification of significantly altered metabolites

[0027] After normalizing the data to total peak intensity, the processed data were uploaded and imported into SIMCA-P software (version 14.1, Umetrics, Umea, Sweden), in which multivariate data analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), was performed. Seven-fold cross-validation and response permutation tests were used to assess the robustness of the models. The variable importance in the projection (VIP) values of each variable in the OPLS-DA model were calculated to indicate their contribution to classification. The unpaired Student’s t-test was used to determine significance. VIP values > 1 and p < 0.05 were considered statistically significant.

[0028] 1.5 Pathway enrichment analysis

[0029] Metabolites were aligned against the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database to retrieve their entries, which were subsequently mapped to pathways in KEGG. The corresponding KEGG pathways were extracted. For further exploration of the impact of the differential metabolites, enrichment analysis was performed. KEGG pathway enrichment analysis was performed based on Fisher's exact test, with all metabolites of each pathway as the background dataset. Only pathways with p-values lower than the 0.05 threshold were considered significant.

[0030] For hierarchical clustering, Cluster 3.0 software (http: / / bonsai.hgc.jp / ~mdehoon / software / cluster / software.htm) and Java Treeview software (http: / / jtreeview.sourceforge.net) were used. When performing hierarchical clustering, the Euclidean distance algorithm was chosen for similarity measurement, and the average linkage clustering algorithm (clustering using the centroid of the observations) was chosen for clustering.

[0031] 1.6 Targeted identification of differential metabolites

[0032] Finally, we performed targeted quantitative identification of several key metabolites (L- kynurenine, betaine aldehyde), and added 12 cases to the control group, for a total of 22 cases in the control group. 100 microliters of bone marrow extract was added to 100 microliters of internal standard, followed by the addition of an extraction solvent mixture (n-butanol: methanol = 1 : 1, by volume) and 10 millimoles / liter ammonium formate. The mixture was immediately vortexed, sonicated in ice water, and then centrifuged to obtain the supernatant for LC-MS / MS analysis. Instrument analysis was performed using a ZORBAX Eclipse Plus C18 column on a binary liquid chromatography system. Quality assurance and quality control procedures included solvent blanks, solvent with added internal standard, matrix blanks without added internal standard, and a process of mixed plasma samples as quality control samples to determine the within-batch and between-batch coefficient of variation.

[0033] 1.7 Statistical analysis

[0034] Statistical analysis was performed using GraphPad Prism 8.0. Data that were normally distributed were expressed as mean, and data that were not normally distributed were expressed as median and interquartile range. Categorical variables in two-group comparisons were analyzed using Fisher’s exact test, while continuous variables were analyzed using independent sample t-test or Mann-Whitney U test, depending on the parametric distribution. For more than two-group comparisons, Kruskal-Wallis test was performed first, followed by Bonferroni correction for multiple comparisons. In addition, we calculated the area under the curve (AUC), sensitivity, and specificity. Kaplan-Meier method was used for relapse-free survival (RFS) analysis, and the outcomes between subgroups were compared by log-rank test. The significance level was set at *p < 0.05; **p < 0.01; ***p < 0.001.

[0035] 2 Results

[0036] 2.1 Metabolic reprogramming in newly diagnosed (ND) children with Langerhans cell histiocytosis (LCH)

[0037] To analyze metabolites in bone marrow aspirates of LCH, we performed a non-targeted metabolomics analysis of metabolites in the centrifuged supernatant of bone marrow aspirates of 28 children with LCH (21 boys [75%] and 7 girls [25%]; median [range] age, 21 [1-146] months) and 10 healthy controls (Table 2).

[0038] Table 2 Characteristics of 28 newly diagnosed LCH patients and controls

[0039]

[0040] LCH: Langerhans cell histiocytosis; SS: single system disease; MS-RO - : multi-system disease without high-risk organ. MS-RO + : multi-system disease with high-risk organ. N / A, not applicable. # Indicates two-sample variance homogeneity test to compare the distribution between LCH cases and healthy controls. & Indicates that of these 16 patients, 5 were multi-single system disease patients.

[0041] Sample quality control results showed that the average of Spearman correlation coefficient was 0.90, indicating that the metabolomics data generated by LC-MS analysis had high reproducibility. The quantitative results of metabolites were consistent between test samples and quality control samples. A total of 1516 metabolites were identified, of which 895 and 621 were detected in cation and anion modes, respectively Figure 1A) These metabolites belong to 12 major categories. Notably, lipids and lipid molecules (34.433%) and organic acids and their derivatives (22.625%) are the two most numerous categories. Figure 1 B). These findings suggest that lipids and lipid molecules, as well as organic acids and their derivatives, may play a crucial role in the pathogenesis of childhood LCH. Volcano plots were used to visually represent the differentially metabolites identified in each pattern by univariate statistical analysis, with fold change (FC) values ​​greater than 1.5 or less than 0.67, and p < 0.05. Figure 1 CD). Discriminant analysis was performed using an orthogonal partial least squares discriminant analysis (OPLS-DA) model. The results showed a clear clustering pattern between the LCH group and the control group, such as... Figure 1 As shown in Figure EF. Clearly, the metabolites in the LCH group showed a significant separation from the control group, indicating a significant difference in bone marrow aspirate (BMA) metabolites and demonstrating the robustness of the analytical model. To further validate the OPLS-DA model, permutation analysis was performed, as shown below. Figure 1 As shown in GH.

[0042] Using variable importance projection (VIP) > 1 and p < 0.05 as the criterion, a total of 115 differentially expressed metabolites were identified. Hierarchical clustering heatmaps showed significant differences in metabolite level patterns between the LCH group and the control group, particularly in anion and cation patterns. Figure 2 A). Analysis revealed that among the top 10 differentially expressed metabolites between LCH patients and the control group, the significantly different metabolites could be mainly divided into two subclasses: amino acids / peptides and their analogues (such as glutamyl-threonyl-arginine, alanine, creatine and arginyl-alanine) and fatty acids and their conjugates (such as oleic acid, 8,11-tridecadienoic acid, etc.).

[0043] To further investigate the exact metabolic pathways involved in metabolic reprogramming in children with LCH, we performed KEGG analysis on significantly altered metabolites and then identified 33 significantly affected metabolic and signaling pathways. Figure 2 The bubble diagram in B shows the top 10 most significantly enriched signaling pathways, among which choline metabolism, glycerophospholipid metabolism, and unsaturated fatty acid biosynthesis are the main pathways, indicating that fatty acid metabolism is severely disrupted in LCH. Notably, fatty acid metabolism is tightly regulated by the MAPK signaling pathway, which is a key factor in the dysregulation of LCH.

[0044] 2.2 Potential diagnostic metabolic markers for childhood LCH

[0045] To explore potential diagnostic biomarkers in childhood LCH, we employed various feature selection methods and determined the weights of pre-screened metabolic biomarkers. Thirty-three metabolites were selected based on their weights (VIP>1 and p<0.01), and these metabolites are indicated by “★”. Figure 2 A). Subsequently, we performed receiver operating characteristic (ROC) curve analysis to assess the impact of these selected metabolites on sample classification. The cumulative AUC curves showed that top-ranked features, such as fatty acyls (oleamide), carboxylic acids and their derivatives (glutamyl-threonyl-arginine), organooxygen compounds (inositol, N-acetyl-D-glucosamine 6-phosphate, and D-mannose), imidazopyrimidines (hypoxanthine and xanthine), and pyridines and their derivatives (nicotinamide), significantly enhanced the ability to distinguish LCH patients from controls. Figure 2 CJ). ROC curves showed that these diagnostic biomarkers exhibited excellent classification performance, with AUCs exceeding 0.9. Therefore, we identified these 8 differentially expressed metabolites as potential diagnostic biomarkers; except for oleamide and glutamate-threonine-arginine, the abundance of all other metabolites in the LCH group was significantly increased (CJ). Figure 6 ).

[0046] 2.3 Metabolomics analysis of differentially expressed metabolites among different clinical subtypes

[0047] To investigate the specific roles of metabolites in LCH classification, we first divided LCH patients into two groups: single-system involvement (SS) and multi-system involvement (MS). We then compared the levels of common differentially expressed metabolites between these two groups, identifying 14 metabolites with significant differences between the two groups. Notably, 11 of these metabolites were classified as lipids and lipid-like molecules, including 4 fatty acyl groups, 4 glycerophospholipids, 2 isoprenol lipids, and 1 sterol derivative. The scatter plots corresponding to the top 10 most significantly different metabolites are shown below. Figure 3 As shown in Figure A. Specifically, the concentration of fatty acyl groups was significantly decreased in the SS group, while the concentration of glycerophospholipids was significantly increased. These results indicate that lipid metabolism is significantly altered in LCH patients with involvement of different organs or systems.

[0048] In clinical practice, multi-system involvement of critical organs is often insidious and difficult to identify. We wanted to determine whether metabolites found in bone marrow aspirate (BMA) have the potential to predict multi-system critical organ involvement. Therefore, we further divided patients with multi-system involvement into a non-critical organ group (MS-RO) based on the presence or absence of critical organs, including the liver, kidneys, lungs, brain, and bone marrow. - ) and the group with dangerous organ involvement (MS-RO) +) two subgroups. The cluster plot shows 6 different metabolites that are significantly different between these different groups Figure 3 B), where the content of 1-heptadecanoyl-sn-glycero-3-phosphocholine [PC(17:0 / 0:0)] significantly decreased with disease severity Figure 3 C), which is expected to be a molecular marker for LCH with or without involvement of dangerous organs.

[0049] According to clinical symptoms, we divided LCH patients into two groups: inflammatory symptom group (IS) and tumor-like symptom group (TLS). We want to know whether the metabolites of bone marrow aspirate can distinguish these two types of patients. It was found that there were 87 metabolites significantly changed between the two groups, and the top 30 metabolites were: taurochenodeoxycholate sulfate, betaine aldehyde, biphenyl indenone A, 3-hydroxy canrenone glucuronide, propyl pyrazole triol, L-kynurenine, 3-(N-maleimidopropionyl) biocytin, cortisol 21-sulfate, 2-cis-4-trans-abscisic acid, ganoderic acid F, human enterostatin, ketone [6-hydroxy-1-[2-(4-morpholinyl)ethyl]-1H-indol-3-yl]-1-naphthalenyl-, mangiferin, estrone glucuronide, 4-pregnen-17a, 20b-diol-3-keto-20-sulfate, (1-acetyloxy-3-hydroxy-6,8a-dimethyl-7-oxo-3-isopropyl-2,3a,4,8-tetrahydro-1H-azulen-4-yl) 4-hydroxybenzoate, 2,5,7,8-tetramethyl-2-(beta-carboxyethyl)-6-hydroxychromane, adipyl-L-carnitine, 3-methoxy-4-hydroxyphenylglycol sulfate, artesunate, 1-methyladenosine, N-lauroyl-D-erythro-sphingosylphosphocholine, PE(16:1e / 14-HDoHE), eplerenone hydroxy acid, Asn-Gly-Lys, 7a, 17a-dimethyl-5beta-androstan-3a, 17beta-diol glucuronide, PE(18:1e / 7-HDoHE), melezitose, alisol A 24-acetate, 5alpha-pregnan-3alpha, 17-diol-20-one 3-sulfate. The hierarchical cluster heat map shows that there are significant differences in the change patterns of metabolites between the TLS group and the IS group Figure 3 D), where L-kynurenine and betaine aldehyde in TLS patients are significantly increased, and both of these two metabolites can effectively distinguish the two groups of patients Figure 3 E). Therefore, specific metabolites can be used as markers to distinguish LCH patients with single system involvement (SS), multiple system involvement without involvement of dangerous organs (MS-RO - ), multiple system involvement with involvement of dangerous organs (MS-RO + ), or tumor-like symptoms (TLS) and inflammatory symptoms (IS).

[0050] 2.4 The role of metabolites in bone marrow extracts in the prognosis of childhood LCH

[0051] It has been reported that the prognosis of childhood Langerhans cell histiocytosis (LCH) is related to BRAF. V600E Mutations, multi-system involvement, organs at risk, and age at diagnosis were highly correlated. To determine whether bone marrow aspirate metabolites could be used to predict clinical outcomes in children with LCH, we followed up for a median of 657 days (range, from 3 days to 1969 days). In the initial cohort of 28 patients with ND LCH, there were 3 relapses (10.7%) and 2 deaths (7.14%), with an event-free survival (EFS) of 82.1%. Patients were divided into two groups based on prognosis: poor prognosis (PPG) and good prognosis (GPG). We further analyzed the differences in metabolites between the two groups ( Figure 4 A). Subsequent follow-up showed that at diagnosis, the subgroup of patients who relapsed or died (n=5) had significantly increased levels of L-kynurenine and 2-arachido-1-stearoyl-ceramide-3-glyceroethanolamine compared with patients who maintained complete remission after treatment, while 1-heptadecanoyl-ceramide-3-phosphatidylcholine showed the opposite trend. Figure 4 B). Furthermore, when all 28 patients in the cohort were divided into two groups based on the concentration of each metabolite, patients with higher levels of L-kynurenine and 2-arachido-1-stearoyl-ceramide-3-glycerophosphoethanolamine at diagnosis were observed to have a lower EFS rate (82.1%), while patients with relatively lower levels had an EFS rate of 100% within approximately 2000 days. Statistical analysis showed p-values ​​less than 0.05 for both groups. Figure 4 C). The EFS was 100% in the group with a high relative content of 1-heptadecanoyl-ceramide-3-phosphatidylcholine, while the EFS was significantly lower in the group with a low relative content. Figure 4 C). These findings further suggest a potential association between these metabolites and poor prognosis, with increased L-kynurenine and 2-arachido-1-stearoyl-ceramide-3-glycerophosphoethanolamine and decreased 1-heptadecanoyl-ceramide-3-phosphatidylcholine predicting poor prognosis in children with LCH.

[0052] 2.5 Multi-system involvement with critical organ involvement (MS-RO) + Metabolic changes in LCH patients after treatment

[0053] To gain a deeper understanding of the changes in metabolites in these patients after treatment, we collected data from 6 MS-RO patients who achieved complete remission after treatment. +Bone marrow aspirates of LCH patients (CR group) were taken and compared with those of patients before treatment (ND group). The application of OPLS-DA analysis revealed a clear clustering pattern of metabolites in the positive and negative models after treatment induction in the CR group as Figure 5 A-B. The CR group showed a clear separation from the ND group, indicating a substantial change in metabolites and demonstrating the robustness of the analysis model, which was further validated by permutation analysis Figure 5 C-D).

[0054] Differential metabolite screening identified 69 differential metabolites. KEGG analysis showed that these metabolites were mainly enriched in pathways such as ABC transporters, biosynthesis of steroid hormones, and primary bile acid biosynthesis, indicating their potential significance in monitoring LCH treatment Figure 5 E). Hierarchical clustering heatmap identified the top 20 differential metabolites Figure 5 F), among which three metabolites, including 1,2-distearyl-sn-glycero-3-phospho-1-serine (DSPS), 1-oleoyl-2-hydroxy-sn-glycero 3-phosphate-(1'-rac glycerol) (18:1 Lyso PG), and dodecanoic acid, showed more significant dynamic changes after treatment. MS-RO + The concentrations of these metabolites were very high in LCH patients, but decreased significantly and returned to normal levels after treatment reached complete remission Figure 5 G). These results suggest that these metabolites can be used as potential biomarkers for evaluating the efficacy of treatment and promoting the implementation of appropriate intervention strategies according to MS-RO + LCH treatment and complete remission, and thus can be used as potential biomarkers for evaluating the efficacy of treatment and promoting the implementation of appropriate intervention strategies.

[0055] 3. Conclusion

[0056] In this study, a non-targeted metabolomics method based on ultra-high performance liquid chromatography-AB6600 / mass spectrometry (UPLC-AB6600 / MS) was used to systematically investigate BMA samples from different groups of pediatric LCH patients. Eight plasma metabolites were identified as excellent diagnostic biomarkers. Phosphatidylcholine (PC, 17:0 / 0:0), L-kynurenine, and betaine aldehyde had clinical significance in patient subtype classification. Moreover, L-kynurenine, 2-arachidonoyl-1-stearoyl-ceramide-3-glycero-phosphoethanolamine, and 1-heptadecanoyl-ceramide-3-phosphatidylcholine could predict the prognosis of pediatric LCH patients. In addition, a group of metabolites (such as dodecanoic acid and 1,2-distearyl-sn-glycero-3-phospho-1-serine) could be used as potential biomarkers for evaluating MS-RO +The index of the clinical treatment efficacy of LCH. The data of the present application provide a comprehensive view of the metabolic product changes of the children LCH patients, which can provide valuable treatment insights for strengthening the diagnosis, prognosis, typing and efficacy evaluation. These findings are worthy of further large-scale cohort multicenter study.

[0057] Finally we further quantitatively detected the metabolites (L-kynurenine, betaine aldehyde) in the bone marrow aspirate of the children diagnosed as LCH. 100 microliters of bone marrow extract was added with 100 microliters of standard, then mixed with extraction solvent mixture (n-butanol: methanol = 1:1, volume ratio) and 10 millimoles / liter ammonium formate. The mixture was immediately vortexed, ultrasonicated in ice water, and then centrifuged to obtain the supernatant for LC-MS / MS analysis, while setting up solvent blank, solvent with internal standard, matrix blank without internal standard, etc. for quality control. The standard curve is as shown in Figure 7 The results of L-kynurenine and betaine aldehyde show the same results as the research of the present application, and the results are as shown in Figures 8-10

[0058] The above is the preferred embodiment of the present application, it should be pointed out that for the ordinary skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.​

Claims

1. Use of a reagent for detecting the abundance of a metabolite in bone marrow aspirate in the manufacture of a product for diagnosing Langerhans cell histiocytosis in a child, wherein, The metabolites include oleamide and / or glutamyl-threonyl-arginine and / or myo-inositol and / or N-acetyl-D-glucosamine 6-phosphate and / or D-mannose and / or hypoxanthine and / or xanthine and / or nicotinamide.

2. A product for diagnosing Langerhans cell histiocytosis in children, characterized in that, The products include reagents for detecting the abundance of oleamide and / or glutamyl-threonyl-arginine and / or myo-inositol and / or N-acetyl-D-glucosamine 6-phosphate and / or D-mannose and / or hypoxanthine and / or xanthine and / or nicotinamide.

3. Use of metabolites in bone marrow aspirates for the manufacture of a product for diagnosing the presence or absence of risk organ involvement in children with multi-system involvement Langerhans cell histiocytosis, wherein, The metabolites include 1-heptadecanoyl-sn-glycero-3-phosphocholine.

4. A product for diagnosing multi-system involvement in childhood Langerhans cell histiocytosis with or without involvement of critical organs, characterized in that, The products include reagents for detecting the abundance of 1-heptadecanoyl-sn-glycero-3-phosphocholine.

5. Use of metabolites in bone marrow aspirates for the preparation of a product for the diagnosis of children with Langerhans cell histiocytosis of the symptomatic inflammatory type and children with Langerhans cell histiocytosis of the symptomatic tumour type, wherein, The metabolites include L-kynurenine and / or betaine aldehyde.

6. A product for diagnosing the differentiation of an inflammatory symptom type child Langerhans cell histiocytosis patient from a tumor-like symptom type child Langerhans cell histiocytosis patient, characterized by, The products include reagents for detecting the abundance of L-kynurenine and / or betaine aldehyde.

7. Use of metabolites in bone marrow aspirates for the manufacture of a product for the prognosis of Langerhans cell histiocytosis in children, wherein, The metabolites include L-kynurenine and / or 2-arachidonoyl-1-stearoyl-sphingosyl-3- glycerophosphoethanolamine and / or 1-heptadecanoyl-sphingosyl-3-phosphocholine.

8. A prognosis product for childhood Langerhans cell histiocytosis, characterized by, The products include reagents for detecting the abundance of L-kynurenine and / or 2-arachidonoyl-1-stearoyl-sphingosyl-3-glycerophosphoethanolamine and / or 1-heptadecanoyl-sphingosyl-3-phosphocholine.

9. Use of metabolites in bone marrow aspirates for the manufacture of a product for assessing the therapeutic efficacy of a treatment of a child with Langerhans cell histiocytosis of the multisystemic with risk organ involvement type, wherein, The metabolites include 1,2-distearyl-sn-glycero-3-phospho-1-serine and / or 1-oleoyl-2- hydroxy-sn-glycero 3-phosphate-(1 '-rac glycerol) and / or dodecanoic acid.

10. A product for assessing the therapeutic efficacy of treatment of childhood Langerhans cell histiocytosis with multisystem involvement and dangerous organ compromise, characterized in that, The products include reagents for detecting the abundance of 1,2-distearyl-sn-glycero-3-phospho-1-serine and / or 1-oleoyl-2-hydroxy-sn-glycero 3-phosphate-(1 '-rac glycerol) and / or dodecanoic acid.

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