Application of composition in preparation of medicine for diagnosing infantile hemangioma and / or monitoring progress and prognosis of infantile hemangioma

Abstract

The invention provides application of a composition in preparation of a medicine for diagnosing infant hemangioma and / or monitoring the progress and prognosis of the infant hemangioma. The composition at least comprises one of the following four groups of substances: (1) at least one of total fatty acid or total saturated fatty acid or total monounsaturated fatty acid or specific fatty acid; (2) at least one of specific metabolites; (3) at least one of specific proteins; and (4) at least one of a specific lipid subclass or a lipid molecule. The invention also provides a detection kit comprising the composition and the specification. According to the application and the kit, early warning / prediction and early diagnosis of infant hemangioma can be achieved, the progress degree of the disease course can be closely monitored, the treatment effect of the medicine can be objectively evaluated, and the prognosis condition can be accurately reflected. The invention further provides a medicine for treating IH, wherein the medicine comprises substances for regulating expression of LDHB, KRT1, BCHE, CFHR4, CPQ, APOC-3, ARHGDIB and LAMA5, so that a new effective way is provided for treating infantile hemangioma.

Description

[0001] This application is a divisional application of an application with a filing date of March 8, 2021, a national application number of 202110249219.5, and an invention name of "Application of compositions in the preparation of drugs for diagnosing infantile hemangiomas and / or monitoring their progression and prognosis, as well as kits and drugs". Technical Field

[0002] The present application relates to the use of a composition in the preparation of a drug for diagnosing hemangioma and / or monitoring its progression and prognosis, as well as a kit and a drug. More specifically, the present application relates to the use of a composition in the preparation of a drug for diagnosing infantile hemangioma and / or monitoring its progression and prognosis, as well as a detection kit and a drug for treating infantile hemangioma. Background Art

[0003] Infantile hemangioma (IH) is the most common benign tumor in infants and young children, commonly known as "red birthmark", with an incidence rate of up to 1-10%. It often appears a few weeks after birth as a red mass, and then rapidly proliferates in the following months to form a red mass. If not treated in time, 10%-15% of cases will develop complications, leading to facial damage, large-area ulcer infection, respiratory compression and obstruction, and even life-threatening.

[0004] Therefore, early diagnosis and timely treatment intervention are crucial to achieve a satisfactory prognosis. Infantile hemangioma is difficult to distinguish from other common skin diseases such as neonatal erythema (Nevus Simplex, NS) in the early stages of the disease. Currently, there is a lack of effective prediction / warning and early differential diagnosis technology for infantile hemangioma, but due to the rapid progression of infantile hemangioma, once the treatment opportunity is missed, it will cause serious consequences. On the contrary, if other common skin diseases are misdiagnosed as infantile hemangioma, resulting in overtreatment, it may also cause unnecessary iatrogenic damage to the child.

[0005] In addition, the current evaluation of the effectiveness of drugs in treating infantile hemangiomas still uses traditional methods such as doctors measuring the size of hemangiomas based on experience, observing texture and color changes, etc., which has large measurement errors, and cannot objectively, timely and accurately evaluate the efficacy, as well as the difficulty in accurately determining the endpoint of the disease. Moreover, for deep-seated infantile hemangiomas, since they have no obvious symptoms on the body surface or only have bulges on the skin surface, they are very easy to be missed or misdiagnosed, causing more serious damage to the children. Currently, there is a lack of technology for early and accurate differential diagnosis. In addition, infantile hemangiomas have a certain recurrence rate. Even if the surface symptoms disappear or stop developing, they may still recur after stopping drug treatment. How to determine the endpoint of medication is also a difficult problem that existing technologies have not yet overcome.

[0006] In summary, there is an urgent need for fast, accurate, sensitive and quantifiable prediction / warning and early diagnosis technologies for infantile hemangiomas, technologies for monitoring the progression of the disease, and technologies for effectively predicting the effectiveness of drug treatment and whether recurrence will occur after cessation of treatment. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing international lack of technology for predicting and warning infantile hemangiomas, the low detection rate of early diagnosis methods for infantile hemangiomas, poor detection sensitivity, easy confusion with other skin diseases with similar appearance, large technical measurement errors for treatment effect and prognosis evaluation, and the inability to quantify detection indicators. A detection technology is provided to quickly, accurately, sensitively and quantitatively achieve prediction / warning of infantile hemangiomas, precise early diagnosis, close monitoring of disease progression, objective evaluation of drug treatment effects, and accurate reflection of prognosis.

[0008] The present invention provides an application of a composition in the preparation of a drug for diagnosing infantile hemangioma and / or monitoring its progression and prognosis, characterized in that the composition comprises at least one of the following four groups of substances:

[0009] (1) fatty acids, wherein the fatty acids are total fatty acids, total saturated fatty acids, total monounsaturated fatty acids, or at least one selected from the group consisting of fatty acids of C10:0, C11:0, C12:0, C14:0, C15:0, C15:1N5, C16:0, C17:0, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C21:0, C22:1N9, C23:0, C24:0, and C8:0;

[0010] (2) metabolites, wherein the metabolites are selected from the group consisting of (3-carboxypropyl) trimethylammonium cation, α-D-(+)-talose, 1-aminocyclopropanecarboxylic acid, 1-myristoyl-sn-glycero-3-phosphocholine, 1-oleoyl-sn-glycero-3-phosphocholine, 1-palmitoyl-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycero-3-phosphocholine, bilirubin, choline, D-2-piperidinic acid, DL-indole-3-lactic acid, glycylglutamic acid, glycerophosphocholine, isomaltose, L-arginine, L-glutamic acid, L-phenylalanine, L-tryptophan, NG, N At least one of the group consisting of G-dimethyl-L-arginine, tyramine, phenylacetic acid, (S)-citramalic acid, 1-oleoyl-L-α-lysophosphatidic acid, 1-palmitoyl-lysophosphatidic acid, α-mangostin, arachidonic acid (peroxide-free), citramalic acid, cyclamates / salts, D-galactates / salts, D-threitol, D(-)-β-hydroxybutyric acid, inositol galactoside, glyceric acid, L-aspartic acid, L-carnosine, L-malic acid, N-acetyl-L-aspartic acid, N-acetylneuraminic acid, norethindrone acetate, choline phosphate, sn-glycero-3-phosphoethanolamine and succinates / salts;

[0011] (3) Protein, wherein the protein is selected from COLEC10, CPQ, LDHB, ACAN, KRT1, ARHGDIB, POSTN, LAMA5, CPN1, PROC, C1S, HPX, SUPT6H, BCAR3, BCHE, SOX30, C1R, PON1, CFHR4, PROZ, IGKV1D-16, APOC-3, COMP, SELENOP, PMFBP1, GSN, SELL, FUCA 2. At least one of the group consisting of HLA-B, FGFBP2, FKBP1A, HSPE1, SAA1, PGD, CDH1, SOD1, ISLR, CORO1A, ZYX, RLBP1, IDH1, IGKV6-21, SORL1, FAM3C, CST6, COTL1, PIGR, A0A1W6IYJ2, PRDX1, CAT, PROS1, CD5L, HMFT1766, C1QA, KRT2 and APP;

[0012] and

[0013] (4) lipid subclass substances or lipid molecules, wherein the lipid subclass substances are at least one selected from the group consisting of acetylcarnitine, ceramide, monoglycosylceramide, disaccharide ceramide, glycosylceramide series, triglycosylceramide, cholesterol ester, cardiolipin, ganglioside, lysophosphatidylcholine, lysophosphatidylethanolamine, monogalactosyldiacylglycerol, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phytosphingosine, phosphatidylinositol, phosphatidylserine, sphingomyelin and triglyceride,And wherein the lipid molecules are selected from PS (39: 4) -H, SM (d45: 6) + H, PC (14: 0 / 20: 4) + HCOO, SM (d20: 0 / 18: 1) + HCOO, PS (49: 4) -H, LPC (26: 0) + HCOO, SM (d20: 0 / 16: 0) + HCOO, PA (50: 4) -H, LPC (20: 4) + HCOO, PC (36: 5) + H, LPC (20: 1) + HCOO, PC (38: 8) + H, PC (46: 5) + H, PE (18: 0p / 22: 5) -H , SM(d22:0 / 16:0)+HCOO, PC(15:0 / 20:4)+HCOO, SM(d18:0 / 20:4)+HCOO, LPC(22:4)+HCOO, PI(18:0 / 22:4)-H, PC(16:1 / 18:2)+H COO, PS(40:4)-H, Cer(d20:0 / 24:1)+H, SM(d37:0)+HCOO, PC(17:1 / 16:0)+HCOO, SM(d20:0 / 22:5)+HCOO, PC(44:11)+H, SM(d32: 0)+HCOO,LPC(22:1)+H,PC(33:0e)+H,SM(d56:2+pO)+H,SM(d42:1)+HCOO,LPC(17:1)+HCOO,SM(d17:0 / 18:2)+HCOO,Cer(d18: 1 / 24:0)+H, SM(d36:3)+HCOO, AcCa(14:2)+H, Cer(d18:1 / 24:0+O)+H, PE(20:0p / 20:4)-H, LPC(26:1)+HCOO, PC(37:5)+H, SM(d4 At least one of the group consisting of 4:1)+HCOO, Cer(d18:1 / 26:1)+H, PE(40:6e)+H, TG(18:1 / 20:2 / 22:4)+NH4, SM(d34:3)+H, PE(40:4)-H, SM(d22:0 / 18:2)+HCOO, Cer(d18:0 / 22:0)+H, SM(d44:0)+HCOO, PI(16:1 / 20:4)-H, PC(20:3 / 20:4)+HCOO, PE(38:1p)-H and SM(d39:0)+HCOO. ,

[0014] The present invention also provides a detection kit, which comprises the composition of the present invention and instructions for using the kit.

[0015] The above-mentioned application and detection kit of the present invention can quickly, accurately, sensitively and quantitatively realize the prediction / early warning, early diagnosis, close monitoring of the progression of infantile hemangioma, objective evaluation of the therapeutic effect of the drug, and accurately reflect the prognosis.

[0016] In addition, the present application also provides a drug for treating infantile hemangioma, including a substance that regulates the expression of LDHB, KRT1, BCHE, CFHR4, CPQ, APOC-3, ARHGDIB and LAMA5, thereby providing a new and effective approach for treating infantile hemangioma. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A photograph of the lesion in the antecubital fossa of a newborn baby with infantile hemangioma at 6 weeks of age according to Example 1 of the present invention;

[0018] Figure 2 A photograph of the back lesion of a newborn baby with infantile hemangioma at 6 weeks of age according to Example 2 of the present invention;

[0019] Figure 3 A photograph of a lesion on the head of a newborn baby suffering from erythema neonatorum at 6 weeks of age according to Example 3 of the present invention;

[0020] Figure 4 A photograph of a lesion on the head of a newborn baby suffering from erythema neonatorum according to Example 4 of the present invention at 6 weeks of age;

[0021] Figure 5 A photograph of the lesion in the antecubital fossa of a newborn baby with infantile hemangioma at 12 weeks of age according to Example 1 of the present invention;

[0022] Figure 6 A photograph of the back lesion of a newborn baby with infantile hemangioma at 12 weeks of age according to Example 2 of the present invention;

[0023] Figure 7 A photograph of a lesion on the head of a newborn baby suffering from erythema neonatorum at 12 weeks of age according to Example 3 of the present invention;

[0024] Figure 8 A photograph of a lesion on the head of a newborn baby suffering from erythema neonatorum at 12 weeks of age according to Example 4 of the present invention;

[0025] Fig. 9 Comparative photographs of eyelid lesions of a newborn baby with infantile hemangioma before and after treatment and before and after drug withdrawal according to Example 7 of the present invention;

[0026] Fig.10Statistical results of lipid subclasses and the number of lipid species identified in each class;

[0027] Fig.11 Figure of total lipid content in the control group and IH group;

[0028] Fig.12 Diagram of the content of general differential lipid subclasses between the control group and the IH group;

[0029] Fig.13 Diagram of the contents of lipid subtypes with significant differences between the control group and the IH group;

[0030] Fig.14 ROC curve of the fitting model for high-throughput Swissport proteomics detection;

[0031] Fig.15 ROC curve of the fitting model for high-throughput uniport proteomics detection;

[0032] Fig.16 ROC curve of the metabolite group fitting model;

[0033] Fig.17 ROC curve of the fitted model between the IH group and the control group in the fatty acid group; and

[0034] Fig.18 ROC curve of the fitting model between IH group and NS group in the fatty acid group. DETAILED DESCRIPTION

[0035] The present invention provides an application of a composition in the preparation of a drug for diagnosing infantile hemangioma and / or monitoring its progression and prognosis, characterized in that the composition comprises at least one of the following four groups of substances:

[0036] (1) fatty acids, wherein the fatty acids are total fatty acids, total saturated fatty acids, total monounsaturated fatty acids, or at least one selected from the group consisting of fatty acids of C10:0, C11:0, C12:0, C14:0, C15:0, C15:1N5, C16:0, C17:0, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C21:0, C22:1N9, C23:0, C24:0, and C8:0;

[0037] (2) metabolites, wherein the metabolites are selected from (3-carboxypropyl)trimethylammonium cation, alpha-D-(+)-talose, 1-aminocyclopropanecarboxylic acid, acid), 1-myristoyl-sn-glycero-3-phosphocholine, 1-oleoyl-sn-glycero-3-phosphocholine, 1-palmitoyl-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycerol-3-phosphocholine, bilirubin, choline, D-pipecolinic acid, DL-Indole-3-lactic acid acid), Glycylglutamic acid (Gly-Glu), Glycerophosphocholine, Isomaltose, L-Arginine, L-Glutamate, L-Phenylalanine, L-Tryptophan, NG,NG-dimethyl-L-arginine (ADMA), Tyramine, Phenylacetic acid, (S)-Citramalic acid, 1-Oleoyl-L-.alpha.-lysophosphatidic acid、1-Palmitoyl Lysophosphatidic Acid、alpha-Mangostin、Arachidonic Acid(peroxide free)、Citramalic acid、Cyclohexylsulfamate、D-Galactarate、D-Threitol、D(-)-beta-hydroxy butyric acid、Galactinol、Glyceric acid、L-Aspartate、L-Carnosine、L-Malic acid、N-Acetyl-L-aspartic acid、N-Acetylneuraminic acid At least one of the group consisting of choline, sn-glycerol-3-phosphoethanolamine, succinate, and norethindrone acetate;

[0038] (3) Protein, wherein the protein is selected from COLEC10, CPQ, LDHB, ACAN, KRT1, ARHGDIB, POSTN, LAMA5, CPN1, PROC, C1S, HPX, SUPT6H, BCAR3, BCHE, SOX30, C1R, PON1, CFHR4, PROZ, IGKV1D-16, APOC-3, COMP, SELENOP, PMFBP1, GSN, SELL, FUCA2 , HLA-B, FGFBP2, FKBP1A, HSPE1, SAA1, PGD, CDH1, SOD1, ISLR, CORO1A, ZYX, RLBP1, IDH1, IGKV6-21, SORL1, FAM3C, CST6, COTL1, PIGR, A0A1W6IYJ2, PRDX1, CAT, PROS1, CD5L, HMFT1766, C1QA, KRT2, and APP; and

[0039] (4) lipid subclass substances or lipid molecules, wherein the lipid subclass substances are at least one selected from the group consisting of acetylcarnitine, ceramide, monoglycosylceramide, disaccharide ceramide, glycosylceramide series, triglycosylceramide, cholesterol ester, cardiolipin, ganglioside, lysophosphatidylcholine, lysophosphatidylethanolamine, monogalactosyldiacylglycerol, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phytosphingosine, phosphatidylinositol, phosphatidylserine, sphingomyelin and triglyceride,And wherein the lipid molecules are selected from PS (39: 4) -H, SM (d45: 6) + H, PC (14: 0 / 20: 4) + HCOO, SM (d20: 0 / 18: 1) + HCOO, PS (49: 4) -H, LPC (26: 0) + HCOO, SM (d20: 0 / 16: 0) + HCOO, PA (50: 4) -H, LPC (20: 4) + HCOO, PC (36: 5) + H, LPC (20: 1) + HCOO, PC (38: 8) + H, PC (46: 5) + H, PE (18: 0p / 22: 5) -H , SM(d22:0 / 16:0)+HCOO, PC(15:0 / 20:4)+HCOO, SM(d18:0 / 20:4)+HCOO, LPC(22:4)+HCOO, PI(18:0 / 22:4)-H, PC(16:1 / 18:2)+H COO, PS(40:4)-H, Cer(d20:0 / 24:1)+H, SM(d37:0)+HCOO, PC(17:1 / 16:0)+HCOO, SM(d20:0 / 22:5)+HCOO, PC(44:11)+H, SM(d32: 0)+HCOO,LPC(22:1)+H,PC(33:0e)+H,SM(d56:2+pO)+H,SM(d42:1)+HCOO,LPC(17:1)+HCOO,SM(d17:0 / 18:2)+HCOO,Cer(d18: 1 / 24:0)+H, SM(d36:3)+HCOO, AcCa(14:2)+H, Cer(d18:1 / 24:0+O)+H, PE(20:0p / 20:4)-H, LPC(26:1)+HCOO, PC(37:5)+H, SM(d4 At least one of the group consisting of 4:1)+HCOO, Cer(d18:1 / 26:1)+H, PE(40:6e)+H, TG(18:1 / 20:2 / 22:4)+NH4, SM(d34:3)+H, PE(40:4)-H, SM(d22:0 / 18:2)+HCOO, Cer(d18:0 / 22:0)+H, SM(d44:0)+HCOO, PI(16:1 / 20:4)-H, PC(20:3 / 20:4)+HCOO, PE(38:1p)-H and SM(d39:0)+HCOO. ,

[0040] In the present invention, the C10:0 refers to decanoic acid. The C11:0 refers to undecanoic acid. The C12:0 refers to dodecanoic acid. The C14:0 refers to myristic acid. The C15:0 refers to pentadecanoic acid. The C15:1N5 refers to cis-10-pentadecenic acid. The C16:0 refers to palmitic acid. The C17:0 refers to heptadecanoic acid. The C18:0 refers to stearic acid. The C18:2N6 refers to linoleic acid. The C20:0 refers to arachidic acid. The C20:3N3 refers to cis-11,14,17-eicosatrienoic acid. The C20:5N3 refers to cis-5,8,11,14,17-eicosapentaenoic acid. The C21:0 refers to heneicosanic acid. The C22:1N9 refers to erucic acid. The 23:0 refers to tricosanic acid. The C24:0 refers to tetracosanoic acid. The C8:0 refers to octanoic acid. The fatty acid of the present invention is further preferably at least one of the group consisting of C8:0, C10:0, C11:0, C16:0, C20:0, C20:5N3, C22:1N9, C23:0 and C24:0; further preferably at least one of the group consisting of C8:0, C10:0, C16:0, C20:0, C20:5N3, C22:1N9, C23:0, C24:0; most preferably at least one of the group consisting of C20:5N3, C22:1N9, C23:0 and C24:0.

[0041] Preferably, the metabolite is selected from the group consisting of choline, 1-aminocyclopropanecarboxylic acid, 1-myristoyl-sn-glycero-3-phosphocholine, D-2-pipecolinic acid, DL-indole-3-lactic acid, Gly-Glu, L-Glutamate, L-Phenylalanine, L-Tryptophan, Tyramine, Phenylacetic acid, Glyceric acid, Cyclohexylsulfamate, D-galactarate and sn-Glycerol-3-phosphoethanolamine. The metabolite is most preferably selected from at least one of the group consisting of 1-myristoyl-sn-glycero-3-phosphocholine, glycylglutamic acid (Gly-Glu), L-phenylalanine (L-Phenylalanine), DL-indole-3-lactic acid (DL-Indole-3-lactic acid), L-tryptophan (L-Tryptophan), phenylacetic acid (Phenylacetic acid) and glyceric acid (Glyceric acid).

[0042] Among the proteins involved in the present invention, COLEC10 refers to Collectin-10. CPQ refers to Carboxypeptidase Q. LDHB refers to L-lactate dehydrogenase B chain. ACAN refers to Aggrecan core protein. KRT1 refers to Keratin, type II cytoskeletal 1. POSTN refers to Periostin. LAMA5 refers to Laminin subunit alpha-5. CPN1 refers to Carboxypeptidase N catalytic chain. PROC refers to Vitamin K-dependent protein C. C1S refers to Complement C1s subcomponent. HPX refers to Hemopexin. The SUPT6H refers to transcription elongation factor SPT6. The BCAR3 refers to breast cancer anti-estrogen resistance protein 3. The BCHE refers to cholinesterase. The SOX30 refers to transcription factor SOX-30. The C1R refers to complement C1r subcomponent. The PON1 refers to serum paraoxonase / arylesterase 1. The CFHR4 refers to complement factor H-related protein 4. The PROZ refers to vitamin K-dependent protein Z. The IGKV1D-16 refers to immunoglobulin kappa variable 1D-16. The APOC-3 refers to apolipoprotein C-III.COMP refers to cartilage oligomeric matrix protein. SELENOP refers to selenoprotein P. PMFBP1 refers to polyamine-modulated factor 1-binding protein 1. GSN refers to gelsolin. SELL refers to L-selectin. FUCA2 refers to plasma alpha-L-fucosidase. HLA-B refers to human leukocyte antigen B antigen (HLA class I histocompatibility antigen, B alpha chain). FGFBP2 refers to fibroblast growth factor-binding protein 2. FKBP1A refers to peptidyl-prolyl cis-trans isomerase. HSPE1 refers to heat shock protein 10 (10kDa heat shock protein). The SAA1 refers to serum amyloid A-1 protein. The PGD refers to 6-phosphogluconate dehydrogenase. The CDH1 refers to Cadherin-1. The SOD1 refers to Superoxide dismutase [Cu-Zn]. The ISLR refers to Immunoglobulin superfamily containing leucine-rich repeats. The CORO1A refers to Coronin-1A. The ZYX refers to Zyxin. The RLBP1 refers to Retinaldehyde-binding protein 1. The IDH1 refers to Isocitrated dehydrogenase [NADP] cytoplasmic. The IGKV6-21 refers to immunoglobulin kappa chain variable region 6-21 (Immunoglobulin kappa variable 6-21).The SORL1 refers to Sortilin-related receptor. The FAM3C refers to Protein FAM3C. The CST6 refers to Cystatin-M. The COTL1 refers to Coactosin-like protein. The PIGR refers to Polymeric immunoglobulin receptor. The A0A1W6IYJ2 refers to N90-VRC38.05 heavy chain variable region. The PRDX1 refers to Peroxiredoxin-1. The CAT refers to Catalase. The PROS1 refers to Vitamin K-dependent protein S. The CD5L refers to CD5 antigen-like. The HMFT1766 refers to Leucine-rich alpha-2-glycoprotein. The C1QA refers to the complement C1q-α subcomponent (Complement C1q subcomponent subunit A). The KRT2 refers to keratin 2 (Cytokeratin-2). The APP refers to amyloid-beta precursor protein (Amyloid-beta precursor protein).

[0043] Wherein said LDHB, KRT1, BCHE, CFHR4 and CPQ are up-regulated proteins, while APOC-3, ARHGDIB and LAMA5 are down-regulated proteins. Said protein is further preferably selected from at least one of the group consisting of HSPE1, PGD, CDH1, ISLR, CPQ, IDH1, PIGR, POSTN, PROC, PROZ, PMFBP1, SELL, A0A1W6IYJ2, CAT, PROS1, CD5L, C1QA, KRT1 and FGFBP2. Said protein is most preferably selected from at least one of the group consisting of HSPE1, CDH1, ISLR, IDH1, COTL1 and FGFBP2.

[0044] The lipids described in the present invention are a class of hydrophobic or amphiphilic small molecules, including eight categories ( Systematic nomenclature) (Fahy et al., 2009): fatty acids (such as linoleic acid, arachidic acid, etc.), glycerolipids (such as TG, DG, etc.), glycerophospholipids (such as PC, PE, PG, PA, etc.), sphingolipids (such as Cer, SM, etc.), sterol lipids (such as sterol esters, etc.), glycolipids (such as MGDG, SQDG, etc.), pregnenolone lipids (such as CoA, etc.) and polyethylene (antibiotics, etc.). Lipids are the main components of biological membrane structures (such as extracellular membranes, mitochondria, exosomes, endoplasmic reticulum, exosomes and other subcellular), and are also signal small molecules and energy substances. Specifically, the names of lipid subclass substances involved in the present invention and their English abbreviations are shown in Table 1 below:

[0045] Table 1 Names and English abbreviations of lipid subclasses

[0046]

[0047]

[0048] The lipid subclass substance of the present invention is further preferably selected from the group consisting of Cer, ChE, LPC, LPE, PA, PC, PE, PI, PS and SM, and most preferably selected from the group consisting of LPC, LPE, PA, PC and PE.

[0049] Preferably, the lipid molecule is selected from PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO, SM(d20:0 / 18:1)+HCOO, PS(49:4)-H, LPC(26:0)+HCOO, SM(d20:0 / 16:0)+HCOO, PA(50:4)-H, LPC(20:4)+HCOO, PC(36:5)+H, LPC(20:1)+HCOO, PC(38:8)+H, PC(46:5)+H, PE(18:0p / 22:5)- H, SM(d22:0 / 16:0)+HCOO, PC(15:0 / 20:4)+HCOO, SM(d18:0 / 20:4)+HCOO, LPC(22:4)+HCOO, PI(18:0 / 22:4)-H, PC(16:1 / 18:2) +HCOO, PS(40:4)-H, Cer(d20:0 / 24:1)+H, SM(d37:0)+HCOO, PC(17:1 / 16:0)+HCOO, SM(d20:0 / 22:5)+HCOO, PC(44:11)+H, SM(d 32:0)+HCOO, LPC(22:1)+H, PC(33:0e)+H, SM(d56:2+pO)+H, SM(d42:1)+HCOO, LPC(17:1)+HCOO, SM(d17:0 / 18:2)+HCOO, Cer(d 18:1 / 24:0)+H, SM(d36:3)+HCOO, AcCa(14:2)+H, Cer(d18:1 / 24:0+O)+H, PE(20:0p / 20:4)-H, LPC(26:1)+HCOO, PC(37:5)+H, S The group consists of M(d44:1)+HCOO, Cer(d18:1 / 26:1)+H, PE(40:6e)+H, TG(18:1 / 20:2 / 22:4)+NH4, SM(d34:3)+H, PE(40:4)-H, SM(d22:0 / 18:2)+HCOO, Cer(d18:0 / 22:0)+H, SM(d44:0)+HCOO, PI(16:1 / 20:4)-H, PC(20:3 / 20:4)+HCOO, PE(38:1p)-H and SM(d39:0)+HCOO. Further preferably, the lipid molecule is selected from the group consisting of: PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO, SM(d20:0 / 18:1)+HCOO, PS(49:4)-H, LPC(26:0)+HCOO, SM(d20:0 / 16:0)+HCOO, PA(50:4)-H and LPC(20:4)+HCOO.Most preferably, the lipid molecule is selected from the group consisting of PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO and SM(d20:0 / 18:1)+HCOO.

[0050] The inventors of the present invention collected 3089 cases of umbilical cord blood from newborns, including 63 positive specimens of infantile hemangioma, and compared and studied thousands of metabolites in healthy newborns, patients with neonatal erythema and patients with infantile hemangioma through high-throughput metabolomics, targeted quantitative metabolomics of fatty acid profiles, general metabolomics, proteomics and lipidomics technologies. They successfully found that the specific fatty acids, metabolites, proteins and lipid subclasses or lipid molecules listed in the present invention have surprising specificity and sensitivity in early warning / prediction, early diagnosis of infantile hemangioma and / or monitoring its progression and prognosis.

[0051] Any of the fatty acids, metabolites, proteins, and lipid subclasses or lipid molecules listed herein can be used alone to determine whether a patient has infantile hemangioma and whether a drug for treating infantile hemangioma is effective. In order to further verify the test results, two or more substances can be used to verify each other. Preferably, the fatty acids are at least two of the fatty acids defined in (1); the metabolites are at least two of the metabolites defined in (2); the proteins are at least two of the proteins defined in (3); or the lipid subclasses or lipid molecules are at least two of the lipid subclasses or lipid molecules defined in (4). Further preferably, the fatty acids are at least three of the fatty acids defined in (1); the metabolites are at least three of the metabolites defined in (2); the proteins are at least three of the proteins defined in (3); or the lipid subclasses or lipid molecules are at least three of the lipid subclasses or lipid molecules defined in (4). Taking into account the issues of cost and detection efficiency, the fatty acid is 1 to 6 of the fatty acids defined in (1); the metabolite is 1 to 6 of the metabolites defined in (2); or the protein is 1 to 6 of the proteins defined in (3); or the lipid subclass substance or lipid molecule is 1 to 6 of the lipid subclass substances or lipid molecules defined in (4).

[0052] Preferably, the fatty acid in (1) is selected from the group consisting of C8:0, C10:0, C11:0, C16:0, C20:0, C20:5N3, C22:1N9, C23:0 and C24:0; the metabolite in (2) is selected from the group consisting of choline, 1-aminocyclopropanecarboxylic acid, 1-myristoyl-sn-glycero-3-phosphocholine, D-2-piperidinic acid, DL-indole-3-lactic acid, glycylglutamate, L-glutamate, L-phenylalanine, L-tryptophan, tyramine, glyceric acid, cyclohexylaminosulfonate, D-galactate / salt and sn-glycero-3-phosphoethanolamine; the protein in (3) is selected from the group consisting of HSPE1, PGD, CDH1, ISLR, CPQ, IDH1, PIGR, POSTN, PRO C, PROZ, PMFBP1, SELL, A0A1W6IYJ2, CAT, PROS1, CD5L, C1QA, KRT1 and FGFBP2; or the (4) lipid subclass substance is selected from the group consisting of Cer, ChE, LPC, LPE, PA, PC, PE, PI, PS and SM, and the lipid molecule is selected from the group consisting of PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO, SM(d20:0 / 18:1)+HCOO, PS(49:4)-H, LPC(26:0)+HCOO, SM(d20:0 / 16:0)+HCOO, PA(50:4)-H and LPC(20:4)+HCOO. Further preferably, the fatty acid in (1) is selected from the group consisting of C20:5N3, C22:1N9, C23:0 and C24:0; the metabolite in (2) is selected from the group consisting of 1-myristoyl-sn-glycero-3-phosphocholine, glycylglutamate, L-phenylalanine, DL-indole-3-lactic acid, L-tryptophan and glyceric acid; the protein in (3) is selected from the group consisting of HSPE1, CDH1, ISLR, IDH1, COTL1 and FGFBP2; or the lipid subclass in (4) is selected from the group consisting of LPC, LPE, PA, PC and PE, or the lipid molecule is selected from the group consisting of PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO and SM(d20:0 / 18:1)+HCOO.

[0053] The application also provides a detection kit, the kit includes the composition of the present invention and instructions for using the kit. The fatty acids, metabolites and lipid subclass substances or lipid molecules of the present application can be measured using conventional methods, for example, spectrophotometry, gas chromatography, liquid chromatography, gas-liquid chromatography, mass spectrometry, gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry conventional serum total lipid determination method, etc. Similarly, the protein can also be measured using conventional methods. The kit can optionally include other reagents required for performing diagnostic determinations or promoting quality control assessments, such as buffers, salts, enzymes, enzyme cofactors, substrates, detection reagents, etc. Other components such as buffers and solutions (e.g., pretreatment reagents) for separating and / or treating test samples can also be included in the kit. Optionally, the kit can also include at least one calibrator or control. Any calibrator or control can be included in the kit. The kit can additionally include one or more other controls. One or more of the kit components can be lyophilized, and the kit can further include reagents suitable for reconstructing lyophilized components.

[0054] The various components of the test kit are optionally provided in a suitable container. One or more of the containers can be a microtiter plate. The test kit can further include a container for holding or storing a sample (e.g., a container or a cartridge for a blood or urine sample). When appropriate, the test kit can also optionally include a reaction vessel, a mixing container, and other components that promote the preparation of reagents or test samples. The test kit can also include one or more instruments for assisting in obtaining a test sample, such as a syringe, a pipette, tweezers, a measured spoon, etc.

[0055] The instructions for using the kit of the present invention can be provided in paper form or in a computer-readable form such as a disk, CD, DVD, flash memory, online download link, mobile phone APP application, etc. The instructions may include a baseline value data set of normal indicators for comparison.

[0056] The sample detected by the kit is selected from the group consisting of whole blood, plasma, serum, lymphocyte suspension, cerebrospinal fluid, bone marrow, pleural effusion, urine, saliva and peritoneal effusion. Preferably, the sample is non-invasive and harmful to the subject. More preferably, the sample detected by the kit is selected from the group consisting of umbilical cord blood and peripheral blood.

[0057] The present application also provides a drug for treating infantile hemangioma, the drug comprising a substance that regulates the expression of LDHB, KRT1, BCHE, CFHR4, CPQ, APOC-3, ARHGDIB and LAMA5. The inventor of the present invention is the first to discover that the relevant protein is related to infantile hemangioma, and that the treatment of infantile hemangioma can be achieved by regulating the expression of the relevant protein.

[0058] Example

[0059] The present invention is further described below by way of embodiments in conjunction with the accompanying drawings, but the present invention is not limited to the following embodiments.

[0060] Sample collection example:

[0061] The inventors of the present application collected and preserved 3089 newborn umbilical cord blood samples, and compared the sick children with normal newborns in multiple factors and dimensions. Finally, they found that there were significant statistical differences in (1) fatty acids, (2) metabolites, (3) proteins and (4) lipid subclasses or lipid molecular indicators.

[0062] (1) Subject recruitment

[0063] Prepare a popular science brochure on infantile hemangioma and provide it to the delivery room of the cooperative hospital. Distribute the brochure to the pregnant women and their families who are admitted to the hospital to introduce the incidence and treatment of infantile hemangioma to the pregnant women and their families, and inform them of the rights and benefits of participating researchers. At the same time, introduce the purpose and follow-up time of this study, and explain that the follow-up time designated by the project team members is of great significance for the early intervention of IH. With the full knowledge of the pregnant women and their families, their wishes will be fully respected, and their medical treatment and rights will not be affected regardless of their consent or not. Under the condition that the pregnant women and their families agree to participate in the study, they will sign the relevant informed consent forms for the collection of umbilical cord blood, placental tissue, maternal and neonatal medical history, data, etc.

[0064] (2) Collect specimens and record information about the mother and newborn

[0065] After the fetus and placenta were delivered and the umbilical cord was cut off, 30 mL of umbilical cord blood from the discarded umbilical vein and placenta was collected aseptically to ensure that the health of the fetus and the mother was not affected. The blood was injected into a procoagulant blood collection tube (purchased from Fuzhou Chang Gung Medical Device Co., Ltd. (CHANGGENG), containing diatomaceous earth), and slowly shaken. After standing at room temperature for half an hour, the neonatal umbilical cord blood was centrifuged at 3000g / min for 15 minutes, and the supernatant was taken and frozen at -80℃. A total of 3089 neonatal umbilical cord blood samples were collected.

[0066] Clinical diagnosis and treatment data will be established in a database and entered under the guidance of statistical professionals; their specimens and personal information will be identified by research number numbers rather than names.

[0067] (3) Follow-up

[0068] Telephone follow-up is conducted at the 6th and 12th weeks after the birth of the newborn to track whether IH occurs. The telephone follow-up staff consists of two graduate students who have undergone rigorous training. The follow-up time avoids the rest time of the newborn's family. No newborn is missed during the follow-up process. The reasons for the withdrawal of newborns will be registered. During the follow-up process, if the symptom description is consistent with infantile hemangioma, the follow-up staff will ask the baby's parents to send pictures of the lesion to help with diagnosis, and notify all children who are initially diagnosed with infantile hemangioma to go to the outpatient clinic for confirmation.

[0069] (4) Matching groups to control confounding factors

[0070] After the follow-up, all the umbilical cord blood samples of infants diagnosed with infantile hemangioma in the outpatient clinic were selected as the case group. Then, the infants without infantile hemangioma were matched according to the relevant information such as the gender, birth weight, and gestational weeks as the matching conditions to serve as the control group.

[0071] (5) Obtaining sample information

[0072] A total of 63 IH-positive specimens were collected during follow-up, and 28 of them were randomly selected as the IH group. And according to the relevant information such as infant gender, birth weight, and gestational weeks of the IH group, 32 control group samples with the same conditions were screened out. And 37 umbilical cord blood samples from children with neonatal erythema (Nevus Simplex, NS) were collected (NS group), a total of 97 samples. Each umbilical cord blood sample in the IH group and the control group was divided into 100 μL / tube in a 1.5 ml centrifuge tube and stored at -80 degrees for inspection.

[0073] Table 2

[0074]

[0075] (6) Brief introduction to the statistical methods and indicators used in the present invention

[0076] Table 3 below shows the results of diagnosing a specific disease using a specific diagnostic method.

[0077] Table 3

[0078]

[0079] Specificity: Sp = d / (b+d) × 100% represents the ability of the diagnostic test to identify non-patients (i.e. the probability of a person who is not actually sick being diagnosed as negative), i.e. the true negative rate. The larger the value, the more accurate the diagnostic test (method).

[0080] Sensitivity: Se = a / (a+c) × 100% represents the ability to diagnose patients, that is, the true positive rate. The larger the value, the more sensitive the diagnostic method is.

[0081] Receiver Operator Characteristic Curve (ROC Curve): Also known as sensitivity curve: It is a coordinate graph with false positive rate (1-specificity) as the horizontal axis and true positive rate (sensitivity) as the vertical axis, and a curve drawn by different results obtained by the subjects under specific stimulation conditions due to different judgment criteria. The horizontal and vertical coordinates can be calculated by software (SPSS\Origin\Graphpad Prism), and the results obtained in the software can obtain the area under the ROC curve, etc., to compare the diagnostic value of the reaction diagnostic test.

[0082] The role of ROC curve:

[0083] 1) Find the “optimal” diagnostic cut-off value for a diagnostic test.

[0084] 2) Calculate the area under the ROC curve (AUC) to evaluate the discrimination ability of a diagnostic test.

[0085] 3) Compare the areas under the ROC curves of multiple tests to screen out the best diagnostic solution.

[0086] Area Under the Curve (AUC):

[0087] The AUC of the ROC curve can reflect the value of the diagnostic test, and the value range is usually 0.5-1. When AUC>0.5, the closer the AUC is to 1, the better the diagnostic effect. A completely worthless diagnosis is 0.5, and a completely ideal diagnosis is 1.

[0088] It is generally believed that: AUC: between 0.5-0.7: indicates low diagnostic value and low accuracy;

[0089] Between 0.7 and 0.9: indicates moderate diagnostic value and a certain degree of accuracy;

[0090] >0.9: indicates high diagnostic value and high accuracy.

[0091] The larger the area under the curve AUC, the higher the overall diagnostic rate:

[0092] Significance: Also known as statistical significance, it refers to the risk level of rejecting the null hypothesis when the null hypothesis is true, also known as probability level, or significance level. It indicates the ability to distinguish between groups. In statistical hypothesis testing, the probability value of a recognized low-probability event is called the significance level of statistical hypothesis testing. The same quantity is measured multiple times and then the average value is calculated.

[0093] Fatty acid group index example:

[0094] 1. Experimental methods

[0095] 1. Preparation of standard products

[0096] 40 kinds of fatty acid methyl ester mixed standard solutions were used, and ten standard concentration gradients of 0.5 mg / L, 1 mg / L, 5 mg / L, 10 mg / L, 25 mg / L, 50 mg / L, 100 mg / L, 250 mg / L, 500 mg / L and 1000 mg / L were prepared for each solution.

[0097] Take 500 μL of 40 fatty acid methyl ester mixed standards at the same concentration, add 25 μL of 500 ppm methyl nonadecanoate as an internal standard, mix well and add to the injection bottle, enter GC-MS detection, injection volume 1 μL, split ratio 10:1, split injection.

[0098] 2. Extraction of metabolites from samples to be tested

[0099] Thaw the sample on ice, take 60μl of the sample in a 2mL glass centrifuge tube. Add 1mL of chloroform-methanol solution, sonicate for 30min, take the supernatant, add 2mL of 1% sulfuric acid-methanol solution, place in an 80℃ water bath, methylate for half an hour, then add 1mL of n-hexane for extraction, wash with 5mL of pure water, take 500μL of the supernatant, add 25μL of methyl salicylate as an internal standard, mix well and add to the injection bottle, enter GC-MS detection, injection volume 1μL, split ratio 10:1, split injection.

[0100] 3. Chromatography-mass spectrometry analysis

[0101] (1) Gas chromatography conditions: The samples were separated using an Agilent DB-WAX capillary column (30 m × 0.25 mm ID × 0.25 μm) gas chromatography system. Program temperature rise: initial temperature 50 °C; maintain for 3 min, increase the temperature to 220 °C at 10 °C / min; and maintain for 5 min. The carrier gas was helium, and the carrier gas flow rate was 1.0 mL / min. A quality control (QC) sample was set every 7 experimental samples in the sample queue to detect and evaluate the stability and repeatability of the system.

[0102] (2) Mass spectrometry analysis: Agilent 7890 / 5975C gas-mass spectrometer was used for mass spectrometry analysis. The injection port temperature was 280°C; the ion source temperature was 230°C; the transfer line temperature was 250°C. Electron impact ionization (EI) source, SIM scanning mode, electron energy 70 eV.

[0103] (3) Data processing: The chromatographic peak area and retention time were extracted using MSD ChemStation software. The calibration curve was drawn to calculate the content of medium- and long-chain fatty acids in the sample.

[0104] (4) Statistics: Data analysis was performed using SPSS software (version 25.0; IBM, Armonk NY, USA). Analysis of variance (ANOVA) was used to analyze the differences among the three groups, and the SNK-q test was further used for multiple comparisons. ROC (receiver operating characteristic curve) analysis was used to determine the diagnostic value of the difference variable. Binary logistics regression analysis was used to construct a logistics regression model to further improve the prediction sensitivity and specificity and improve the diagnostic value.

[0105] (III) Results

[0106] (1) Test results

[0107] A total of 39 medium-chain fatty acids were detected, and the total fatty acid amounts of five medium-chain fatty acid subcategories were calculated: total SFA (saturated fatty acids), total MUFA (monounsaturated fatty acids), total PUFA (polyunsaturated fatty acids), total N3 (ω-3 cluster fatty acids, the first unsaturated bond in unsaturated fatty acids appears at the third position from the methyl end of the carbon chain) and total N6 (ω-6 cluster fatty acids, the first unsaturated bond in unsaturated fatty acids appears at the sixth position from the methyl end of the carbon chain).

[0108] (2) ANOVA: ANOVA was used to compare whether there were differences in the medium-chain fatty acids among the three groups, and the SNK-q test was further used for pairwise comparisons among the three groups. The results of ANOVA showed that 18 of the 39 medium-chain fatty acids were significantly different among the three groups (P < 0.05), namely: C10:0, C11:0, C12:0, C14:0, C15:0, C15:1N5, C16:0, C17:0, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C21:0, C22:1N9, C23:0, C24:0 and C8:0. In addition, there were significant differences in the total saturated fatty acids (SFA) and total monounsaturated fatty acids (MUFA) among the five medium- and long-chain fatty acid subcategories (P < 0.05) (see Table 4, unit: μg / ml).

[0109] Table 4

[0110]

[0111]

[0112] (3) SNK-q test: For the medium-chain fatty acids that showed significant differences among the three groups in the variance analysis, the SNK-q test was further used to compare the three groups. The results showed that there were 17 medium-chain fatty acids with significant differences between the IH group and the control group (P < 0.05), namely: C10:0, C11:0, C12:0, C14:0, C15:0, C15:1N5, C16:0, C17:0, C18:0, C18:2N6, C20:0, C20:5N3, C21:0, C22:1N9, C23:0, C24:0 and C8:0, and there were also significant differences in total saturated fatty acids (SFA) and total monounsaturated fatty acids (MUFA) (P < 0.05); there were 13 medium-chain fatty acids with significant differences between the IH group and the NS group. There were significant differences in medium and long-chain fatty acids (P < 0.05), namely C10:0, C11:0, C12:0, C15:1N5, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C21:0, C22:1N9, C23:0 and C24:0, and there were also significant differences in total monounsaturated fatty acids (MUFA) (P < 0.05). Compared with the control group, there were significant differences in 3 medium and long-chain fatty acids between the NS group (P < 0.05), namely C15:0, C20:3N3 and C22:1N9 (see Table 5).

[0113] (4) ROC curve analysis: The ROC curves of the above 44 differential metabolites were drawn and the AUC values ​​were calculated. Compared with the control group, the AUC values ​​of 12 medium-chain fatty acids in the IH group were greater than 0.7, namely: C10:0, C11:0, C15:1N5, C18:0, C20:0, C20:5N3, C21:0, C22:1N9, C23:0, C24:0, C6:0 and C8:0; while compared with the NS group, the AUC values ​​of 9 medium-chain fatty acids in the IH group were greater than 0.7, namely: C10:0, C12:0, C15:1N5, C20:0, C20:3N3, C20:5N3, C21:0, C23:0 and C24:0. (See Table 5)

[0114] Table 5

[0115]

[0116]

[0117]

[0118] (5) Binary logistics regression analysis was performed on the IH group VS the control group: the different even-carbon medium-chain fatty acids between the IH group and the control group were screened: C10:0, C12:0, C14:0, C16:0, C18:0, C18:2N6, C20:0, C20:5N3, C22:1N9, C24:0 and C8:0, a total of 11, and binary logistics regression analysis was performed. The stepwise method was used to obtain the optimal model, and the optimal model constructed with 6 variables was established (as shown in Table 6 below). Thus, the new predicted value (Y) is calculated: Y = logit (P) = Ln (P / (1-P)) = 126.02 × C10: 0 - 0.16 × C16: 0 + 5.38 × C20: 0 + 23.28 × C20: 5N3 + 0.78 × C22: 1N9 - 67.68 × C8: 0 + 1.79

[0119] Table 6: Binary logistic regression of IH group VS control group

[0120]

[0121] (6) Fitting model ROC curve analysis: Generate predicted value Y value with the model and draw ROC curve with Y value (such as Fig.17 As shown), the AUC was calculated to be 0.960, the sensitivity of the model was 0.917, and the specificity was 0.923.

[0122] (7) Binary logistic regression analysis of IH group VS NS group:

[0123] The difference in even-carbon medium-chain fatty acids between the IH group and the NS group was screened: C10:0, C12:0, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C22:1N9 and C24:0, a total of 9 fatty acids, and binary logistics regression analysis was performed. The stepwise method was used to obtain the optimal model, and the optimal model constructed with 4 variables was established (as shown in Table 7 below).

[0124] Thus, the new predicted value (Y) is calculated as follows: Y = logit (P) = Ln (P / (1-P)) = 12.41 × C12:0 - 19.42 × C20:3N3 + 14.10 × C20:5N3 - 0.44 × C22:1N9 + 5.56

[0125] Table 7: Binary logistic regression of IH group VS NS group

[0126]

[0127]

[0128] (8) Fitting model ROC curve analysis: Generate the predicted value Y value with the model, and draw the ROC curve with the Y value ( Fig.18 ), and the calculated AUC was 0.966. At this time, the sensitivity of the model was 0.917 and the specificity was 0.946.

[0129] In summary, there are significant differences between the fatty acid indexes of infantile hemangioma patients and those of erythema neonatorum with similar clinical symptoms. The inventors of the present invention have found that the fatty acid indexes of infantile hemangioma patients are not only significantly different from those of normal children, but also significantly different from those of erythema neonatorum with similar clinical symptoms, which is helpful for clinically distinguishing other diseases with similar symptoms to infantile hemangioma, and avoiding overtreatment or missed treatment opportunities caused by misdiagnosis.

[0130] Metabolome indicators example:

[0131] 1. Experimental methods

[0132] 1. Sample processing: Take out the samples (all samples shown in Table 2) at -80℃, slowly dissolve at 4℃, take 100ul of each sample, add 400ul pre-cooled methanol acetonitrile solution (1:1, v / v), vortex for 60s, place at -20℃ for 1h to precipitate protein, centrifuge at 14000rcf, 4℃ for 20min, take the supernatant and freeze-dry, and store the samples at -80℃.

[0133] 2. Sample detection: The samples were separated using the Agilent 1290 Infinity LC ultra-high performance liquid chromatography system (UHPLC) HILIC column. After the sample detection was completed, the AB Triple TOF 6600 mass spectrometer was used to collect the primary and secondary spectra of the samples.

[0134] 3. Data processing: The metabolome detection data was converted into .mzXML format by ProteoWizard, and then the XCMS program was used for peak alignment, retention time correction and peak area extraction. Metabolite structure identification was performed by accurate mass matching (<25ppm) and secondary spectrum matching, and the laboratory's self-built database was searched. XCMS software was used to extract metabolite ion peaks, and 10733 and 9688 peaks were detected in positive and negative ion modes, respectively. The laboratory's self-built database was used for qualitative analysis, and 228 and 189 metabolites were qualitatively identified in positive and negative ion modes, respectively.

[0135] Table 8

[0136]

[0137] 4. Statistics:

[0138] Data were analyzed using SPSS software (version 25.0; IBM, Armonk NY, USA) and SIMCA-P software (version 14.1; Umetrics, Sweden). Two independent sample T test (Student's t-test) was used to analyze whether there were differences in variables. The two-tailed test P value was less than 0.05, which was considered significant. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to establish a relationship model between metabolite expression and sample category to predict the sample category. At the same time, the variable projection importance (VIP) was calculated to measure the influence and explanatory power of the expression pattern of each metabolite on the classification and discrimination of each group of samples, thereby assisting the screening of marker metabolites (VIP>1.0 was used as the screening standard). ROC (receiver operating characteristic curve) analysis was used to determine the diagnostic value of differential variables. Binary logistics regression analysis was used to construct a logistics regression model to further improve the prediction sensitivity and specificity and improve the diagnostic value.

[0139] (II) Results:

[0140] (1) Analysis of differential metabolites: Metabolites with orthogonal partial least squares discriminant analysis VIP>1 and T-test analysis P value <0.05 were selected as metabolites with significant differences. There were 21 metabolites with significant differences in the positive ion mode and 21 metabolites with significant differences in the negative ion mode.

[0141] (2) ROC curve analysis: The ROC curves of the above 42 differential metabolites were drawn and the AUC values ​​were calculated. Among them, the AUC values ​​of 12 differential metabolites were greater than 0.7, and the AUC values ​​of 7 differential metabolites were greater than 0.75. See Table 9 below for details.

[0142] Table 9: Differential metabolites

[0143]

[0144]

[0145] (3) Binary logistics regression: Binary logistics regression analysis was performed on the above 42 differential metabolites, and the optimal model was obtained using a stepwise algorithm. The optimal model constructed with 6 variables was established (see Table 10), thereby calculating the new predicted value (Y): Y = logit (P) = Ln (P / (1-P)) = -5.55 × 10 -5 ×DL-indole-3-lactic acid+3.17×10 -4 ×L-tryptophan+3.50×10 -5 ×Cyclohexylaminosulfonate-4.15×10 -5 ×D-galactate+4.13×10 -5 × Glyceric acid + 1.45 × 10 -5 ×sn-glycero-3-phosphoethanolamine-9.32

[0146] Table 10: Logistics regression analysis and prediction model establishment.

[0147]

[0148]

[0149] (4) Fitting model ROC curve analysis: Use the model to generate predicted value Y value and draw ROC curve with Y value (such as Fig.16 The AUC was calculated to be 0.952, with a sensitivity of 0.893 and a specificity of 0.906.

[0150] Example of protein group indicators:

[0151] High-throughput Swissport proteomics detection

[0152] 1. Experimental methods

[0153] 1. Glossary

[0154] DDA: Data Dependent Acquisition, used in this article to build the library in the DIA analysis process.

[0155] DIA: Data Independent Acquisition, specifically based on Thermo Scientific TM Data-independent acquisition of electrostatic field orbitrap.

[0156] Spectronaut Pulsar X: Proteomics software for the analysis of data independent acquisition (DIA) measurements. Well-known DIA data analysis software from Biognosys.

[0157] MaxQuant: A famous proteomics software from Mann M. laboratory, used for checking libraries when establishing libraries.

[0158] Direct DIA: Integrated database search engine called Pulsar enabling spectral-libraryfree DIA workflow. The algorithm from Biognosys can directly query the DIA data library to increase the capacity of the library and improve the number of final qualitative and quantitative DIA results.

[0159] 2. Protein detection:

[0160] First, all samples to be tested were combined in equal amounts as a pool, and the pool samples were separated into high-abundance components using Aglient-H14. The high-abundance and low-abundance components were enzymatically hydrolyzed, and the peptides of the low-abundance group were HPRP graded (10 copies). The above samples were analyzed by LC-MS / MS (QE-HF_DDA mode) to obtain a proteome database, and the protein self-built library of APT was merged as the database for DIA work. The specific steps are: the mixture of all samples was subjected to sample enzymatic hydrolysis and HPRP classification, and after data dependent acquisition (DDA) mass spectrometry data acquisition, MaxQuant was used for library search and identification, and the spectral library was constructed by Spectronaut pulsar X software as the database for subsequent data independent acquisition (DIA) protein spectrum quantification. Download the swissport data package to build a proteome database.

[0161] After each sample in the project was prepared and enzymatically hydrolyzed, it was analyzed by DIA (data independent acquisition) on the computer. The obtained DIA raw files were imported into Spectronaut Pulsar X for analysis, and the qualitative and quantitative protein expression data of each sample were obtained (the numerical value reflects the area under the curve of the protein chromatographic peak, and the larger the value, the higher the protein content).

[0162] 3. Statistics:

[0163] Data analysis was performed using SPSS software (version 25.0; IBM, Armonk NY, USA). Two independent sample T tests (Student's t-test) were used to analyze whether there were differences in the variables. The two-tailed test P value was less than 0.05, indicating that the difference was significant. ROC (receiver operating characteristic curve) was used to analyze the diagnostic value of the difference variables. Binary logistics regression analysis was used to construct a logistics regression model to further improve the prediction sensitivity and specificity and improve the diagnostic value.

[0164] (II) Results

[0165] (1) Protein screening: A total of 727 proteins were qualitatively and quantitatively identified. Protein variables with missing values ​​> 50% in all samples were filtered to reduce the false positive rate. A total of 603 variables were screened. Missing values ​​were filled with the average value of the variables in the same group to maintain homogeneity within the data group.

[0166] (2) T test: Two independent sample T test was performed on the selected proteins, and variables with P values ​​less than 0.05 were selected, totaling 47: PG.Genes: COLEC10, CPQ, LDHB, ACAN, KRT1, ARHGDIB, POSTN, LAMA5, CPN1, PROC, C1S, HPX, SUPT6H, BCAR3, BCHE, SOX30, C1R, PON1, CFHR4, PROZ, IGKV1D-16, APOC3, COMP, SELENOP, PMFBP1, GSN, SELL, FUCA2, HLA-B, FGFBP2, FKBP1A, HSPE1, SAA1, PGD, CDH1, SOD1, ISLR, CORO1A, ZYX, RLBP1, IDH1, IGKV6-21, SORL1, FAM3C, CST6, COTL1, and PIGR.

[0167] (3) ROC curve analysis: ROC analysis was performed on the above 47 proteins, and the AUC values ​​were calculated. The results are shown in Table 11 below:

[0168] Table 11: Differential proteins.

[0169]

[0170]

[0171] (4) Binary logistics regression analysis: Binary logistics regression analysis was performed on the above 47 proteins, and the optimal model was obtained using a stepwise algorithm. The optimal model constructed with 6 variables was established (see Table 12), thereby calculating the new predicted value (Y): Y = logit (P) = Ln (P / (1-P)) = 7.02 × 10 -6 ×KRT1-4.09×10 -5 ×POSTN-1.18×10 -4 ×PROC-1.06×10 -4 ×PROZ-2.30×10 -4 ×PMFBP1-1.12×10 -4 ×SELL+51.03

[0172] Table 12: Logistics regression analysis and prediction model establishment.

[0173]

[0174] (5) Fitting model ROC curve analysis: Use the model to generate predicted value Y value and draw ROC curve with Y value (such as Fig.14 The AUC was calculated to be 0.965, with a sensitivity of 0.929 and a specificity of 0.906.

[0175] High-throughput uniport proteomics detection

[0176] The difference from the above high-throughput Swissport proteomics assay is:

[0177] 1. Protein detection:

[0178] First, all samples to be tested were combined in equal amounts as a pool, and the pool samples were separated into high-abundance components using Aglient-H14. The high-abundance and low-abundance components were enzymatically hydrolyzed, and the peptides of the low-abundance group were HPRP graded (10 copies). The above samples were analyzed by LC-MS / MS (QE-HF_DDA mode) to obtain a proteome database, and the protein self-built library of APT was merged as the database for DIA work. The specific steps are: the mixture of all samples was subjected to sample enzymatic hydrolysis and HPRP classification, and after data dependent acquisition (DDA) mass spectrometry data acquisition, MaxQuant was used for library search and identification, and the spectral library was constructed by Spectronaut pulsar X software as the database for subsequent data independent acquisition (DIA) protein spectrum quantification. Download the uniport data package to build a proteome database.

[0179] Each sample in the project was prepared and protein hydrolyzed independently, and then DIA (data independent acquisition) analysis was performed on the computer. The obtained DIA raw files were imported into Spectronaut Pulsar X for analysis to obtain the number of qualitative and quantitative proteins for each sample. (The numerical value reflects the area under the curve of the protein chromatographic peak. The larger the value, the higher the protein content). A total of 2572 proteins were qualitatively and quantitatively analyzed, of which 1672 proteins were detected in more than 80% of the samples.

[0180] 2. Results

[0181] (1) Protein screening: A total of 2572 proteins were qualitatively and quantitatively identified. Protein variables with missing values ​​> 20% in all samples were filtered to reduce the false positive rate, and a total of 1672 variables were screened. Missing values ​​were filled with the median value of the variable in the same group to maintain homogeneity within the data group;

[0182] (2) T test: Two independent sample T test was used for the selected data, and variables with P values ​​less than 0.05 were selected, totaling 71. Immunoglobulins were further removed, and 15 differentially expressed proteins were obtained, PG.Genes: A0A1W6IYJ2, PRDX1, CAT, CPN1, APOC3, HPX, PROS1, CD5L, HMFT1766, C1QA, KRT1, BCHE, KRT2, APP, and FGFBP2.

[0183] (3) ROC curve analysis: ROC curves were drawn for the above 15 variables, and AUC was calculated, as shown in Table 13:

[0184] Table 13

[0185]

[0186] (4) Binary logistics regression analysis: Binary logistics regression analysis was performed on the above 15 proteins, and the optimal model was obtained using a stepwise algorithm. The optimal model constructed with 6 variables was established (see Table 14 below), thereby calculating the new predicted value (Y): Y = logit (P) = Ln (P / (1-P)) = 3.96 × 10 -6 ×A0A1W6IYJ2-3.22×10 -6 ×CAT-1.92×10 -5 ×PROS1-4.10×10 -5 ×CD5L+3.36×10 -6 ×C1QA+4.74×10 -6 ×KRT1+6.81

[0187] Table 14

[0188]

[0189] (5) Fitting model ROC curve analysis: Use the model to generate predicted value Y value and draw ROC curve with Y value (such as Fig.15 The AUC was calculated to be 0.934, with a sensitivity of 0.893 and a specificity of 0.875.

[0190] Lipidome index example:

[0191] 1. Experimental instruments and reagents

[0192] Q-Exactive Plus mass spectrometer (Thermo Scientific)

[0193] UHPLC Nexera LC-30A Ultra High Performance Liquid Chromatograph (SHIMADZU)

[0194] Low temperature high speed centrifuge (Eppendorf 5430R)

[0195] Chromatographic column: Waters, ACQUITY UPLC CSH C18, 1.7μm, 2.1mm×100mmcolumn

[0196] Acetonitrile (Thermo Fisher), Isopropanol (Thermo Fisher), Methanol (Thermo Fisher)

[0197] 13 isotopic internal standards (Cer, LPC, PC, LPE, PE, PI, PS, PA, PG, SM, Chol Ester, DG, TG).

[0198] 2. Experimental Methods

[0199] 2.1 Sample Information

[0200] Preparation of quality control samples (QC): Take equal amounts of samples from each group and mix them as QC. QC samples are not only used to measure the instrument status before injection and balance the chromatography-mass spectrometry system, but are also interspersed in the detection process of the samples to be tested to evaluate the system stability during the entire experiment.

[0201] 2.2 Sample preprocessing method

[0202] 60ul of each sample was aspirated, 200μL of water and 20μL of internal lipid standard mixture were added, vortexed, 800μL of methyl tert-butyl ether (MTBE) was added, vortexed, 240μL of pre-cooled methanol was added, vortexed, sonicated in a low-temperature water bath for 20min, placed at room temperature for 30min, centrifuged at 14000g 10℃ for 15min, the upper organic phase was taken, dried with nitrogen, and 200μL of 90% isopropanol / acetonitrile solution was added for re-dissolution during mass spectrometry analysis, vortexed thoroughly, 90μL of the reconstituted solution was taken, centrifuged at 14000g 10℃ for 15min, and the supernatant was taken for sampling and analysis.

[0203] 2.3 Chromatography-mass spectrometry analysis

[0204] 2.3.1 Chromatographic conditions

[0205] The samples were separated using a UHPLC Nexera LC-30A ultra-high performance liquid chromatography system. C18 column;

[0206] Column temperature 45℃; flow rate 300μL / min. Mobile phase composition A: acetonitrile aqueous solution (acetonitrile: water = 6:4, v / v), B: acetonitrile isopropanol solution (acetonitrile: isopropanol = 1:9, v / v). Gradient elution program is as follows: 0---2min, B maintained at 30%; 2---25min, B linearly changed from 30% to 100%; 25---35min, B maintained at 30%.

[0207] 2.3.2 Mass spectrometry conditions

[0208] Electrospray ionization (ESI) was used for detection in positive and negative ion modes. The samples were separated by UHPLC and analyzed by Q Exactive series mass spectrometer (Thermo Scientific TM ) for mass spectrometry analysis. The ESI source conditions are as follows:

[0209] Heating temperature (Heater Temp) 300℃, sheath gas flow rate (Sheath Gas Flow rate) 45arb, sheath gas flow rate (Aux Gas Flow Rate) 15arb, sweep gas flow rate (Sweep Gas Flow Rate) 1arb, spray voltage (spray voltage) 3.0KV, capillary temperature (Capillary Temp) 350℃, ion lens voltage frequency level (S-Lens RF Level) 50%, MS1 scan range (scan ranges): 200-1800.

[0210] The mass-to-charge ratios of lipid molecules and lipid fragments were collected as follows: 10 fragment spectra (MS2scan, HCD) were collected after each full scan. The resolution of MS1 at M / Z 200 was 70,000, and the resolution of MS2 at M / Z 200 was 17,500.

[0211] 2.4 Data processing and software settings

[0212] LipidSearch was used to perform peak recognition, peak extraction, lipid identification (secondary identification), etc. on lipid molecules and internal standard lipid molecules. The main parameters were: precursor tolerance: 5ppm, product tolerance: 5ppm, product ion threshold: 5%.

[0213] 2.5 Statistical analysis

[0214] SPSS software (version 25.0; IBM, Armonk NY, USA) was used for analysis. Two independent sample T test (Student's t-test) was used to analyze whether there were differences in variables. The two-tailed test P value was less than 0.05, indicating that the difference was significant. The size of the difference was calculated by calculating the Flod change (FC) value. FC>1.5 or FC<0.67 indicated that the difference was significant. ROC (receiveroperating characteristic curve) analysis was used to analyze the diagnostic value of the difference variable.

[0215] 3. Results

[0216] 3.1 Identification Quantity Statistics

[0217] The International Lipid Classification and Nomenclature Committee divides lipid compounds into eight major types. Each type can be divided into different subclasses (lipid class) according to the difference in polar head. Each subclass is divided into different molecules (lipid species) according to differences in carbon chain saturation or length, thus forming a three-level classification of lipid compounds: major class-subclass-molecule.

[0218] The number of lipid compounds in the sample identified in the positive and negative ion modes in this experiment

[0219] Table 15: Lipid subclasses and number of molecules identified

[0220]

[0221] The statistical results of the lipid subclasses and lipid species identified in the positive and negative ion modes are shown in Tables 1 and Fig.10 .

[0222] 3.2 Lipid differential analysis

[0223] 3.2.1 Overall level

[0224] The total lipid content of the sample is obtained by summing up the contents of all quantified lipid molecules in the same sample. The total contents of samples in different groups can then be compared. The T test results showed that the total lipid content of the IH group was lower than that of the control group (P < 0.01). (See Fig.11 )

[0225] 3.2.2 Subcategory level

[0226] The contents of all quantified lipid subclasses in the same sample were summed up to compare the expression changes of lipid subclasses in different samples. T test results showed that the contents of AcCa, Cer, CerG1, CerG2, CerG2GNAc1, CerG3, ChE, CL, GM3, LPC, LPE, MGDG, PA, PC, PE, phSM, PI, PS, SM and TG were different between the two groups (P < 0.05), among which the content of phSM in the IH group was higher than that in the control group, and the others were lower than those in the control group. There was no significant difference in the contents of Co, DG, LPI, So and WE between the two groups (P > 0.05) (see Table 16, Fig.12 and Fig.13 ).

[0227] ROC analysis was further used to analyze the diagnostic value of each differential lipid subclass, and the AUC value was calculated. The results showed that the AUC values ​​of Cer, ChE, LPC, LPE, PA, PC, PE, PI, PS and SM were greater than 0.700, and the AUC value of PA was greater than 0.8, which had good diagnostic value (see Table 16).

[0228] Table 16: Differences in the expression of different subtypes of lipids between the two groups

[0229]

[0230] 3.2.3 Lipid molecular level

[0231] Two-sample T-tests were performed on 821 lipid molecules that were qualitatively and quantitatively analyzed between the two groups. The results showed that 567 lipid molecules were different (P < 0.05). The fold change (FC) was further screened, and 6 differential molecules met FC>1.5, and 130 met FC<0.67. Finally, 136 differential molecules were screened with T-test P value < 0.05 and FC> 1.5 or FC < 0.67 as the screening conditions.

[0232] ROC analysis was further used to analyze the diagnostic value of each differential molecule, and the AUC value was calculated. The results showed that 115 differential molecules had an AUC ≥ 0.700, of which 53 had an AUC ≥ 0.750 (see Table 17). Among them, there were 9 differential molecules with an AUC ≥ 0.80, namely PS (39: 4) -H, SM (d45: 6) + H, PC (14: 0 / 20: 4) + HCOO, SM (d20: 0 / 18: 1) + HCOO, PS (49: 4) -H, LPC (26: 0) + HCOO, SM (d20: 0 / 16: 0) + HCOO, PA (50: 4) -H, and LPC (20: 4) + HCOO.

[0233] Table 17: There are significant differences between the lipid molecular indicators of children with infantile hemangioma and normal children, lipid molecules with AUC ≥ 0.75.

[0234]

[0235]

[0236] Fitting model validation example

[0237] The metabolite contents detected in the first sample of the positive group and the control group were respectively substituted into the above-mentioned proteomics model, metabolomics model, and fatty acid omics model to obtain the Y value and the prediction results. The results showed that in the four models, the Y values ​​of the samples of the IH group and the control group were significantly different, indicating that the four models all had good prediction effects. (See Table 18 below for details)

[0238] Table 18

[0239]

[0240]

[0241] Verification example:

[0242] Examples 1-6

[0243] This example is used to verify that the fatty acid index with significant differences obtained by the present invention can distinguish patients with infantile hemangioma, normal children and patients with neonatal erythema. The specific examples 1-6 are shown in Table 19 below.

[0244] Table 19

[0245] Example Corresponding detection object Diseased area Example 1 Infantile hemangioma patient 01 Cubital fossa Example 2 Infantile hemangioma patients 02 Back Example 3 Neonatal erythema patient 01 head Example 4 Neonatal erythema patient 02 head Example 5 Normal children 01 none Example 6 Normal children 02 none

[0246] After birth, the umbilical cord blood samples of the patients of Examples 1-6 or normal children or patients with neonatal erythema were tested for the relevant indicators of the present invention according to the methods described above. The following Table 20 shows the specific test results.

[0247] Table 20 Fatty acid test results of umbilical cord blood samples

[0248]

[0249]

[0250] Figures 1 to 8 The growth of lesions in a patient with infantile hemangioma and a patient with erythema neonatorum is shown. Figure 1 and Figure 2 The images show two infantile hemangiomas with reddish patches in the antecubital fossa and back at 6 weeks of age. Figure 3 and Figure 4 Symptoms of patients with erythema neonatorum are not easily distinguishable; Figure 3 and Figure 4 Two patients with erythema neonatorum were shown with occipital erythema neonatorum, which also showed reddish patches, at 6 weeks after birth; Figure 5 and Figure 6The images show that at 12 weeks after birth, the infantile hemangiomas in the antecubital fossa and back of two patients gradually increased in size and darkened in color, appearing as bright red masses that were higher than the skin surface. Figure 7 and Figure 8 The results show that the occipital neonatal erythema remained unchanged or faded in color in two patients with neonatal erythema 12 weeks after birth. (Note: The parents of infantile hemangioma patients who agreed to participate in the experiment did not agree to start treatment for IH in advance for children whose umbilical cord blood samples showed abnormalities before 12 weeks after birth.)

[0251] As can be seen from Table 20, by detecting the indicators discovered by the inventors in their umbilical cord blood samples, it is possible to screen out infantile hemangioma patients in advance and accurately diagnose them, thus providing the possibility for timely control of the onset and progression of the disease in infantile hemangioma patients.

[0252] Example 7-37

[0253] This example is used to verify that the metabolites, proteins, lipid subclasses and lipid molecular indicators with significant differences obtained by the present invention can distinguish infantile hemangioma patients from normal children. Specific test results are shown in Tables 21-24.

[0254] Table 21 Metabolite detection results of umbilical cord blood samples of Examples 7-16

[0255]

[0256]

[0257] Table 21

[0258]

[0259]

[0260] Table 21

[0261]

[0262]

[0263] Table 21

[0264] Metabolites Example 13 (IH Child 06) Example 14 (Normal Child 06) IH compared with normal children (3-Carboxypropyl)trimethylammonium cation 629036.2 230257.3 high α-D-(+)-Talose 44115.07 16960.69 high 1-Aminocyclopropanecarboxylic acid 94329.05 49168.4 high 1-Myristoyl-sn-glycero-3-phosphocholine 580831.1 179074.7 high 1-oleoyl-sn-glycero-3-phosphocholine 166464.6 59803.85 high 1-Palmitoyl-sn-glycero-3-phosphocholine 31923571 5171830 high choline 307412.2 108971.7 high DL-Indole-3-lactic acid 1837840 521566.6 high Glycerylphosphorylcholine 1355195 692020.8 high Isomaltose 414269 152420.9 high L-Glutamic Acid 140465.1 23508.76 high L-Phenylalanine 996992.2 401872.1 high L-Tryptophan 360420.5 103943 high Tyramine 2133112 973692.5 high Phenylacetic acid 20765.4 31707.5 Low (S)-Citral 12353.27 4792.731 high α-Mangostin 916127.6 231627.4 high Cyclamate 48629.76 15112.53 high D-galactate 31600.3 55556.12 Low Inositol galactoside 3007417 982875.2 high Glyceric acid 79903.29 27278.19 high L-Aspartic Acid 102114.2 11808.49 high L-Carnosine 38562.33 112370.8 Low L-Malic Acid 102599.6 6392.867 high N-Acetyl-L-Aspartic Acid 38679.11 13727.85 high N-Acetylneuraminic acid 176493.9 22262.77 high Phosphocholine 58819.83 20118.58 high sn-Glycero-3-phosphoethanolamine 339402.5 50557.39 high Succinate 58534.13 13912.63 high

[0265] Table 21

[0266]

[0267]

[0268] Table 22 Protein detection results of umbilical cord blood samples of Examples 17-26

[0269]

[0270]

[0271] Table 22

[0272] protein Example 19 (IH Child 09) Example 20 (IH Child 09) IH compared with normal children COLEC10 1694103.5 30962.67 high ACAN 2405567.3 22159.97 high KRT1 1201667.6 338370.22 high POSTN 38748.93 92234.64 Low LAMA5 91258.05 325520.4 Low CPN1 176122.81 312381.63 Low PROC 59420.95 167681.98 Low BCAR3 453915.63 322910.13 high SOX30 46228.37 212304.75 Low PROZ 50152.57 159895.33 Low IGKV1D-16 423852.69 223151.53 high PMFBP1 36022 57853.33 Low FGFBP2 212745.72 26039.64 high HSPE1 919539 7279872.4 Low PGD 574242.25 92360.25 high SOD1 66378.97 48647.85 high COTL1 36463.08 108060.94 Low CAT 66668.719 1332205.3 Low PROS1 232601.3 191883.78 high CD5L 288586.03 124116.71 high HMFT1766 703811.06 574892.5 high

[0273] Table 22

[0274]

[0275]

[0276] Table 22

[0277] protein Example 23 (IH child 11) Example 24 (IH child 11) IH compared with normal children COLEC10 3388055.3 40016.11 high POSTN 41532.77 92365.08 Low PROC 56539.59 149606.16 Low C1S 742738.81 966209.13 Low SUPT6H 32310.08 10553.28 high BCAR3 502538.63 216837.97 high BCHE 166995.64 277742.38 Low SOX30 121270.22 233738.78 Low C1R 289904.19 359219.97 Low IGKV1D-16 425436.22 264957.34 high APOC3 3651022.5 5632990.5 Low GSN 505719.47 866859.94 Low SELL 121987.74 159236.16 Low CDH1 24045.53 90848.18 Low ZYX 30331.08 128526.67 Low SORL1 5664647.5 971401.38 high FAM3C 13536.82 18625.73 Low CST6 22128.1 88522.59 Low PRDX1 186841.38 559074.75 Low Q1Q 1726583 1364217.3 high APP 45928.97 21351.4 high

[0278] Table 22

[0279]

[0280]

[0281] Table 23 Lipid subclass test results of umbilical cord blood samples of Examples 27-32

[0282]

[0283]

[0284] Table 24 Lipid molecule detection results of umbilical cord blood samples of Examples 33-36

[0285]

[0286]

[0287] Embodiment 37:

[0288] This example is used to illustrate the use of the indicator of the present invention for monitoring the progression of IH and the therapeutic effect of a drug.

[0289] like Fig. 9 Shown: The regression of infantile hemangioma on the left upper eyelid after oral propranolol. (A) Before starting oral propranolol, the tumor proliferated significantly; (B) After taking propranolol for 1 month, the tumor gradually regressed; (C) After taking propranolol for 12 months, IH basically disappeared and the drug was discontinued; (D) After stopping the drug for 2 months, IH rebounded.

[0290] Table 25: Changes in the concentrations of fatty acids C20:0 and C22:6N3 in peripheral blood of children with IH before and after oral administration of propranolol.

[0291]

[0292] It can be seen from Table 25 that the fatty acid index of the present invention is fully capable of effectively monitoring the progress of IH and the therapeutic effect of the drug.

[0293] Although the subject matter of the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and modifications can be made without departing from the spirit and scope of the present invention as defined by the appended claims. In addition, the scope of the present application is not intended to be limited to the specific embodiments of the purposes, material compositions, kits, methods and steps described in the specification. Those of ordinary skill in the art will easily understand from the disclosure of the disclosed subject matter that, according to the subject matter of the present disclosure, existing or to-be-developed purposes, material compositions, kits, methods or steps that perform substantially the same functions as the corresponding embodiments described herein or achieve substantially the same results can be used. Therefore, the appended claims are intended to include such purposes, material compositions, kits, methods or steps within their scope.

[0294] Patents, patent applications, publications, product descriptions, and protocols are cited throughout this application, the disclosures of which are incorporated herein by reference in their entirety for all purposes.

Claims

1. Use of the composition in the preparation of a drug for diagnosing infantile hemangioma and / or monitoring its progression and prognosis, characterized in that: The composition comprises at least one of the following four groups of substances: (1) fatty acids, wherein the fatty acids are total fatty acids, total saturated fatty acids, total monounsaturated fatty acids, or at least one selected from the group consisting of fatty acids of C10:0, C11:0, C12:0, C14:0, C15:0, C15:1N5, C16:0, C17:0, C18:0, C18:2N6, C20:0, C20:3N3, C20:5N3, C21:0, C22:1N9, C23:0, C24:0, and C8:0; (2) metabolites, wherein the metabolites are selected from the group consisting of (3-carboxypropyl) trimethylammonium cation, α-D-(+)-talose, 1-aminocyclopropanecarboxylic acid, 1-myristoyl-sn-glycero-3-phosphocholine, 1-oleoyl-sn-glycero-3-phosphocholine, 1-palmitoyl-sn-glycero-3-phosphocholine, 1-stearoyl-sn-glycero-3-phosphocholine, bilirubin, choline, D-2-piperidinic acid, DL-indole-3-lactic acid, glycylglutamic acid, glycerophosphocholine, isomaltose, L-arginine, L-glutamic acid, L-phenylalanine, L-tryptophan, NG, N At least one of the group consisting of G-dimethyl-L-arginine, tyramine, phenylacetic acid, (S)-citramalic acid, 1-oleoyl-L-α-lysophosphatidic acid, 1-palmitoyl-lysophosphatidic acid, α-mangostin, arachidonic acid (peroxide-free), citramalic acid, cyclamates / salts, D-galactates / salts, D-threitol, D(-)-β-hydroxybutyric acid, inositol galactoside, glyceric acid, L-aspartic acid, L-carnosine, L-malic acid, N-acetyl-L-aspartic acid, N-acetylneuraminic acid, norethindrone acetate, choline phosphate, sn-glycero-3-phosphoethanolamine and succinates / salts; (3) Protein, wherein the protein is selected from COLEC10, CPQ, LDHB, ACAN, KRT1, ARHGDIB, POSTN, LAMA5, CPN1, PROC, C1S, HPX, SUPT6H, BCAR3, BCHE, SOX30, C1R, PON1, CFHR4, PROZ, IGKV1D-16, APOC-3, COMP, SELENOP, PMFBP1, GSN, SELL, FUCA2 , HLA-B, FGFBP2, FKBP1A, HSPE1, SAA1, PGD, CDH1, SOD1, ISLR, CORO1A, ZYX, RLBP1, IDH1, IGKV6-21, SORL1, FAM3C, CST6, COTL1, PIGR, A0A1W6IYJ2, PRDX1, CAT, PROS1, CD5L, HMFT1766, C1QA, KRT2, and APP; and (4) lipid subclass substances or lipid molecules, wherein the lipid subclass substances are at least one selected from the group consisting of acetylcarnitine, ceramide, monoglycosylceramide, disaccharide ceramide, glycosylceramide series, triglycosylceramide, cholesterol ester, cardiolipin, ganglioside, lysophosphatidylcholine, lysophosphatidylethanolamine, monogalactosyldiacylglycerol, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phytosphingosine, phosphatidylinositol, phosphatidylserine, sphingomyelin and triglyceride,And wherein the lipid molecules are selected from PS (39: 4) -H, SM (d45: 6) + H, PC (14: 0 / 20: 4) + HCOO, SM (d20: 0 / 18: 1) + HCOO, PS (49: 4) -H, LPC (26: 0) + HCOO, SM (d20: 0 / 16: 0) + HCOO, PA (50: 4) -H, LPC (20: 4) + HCOO, PC (36: 5) + H, LPC (20: 1) + HCOO, PC (38: 8) + H, PC (46: 5) + H, PE (18: 0p / 22: 5) -H , SM(d22:0 / 16:0)+HCOO, PC(15:0 / 20:4)+HCOO, SM(d18:0 / 20:4)+HCOO, LPC(22:4)+HCOO, PI(18:0 / 22:4)-H, PC(16:1 / 18:2)+H COO, PS(40:4)-H, Cer(d20:0 / 24:1)+H, SM(d37:0)+HCOO, PC(17:1 / 16:0)+HCOO, SM(d20:0 / 22:5)+HCOO, PC(44:11)+H, SM(d32: 0)+HCOO,LPC(22:1)+H,PC(33:0e)+H,SM(d56:2+pO)+H,SM(d42:1)+HCOO,LPC(17:1)+HCOO,SM(d17:0 / 18:2)+HCOO,Cer(d18: 1 / 24:0)+H, SM(d36:3)+HCOO, AcCa(14:2)+H, Cer(d18:1 / 24:0+O)+H, PE(20:0p / 20:4)-H, LPC(26:1)+HCOO, PC(37:5)+H, SM(d4 At least one of the group consisting of 4:1)+HCOO, Cer(d18:1 / 26:1)+H, PE(40:6e)+H, TG(18:1 / 20:2 / 22:4)+NH4, SM(d34:3)+H, PE(40:4)-H, SM(d22:0 / 18:2)+HCOO, Cer(d18:0 / 22:0)+H, SM(d44:0)+HCOO, PI(16:1 / 20:4)-H, PC(20:3 / 20:4)+HCOO, PE(38:1p)-H and SM(d39:0)+HCOO. , 2. The use according to claim 1, characterized in that: The fatty acids are at least two of the fatty acids defined in (1); the metabolites are at least two of the metabolites defined in (2); and the proteins are at least two of the proteins defined in (3); Alternatively, the lipid subtype substances or lipid molecules are at least two of the lipid subtype substances or lipid molecules defined in (4).

3. The use according to claim 1, characterized in that: The fatty acids are at least three of the fatty acids defined in (1); the metabolites are at least three of the metabolites defined in (2); and the proteins are at least three of the proteins defined in (3); Alternatively, the lipid subtype substances or lipid molecules are at least three of the lipid subtype substances or lipid molecules defined in (4).

4. The use according to claim 1, characterized in that: The fatty acids are 1 to 6 of the fatty acids defined in (1); the metabolites are 1 to 6 of the metabolites defined in (2); or the proteins are 1 to 6 of the proteins defined in (3); or the lipid subclasses or lipid molecules are 1 to 6 of the lipid subclasses or lipid molecules defined in (4).

5. The use according to any one of claims 1 to 4, wherein the fatty acids in (1) are selected from the group consisting of C8:0, C10:0, C11:0, C16:0, C20:0, C20:5N3, C22:1N9, C23:0 and C24:0; the metabolites in (2) are selected from the group consisting of choline, 1-aminocyclopropanecarboxylic acid, 1-myristoyl-sn-glycero-3-phosphocholine, D-2-piperidinic acid, DL-indole-3-lactic acid, glycylglutamate, L-glutamate, L-phenylalanine, L-tryptophan, tyramine, glyceric acid, cyclohexylaminosulfonate, D-galactate / salt and sn-glycero-3-phosphoethanolamine; and the proteins in (3) are selected from the group consisting of HSPE1, PGD, CDH1, ISLR, CPQ, IDH1, PIGR , POSTN, PROC, PROZ, PMFBP1, SELL, A0A1W6IYJ2, CAT, PROS1, CD5L, C1QA, KRT1 and FGFBP2; or the (4) lipid subclass substance is selected from the group consisting of Cer, ChE, LPC, LPE, PA, PC, PE, PI, PS and SM, and the lipid molecule is selected from the group consisting of PS(39:4)-H, SM(d45:6)+H, PC(14:0 / 20:4)+HCOO, SM(d20:0 / 18:1)+HCOO, PS(49:4)-H, LPC(26:0)+HCOO, SM(d20:0 / 16:0)+HCOO, PA(50:4)-H and LPC(20:4)+HCOO.

6. The use according to claim 5, wherein the fatty acid in (1) is selected from the group consisting of C20:5N3, C22:1N9, C23:0 and C24:0; The metabolite in (2) is selected from the group consisting of 1-myristoyl-sn-glycero-3-phosphocholine, glycylglutamate, L-phenylalanine, DL-indole-3-lactic acid, L-tryptophan and glyceric acid; The (3) protein is selected from the group consisting of HSPE1, CDH1, ISLR, IDH1, COTL1 and FGFBP2; or The (4) lipid subclass substance is selected from the group consisting of LPC, LPE, PA, PC and PE, or the lipid molecule is selected from the group consisting of PS (39:4)-H, SM (d45:6) + H, PC (14:0 / 20:4) + HCOO and SM (d20:0 / 18:1) + HCOO.

7. A detection kit, comprising the composition according to any one of claims 1 to 6 and instructions for using the kit.

8. A detection kit, comprising reagents for determining the contents of C12:0, C20:3N3, C20:5N3 and C22:1N9 fatty acids in a sample to be tested, and instructions for using the kit; in, Substitute the fatty acid content measured in the sample to be tested into the following formula: Y=12.41×C12:0-19.42×C20:3N3+14.10×C20:5N3-0.44×C22:1N9+5.56; The Y value obtained from the sample to be tested is compared with the Y value of the corresponding fatty acid content obtained from a normal person. If there is a significant difference, the individual providing the sample is determined to be positive for infantile hemangioma.

9. The detection kit according to claim 7 or 8, wherein The sample detected by the kit is selected from the group consisting of whole blood, plasma, serum, lymphocyte suspension, cerebrospinal fluid, bone marrow, pleural effusion, urine, saliva and peritoneal effusion of the subject.

10. The detection kit according to claim 7 or 8, wherein The sample detected by the kit is selected from the group consisting of umbilical cord blood and peripheral blood.

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