JP2023162292A - Method for detecting infant atopic dermatitis - Google Patents

Abstract

To provide a marker for detecting infant atopic dermatitis, and a method for detecting infant atopic dermatitis using the marker.SOLUTION: A method for detecting infant atopic dermatitis in a subject includes the step of measuring the expression level of at least one gene selected from seven gene groups consisting of IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMICA1 and HNRNPA1 or an expression product thereof in a biological sample taken from the subject.SELECTED DRAWING: None

Description

The present invention relates to a method for detecting infant atopic dermatitis using an infant atopic dermatitis marker.

Atopic dermatitis (hereinafter also referred to as "AD") is an eczematous skin disease that mainly occurs in people with atopic predisposition. Typical symptoms of atopic dermatitis include chronic and recurrent itching that occurs contralaterally, skin eruptions, erythema, etc., as well as hypokeratosis, decreased barrier function, and dry skin. Most cases of atopic dermatitis occur in infants and tend to improve as they grow older, but in recent years, adult-type and refractory atopic dermatitis have also increased.

Newborns/infants who have a genetic predisposition to allergies and atopic dermatitis may develop various allergic diseases as they age, such as infant eczema, atopic dermatitis, food allergies, and even bronchial asthma and allergic rhinitis. march) is known. In this way, once an allergic disease develops, there is a high possibility of developing another allergic disease, and the treatment for these diseases often takes a long time, so it is necessary to suppress the onset of allergic diseases at the infant stage. It is said that

As a method for detecting atopic dermatitis using biomarkers, the peripheral blood eosinophil count, serum total IgE level, LDH (lactate dehydrogenase) level, Thymus an
d Activation-Regulated Chemokine (TARC) and S
Detecting quamous cell carcinoma antigen 2 (SCCA2) (Non-Patent Documents 1, 2, 3), and detecting the agrC mutation-dependent RNAIII gene expression level of Staphylococcus aureus in skin flora ( Patent Document 1) and the like have been proposed.
However, it is unclear whether these markers are also applicable to atopic dermatitis in infants.

On the other hand, techniques have been developed to investigate the current and future physiological state of a human body by analyzing nucleic acids such as DNA and RNA in biological samples. For analysis using nucleic acids, a comprehensive analysis method has been established, and a wealth of information can be obtained with a single analysis. It has the advantage of being easy to link. Nucleic acids derived from living organisms can be extracted from body fluids such as blood, secretions, tissues, etc., but recently RNA contained in skin surface lipids (SSL) has been used as a sample for analysis of living organisms. In particular, it has been reported that marker genes for the epidermis, sweat glands, hair follicles, and sebaceous glands can be detected from SSL (Patent Document 2).

JP 2019-30272 Publication International Publication No. 2018/008319

Sugawara et al., Allergy (2002) 57:180-181 Ohta et al., Ann Clin Biochem.(2012) 49:277-84 Kato et al., Journal of the Japanese Dermatological Society (2018) 128: 2431-2502

The present invention relates to providing a marker for detecting atopic dermatitis in infants and a method for detecting atopic dermatitis in infants using the marker.

The present inventors collected SSL from infants with atopic dermatitis and infants with healthy skin without allergic predisposition, and comprehensively analyzed the expression status of RNA contained in SSL as sequence information. We found that the gene expression level was significantly different between the two, and that this could be used as an indicator to detect infantile atopic dermatitis.

That is, the present invention relates to the following 1) to 3).
1) Regarding biological samples collected from subjects, IMPDH2, ERI1, FBXW2,
A method for detecting infantile atopic dermatitis in a subject, comprising the step of measuring the expression level of at least one gene selected from the seven gene group consisting of STK17B, TAGLN2, AMICA1, and HNRNPA1 or its expression product.
2) A test kit for detecting infantile atopic dermatitis used in the method of 1), which contains an oligonucleotide that specifically hybridizes with the gene or an antibody that recognizes the expression product of the gene.
3) A detection marker for infantile atopic dermatitis consisting of at least one gene or its expression product selected from the gene groups shown in Tables B-1 and B-2 below.

According to the present invention, infant atopic dermatitis can be diagnosed easily and non-invasively at an early stage, with high precision, and
It becomes possible to detect with high sensitivity and specificity.

All patents, non-patent documents, and other publications cited herein are incorporated by reference in their entirety.

In the present invention, the term "nucleic acid" or "polynucleotide" refers to DNA or RNA.
means. DNA includes cDNA, genomic DNA, and synthetic DNA, and "RNA" includes total RNA, mRNA, rRNA, tRNA, non-co
Both ding RNA and synthetic RNA are included.

In the present invention, "gene" refers to double-stranded DNA including human genomic DNA, as well as cDNA.
Single-stranded DNA (positive strand) containing a single-stranded DNA (positive strand), single-stranded DNA (complementary strand) having a sequence complementary to the normal strand
, and these fragments, and the sequence information of the bases constituting DNA includes:
It means something that contains some kind of biological information.
In addition, the "gene" includes not only a "gene" expressed by a specific base sequence, but also nucleic acids encoding homologues (i.e., homologs or orthologs), variants such as genetic polymorphisms, and derivatives. be done.
The names of genes disclosed herein follow the Official Symbols described in NCBI ([www.ncbi.nlm.nih.gov/]). On the other hand, regarding Gene Ontology (GO), Pathway ID. as described in String ([string-db.org/]). Follow.

In the present invention, the "expression product" of a gene is a concept that includes transcription products and translation products of the gene. A "transcription product" is an RNA produced by transcription from a gene (DNA), and a "translation product" means a protein encoded by a gene that is translated and synthesized based on RNA.

In the present invention, "atopic dermatitis" refers to a disease mainly caused by itchy eczema that repeatedly undergoes exacerbations and remissions, and many of its patients are said to have a predisposition to atopic dermatitis. Predisposing factors for atopic dermatitis include i) family history/medical history (bronchial asthma, allergic rhinitis/conjunctivitis, or atopic dermatitis, or multiple diseases), or ii) predisposition to producing IgE antibodies. .
In the present invention, infant atopic dermatitis refers to infants from ages 0 to school age, specifically infants from 0 to 5 years old. During infancy, the eruption begins on the head and face and often moves down to the trunk and extremities.In infancy, the eruption decreases on the face and appears mainly on the neck and joints of the extremities. Regarding the difference between infantile atopic dermatitis and adult atopic dermatitis, it has been reported in recent years that adult atopic dermatitis has abnormal epidermal keratosis associated with chronic inflammation compared to infantile atopic dermatitis. (journal of allergy)
and clinical immunology, volume 141, issue 6, June 2018, pageg 2094-2106), the number of reports is small and it is not clear.

In the present invention, "detection" of infantile atopic dermatitis means clarifying the presence or absence of infantile atopic dermatitis, and can also be expressed in terms of examination, measurement, judgment, evaluation, or evaluation support. can. Note that in this specification, the term "determination" or "evaluation" does not include determination or evaluation by a doctor.

In this specification, "feature amount" is synonymous with "explanatory variable" in machine learning, and includes genes used in machine learning selected from atopic dermatitis detection markers or their expression products. ”.

As shown in the examples below, SSL of 28 healthy children/25 children with atopic dermatitis
DESeq2 (
The count value (Normaliz
ed count value) to extract RNAs with a corrected p value (FDR) of less than 0.25 by likelihood ratio test in children with atopic dermatitis compared to healthy children. 61 genes, 310 genes with decreased expression (371 genes in total (Tables 1-1 to 1-9)
) was identified. In the table, the genes indicated by "UP" are genes whose expression level increases in children with atopic dermatitis, and the genes indicated by "DOWN" are genes whose expression level decreases in children with atopic dermatitis. .
Therefore, genes selected from the group of 371 genes or their expression products can serve as infantile atopic dermatitis markers for detecting infantile atopic dermatitis. Of this gene group, 318 genes (marked with * and shown in bold in Tables 1-1 to 1-9) are genes that have not been reported to be associated with atopic dermatitis.

In addition, we used the expression level data of all SSL-derived RNAs detected from the subjects (Log ₂ (RPM+1) values of 3486 genes) as explanatory variables, healthy subjects and infantile atopic dermatitis patients as objective variables, and used a machine learning algorithm. Random Forest (Breiman L. Machine
Learning (2001) 45;5-32), we attempted to extract feature genes and construct a prediction model. As shown in the examples below, the top 100 genes with variable importance based on the Gini coefficient (Table 3
-1 to 3-3) were selected as feature genes, and it was shown that a model using these genes could predict atopic dermatitis in infants.
Therefore, genes selected from the group of 100 genes or their expression products can be suitable markers for infantile atopic dermatitis for detecting infantile atopic dermatitis. Of this gene group, 92 genes (marked with * and shown in bold in Tables 3-1 to 3-3) are genes that have not been reported to be associated with atopic dermatitis, and are a new type of atopic dermatitis in infants. It is a sexual dermatitis marker. As shown in the Examples described below, it is also possible to predict atopic dermatitis in infants using a prediction model using these new infant atopic dermatitis markers.

In addition, using the expression level data (Log ₂ (RPM + 1) value) of the above 371 genes or 318 genes among which expression changes were observed between healthy children and children with atopic dermatitis, random forest was similarly used. Attempts were made to construct predictive models based on these methods, and it was shown that it was possible to predict infant atopic dermatitis using both methods.

In addition, the Boruta method (Kursa et al. Fundamental Infor
maticae (2010) 101;271-286) to extract feature genes (maximum number of trials: 1000,
As a result, nine genes (Table 4) were extracted as feature genes, and as shown in the examples below, a prediction model using a random forest using these genes was used to predict atopy in infants. It was shown that it is possible to predict dermatitis.
Therefore, a gene selected from the nine gene groups or its expression product can serve as an infant atopic dermatitis marker for detecting infant atopic dermatitis. Seven of the genes in this gene group (marked with an asterisk and shown in bold in Table 4) are genes that have not been reported to be associated with atopic dermatitis, and are novel markers of infantile atopic dermatitis. be. As shown in the Examples described below, it is also possible to predict atopic dermatitis in infants using a prediction model using these new infant atopic dermatitis markers.

The 371 gene groups shown in Tables 1-1 to 1-9 extracted through the expression variation analysis described above (
A), 100 gene groups shown in Tables 3-1 to 3-3 selected as feature genes by random forest (B), and 9 genes shown in Table 4 selected as feature genes by Boruta method All of the 441 genes (Tables A-1 to A-2), which is the sum of the species gene group (C) (A∪B∪C), can be markers for infantile atopic dermatitis, of which 383 (Table B-1 to B-2) are new infantile atopic dermatitis markers.

IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMI of the present invention
The seven genes consisting of CA1 and HNRNPA1 are the 100 gene groups (B) listed in Tables 3-1 to 3-3 selected as feature genes by the random forest described above,
Nine gene groups listed in Table 4 selected as feature genes by the Boruta method (C)
This is a gene (B∩C) that is common to atopic dermatitis, and has not been previously reported to be associated with atopic dermatitis (indicated in bold with an asterisk in each table). Therefore, at least one gene selected from these gene groups or its expression product is particularly useful as a novel infantile atopic dermatitis marker for detecting infantile atopic dermatitis.
Furthermore, among these, IMPDH2, ERI1, and FBXW2 are genes (
A∩B∩C), it can be said to be a more preferable novel marker for infant atopic dermatitis.
Each of these seven genes can independently serve as a marker for infantile atopic dermatitis;
It is preferable to use a combination of two or more types, preferably four or more types, more preferably six or more types, and it is even more preferable to use all seven types.

In addition, ABHD8, GPT2, PLIN2, FAM100B, YPEL2, M
AP1LC3B2, RLF, KIAA0930, UBE2R2, HK2, USF2, PD
IA3P, HNRNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7
The 23 genes consisting of , ECH1, CASS4, CLEC4A, SNRPD1, SLC7A11 and SNX8 are the 371 gene group (A) listed in Tables 1-1 to 1-9 extracted by the above-mentioned expression variation analysis, It is included in the common part of the 100 gene groups (B) listed in Tables 3-1 to 3-3, which were selected as feature genes by random forest, and no relationship with atopic dermatitis has been previously reported. This is a gene obtained by removing the above-mentioned IMPDH2, ERI1, and FBXW2. Therefore, at least one gene selected from these gene groups or its expression product is also useful as a novel infantile atopic dermatitis marker for detecting infantile atopic dermatitis.

The genes that can be used as markers for infantile atopic dermatitis (hereinafter also referred to as "target genes") include the bases of the DNA that constitutes the genes as long as they can be used as biomarkers for detecting infantile atopic dermatitis. Genes having substantially the same base sequence as the sequence are also included. Here, substantially the same base sequence means, for example, a homology calculation algorithm
When searching using NCBI BLAST with the following conditions: expected value = 10; allow gaps; filtering = ON; match score = 1; mismatch score = -3, the DN that constitutes the gene
This means that there is an identity of 90% or more, preferably 95% or more, and even more preferably 98% or more with the base sequence of A.

The method for detecting infantile atopic dermatitis of the present invention includes target genes such as IMPDH2, ERI1, FBXW2, STK17B,
The method includes a step of measuring the expression level of at least one gene selected from a group of seven genes consisting of TAGLN2, AMICA1, and HNRNPA1 or its expression product.

The biological sample used in the present invention may be any tissue or biological material in which the expression of the gene of the present invention changes with the onset and progression of atopic dermatitis. Specific examples include body fluids such as organs, skin, blood, urine, saliva, sweat, skin surface lipids (SSL), tissue exudates, serum prepared from blood, plasma, others, stool, hair, etc., and are preferred. is the skin or skin surface lipid (SS
L), more preferably skin surface lipids (SSL).
Furthermore, the subject from whom the biological sample is collected is not particularly limited in gender or race as long as it is an infant, but infants whose atopic dermatitis needs to be detected or infants who are suspected of developing atopic dermatitis are preferable.

Here, "skin surface lipid (SSL)" refers to the fat-soluble fraction present on the surface of the skin,
It is also called sebum. In general, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and exists on the skin surface in the form of a thin layer that covers the skin surface. SSL
contains RNA expressed in skin cells. (See Patent Document 3 above). Furthermore, in this specification, "skin" is a general term for a region including tissues such as the epidermis, dermis, hair follicles, sweat glands, sebaceous glands, and other glands on the body surface, unless specifically limited.

Any means used for collecting or removing SSL from the skin can be used to collect SSL from the skin of the subject. Preferably, the SSL absorbent material described below, S
SL adhesive materials or devices that scrape the SSL from the skin can be used. S.S.
The L-absorbing material or the SSL adhesive material is not particularly limited as long as it has an affinity for SSL, and examples thereof include polypropylene, pulp, and the like. A more detailed example of the procedure for collecting SSL from the skin is to apply SS to a sheet material such as oil blotting paper or oil blotting film.
Examples include a method of absorbing L, a method of adhering SSL to a glass plate, tape, etc., and a method of collecting SSL by scraping it off with a spatula, scraper, etc. In order to improve the adsorption of SSL, an SSL-absorbing material pre-impregnated with a highly fat-soluble solvent may be used. On the other hand, if the SSL-absorbing material contains a highly water-soluble solvent or water, the adsorption of SSL will be inhibited, so it is preferable that the content of a highly water-soluble solvent or water is small. The SSL absorbent material is preferably used in a dry state. The site of the skin from which SSL is collected is not particularly limited, and includes the skin of any part of the body such as the head, face, neck, trunk, limbs, etc., and includes areas that secrete a lot of sebum, such as the skin of the face. is preferred.

RNA-containing SSL collected from a subject may be stored for a certain period of time. SS collected
L is preferably stored under low temperature conditions as soon as possible after collection in order to suppress the degradation of the RNA contained therein as much as possible. The temperature conditions for storage of the RNA-containing SSL in the present invention are 0°C.
It may be below, preferably -20±20℃ to -80±20℃, more preferably -20℃
±10°C to -80±10°C, more preferably -20±20°C to -40±20°C, even more preferably -20±10°C to -40±10°C, even more preferably -20±10°C, and Preferably it is -20±5°C. The period of storage of the RNA-containing SSL under the low-temperature conditions is not particularly limited, but is preferably 12 months or less, such as 6 hours or more and 12 months or less, more preferably 6 months or less, such as 1 day or more and 6 months or less. More preferably, the period is 3 months or less, for example, 3 days or more and 3 months or less.

In the present invention, the expression level of the target gene or its expression product is measured by RN
cDNA artificially synthesized from A, DNA encoding the RNA, protein encoded by the RNA, molecules that interact with the protein, molecules that interact with the RNA, or molecules that interact with the DNA. etc. Here, molecules that interact with RNA, DNA, or proteins include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, and their phosphorylated products, alkylated products, sugar adducts, etc. Examples include any of the above complexes. Moreover, the expression level comprehensively means the expression amount and activity of the gene or expression product.

In the method of the present invention, as a preferred embodiment, SSL is used as a biological sample. In this case, the expression level of RNA contained in SSL is analyzed, and specifically, after converting RNA into cDNA by reverse transcription, , the cDNA or its amplification product is measured.
Extraction of RNA from SSL can be performed using methods commonly used for the extraction or purification of RNA from biological samples, such as the phenol/chloroform method, AGPC (acid guanidin
ium thiocyanate-phenol-chloroform extrac
tion) method, or TRIzol (registered trademark), RNeasy (registered trademark), QIAzo
1 (registered trademark), a method using special magnetic particles coated with silica, Solid Phase Reversible Immobilizat
A method using ion magnetic particles, extraction using a commercially available RNA extraction reagent such as ISOGEN, etc. can be used.

Although primers targeting a specific RNA to be analyzed may be used for the reverse transcription, it is preferable to use random primers for more comprehensive preservation and analysis of nucleic acids.
A common reverse transcriptase or reverse transcription reagent kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcription reagent kit is used, such as M-MLV Reverse Transcriptase and its modified versions, or commercially available reverse transcriptase or reverse transcription reagent kits, For example, PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.), Supe
rScript (registered trademark) Reverse Transcriptase series (T
hermo Scientific), etc. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (
(Registered trademark) VILO cDNA Synthesis kit (Both are from Thermo
Scientific, Inc.) etc. are preferably used.
The elongation reaction in reverse transcription is performed at a temperature of preferably 42°C ± 1°C, more preferably 42°C.
It is preferable to adjust the temperature to ±0.5°C, more preferably to 42°C ±0.25°C, while adjusting the reaction time to preferably 60 minutes or more, more preferably 80 to 120 minutes.

When the target is RNA, cDNA, or DNA, representative methods for measuring the expression level include PCR using DNA that hybridizes to these as primers, real-time RT-PCR, multiplex PCR, SmartAmp, LAMP, etc. Nucleic acid amplification methods that hybridize to these, hybridization methods that use nucleic acids that hybridize to these as probes (
DNA chip, DNA microarray, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), a method for determining base sequences (sequencing), or a combination of these methods.

In PCR, a primer pair targeting a specific DNA to be analyzed is used to target the specific DNA.
Although only the species DNA may be amplified, a plurality of specific DNAs may be amplified simultaneously using a plurality of primer pairs. Preferably, the PCR is a multiplex PCR. Multiplex PCR is a method of simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using commercially available kits (e.g., Ion AmpliSeq Transcriptome Human Gen
e Expression Kit; Life Technologies Japan Co., Ltd., etc.).
The temperature of the annealing and extension reaction in the PCR depends on the primers used, so it cannot be generalized, but when using the above multiplex PCR kit, it is preferably 62°C ± 1°C, more preferably 62°C ± 0°C. .5℃, more preferably 62℃±0.25
It is ℃. Therefore, in the PCR, annealing and extension reactions are preferably performed in one step. The time of the annealing and extension reaction steps can be adjusted depending on the size of the DNA to be amplified, etc., but is preferably 14 to 18 minutes. The conditions for the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but are preferably 95 to 99
℃ for 10 to 60 seconds. Reverse transcription and PCR at the temperature and time described above can be performed using a thermal cycler commonly used for PCR.

Purification of the reaction product obtained by the PCR is preferably performed by size separation of the reaction product. Size separation allows the desired PCR reaction product to be separated from primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be done, for example, by
This can be carried out using a size separation column, a size separation chip, magnetic beads that can be used for size separation, or the like. Preferred examples of magnetic beads that can be used for size separation include Ampu
Solid Phase Reversible Immobiliza such as re XP
tion (SPRI) magnetic beads.

The purified PCR reaction product may be subjected to further processing necessary for subsequent quantitative analysis. For example, for DNA sequencing, a purified PCR reaction product may be prepared into an appropriate buffer solution, a PCR primer region contained in PCR amplified DNA may be cleaved, or an adapter sequence may be added to the amplified DNA. may be further added. For example, the purified PCR reaction product is prepared into a buffer solution, the PCR primer sequence is removed and adapter ligated to the amplified DNA, and the obtained reaction product is amplified as necessary for quantitative analysis. libraries can be prepared. These operations can be performed using, for example, SuperScript® VILO cDNA Sy
5x included with nthesis kit (Life Technologies Japan Co., Ltd.)
VILO RT Reaction Mix and Ion AmpliSeq Tran
5×Ion AmpliSeq HiF attached to scriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.)
i Mix, and Ion AmpliSeq Transcriptome Human
This can be carried out using Gene Expression Core Panel according to the protocol attached to each kit.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using the Northern blot hybridization method, for example, the probe DNA is first labeled with a radioactive isotope, a fluorescent substance, etc., and then the obtained labeled DNA is and hybridize with RNA derived from a biological sample transferred to a nylon membrane or the like according to a conventional method. Thereafter, a method of measuring the formed double strand of labeled DNA and RNA by detecting a signal derived from the labeled substance is included.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using the RT-PCR method, for example, cDNA is first prepared from RNA derived from a biological sample according to a conventional method, and then the target gene of the present invention is used as a template. A pair of primers prepared for amplification (the above cDNA (
The positive strand (which binds to the − strand) and the reverse strand (which binds to the + strand) are hybridized with this. after that,
PCR is performed according to a conventional method, and the resulting amplified double-stranded DNA is detected. To detect the amplified double-stranded DNA, use primers that have been labeled in advance with RI, a fluorescent substance, etc.
A method of detecting labeled double-stranded DNA produced by performing R can be used.

When measuring the expression level of a target gene or a nucleic acid derived therefrom using a DNA microarray, for example, an array in which at least one type of nucleic acid (cDNA or DNA) derived from the target gene of the present invention is immobilized on a support is used. , labeled cDNA or cRNA prepared from mRNA
By binding A to a microarray and detecting the label on the microarray, m
The expression level of RNA can be measured.
The nucleic acids to be immobilized on the array may be any nucleic acids that hybridize specifically (that is, substantially only to the target nucleic acid) under stringent conditions. The nucleic acid may have a sequence or may be a nucleic acid consisting of a partial sequence. Here, the "partial sequence" includes a nucleic acid consisting of at least 15 to 25 bases. Stringent conditions here usually include washing conditions such as "1x SSC, 0.1% SDS, 37°C", and more severe hybridization conditions include "0.5x SS
C, 0.1% SDS, 42℃", and even more severe hybridization conditions are "0.1% SDS, 42℃".
xSSC, 0.1% SDS, 65°C." Hybridization conditions were described by J. Sambrook et al., Molecular Cloning: A Laboratory Manual, Thrd Edition.
, Cold Spring Harbor Laboratory Press (2001), etc.

When measuring the expression level of a target gene or a nucleic acid derived therefrom by sequencing, for example, analysis may be performed using a next-generation sequencer (eg, Ion S5/XL system, Life Technologies Japan Co., Ltd.). RNA expression can be quantified based on the number of reads generated by sequencing (read count).

Probes or primers used in the above measurements, i.e. primers for specifically recognizing and amplifying the target gene of the present invention or nucleic acids derived therefrom, or primers for specifically detecting the RNA or nucleic acids derived therefrom. Probes fall under this category, and these can be designed based on the base sequence that constitutes the target gene. Here, "specifically recognize" means that substantially only the target gene of the present invention or a nucleic acid derived therefrom can be detected in, for example, Northern blotting, and that substantially only the nucleic acid can be detected in, for example, RT-PCR. This means that it can be determined that the detected substance or product is the gene or a nucleic acid derived therefrom, such that the detected substance or product is amplified.
Specifically, an oligonucleotide containing a certain number of nucleotides complementary to a DNA consisting of a base sequence constituting the target gene of the present invention or a complementary strand thereof can be used. Here, "complementary strand" refers to double-stranded DNA consisting of A:T (U in the case of RNA) and G:C base pairs.
refers to one strand relative to the other. Furthermore, "complementary" does not mean that the sequences are completely complementary in the certain number of consecutive nucleotide regions, but preferably 80% or more, more preferably 90% or more, still more preferably 95% or more of the bases. It is sufficient that they have sequence identity.
The identity of base sequences can be determined by an algorithm such as the above-mentioned BLAST.
When such an oligonucleotide is used as a primer, it is sufficient that it can perform specific annealing and chain elongation, and is usually, for example, 10 bases or more, preferably 15 bases or more, more preferably 20 bases or more, and, for example, 100 bases or less, The chain length is preferably 50 bases or less, more preferably 35 bases or less. In addition, when used as a probe, it is sufficient that specific hybridization is possible, and the DNA has at least a part or all of the base sequence (or its complementary strand) that constitutes the target gene of the present invention, for example. A chain length of 10 bases or more, preferably 15 bases or more, and, for example, 100 bases or less, preferably 50 bases or less, and more preferably 25 bases or less is used.
Note that here, the "oligonucleotide" can be DNA or RNA,
It may be synthetic or natural. Alternatively, the probe used for hybridization is usually labeled.

In addition, when measuring the translation product (protein) of the target gene of the present invention, molecules that interact with the protein, molecules that interact with RNA, or molecules that interact with DNA, protein chip analysis, immunoassay method ( (e.g., ELISA, etc.), mass spectrometry (e.g., LC-
MS/MS, MALDI-TOF/MS), 1-hybrid method (PNAS 100, 12271-1227
6 (2003)) or the two-hybrid method (Biol. Reprod. 58, 302-311 (1998)), which can be appropriately selected depending on the subject.
For example, when a protein is used as the measurement target, the measurement is carried out by bringing an antibody against the expression product of the present invention into contact with a biological sample, detecting the protein in the sample that has bound to the antibody, and measuring its level. For example, according to Western blotting, the above-mentioned antibody is used as the primary antibody, and then an antibody that binds to the primary antibody labeled with a radioactive isotope, a fluorescent substance, an enzyme, etc. is used as the secondary antibody, and the primary antibody is Labeling is performed, and signals derived from these labeled substances are measured using a radiation meter, a fluorescence detector, or the like.
Note that the antibody against the above translation product may be a polyclonal antibody or a monoclonal antibody. These antibodies can be produced according to known methods. Specifically, a polyclonal antibody is produced by immunizing a non-human animal such as a domestic rabbit using a protein expressed and purified in Escherichia coli etc. according to a conventional method, or by synthesizing a partial polypeptide of the protein according to a conventional method. It can be obtained from the serum of the immunized animal according to a conventional method.
On the other hand, monoclonal antibodies are produced by immunizing a non-human animal such as a mouse with a protein expressed and purified in Escherichia coli etc. or a partial polypeptide of the protein according to a conventional method, and then fusing the obtained spleen cells with myeloma cells. It can be obtained from prepared hybridoma cells. Monoclonal antibodies may also be produced using phage display (Griffiths, A.
.D.; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February
y 1998, pp. 102-108(7)).

In this way, the expression level of the target gene of the present invention or its expression product in the biological sample collected from the subject is measured, and infant atopic dermatitis is detected based on the expression level. Detection is specifically performed by comparing the measured expression level of the target gene of the present invention or its expression product with a control level.
When analyzing the expression levels of multiple target genes by sequencing, as described above, the read count value, which is the expression level data, and the RPM value, which is the read count value corrected for the difference in the total number of reads between samples, are used. , the value obtained by converting the RPM value to a base 2 logarithm value (Log ₂
RPM value) or a base 2 logarithm value (Log ₂ (RPM+1) value) obtained by adding an integer 1, or a count value corrected using DESeq2 (Normalized count value) or a base 2 logarithm value obtained by adding an integer 1 ( It is preferable to use the Log ₂ (count+1) value) as an index. In addition, fragments p
er kilobase of exon per million reads ma
pped (FPKM), reads per kilobase of exon p
million reads mapped (RPKM), transcript
It may be a value calculated by ts per million (TPM) or the like.
Moreover, the signal value obtained by the microarray method and its correction value may be used.
In addition, when analyzing the expression level of only a specific target gene by RT-PCR etc.
The expression level of the target gene can be analyzed by converting it into a relative expression level based on the expression level of the housekeeping gene (relative quantification), or the absolute copy number can be determined using a plasmid containing the target gene region. A method of quantitative analysis (absolute quantification) is preferred. The copy number obtained by digital PCR may also be used.
Here, the "control level" includes, for example, the expression level of the target gene or its expression product in a healthy person. The expression level in a healthy person may be a statistical value (eg, average value, etc.) of the expression level of the gene or its expression product measured from a population of healthy people. When there are multiple target genes, it is preferable to determine a reference expression level for each gene or its expression product.

Furthermore, infant atopic dermatitis in the present invention can also be detected by increasing/decreasing the expression level of the target gene of the present invention or its expression product. In this case, the expression level of the target gene or its expression product in the biological sample derived from the subject is compared with the cutoff value (reference value) of each gene or its expression product. The cutoff value can be determined as appropriate by obtaining the expression level of the target gene or its expression product in healthy subjects as standard data in advance, and then determining it as appropriate based on statistical values such as the average value and standard deviation of the expression level. good.

Furthermore, the expression level of the target gene or its expression product derived from a child suffering from atopic dermatitis,
A discriminant (prediction model) for separating children with atopic dermatitis from healthy children is constructed using the measured expression level of target genes or their expression products derived from healthy children, and using the discriminant, , can detect atopic dermatitis in infants. That is, the expression level of the target gene or its expression product derived from a child with atopic dermatitis and the expression level of the target gene or its expression product derived from a healthy child are used as teacher samples to compare the expression level of the target gene or its expression product derived from a child with atopic dermatitis and the healthy child. A discriminant (prediction model) for separating children is constructed, and a cutoff value (reference value) for distinguishing between children with atopic dermatitis and healthy children is determined based on the discriminant. Note that in creating the discriminant, dimension reduction is performed by principal component analysis (PCA), and the principal components can be used as explanatory variables.
Then, the level of the target gene or its expression product is similarly measured from the biological sample collected from the subject, the obtained measured value is substituted into the discriminant, and the result obtained from the discriminant is compared with the reference value. By doing so, the presence or absence of infantile atopic dermatitis in a subject can be evaluated.

The variables used to construct the discriminant include explanatory variables and objective variables. As the explanatory variable, for example, the expression level of a target gene or its expression product selected by the method described below can be used. As an objective variable, for example, whether the sample is derived from a healthy child or a child suffering from atopic dermatitis can be used.

The selection of features requires a statistically significant difference between the two groups to be discriminated, for example, an expression level of 2.
The expression level of a gene that varies significantly between groups (expression-varied gene) or its expression product can be used. Furthermore, feature genes can be extracted using known algorithms such as those used for machine learning, and their expression levels can be used. For example, use the expression level of a gene or its expression product with high variable importance in the random forest shown below, or extract feature genes using the "Boruta" package of R language and use the expression level. be able to.

As an algorithm for constructing the discriminant, a known algorithm such as an algorithm used for machine learning can be used. An example of a machine learning algorithm is Random Forest (
random forest), linear kernel support vector machine (SVM li
near), rbf kernel support vector machine (SVM rbf) neural network (Neural net), general linear model (Generalized l
regularized linea discriminant analysis (inear model), regularized linear discriminant analysis
r discriminant analysis), regularized logistic regression (Re
gularized logistic regression). Input verification data into the constructed prediction model to calculate a predicted value, and select the model whose predicted value most closely matches the actual measured value, for example, the model with the highest accuracy rate, as the optimal prediction model. I can do it. In addition, the detection rate (Recall),
Precision and the F value, which is the harmonic mean thereof, can be calculated, and the model with the largest F value can be selected as the optimal predictive model.

When using a random forest algorithm to construct a discriminant, the estimation error rate for unknown data (OOB error rate) is an indicator of the accuracy of the predictive model.
can be calculated (Breiman L. Machine Learning (2001) 45;5-32).

In random forest, a classifier called a decision tree is created by randomly extracting about two-thirds of the total number of samples, allowing for duplication, according to a technique called the bootstrap method. Samples that were not extracted at this time are Out of bug (OO
B), it is possible to predict the objective variable of OOB using one decision tree, and calculate the error rate by comparing it with the correct answer label (OOB error r in decision tree).
ate). The same operation was repeated 500 times and the OOB er of 500 decision trees was calculated.
The average value of the ror rate is calculated as the OOB err of the random forest model.
or rate.

Note that the number of decision trees (ntree value) for constructing a random forest model is 500 by default, but can be changed from 1 to any number as necessary.
Furthermore, the number of variables used to create the sample discriminant in one decision tree (mtry value)
By default, is the square root of the number of explanatory variables, but can be changed to any value from 1 to the total number of explanatory variables as needed.

The “caret” package of the R language can be used to determine the mtry value. “c
By specifying random forest in the method of the "aret" package, it is possible to try 8 different mtry values and select, for example, the mtry value with the maximum accuracy as the optimal mtry value.The number of trials of the mtry value is The number of trials can be changed to an arbitrary number if necessary.

When a random forest algorithm is used in constructing a discriminant, the importance of explanatory variables used in constructing a model can be expressed as a numerical value (variable importance). For example, the amount of decrease in the Gini coefficient (Mean Decrease Gini) can be used as the value of the variable importance.

The method for determining the cutoff value (reference value) is not particularly limited, and can be determined according to a known method. For example, ROC (Receiver Operator) created using discriminant
Characteristic Curve). In an ROC curve, the vertical axis plots the probability of a positive result in a positive patient (sensitivity), and the horizontal axis plots the probability of a negative result in a negative patient (specificity) subtracted from 1 (false positive rate). be done. Regarding "true positive (sensitivity)" and "false positive (1-specificity)" shown in the ROC curve, the value at which "true positive (sensitivity)" - "false positive (1-specificity)" is the maximum (Youden
index) can be used as a cutoff value (reference value).

As already mentioned, IMPDH2, ERI1, FBXW2, STK17B, TAGLN
2, 7 genes consisting of AMICA1 and HNRNPA1, and 100 genes shown in Tables 3-1 to 3-3 including the 7 genes, or 9 genes shown in Table 4, or Table 1
A prediction model capable of predicting infant atopic dermatitis can be constructed using the 371 genes indicated by -1 to 1-9 as feature genes.
Therefore, when creating a discriminant to separate the group of children affected by infantile atopic dermatitis and the group of healthy children, the target genes are IMPDH2, ERI1, FBXW2, STK17B, and TAG.
One gene or two or more, preferably five or more, more preferably all seven genes are selected from the seven genes consisting of LN2, AMICA1, and HNRNPA1, and the expression data of the gene or its expression product is used. Furthermore, when selecting a plurality of genes, it is preferable to select them as feature genes in order of variable importance in Tables 3-1 to 3-3, and to create a discriminant. In addition, the 7 genes include the 441 genes shown in Table A, the 100 genes shown in Tables 3-1 to 3-3, the 9 genes shown in Table 4, or the 441 genes shown in Table A, or the 9 genes shown in Tables 1-1 to 1-9. Among the 371 genes shown, a discriminant was created by appropriately adding expression data of at least one, 5 or more, 10 or more, 20 or more, 50 or more genes or their expression products selected from genes other than the 7 genes. It is also possible to detect atopic dermatitis in infants. When selecting genes other than the seven genes concerned from the 100 genes shown in Tables 3-1 to 3-3, select genes in descending order of variable importance, or genes within the top 50 in variable importance, Preferably, the feature amount genes may be selected from genes within the top 30. Furthermore, when selecting genes other than the seven genes as feature genes, they are marked with * and displayed in bold in Tables 1-1 to 1-9, Tables 3-1 to 3-3, and Table 4. It is preferable to select the characteristic amount genes from novel atopic dermatitis markers.
In addition, when adding the 371 genes shown in Tables 1-1 to 1-9, in addition to the 7 genes consisting of IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMICA1, and HNRNPA1 as target genes, ABHD8, GPT2
, PLIN2, FAM100B, YPEL2, MAP1LC3B2, RLF, KIAA0
930, UBE2R2, HK2, USF2, PDIA3P, HNRNPUL1, SEC6
1G, DNAJB11, SDHD, NDUFS7, ECH1, CASS4, IL7R, C
At least one selected from the 25 gene groups LEC4A, AREG, SNRPD1, SLC7A11, and SNX8, preferably at least one or five genes selected from the variable importance of these genes in Tables 3-1 to 3-3. As described above, a discriminant may be created by appropriately adding expression data of 10 or more, 20 or more genes or their expression products.
The 25 genes are the 371 gene group (A) listed in Tables 1-1 to 1-9 extracted by the above-mentioned expression variation analysis, and Table 3- selected as feature genes by random forest. This gene is included in the common part of the 100 gene group (B) described in 1 to 3-3.
Preferably, the above 7 genes, the 371 genes or 318 genes shown in Tables 1-1 to 1-9 (marked with * and shown in bold in Tables 1-1 to 19), and the genes shown in Tables 3-1 to 3-3. 100
Examples include discriminant expressions that use genes or 92 genes (marked with * and shown in bold in Tables 3-1 to 3-3), or the 9 genes shown in Table 4 as feature genes.
More preferably, the above 7 genes, 100 genes or 92 genes shown in Tables 3-1 to 3-3 (indicated in bold with * in Tables 3-1 to 3-3), or 9 genes shown in Table 4 An example is a discriminant that uses genes as feature genes.

The test kit for detecting infantile atopic dermatitis of the present invention contains a test reagent for measuring the expression level of the target gene of the present invention or its expression product in a biological sample isolated from a patient. . Specifically, a reagent for nucleic acid amplification or hybridization containing an oligonucleotide (e.g., a primer for PCR) that specifically binds (hybridizes) to the target gene of the present invention or a nucleic acid derived therefrom; Examples include reagents for immunoassays containing antibodies that recognize the expression products (proteins) of the target genes of the present invention. The oligonucleotides, antibodies, etc. included in the kit can be obtained by known methods as described above.
In addition to the antibodies and nucleic acids mentioned above, the test kit also includes labeling reagents, buffers, coloring substrates, secondary antibodies, blocking agents, equipment necessary for the test, control reagents used as positive controls and negative controls, Equipment for collecting biological samples (e.g.
(e.g., oil-removing film for collecting SSL).

Aspects and preferred embodiments of the invention are shown below.
<1> Regarding biological samples collected from subjects, IMPDH2, ERI1, FBXW2
, STK17B, TAGLN2, AMICA1, and HNRNPA1. A method for detecting infantile atopic dermatitis in a subject, the method comprising the step of measuring the expression level of at least one gene selected from the seven gene groups consisting of , STK17B, TAGLN2, AMICA1, and HNRNPA1 or its expression product.
<2> The method for detecting atopic dermatitis in infants according to <1>, which comprises at least measuring the expression level of a gene selected from the three gene groups consisting of IMPDH2, ERI1, and FBXW2 or its expression product.
<3> The expression level of the gene or its expression product is a measurement of the expression amount of mRNA, <1>
Or method <2>.
<4> The gene or its expression product is RNA contained in lipids on the skin surface of the subject;
Any method from <1> to <3>.
<5> The method according to any one of <1> to <4>, wherein the measured value of the expression level is compared with the reference value of each gene or its expression product to evaluate the presence or absence of infantile atopic dermatitis.
<6> Using the expression level of the gene or its expression product derived from a child with atopic dermatitis and the measured expression level of the same gene or its expression product derived from a healthy child as teacher samples, Create a discriminant to classify healthy children, substitute the measured expression level of the same gene or its expression product obtained from the biological sample collected from the subject into the discriminant, and compare the obtained results with the reference value. The method according to any one of <1> to <4>, which evaluates the presence or absence of infantile atopic dermatitis in a subject.
<7> The algorithm for constructing the discriminant is random forest, linear kernel support vector machine, rbf kernel support vector machine, neural network, general linear model, regularized linear discriminant analysis, or regularized logistic regression. <6
> method.
<8> The method of <6> or <7>, wherein the expression levels of all genes of the seven gene groups or their expression products are measured.
<9> In addition to at least one gene selected from the seven gene groups, Table 3-
The expression level of at least one gene or its expression product selected from the 100 genes shown in 1 to 3-3 and the gene group excluding the 7 genes among the 9 genes shown in Table 4 is measured. Any method from <6> to <8>.
<10> The method of <9>, wherein 100 types shown in Tables 3-1 to 3-3 below are feature genes extracted using random forest.
<11> The method of <9>, wherein the nine types shown in Table 4 below are feature genes extracted using the Boruta method.
<12> In addition to at least one gene selected from the seven gene groups, Table 1 below
The expression level of at least one gene or its expression product selected from the gene group excluding the genes included in the 7 types among the 371 types shown by -1 to 1-9 is measured, <6>
~ Any method of <8>.
<13> In addition to at least one gene selected from the seven gene groups, the following two
The method of <11> or <12>, wherein the expression level of at least one gene selected from five gene groups or its expression product is measured;
ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1LC3B
2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3P, HN
RNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, ECH1, C
ASS4, IL7R, CLEC4A, AREG, SNRPD1, SLC7A11 and SN
X8.
<14> An infant used in any of the methods <1> to <13>, which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene. A test kit for detecting atopic dermatitis.
<15> A detection marker for atopic dermatitis in infants, comprising at least one gene selected from the 383 gene groups shown in B-1 to B-2 above or its expression product.
<16>IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMI
The detection marker for infantile atopic dermatitis according to <15>, which is at least one gene selected from the seven gene group consisting of CA1 and HNRNPA1 or its expression product.
<17>ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1
LC3B2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3
P, HNRNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, EC
At least one gene or its expression product selected from the 23 gene group consisting of H1, CASS4, CLEC4A, SNRPD1, SLC7A11 and SNX8, <1
5> Detection marker for infantile atopic dermatitis.

Hereinafter, the present invention will be explained in more detail based on Examples, but the present invention is not limited thereto.

Example 1 Detection of expression-variable genes related to infantile atopic dermatitis in RNA extracted from SSL 1) SSL collection 28 healthy skin infants (HL) (6 months to 5 years old, male and female) and infants with atopic dermatitis (AD) Twenty-five subjects (6 months to 5 years old, male and female) were used as subjects. Infants with atopic dermatitis have a rash on their entire face and have been diagnosed with atopic dermatitis of mild or moderate severity by a dermatologist. After collecting sebum from the whole face of each subject (AD includes the eruption area) using an oil-blotting film (5 x 8 cm, made of polypropylene, 3M Company), the oil-blotting film is transferred to a vial and used for RNA extraction. It was stored at -80°C for about 1 month.

2) RNA preparation and sequencing Cut the oil-removed film from 1) above into an appropriate size, and use QIAzol Lysis.
RNA was extracted using Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, SuperScript VILO cDNA Synth
Reverse transcription was performed at 42° C. for 90 minutes using an esis kit (Life Technologies Japan Co., Ltd.) to synthesize cDNA. Random primers included in the kit were used as primers for the reverse transcription reaction. A library containing DNA derived from the 20802 gene was prepared from the obtained cDNA by multiplex PCR. Multiplex PCR using Ion AmpliSeq Transcriptome Human Ge
[99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) × 20 cycles → 4°C, Ho
The test was carried out under the following conditions. The obtained PCR product was purified using Ampure XP (Beckman Coulter, Inc.), and then subjected to buffer reconstitution, primer sequence digestion, adapter ligation, purification, and amplification to prepare a library. The prepared library was loaded onto an Ion 540 Chip and sequenced using an Ion S5/XL system (Life Technologies Japan Co., Ltd.).

3) Data Analysis i) Data Used The expression level data (read count value) of RNA derived from the subject measured in 2) above was corrected using a method called DESeq2. However, expression level data that is not a missing value is obtained for 90% or more of the expression level data of all sample subjects348
Only 6 genes were used for the following analysis. For the analysis, a count value (normalized count value) corrected using a method called DESeq2 was used.
ii) RNA expression analysis SSL-derived RNA expression level (Normalized) of healthy subjects and AD measured in i) above
RNA (genes with variable expression) whose corrected p value (FDR) by likelihood ratio test was less than 0.25 in AD compared to healthy subjects was identified. As a result, 31
0 types of RNA decreased (DOWN), and 61 types of RNA increased (UP) (Tables 1-1 to 1-
9).

Regarding the 371 genes shown in Tables 1-1 to 1-9, the public database STRI
Biologic by Gene Ontology (GO) enrichment analysis using NG
al process (BP) was searched. As a result, 144 BPs were found that were associated with genes whose expression was decreased in AD patients, and were found to be associated with cell death, keratinization, immune response (degranulation of neutrophils and leukocytes), myeloid cell activation, and lipid metabolism. It was shown that the term was included (Table 2). In addition, 44 BPs related to groups of genes with increased expression were obtained, including terms related to immune responses to exogenous antigens (Tables 2-1 to 2-4). On the other hand, 318 genes out of the 371 genes shown in Tables 1-1 to 1-9 (marked with * and shown in bold in each table) have been previously shown to be associated with atopic dermatitis. Since there are no reports suggesting this, it was determined that it could be a new atopic dermatitis marker.

Example 2 Construction of a discriminant model using genes with high variable importance in random forest 1) Data used Obtain data on the expression level of SSL-derived RNA (read count value) from subjects using the same method as in Example 1. and converted into an RPM value corrected for differences in the total number of reads between samples. However, only 3486 genes for which expression level data with non-missing values were obtained in 90% or more of all samples were used for the following analysis. To build a machine learning model, in order to approximate the RPM value that follows the negative binomial distribution to a normal distribution, we use the base 2 logarithm (Log ₂ (R
PM+1) value) was used.

2) Selection of feature genes In order to select feature genes using the random forest algorithm, Log ₂ of 3486 genes for which expression data with non-missing values was obtained in more than 90% of all samples was used. The (RPM+1) value was used as an explanatory variable, and healthy subjects (HL) and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of the R language, and the optimal value of the number of variables (mtry value) used to construct one decision tree was tuned. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the top 100 genes with variable importance based on the Gini coefficient (Tables 3-1 to 3-3). These 100 genes or 92 genes among which no relationship with atopic dermatitis has been reported so far (marked with * and shown in bold in each table) were selected as feature genes.

3) Model construction The Log ₂ (RPM+1) values of the above 100 genes or 92 genes are used as explanatory variables, and HL
and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of the R language, and the optimal value of the number of variables (mtry value) used to construct one decision tree was tuned. Using the mtry value determined by tuning, a random forest algorithm is executed to calculate the estimated error rate (OOB erro
r rate) was calculated. As a result, in the model using 100 genes, OOB e
The rror rate was 9.43%, and 13.21% in the model using 92 genes.

Example 3 Construction of a discriminant model using expression-variable genes 1) Data used In the same manner as in Example 1, data on the expression level of SSL-derived RNA (read count value) was obtained from the subjects, and the total number between samples was calculated. It was converted into an RPM value corrected for the difference in the number of reads. In order to approximate the RPM value according to the negative binomial distribution to a normal distribution, a base 2 logarithm value (log ₂ (RPM+1) value) obtained by adding an integer 1 was used to construct the machine learning model.

2) Selection of feature genes The 371 genes (Tables 1-1 to 1-9) whose expression was significantly changed in AD compared to healthy subjects (HL) in Example 2, or those with atopic 318 genes (marked with * and shown in bold in each table) for which no relationship with dermatitis has been reported were selected as feature genes.

3) Model construction The Log ₂ (RPM+1) values of the above 371 genes or 318 genes are used as explanatory variables, and H
L and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of the R language, and the optimal value of the number of variables (mtry value) used to construct one decision tree was tuned. mtry determined by tuning
Using the value, run the random forest algorithm and find the OOB error rate
was calculated. As a result, in the model using 371 genes, OOB error ra
te was 26.42%, and 30.19% in the model using 318 genes.

Example 4 Construction of a discriminant model using feature genes extracted by the Boruta method 1) Data used In the same manner as in Example 1, data on the expression level of SSL-derived RNA (read count value) was obtained from the subjects. , and converted to RPM values corrected for differences in total number of reads between samples. However, only 3486 genes for which expression level data with non-missing values were obtained in 90% or more of all samples were used for the following analysis. To build a machine learning model, in order to approximate the RPM value that follows the negative binomial distribution to a normal distribution, we use the base 2 logarithm (log ₂ (R
PM+1) value) was used.

2) Selection of feature genes Expression data that is not a missing value is obtained in more than 90% of all samples34
Using the Log ₂ (RPM+1) values of 86 genes as explanatory variables and healthy subjects (HL) and AD as objective variables, an algorithm in the "Boruta" package of the R language was executed. The maximum number of trials was set to 1000, and nine genes with a p value of less than 0.01 were calculated (Table 4). Table 4
The 9 genes shown in Figure 1 or 7 genes among which no relationship with atopic dermatitis has been reported so far (marked with * and shown in bold in Table 4) were selected as feature genes.

3) Model construction Using the Log ₂ (RPM+1) values of the above 9 genes or 7 genes as explanatory variables, HL and AD
was used as the objective variable. The random forest algorithm is specified as a method in the “caret” package of the R language, and the number of variables (mt
ry value) was tuned. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the OOB error rate. As a result, the OOB error rate was 9.4 in the model using 9 genes.
3%, and 15.09% in the model using 7 genes.

Claims (14)

Regarding biological samples collected from subjects, IMPDH2, ERI1, FBXW2, ST
A method for detecting infantile atopic dermatitis in a subject, comprising the step of measuring the expression level of at least one gene selected from the seven gene group consisting of K17B, TAGLN2, AMICA1, and HNRNPA1 or its expression product. 2. The method for detecting atopic dermatitis in infants according to claim 1, which comprises at least measuring the expression level of a gene selected from the three gene groups consisting of IMPDH2, ERI1, and FBXW2 or its expression product. The method according to claim 1 or 2, wherein the expression level of the gene or its expression product is a measurement of the expression amount of mRNA. The method according to any one of claims 1 to 3, wherein the gene or its expression product is RNA contained in lipids on the skin surface of the subject. The method according to any one of claims 1 to 4, wherein the measured value of the expression level is compared with a reference value of each gene or its expression product to evaluate the presence or absence of infantile atopic dermatitis. The expression level of the gene or its expression product derived from a child with atopic dermatitis and the measured expression level of the same gene or its expression product derived from a healthy child were used as teacher samples to test children with atopic dermatitis and healthy children. By creating a discriminant to separate the genes, substituting the measured expression level of the same gene or its expression product obtained from the biological sample collected from the subject into the discriminant, and comparing the obtained results with the reference value. The method according to any one of claims 1 to 4, which evaluates the presence or absence of infantile atopic dermatitis in a subject. 7. The method according to claim 6, wherein the expression levels of all genes of the seven gene groups or their expression products are measured. In addition to at least one gene selected from the seven gene groups, the following Tables 3-1 to 3
6. The expression level of at least one gene or its expression product selected from the 100 genes indicated by -3 and the 7 genes out of the 9 genes shown in Table 4 is measured. Or the method described in 7.

In addition to at least one gene selected from the seven gene groups, the following Tables 1-1 to 1
-9 out of 371 types, the expression level of at least one gene selected from the gene group excluding the genes included in the 7 types or its expression product is measured.
Method described.

10. The expression level of at least one gene selected from the following 25 gene groups or its expression product is measured in addition to the at least one gene selected from the 7 gene groups. Method,
ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1LC3B
2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3P, HN
RNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, ECH1, C
ASS4, IL7R, CLEC4A, AREG, SNRPD1, SLC7A11 and SN
X8. The atopic infant used in the method according to any one of claims 1 to 10, which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived therefrom, or an antibody that recognizes the expression product of the gene. Test kit for detecting dermatitis. A detection marker for infantile atopic dermatitis consisting of at least one gene or its expression product selected from the 383 gene groups shown in Tables B-1 and B-2 below.

The detection marker for infantile atopic dermatitis according to claim 12, which is at least one gene selected from the seven gene group consisting of IMPDH2, ERI1, FBXW2, STK17B, TAGLN2, AMICA1, and HNRNPA1 or its expression product. ABHD8, GPT2, PLIN2, FAM100B, YPEL2, MAP1LC3B
2, RLF, KIAA0930, UBE2R2, HK2, USF2, PDIA3P, HN
RNPUL1, SEC61G, DNAJB11, SDHD, NDUFS7, ECH1, C
The detection marker for infantile atopic dermatitis according to claim 12, which is at least one gene selected from the 23 gene group consisting of ASS4, CLEC4A, SNRPD1, SLC7A11, and SNX8 or its expression product.