JP2021175381A - Method for detecting infant atopic dermatitis - Google Patents

Abstract

To provide a marker for detecting atopic dermatitis, and a method for detecting atopic dermatitis using the marker.SOLUTION: A method for detecting atopic dermatitis in a subject includes the step of measuring the expression level of at least one gene selected from seventeen gene groups consisting of MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2 and CSNK1G2 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 atopic dermatitis using an atopic dermatitis marker.

Atopic dermatitis (hereinafter, also referred to as "AD") is an eczema skin disease that mainly develops in persons having an atopic predisposition. Typical symptoms of atopic dermatitis include bilateral, chronic and recurrent itching, rashes, erythema, etc., as well as keratinization deficiency, reduced barrier capacity, and dry skin. Most atopic dermatitis develops in infants and tends to improve with growth, but in recent years, adult-type and refractory atopic dermatitis have also increased.

At present, the degree of atopic dermatitis is determined by relying on the findings with the naked eye. There are various findings such as dryness, erythema, scales, papules, scratches, edema, adhesion of scabs, vesicles, erosions, and pruritus nodules. There is a possibility that the judgment may differ depending on the skill. Thus, there is a problem that the judgment of the degree of atopic dermatitis is not objective.

In recent years, as a method for detecting atopic dermatitis using a biomarker, peripheral blood eosinophil count in blood, total serum IgE value, LDH (lactate dehydrogenase) level, and Symus and Activation-Regulated Chemokine (TARC) in serum have been used. And Squamous cell carcinoma antigen 2 (SCCA2) (Non-Patent Documents 1, 2 and 3), and the agrC mutation-dependent RNAIII gene expression level of Staphylococcus aureus in the skin flora (Patent Document 1). ) Etc. have been proposed, but it cannot always be said that diagnosis is possible with sufficient accuracy.

On the other hand, a technique for investigating the current and future physiological states in a human body by analyzing nucleic acids such as DNA and RNA in a biological sample has been developed. For analysis using nucleic acids, a comprehensive analysis method has been established and abundant information can be obtained by one analysis, and the functional analysis results are based on many research reports on monobasic polymorphism and RNA function. 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. Recently, RNA contained in skin surface lipids (SSL) is used as a sample for analysis of living organisms. It has been reported that marker genes for epidermis, sweat glands, hair follicles and sebaceous glands can be detected from SSL (Patent Document 2).

Japanese Unexamined Patent Publication No. 2019-30272 International Publication No. 2018/0083319

Sugawara et al., Allergy (2002) 57: 180-181 Ohta et al., Ann Clin Biochem. (2012) 49: 277-84 Kato et al., Nikkikai Magazine (2018) 128: 2431-2502

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

The present inventors collected SSL from adult patients with atopic dermatitis and healthy subjects, and comprehensively analyzed the expression state of RNA contained in the SSL as sequence information. As a result, the expression levels of specific genes were both. It was found that atopic dermatitis can be detected using this as an index.

That is, the present invention relates to the following 1) to 3).
1) Regarding biological samples collected from subjects, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1 A method for detecting atopic dermatitis in a subject, comprising the step of measuring the expression level of at least one gene selected from the group or an expression product thereof.
2) A test kit for detecting atopic dermatitis used in the method 1), which contains an oligonucleotide that specifically hybridizes with the gene or an antibody that recognizes an expression product of the gene.
3) A marker for detecting atopic dermatitis, which comprises at least one gene selected from the 210 gene groups shown in Table B below or an expression product thereof.

According to the present invention, it is possible to detect atopic dermatitis at an early stage with high accuracy, sensitivity and specificity in a simple and non-invasive manner.

All patented, non-patented, and other publications cited herein are hereby incorporated by reference in their entirety.

In the present invention, the term "nucleic acid" or "polynucleotide" means DNA or RNA. DNA includes any of cDNA, genomic DNA, and synthetic DNA, and "RNA" includes any of total RNA, mRNA, rRNA, tRNA, non-coding RNA, and synthetic RNA.

In the present invention, the "gene" refers to double-stranded DNA containing human genomic DNA, single-stranded DNA containing cDNA (positive strand), and single-stranded DNA having a sequence complementary to the positive strand (complementary strand). , And those fragments, which include some biological information in the sequence information of the bases constituting the DNA.
In addition, the "gene" includes not only "genes" represented by specific base sequences, but also nucleic acids encoding these homologues (that is, homologs or orthologs), mutants such as gene polymorphisms, and derivatives. Will be done.
The names of the genes disclosed herein follow the Official Symbol described in NCBI ([www.ncbi.nlm.nih.gov/]). On the other hand, regarding Gene Ontology (GO), Pathway ID. Follow.

In the present invention, the "expression product" of a gene is a concept including a transcript and a translation product of the gene. The "transcription product" is RNA produced by being transcribed from a gene (DNA), and the "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 caused by pruritic eczema, which repeats exacerbations and remissions, and many of the patients are said to have an atopic predisposition. Examples of atopic predisposition include i) family history / medical history (one or more of bronchial asthma, allergic rhinitis / conjunctivitis, and atopic dermatitis), or ii) predisposition to easily produce IgE antibody. ..

In the present invention, "detection" of atopic dermatitis means to clarify the presence or absence of atopic dermatitis, and can be paraphrased by the terms inspection, measurement, judgment, evaluation or evaluation support. In addition, the term "judgment" or "evaluation" in this specification does not include judgment or evaluation by a doctor.

In the present specification, "feature amount" is synonymous with "explanatory variable" in machine learning, and a gene used for machine learning selected from atopic dermatitis detection markers or an expression product thereof is combined to form a "feature amount gene". ".

As shown in Examples described later, DESeq2 (Love MI et al. Genome) was used for RNA expression level data (read count value) extracted from the SSL of 14 healthy adult subjects and 29 atopic dermatitis patients. Using the count value (Normalized count value) corrected using Biol. 2014), the correction value (FDR) of the p value by the likelihood ratio test in patients with atopic dermatitis was 0.05 compared to healthy subjects. By extracting RNA that is less than, 48 types of up-expression genes and 75 types of down-expression genes (total of 123 genes (Tables 1-1 to 1-3) were identified. Genes indicated by "UP" in the table. Is a gene whose expression level is increased in patients with atopic dermatitis, and a gene represented by "DOWN" is a gene whose expression level is decreased in patients with atopic dermatitis.
Therefore, a gene selected from the 123 gene groups or an expression product thereof can be a marker for atopic dermatitis for detecting atopic dermatitis. Of the gene group, 107 genes (marked with * in Tables 1-1 to 1-3 and indicated in bold) are genes for which no relationship with atopic dermatitis has been reported so far.

In addition, data on the expression levels of all SSL-derived RNAs from the subjects _{(Log 2} (RPM + 1) value of the 7429 gene) were used as explanatory variables, and healthy subjects and patients with atopic dermatitis were used as objective variables, and a random forest was used as a machine learning algorithm. Using (Breiman L. Machine Learning (2001) 45; 5-32), we attempted to extract feature-quantity genes and construct a prediction model. As shown in Examples described later, the top 150 genes with variable importance based on the Gini coefficient (Tables 3-1 to 3-4) were selected as feature genes, and a prediction model was constructed using them. As a result, atopy was constructed. It has been shown that sexual dermatitis can be predicted.
Therefore, a gene selected from such a group of 150 genes or an expression product thereof can be a suitable marker for atopic dermatitis for detecting atopic dermatitis. Of these, 127 genes (marked with * in Tables 3-1 to 3-4 and indicated in bold) are novel atopic dermatitis markers for which no relationship with atopic dermatitis has been reported so far. .. As shown in Examples described later, atopic dermatitis can be predicted even with a prediction model using these novel atopic dermatitis markers.

_{In addition, using the data (Log 2} (RPM + 1) value) of the expression levels of the above 123 genes or 107 genes of which expression fluctuations were observed in healthy subjects and patients with atopic dermatitis, similarly using a random forest. When we tried to build a predictive model, it was shown that atopic dermatitis can be predicted in any case.

In addition, the feature gene was extracted using the Boruta method (Kursa et al. Fundamental Informaticae (2010) 101; 271-286) as a machine learning algorithm (maximum number of trials 1000 times, p value less than 0.01). , 45 genes (Table 4) were extracted as feature genes, and as shown in Examples described later, it was shown that atopic dermatitis can be predicted by a prediction model using a random forest using these genes.
Therefore, a gene selected from such a group of 45 genes or an expression product thereof can be a suitable marker for atopic dermatitis for detecting atopic dermatitis. Of these, 39 genes (marked with * in Table 4 and indicated in bold) are novel atopic dermatitis markers for which no relationship with atopic dermatitis has been reported so far. As shown in Examples described later, atopic dermatitis can be predicted even with a prediction model using these novel atopic dermatitis markers.

The 123 gene groups (A) shown in Tables 1-1 to 1-3 extracted by the above-mentioned expression fluctuation analysis and Tables 3-1 to 3-4 selected as characteristic amount genes by a random forest are shown. 245 genes (Table A), which is the sum (A∪B∪C) of the 150 gene groups (B) and the 45 gene groups (C) shown in Table 4 selected as the feature amount genes by the Boruta method. ) Is a marker for atopic dermatitis, of which 210 genes (Table B) are novel markers for atopic dermatitis.

MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2 and CSNK1 123 gene groups (A) shown in Tables 1-1 to 1-3 extracted and 150 gene groups shown in Tables 3-1 to 3-4 selected as feature genes by random forest. (B) and the gene (A∩B∩C) common to the 45 gene groups (C) shown in Table 4 selected as the feature gene by the Boruta method, and are conventionally associated with atopic dermatitis. Genes that have not been identified (marked with * in each table and shown in bold). Therefore, at least one gene selected from these gene groups or an expression product thereof is particularly useful as a novel atopic dermatitis marker for detecting atopic dermatitis.
Each of these 17 genes can be a marker for atopic dermatitis alone, but it is preferable to use 2 or more, preferably 5 or more, more preferably 10 or more in combination, and it is preferable to use all 17 genes. Even more preferable.

The gene that can be a marker for atopic dermatitis (hereinafter, also referred to as "target gene") includes the base sequence of the DNA that constitutes the gene as long as it can be a biomarker for detecting atopic dermatitis. Genes having substantially the same base sequence are also included. Here, with substantially the same base sequence, for example, the homology calculation algorithm NCBI BLAST is used, and the condition of expected value = 10; gap is allowed; filtering = ON; match score = 1; mismatch score = -3. It means that there is 90% or more, preferably 95% or more, and even more preferably 98% or more identity with the base sequence of the DNA constituting the gene.

The method for detecting atopic dermatitis of the present invention is a target gene, as one embodiment, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, for a biological sample collected from a subject. It comprises a step of measuring the expression level of at least one gene selected from a group of 17 genes consisting of DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2 and CSNK1G2 or an expression product thereof.

The biological sample used in the present invention may be a tissue or a biomaterial whose expression changes with the onset and progression of atopic dermatitis. Specific examples thereof include body fluids such as organs, skin, blood, urine, saliva, sweat, skin surface lipids (SSL) and tissue exudates, serum prepared from blood, plasma, others, stool, hair and the like, which are preferable. Is skin or skin surface lipid (SSL), more preferably skin surface lipid (SSL).
In addition, the subject for which the biological sample is collected is preferably a person who needs to detect atopic dermatitis or a person who is suspected of developing atopic dermatitis, and is not limited in gender and age, but is preferably an adult. Specifically, it is a person 16 years or older, preferably a person 20 years or older.

Here, "skin surface lipid (SSL)" refers to a fat-soluble fraction existing on the surface of the skin, and is sometimes called sebum. In general, SSL mainly contains secretions secreted from exocrine glands such as sebaceous glands in the skin, and is present on the surface of the skin in the form of a thin layer covering the skin surface. SSL contains RNA expressed in skin cells. (See Patent Document 3 above). Further, in the present specification, "skin" is a general term for regions including the epidermis, dermis, hair follicles, and tissues such as sweat glands, sebaceous glands, and other glands on the body surface, unless otherwise specified.

For the collection of SSL from the subject's skin, any means used to recover or remove SSL from the skin can be employed. Preferably, an SSL-absorbing material, an SSL-adhesive material, or an instrument that scrapes the SSL from the skin, which will be described later, can be used. The SSL-absorbing material or the SSL-adhesive material is not particularly limited as long as it is a material having an affinity for SSL, and examples thereof include polypropylene and pulp. More detailed examples of the procedure for collecting SSL from the skin include a method of absorbing SSL into a sheet-like material such as oil removal paper and oil removal film, a method of adhering SSL to a glass plate, tape, etc., a spatula, a scraper, etc. There is a method of scraping off the SSL and collecting the SSL. In order to improve the adsorptivity of SSL, an SSL-absorbing material containing a highly lipophilic solvent in advance may be used. On the other hand, if the SSL-absorbent material contains a highly water-soluble solvent or water, the adsorption of SSL is inhibited, so that the content of the highly water-soluble solvent or water is preferably small. The SSL-absorbent material is preferably used in a dry state. The part of the skin from which the SSL is collected is not particularly limited, and examples thereof include the skin of any part of the body such as the head, face, neck, trunk, and limbs, and the part where sebum is secreted, for example, the skin of the face. Is preferable.

RNA-containing SSL collected from the subject may be stored for a period of time. The collected SSL is preferably stored under low temperature conditions as soon as possible after collection in order to suppress the decomposition of the contained RNA as much as possible. The storage temperature condition of the RNA-containing SSL in the present invention may be 0 ° C. or lower, preferably −20 ± 20 ° C. to −80 ± 20 ° C., more preferably −20 ± 10 ° C. to −80 ± 10 ° C. , More preferably −20 ± 20 ° C. to −40 ± 20 ° C., further preferably −20 ± 10 ° C. to −40 ± 10 ° C., further preferably −20 ± 10 ° C., still more preferably −20 ± 5 ° C. .. The storage period of the RNA-containing SSL under the low temperature condition is not particularly limited, but is preferably 12 months or less, for example, 6 hours or more and 12 months or less, more preferably 6 months or less, for example, 1 day or more and 6 months or less. More preferably, it is 3 months or less, for example, 3 days or more and 3 months or less.

In the present invention, as a target for measuring the expression level of a target gene or an expression product thereof, a cDNA artificially synthesized from RNA, a DNA encoding the RNA, a protein encoded by the RNA, and an interaction with the protein are used. Examples include molecules that interact with RNA, molecules that interact with DNA, and the like. Here, examples of molecules that interact with RNA, DNA or protein include DNA, RNA, proteins, polysaccharides, oligosaccharides, monosaccharides, lipids, fatty acids, their phosphorylates, alkylated products, sugar adducts and the like, and the like. Any of the above complexes can be mentioned. The expression level comprehensively means the expression level and activity of the gene or expression product.

In the method of the present invention, SL is used as a biological sample as a preferred embodiment. In this case, the expression level of RNA contained in SSL is analyzed, and specifically, after RNA is converted to cDNA by reverse transcription. , The cDNA or its amplification product is measured.
For the extraction of RNA from SSL, methods commonly used for extracting or purifying RNA from biological samples, such as the phenol / chloroform method, the AGPC (acid guanidinium thiocyanate-phenyl-chromoform extraction) method, or TRIzol®. ), RNAy®, QIAzol® and other columns, silica-coated special magnetic particles, Solid Phase Reversible Imaging magnetic particles, ISOGEN and other commercially available products. Extraction with an RNA extraction reagent or the like can be used.

For the reverse transcription, primers targeting the specific RNA to be analyzed may be used, but it is preferable to use random primers for more comprehensive nucleic acid storage and analysis. A general reverse transcriptase or reverse transcriptase kit can be used for the reverse transcription. Preferably, a highly accurate and efficient reverse transcriptase or reverse transcriptase kit is used, and examples thereof include M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcriptase kits. For example, PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Co., Ltd.), SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific) and the like can be mentioned. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both from Thermo Scientific) and the like are preferably used.
For the extension reaction in the reverse transcription, the temperature is preferably adjusted to 42 ° C. ± 1 ° C., more preferably 42 ° C. ± 0.5 ° C., still more preferably 42 ° C. ± 0.25 ° C., while the reaction time is preferably adjusted to 42 ° C. ± 0.25 ° C. It is preferably adjusted for 60 minutes or more, more preferably 80 to 120 minutes.

When RNA, cDNA or DNA is targeted, the method for measuring the expression level is represented by the PCR method using the DNA hybridizing to these as a primer, the real-time RT-PCR method, the multiplex PCR, the SmartAmp method, the LAMP method, and the like. Nucleic acid amplification method, hybridization method using nucleic acid hybridizing to these as a probe (DNA chip, DNA microarray, dot blot hybridization, slot blot hybridization, Northern blot hybridization, etc.), method for determining base sequence ( Sequencing) or a combination of these methods can be selected.

In PCR, only one specific DNA may be amplified using a primer pair targeting a specific DNA to be analyzed, but a plurality of specific DNAs may be amplified at the same time using a plurality of primer pairs. May be good. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method of simultaneously amplifying a plurality of gene regions by simultaneously using a plurality of primer pairs in a PCR reaction system. Multiplex PCR can be carried out using a commercially available kit (for example, Ion AmpliSeqTranscriptome Human Gene Expression Kit; Life Technologies Japan Co., Ltd., etc.).
The temperature of the annealing and extension reactions in the PCR cannot be unequivocally determined because it depends on the primers used, but when the above multiplex PCR kit is used, it is preferably 62 ° C ± 1 ° C, more preferably 62 ° C ± 0. It is 5.5 ° C., more preferably 62 ° C. ± 0.25 ° C. Therefore, in the PCR, the annealing and extension reactions are preferably carried out 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 and the like, but is preferably 14 to 18 minutes. The conditions of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but are preferably 95 to 99 ° C. for 10 to 60 seconds. Reverse transcription and PCR at temperature and time as 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. By size separation, the PCR reaction product of interest can be separated from the primers and other impurities contained in the PCR reaction solution. DNA size separation can be performed by, for example, 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 Solid Phase Reversible Imaging (SPRI) magnetic beads such as Aple XP.

The purified PCR reaction product may be subjected to further treatment necessary for subsequent quantitative analysis. For example, for DNA sequencing, the purified PCR reaction product can be prepared into a suitable buffer solution, the PCR primer region contained in the PCR-amplified DNA can be cleaved, or the adapter sequence can 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 ligation is performed on the amplified DNA, and the obtained reaction product is amplified as necessary for quantitative analysis. Library can be prepared. These operations are performed, for example, by 5 × VILO RT Reaction Mix attached to SuperScript (registered trademark) VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) and Ion AmpliSeq Transcriptome Human Gene Expression Japan Co., Ltd. It can be performed according to the protocol included in each kit using the 5 × Ion Ampliseq HiFi Mix and the Ion Ampliseq Transcriptome Human Gene Expression Core Panel included in the kit.

When measuring the expression level of a target gene or a nucleic acid derived from it using the Northern blot hybridization method, for example, the probe DNA is first labeled with a radioactive isotope, a fluorescent substance, or the like, and then the obtained labeled DNA is used. , Hybridize with RNA derived from a biological sample transferred to a nylon membrane or the like according to a conventional method. Then, a method of measuring the double chain of the formed labeled DNA and RNA by detecting a signal derived from the labeled substance can be mentioned.

When measuring the expression level of a target gene or a nucleic acid derived from the target gene using the RT-PCR method, for example, cDNA is first prepared from RNA derived from a biological sample according to a conventional method, and the target gene of the present invention is used as a template. A pair of primers prepared for amplification (a normal chain that binds to the above-mentioned cDNA (-chain) and a reverse chain that binds to the + chain) are hybridized with this. Then, the PCR method is performed according to a conventional method, and the obtained amplified double-stranded DNA is detected. To detect the amplified double-stranded DNA, a method for detecting the labeled double-stranded DNA produced by performing the above PCR using a primer labeled with RI, a fluorescent substance, or the like in advance is used. Can be done.

When measuring the expression level of a target gene or a nucleic acid derived from the target gene using a DNA microarray, for example, an array in which at least one of the nucleic acids (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 can be bound on a microarray, and the expression level of mRNA can be measured by detecting the label on the microarray.
The nucleic acid immobilized on the array may be a nucleic acid that specifically hybridizes under stringent conditions (that is, substantially only to the nucleic acid of interest), and for example, all of the target genes of the present invention. It may be a nucleic acid having a sequence or a nucleic acid consisting of a partial sequence. Here, the "partial sequence" includes nucleic acids consisting of at least 15 to 25 bases. Here, stringent conditions can usually include cleaning conditions of about "1 x SSC, 0.1% SDS, 37 ° C.", and stricter hybridization conditions include "0.5 x SSC, 0.1". % SDS, 42 ° C. ”, and more severe hybridization conditions include“ 0.1 × SSC, 0.1% SDS, 65 ° C. ”. Hybridization conditions are described in 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 from the target gene by sequencing, for example, analysis using a next-generation sequencer (for example, Ion S5 / XL system, Life Technologies Japan Co., Ltd.) can be mentioned. RNA expression can be quantified based on the number of reads created by sequencing (read count).

A probe or primer used for the above measurement, that is, a primer for specifically recognizing and amplifying the target gene of the present invention or a nucleic acid derived from it, or a primer for specifically detecting the RNA or a nucleic acid derived from it. Probes fall into this category, but they can be designed based on the nucleotide sequences that make up the target gene. Here, "specifically recognizing" means that, for example, in Northern blotting, substantially only the target gene of the present invention or a nucleic acid derived from the target gene can be detected, and in, for example, in the RT-PCR method, substantially only the nucleic acid. Means that the detection or product can be determined to be the gene or nucleic acid derived from it so that is amplified.
Specifically, an oligonucleotide containing a DNA consisting of a base sequence constituting the target gene of the present invention or a certain number of nucleotides complementary to the complementary strand thereof can be used. Here, the "complementary strand" refers to the other strand of a double-stranded DNA consisting of A: T (U in the case of RNA) and G: C base pairs with respect to one strand. Further, "complementary" is not limited to the case where the sequence is completely complementary in the fixed number of continuous nucleotide regions, and is preferably 80% or more, more preferably 90% or more, still more preferably 95% or more bases. It suffices to have the sameness on the sequence. The identity of the base sequence can be determined by an algorithm such as BLAST.
When such an oligonucleotide is used as a primer, it suffices if it can perform specific annealing and chain extension, 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. Those having a chain length of 50 bases or less, more preferably 35 bases or less can be mentioned. Further, when it is used as a probe, it suffices if specific hybridization can be performed, and it has at least a part or all of a sequence of DNA (or its complementary strand) consisting of a base sequence constituting 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, more preferably 25 bases or less is used.
Here, the "oligonucleotide" can be DNA or RNA, and may be synthesized or natural. Alternatively, as the probe used for hybridization, a labeled probe is usually used.

In addition, when measuring a translation product (protein) of the target gene of the present invention, a molecule that interacts with the protein, a molecule that interacts with RNA, or a molecule that interacts with DNA, protein chip analysis and immunoassay (immunassay) For example, ELISA, etc.), mass spectrometry (eg, LC-MS / MS, MALDI-TOF / MS), 1-hybrid method (PNAS 100, 12271-12276 (2003)) and 2-hybrid method (Biol. Reprod. 58). , 302-311 (1998)) can be used and can be appropriately selected according to the target.
For example, when a protein is used as a measurement target, it is carried out by contacting an antibody against the expression product of the present invention with a biological sample, detecting the protein in the sample bound to the antibody, and measuring the level thereof. For example, according to Western blotting, the above antibody is used as the primary antibody, and then the primary antibody is used as the secondary antibody by using an antibody that binds to the primary antibody labeled with a radioactive isotope, a fluorescent substance, an enzyme or the like. Labeling is performed, and signals derived from these labeling substances are measured with a radiation measuring device, a fluorescence detector, or the like.
The antibody against the translation product may be a polyclonal antibody or a monoclonal antibody. These antibodies can be produced according to known methods. Specifically, the polyclonal antibody immunizes non-human animals such as rabbits by using a protein expressed and purified in Escherichia coli or the like 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, a monoclonal antibody is obtained by immunizing a non-human animal such as a mouse with a protein expressed and purified in Escherichia coli or the like according to a conventional method or a partial polypeptide of the protein, and fusing the obtained spleen cells and myeloma cells. It can be obtained from the prepared hybridoma cells. Monoclonal antibodies may also be prepared using a phage display (Griffiths, AD; Duncan, AR, Current Opinion in Biotechnology, Volume 9, Number 1, February 1998, pp. 102-108 (7)).

Thus, the expression level of the target gene of the present invention or the expression product thereof in the biological sample collected from the subject is measured, and 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 invention or its expression product with the 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 obtained by correcting the difference in the total number of reads between the samples. , The value obtained by converting the RPM value into the logarithmic value of the base 2 (Log ₂ RPM value), the logarithmic value of the base 2 obtained by adding the integer 1 (Log ₂ (RPM + 1) value), or the count value corrected using DESeq2. It is preferable to use (Normalized count value) or the logarithmic value of the base 2 (Log ₂ (count + 1) value) obtained by adding an integer 1 as an index. Also, as a quantitative value of RNA-seq, it is commonly used as a quantitative value of RNA-seq. It may be a value. Further, it may be a signal value obtained by the microarray method and a correction value thereof. When analyzing the expression level of only a specific target gene by RT-PCR or the like, the expression level of the target gene is converted into a relative expression level based on the expression level of the housekeeping gene (relative quantification). The method of analysis by quantifying (absolute quantification) the absolute number of copies using a plasmid containing the region of the target gene is preferable. It may be the number of copies obtained by the digital PCR method.
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 of a healthy person may be a statistical value (for example, an average value) of the expression level of the gene or its expression product measured from a healthy person population. When there are a plurality of target genes, it is preferable to determine the reference expression level for each gene or its expression product.

Further, the detection of atopic dermatitis in the present invention can also be carried out 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 based on statistical values such as the average value and standard deviation of the expression level based on the expression level of the target gene or its expression product in a healthy subject obtained as reference data in advance. good.

Furthermore, using the measured values of the expression level of the target gene derived from the atopic dermatitis patient or its expression product and the expression level of the target gene derived from the healthy person or its expression product, the atopic dermatitis patient and the healthy person Atopic dermatitis can be detected by constructing a discriminant formula (prediction model) for dividing the disease and using the discriminant formula. That is, using the measured values of the expression level of the target gene derived from the atopic dermatitis patient or its expression product and the expression level of the target gene derived from the healthy person or its expression product as a teacher sample, the atopic dermatitis patient and the healthy person are selected. A discriminant formula (prediction model) for dividing is constructed, and a cutoff value (reference value) for discriminating between atopic dermatitis patients and healthy subjects is obtained based on the discriminant formula. In creating the discriminant, dimensional compression can be performed by principal component analysis (PCA), and the main component can be used as an explanatory variable.
Then, the level of the target gene or its expression product is measured in the same manner 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 atopic dermatitis in the 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 the target gene or its expression product selected by the following method can be used. As the objective variable, for example, whether the sample is derived from a healthy person or a patient with atopic dermatitis can be used.

For the selection of the feature amount, a statistically significant difference between the two groups to be discriminated, for example, a gene whose expression level significantly fluctuates between the two groups (expression fluctuation gene) or the expression level of the expression product thereof is used. Can be done. In addition, a well-known gene such as an algorithm used for machine learning can be used to extract a feature gene and its expression level can be used. For example, the expression level of a gene with high variable importance or its expression product in the random forest shown below is used, or the feature amount gene is extracted using the "Boruta" package of R language and the expression level is used. be able to.

As an algorithm for constructing the discriminant, a known algorithm such as an algorithm used for machine learning can be used. Examples of machine learning algorithms include random forest (Random forest), linear kernel support vector machine (SVM liner), rbf kernel support vector machine (SVM rbf) neural network (Regular net), and generalized linear model. ), Regularized linear discriminant analysis, Regularized logistic regression, and the like. Input verification data into the constructed prediction model to calculate the prediction value, and select the model whose prediction value best matches the measured value, for example, the model with the largest accuracy rate, as the optimum prediction model. Can be done. In addition, the detection rate (Recall), accuracy (Precision), and F value, which is the harmonic mean thereof, can be calculated from the predicted value and the measured value, and the model with the largest F value can be selected as the optimum prediction model. ..

When the random forest algorithm is used in the construction of the discriminant, the estimated error rate (OOB rate) for unknown data can be calculated as an index of the accuracy of the prediction model (Breiman L. Machine Learning (2001)). 45; 5-32).

In a random forest, a classifier called a decision tree is created by randomly extracting about two-thirds of the number of samples by allowing duplication from all the samples according to a technique called the bootstrap method. The sample not extracted at this time is called Out of bug (OOB), and the objective variable of OOB is predicted using one decision tree, and the wrong answer rate is calculated by comparing it with the correct answer label. Can be (OOB variable rate in decision tree). The same operation is repeated 500 times, and the value obtained by taking the average value of the OOB erase in 500 decision trees can be used as the OOB erase of the model of the random forest.

The number of decision trees (stream values) for constructing a random forest model is 500 by default, but it can be changed to any number as needed. Furthermore, the number of variables (mtry value) used to create the sample discriminant formula in one decision tree is the value obtained by taking the square root of the number of explanatory variables by default, but if necessary, one to all explanatory variables Can be changed to any of the values up to.

The R language "caret" package can be used to determine the mtry value. A random forest can be specified in the method of the "caret" package, eight mtry values can be tried, and for example, the mtry value that maximizes the accuracy can be selected as the optimum mtry value. The number of trials of the mtry value can be changed to any number of trials as needed.

When the random forest algorithm is used in the construction of the discriminant, the importance of the explanatory variables used in the construction of the model can be quantified (variable importance). For the value of the variable importance, for example, the amount of decrease in the Gini coefficient (Mean Decrease Gini) can be used.

The method for determining the cutoff value (reference value) is not particularly limited, and the cutoff value (reference value) can be determined according to a known method. For example, it can be obtained from a ROC (Receiver Operating Characteristic Curve) curve created by using a discriminant. In the 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). Will be done. Regarding "true positive (sensitivity)" and "false positive (1-specificity)" shown in the ROC curve, "true positive (sensitivity)"-"false positive (1-specificity)" is the maximum value (Youden). index) can be a cutoff value (reference value).

As already mentioned, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, SPDYE7P, DNASE1L1, GNB2 and CSN1 123 genes shown in Tables 1-1 to 1-3, 150 genes shown in Tables 3-1 to 3-4, or 45 genes shown in Table 4 containing genes are used as characteristic amount genes for atopic dermatitis. It is possible to build a predictive model that can be predicted.

Therefore, when creating a discriminant that separates the atopic dermatitis patient group from the healthy subject group, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA are used as characteristic gene. , PPP1R9B, SPDYE7P, DNASE1L1, GNUB2 and CSNK1G2, one gene or two or more, preferably 5 or more, more preferably 10 or more, still more preferably all 17 , The expression data of the gene or its expression product is used. Further, when a plurality of genes are selected, it is preferable to select the genes having the higher variable importance in Tables 3-1 to 3-4 of these genes as the feature amount genes in order to prepare a discriminant. In addition, the 17 kinds of genes include 245 genes shown in Table A, 123 genes shown in Tables 1-1 to 1-3, 150 genes shown in Tables 3-1 to 3-4, or Table 4. A discriminant formula is created by appropriately adding expression data of at least one, 5 or more, 10 or more, 20 or more or 50 or more genes or their expression products selected from genes other than the 17 kinds of genes among the 45 genes. However, it is also possible to detect atopic dermatitis. When selecting genes other than the 17 genes from the 150 genes shown in Tables 3-1 to 3-4, the genes having the highest variable importance are selected in order, or the variable importance is within the 50th position from the highest. Preferably, the feature amount gene may be selected from the genes within the 30th position. Further, when a gene other than the 17 kinds of genes is selected as a feature gene, * is added in Tables 1-1 to 1-3, Tables 3-1 to 3-4 and Table 4 and displayed in bold. It is preferable to select the feature gene from the novel atopic dermatitis markers.
Preferably, the above 17 genes, 123 genes or 107 genes shown in Tables 1-1 to 1-3 (indicated in bold with * in Tables 1-1 to 1-3), Tables 3-1 to 3-4. 150 genes or 127 genes shown in Table 3 to 127 genes (marked with * in Tables 3-1 to 3-4 and shown in bold), or 45 genes or 39 genes shown in Table 4 (marked with * in Table 4 and shown in bold). There is a discriminant formula in which is a feature amount gene.

The test kit for detecting atopic dermatitis of the present invention contains a test reagent for measuring the expression level of the target gene of the present invention or an expression product thereof in a biological sample isolated from a patient. Specifically, a reagent for nucleic acid amplification and hybridization, or a reagent containing an oligonucleotide (for example, a primer for PCR) that specifically binds (hybridizes) to the target gene of the present invention or a nucleic acid derived from the target gene. Examples thereof include reagents for immunological measurement containing an antibody that recognizes an expression product (protein) of the target gene of the present invention. The oligonucleotides, antibodies and the like included in the kit can be obtained by a known method as described above.
In addition to the above antibodies and nucleic acids, the test kit includes labeling reagents, buffer solutions, color-developing substrates, secondary antibodies, blocking agents, instruments necessary for testing, and control reagents used as positive and negative controls. A tool for collecting a biological sample (for example, a grease removing film for collecting an SSL) or the like can be included.

Aspects and preferred embodiments of the present invention are shown below.
<1> Regarding biological samples collected from subjects, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1 and GN A method for detecting atopic dermatitis in a subject, comprising the step of measuring the expression level of at least one gene selected from the gene group or an expression product thereof.
<2> The method of <1>, wherein the expression level of the gene or its expression product is a measurement of the expression level of mRNA.
<3> The method of <1> or <2>, wherein the gene or its expression product is RNA contained in the lipid on the skin surface of the subject.
<4> The method according to any one of <1> to <3>, 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 atopic dermatitis.
<5> Using the measured values of the expression level of the gene or its expression product derived from atopic dermatitis patients and the expression level of the gene or its expression product derived from healthy subjects as a teacher sample, atopic dermatitis patients and healthy subjects By creating a discriminant formula for division, substituting the measured value of the expression level of the gene or its expression product obtained from the biological sample collected from the subject into the discriminant formula, and comparing the obtained result with the reference value. , Any of <1> to <3> for assessing the presence or absence of atopic dermatitis in a subject.
<6> The algorithm for constructing the discriminant formula is a random forest, a support vector machine for a linear kernel, a support vector machine for an rbf kernel, a neural network, a general linear model, a regularized linear discriminant analysis, or a regularized logistic regression. > Method.
<7> The method of <5> or <6>, wherein the expression levels of all genes of the 17 gene groups or their expression products are measured.
<8> In addition to at least one gene selected from the above 7 gene groups, 1123 genes shown in Tables 1-1 to 1-3 below and 150 genes shown in Tables 3-1 to 3-4 below. , Any one of <5> to <7>, wherein the expression level of at least one gene selected from the gene group excluding the 17 genes among the 45 genes shown in Table 4 or an expression product thereof is measured. the method of.
<9> The method of <8>, wherein the 150 species shown in Tables 3-1 to 3-4 below are feature genes extracted using a random forest.
<10> The method of <8>, wherein the 45 species shown in Table 4 below are feature genes extracted by the Boruta method.
<11> Atopy used in any of the methods <1> to <10>, which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived from the gene, or an antibody that recognizes an expression product of the gene. A test kit for detecting sexual dermatitis.
<12> A marker for detecting atopic dermatitis, which comprises at least one gene selected from the 210 gene groups shown in B or an expression product thereof.
<13> MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2 and CSNK1 A marker for detecting atopic dermatitis of <12>, which is a gene or an expression product thereof.

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

Example 1 Detection of expression-variable genes related to atopic dermatitis in RNA extracted from SSL 1) SSL collection 14 healthy adults (HL) (25-57 years old, male) and adults with atopic skin 29 subjects (AD) (23-56 years old, male) were used as subjects. Subjects with atopic dermatitis have been diagnosed by a dermatologist as having a rash at least on the face and with mild or moderate severity of atopic dermatitis. After collecting sebum from the entire face of each subject (including the eruption part in AD patients) using an oil removal film (5 x 8 cm, made of polypropylene, 3M company), the oil removal film is transferred to a vial and used for RNA extraction. The mixture was stored at −80 ° C. for about 1 month.

2) RNA preparation and sequencing The oil-removing film from 1) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. Based on the extracted RNA, cDNA was synthesized by reverse transcription at 42 ° C. for 90 minutes using a SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.). The random primer included in the kit was used as the primer for the reverse transcription reaction. From the obtained cDNA, a library containing DNA derived from the 20802 gene was prepared by multiplex PCR. Multiplex PCR was performed using Ion AmpliSeqTranscriptome Human Gene Expression Kit (Life Technologies Japan Co., Ltd.) [99 ° C, 2 minutes → (99 ° C, 15 seconds → 62 ° C, 16 minutes) × 20 cycles → 4 ° C, Hold. ] Condition. The obtained PCR product was purified by Aple XP (Beckman Coulter, Inc.), followed by buffer reconstitution, primer sequence digestion, adapter ligation and purification, and amplification to prepare a library. The prepared library was loaded into an Ion 540 Chip and sequenced using an Ion S5 / XL system (Life Technologies Japan KK).

3) Data analysis i) Data used The data (read count value) of the expression level of RNA derived from the subject measured in 2) above was corrected using a method called DESeq2. However, only the 7429 gene for which 90% or more of the expression level data of all sample subjects had expression level data that was not a missing value was used in the following analysis. For the analysis, a count value (normalized count value) corrected by using a method called DESeq2 was used.

ii) RNA expression analysis Based on the healthy subjects measured in i) above and the SSL-derived RNA expression level (Normalized count value) of AD, the p-value corrected by the likelihood ratio test in AD compared with healthy subjects (corrected value of p value by likelihood ratio test). RNA (expression variation gene) having FDR) of less than 0.05 was identified. As a result, 75 kinds of RNA decreased (DOWN) in AD, and 48 kinds of RNA increased (UP) in AD (Tables 1-1 to 1-3).

The 123 genes shown in Tables 1-1 to 1-3 were searched for biological processes (BP) by gene ontology (GO) enrichment analysis using STRING, which is a public database. As a result, 27 BPs associated with the underexpressed gene group were obtained in AD patients, and it was shown that they contained terms related to lipid metabolism and amino acid metabolism (Table 2). In addition, four BPs associated with the up-expressed gene group were obtained, and it was shown that they contained terms related to leukocyte activation (Table 2). On the other hand, 107 of the 123 genes shown in Tables 1-1 to 1-3 (indicated in bold with * in each table) have been associated with atopic dermatitis so far. Since there was no suggestive report, it was judged that it could be a novel marker for atopic dermatitis.

Example 2 Construction of a discrimination model using genes with high variable importance in random forest 1) Usage data Obtain data (read count value) of the expression level of SSL-derived RNA from the subject by the same method as in Example 1. Then, it was converted into an RNAM value in which the difference in the total number of reads between the samples was corrected. However, only the 7429 gene for which expression level data that was not a missing value was obtained in 90% or more of the samples was used for the following analysis. _{In constructing the machine learning model, the logarithm of the base 2 (Log 2} (RPM + 1) value) obtained by adding the integer 1 was used in order to approximate the RPM value following the negative binomial distribution to the normal distribution.

2) Selection of feature gene In order to select the feature gene using the random forest algorithm, the expression level data that is not a missing value is obtained in 90% or more of the samples. Log _{2 of the 7429 gene.} 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 R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, a random forest algorithm was executed to calculate the top 150 genes of variable importance based on the Gini coefficient (Tables 3-1 to 3-4). Then, these 150 genes or 127 genes whose relationship with atopic dermatitis has not been reported so far (marked with * in each table and indicated in bold) were selected as feature genes.

3) Model construction The Log ₂ (RPM + 1) value of the above 150 genes or 127 genes was used as an explanatory variable, and HL and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB rate) was calculated. As a result, the OOB erase rate was 6.98% in the model using 150 genes and 6.98% in the model using 127 genes.

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

2) Selection of feature amount genes 123 genes (Tables 1-1 to 1-3) whose expression was significantly changed in AD as compared with healthy subjects (HL) in Example 1, or of which atopic properties have been obtained so far. 107 genes for which no relationship with dermatitis was reported (marked with * in each table and indicated in bold) were selected as feature genes.

3) Model construction The Log ₂ (RPM + 1) value of the 123 gene or 107 gene was used as an explanatory variable, and HL and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the OOB eraser rate was calculated. As a result, the OOB erase rate was 13.95% in the model using 123 genes and 13.95% in the model using 107 genes.

Example 4 Construction of a discrimination model using the feature amount gene extracted by the Boruta method 1) Usage data Data on the expression level of SSL-derived RNA (read count value) from the subject was acquired by the same method as in Example 1. , Converted to an RPM value corrected for the difference in the total number of reads between samples. However, only the 7429 gene for which expression level data that was not a missing value was obtained in 90% or more of the samples was used for the following analysis. _{In constructing the machine learning model, the logarithm of the base 2 (log 2} (RPM + 1) value) obtained by adding the integer 1 was used in order to approximate the RPM value following the negative binomial distribution to the normal distribution.

_{2) Selection of feature amount gene The Log 2} (RPM + 1) value of the 7429 gene, for which expression level data that is not a missing value is obtained in 90% or more of all samples, is used as an explanatory variable, and a healthy subject (HL) is used. Using AD as the objective variable, the algorithm of the R language "Boruta" package was executed. The maximum number of trials was 1000, and 45 genes having a p-value of less than 0.01 were calculated (Table 4). Of these 45 genes, or 39 genes for which no relationship with atopic dermatitis has been reported so far (marked with * in Table 4 and indicated in bold) were selected as feature genes.

3) Model construction The Log ₂ (RPM + 1) value of the above 45 genes or 39 genes was used as an explanatory variable, and HL and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the OOB eraser rate was calculated. As a result, the OOB erase rate was 6.98% in the model using 45 genes and 9.3% in the model using 39 genes.

Example 5 Construction of a discrimination model based on the feature amount genes duplicated in a plurality of Examples 1) Usage data Data on the expression level of SSL-derived RNA from the subject (read count) in the same manner as in Example 1. Value) was obtained and converted to an RMP value in which the difference in the total number of reads between samples was corrected. _{In constructing the machine learning model, the logarithm of the base 2 (log 2} (RPM + 1) value) obtained by adding the integer 1 was used in order to approximate the RPM value following the negative binomial distribution to the normal distribution.

2) Selection of feature amount genes Among the feature amount genes used in Examples 2 to 4, the genes used in all Examples 2 to 4 are MECR, RASA4CP, HMGCS1, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2. , TMPRSS11E, RPS6KB2, CTBP1, ZNF335, CAPN1, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2, CSNK1G2 (Table 5). Of these 19 genes, 17 genes whose relationship with atopic dermatitis has not been reported so far (marked with * in Table 5 and indicated in bold) were selected as feature genes.

3) Model construction The Log ₂ (RPM + 1) value of the above 17 genes was used as an explanatory variable, and HL and AD were used as objective variables. The random forest algorithm was specified as a method in the "caret" package of R language, and the optimum value of the number of variables (mtry value) used for constructing one decision tree was tuned. Using the mtry value determined by tuning, the random forest algorithm was executed and the OOB eraser rate was calculated. As a result, the OOB error rate was 6.98%.

Claims (12)

Regarding the biological samples collected from the subjects, MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1 A method for detecting atopic dermatitis in a subject, comprising the step of measuring the expression level of at least one gene selected or an expression product thereof. The method according to claim 1, wherein the expression level of the gene or an expression product thereof is a measurement of the expression level of mRNA. The method according to claim 1 or 2, wherein the gene or an expression product thereof is RNA contained in the lipid on the skin surface of the subject. The method according to any one of claims 1 to 3, 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 atopic dermatitis. Discrimination between atopic dermatitis patients and healthy subjects using the measured values of the expression level of the gene or its expression product derived from atopic dermatitis patients and the expression level of the gene or its expression products derived from healthy subjects as a teacher sample. By creating a formula, substituting the measured value of the expression level of the gene or its expression product obtained from the biological sample collected from the subject into the discrimination formula, and comparing the obtained result with the reference value, the subject is subjected to. The method according to any one of claims 1 to 4, which evaluates the presence or absence of atopic dermatitis in a examiner. The method according to claim 5, wherein the expression level of all genes of the 17 gene groups or expression products thereof is measured. In addition to at least one gene selected from the 17 gene groups, at least one gene selected from the gene group excluding the 17 genes from the 245 genes shown in Table A below or an expression product thereof. The method according to claim 5 or 6, wherein the expression level of is measured.

In addition to at least one gene selected from the 17 gene groups, 123 species in Tables 1-1 to 1-3 below, 150 species shown in Tables 3-1 to 3-4, or shown in Table 4 The method according to claim 5 or 6, wherein the expression level of at least one gene selected from the gene group excluding the 17 kinds of genes out of 45 kinds of genes or an expression product thereof is measured.

In addition to at least one gene selected from the 17 gene groups, at least 1 selected from the 107 gene group, 127 gene group, and 39 gene group excluding the 17 gene group in the table below. The method according to claim 7 or 8, wherein the expression level of one gene or an expression product thereof is measured.

Atopic skin used in the method according to any one of claims 1 to 9, which contains an oligonucleotide that specifically hybridizes with the gene or a nucleic acid derived from the gene, or an antibody that recognizes an expression product of the gene. Inspection kit for detecting flames. A marker for detecting atopic dermatitis, which comprises at least one gene selected from the 210 gene groups shown in Table B below or an expression product thereof.

MECR, RASA4CP, ARRDC4, EIF1AD, FDFT1, ZNF706, TEX2, TMPRSS11E, RPS6KB2, CTBP1, ZNF335, DGKA, PPP1R9B, SPDYE7P, DNASE1L1, GNB2 and CSNK1G2. The marker for detecting atopic dermatitis according to claim 11, which is an expression product.