Stress marker and method for detecting chronic stress level using same

Abstract

To provide a method for detecting a chronic stress level by using a biomarker.SOLUTION: A method for detecting a chronic stress level of a subject includes measuring the expression level of at least one selected from the group consisting of nucleic acids encoding following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7 and proteins encoded by the genes in a biological sample taken from the subject.SELECTED DRAWING: None

Description

[Technical field]

[0001] The present invention relates to a stress marker and a method for detecting a chronic stress level using the same. [Background technology]

[0002] Stress is considered to be a change in the mind and body functions that occurs when various physical, environmental, and psychological stimuli and loads are received. In modern society, many people, especially those of working age, live with stress. Stress not only has a negative effect on both the mind and body and leads to a decline in quality of life (QOL), but is also known to be involved in the onset and exacerbation of various diseases.

[0003] Stress is largely divided into acute stress and chronic stress caused by continuous stress load. Chronic stress is known to be a factor in various diseases such as mental disorders, and there is a need to understand its mechanism and develop an improvement technique. In order to develop an improvement technique, an evaluation index for stress is necessary, and from this perspective, chromogranin A (Non-Patent Document 1) has been proposed as an indicator for acute stress in addition to cortisol, and α-klotho (Non-Patent Document 2) and stress markers such as those disclosed in Patent Documents 1 to 4 have been proposed as an indicator for chronic stress. However, most of these stress markers can only be collected by invasive methods, and are not necessarily associated with chronic stress. Therefore, an objective or quantitative evaluation method for chronic stress using biomarkers has not yet been established.

[0004] Technologies have been developed to investigate the current and future physiological state of the human body by analyzing nucleic acids such as DNA and RNA in biological samples. Nucleic acids derived from living organisms can be extracted from tissues such as blood, body fluids, secretions, etc. Patent Document 5 describes the isolation of nucleic acids such as RNA derived from skin cells of a subject from surface skin lipids (SSL) and the use of the nucleic acids as samples for biological analysis. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2013-150558 A [Patent Document 2] JP 2008-054590 A [Patent Document 3] JP 2007-110912 A [Patent Document 4] JP 2007-306883 A [Patent Document 5] International Publication No. 2018 / 008319 [Non-patent literature]

[0006] [Non-Patent Document 1] Stress. 2006,9(3):127-131. [Non-Patent Document 2] J Investig Med, 2019,67(7):1082-1086 Summary of the Invention [Problem to be solved by the invention]

[0007] FIELD OF THEINVENTION The present invention relates to detection of chronic stress levels using biomarkers.The present invention relates to a novel stress marker and a method for detecting chronic stress levels using the same. [Means for solving the problem]

[0008] In one aspect, the present invention provides a stress marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes. In another aspect, the present invention provides a method for detecting a chronic stress level in a subject, comprising measuring the expression level of said stress marker in a biological sample taken from the subject. In another aspect, the present invention provides a method for evaluating a chronic stress controller, comprising measuring the expression level of the stress marker in a biological sample collected from a subject to which a test substance has been administered. In another aspect, the present invention provides a kit for detecting a chronic stress level, or a kit for evaluating a chronic stress regulator, which is used in the method for detecting a chronic stress level. Effect of the Invention

[0009] The present invention provides a novel stress marker. The stress marker of the present invention enables detection of chronic stress levels. In addition, the stress marker of the present invention can be collected non-invasively from the skin surface lipids (SSL), which reduces the effort required for collection and the burden on the subject caused by collection. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] All patents, non-patent publications, and other publications cited herein are hereby incorporated by reference in their entirety.

[0011] The names of the genes disclosed herein are based on the official symbols listed in NCBI ([www.ncbi.nlm.nih.gov / ]).

[0012] As used herein, "skin surface lipids (SSL)" refers to the fat-soluble fraction present on the surface of the skin, also known as sebum. Generally, 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 covering the skin surface.

[0013] In this specification, unless otherwise specified, "skin" is a general term for an area including tissues such as the epidermis, dermis, hair follicles, sweat glands, sebaceous glands and other glands on the body surface.

[0014] In this specification, "high stress" (or "subject is under stress") refers to a state in which the stress level is chronically high. In other words, it refers to a high level of chronic stress. The level of chronic stress can be scored based on an interview or a questionnaire. For example, in the simplified version of the occupational stress questionnaire (23 items) according to the revised edition of the Stress Check System Implementation Manual based on the Industrial Safety and Health Act (Ministry of Health, Labor and Welfare, 2016), the level of chronic stress can be evaluated as a stress score based on subjective scoring (maximum score 25 points, minimum score 5 points). The lower the stress score according to the occupational stress questionnaire, the higher the stress. Thus, in this specification, a chronic stress level corresponding to "high stress" refers to a state in which the stress score of the simplified version of the occupational stress questionnaire (23 items) is 11 points or less, and a chronic stress level corresponding to "low stress" refers to a state in which the stress score is 23 points or more. In this specification, a chronic stress level of "healthy" refers to a state in which the stress score of the simplified version of the occupational stress questionnaire (23 items) is 12 to 25 points.

[0015] In this specification, "detection" of chronic stress level can be expressed in other terms such as examination, measurement, judgment, or evaluation assistance. The terms "detection", "examination", "measurement", "judgment" or "evaluation" of chronic stress level in this specification do not include a diagnosis of a stress-related disease by a doctor.

[0016] (1. Stress Markers) Traditionally, chronic stress has been evaluated mainly by scoring based on interviews or questionnaires (e.g., the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)). Such evaluation methods can be less objective and quantitative, and make it difficult to compare evaluations between studies.

[0017] The present inventors have found that the chronic stress level of a subject is reflected in the expression level of a specific gene. As shown in the Examples below, genes with different expression levels were found between the high stress group and the low stress group determined based on the simplified version of the Occupational Stress Simple Questionnaire (23 items). Nucleic acids encoding genes showing different expression levels between the two groups, and proteins encoded by the genes, can be used as stress markers. For example, the expression levels of the nucleic acids encoding these genes or the proteins encoded by the genes can be used as indicators to detect chronic stress levels.

[0018] Therefore, in one aspect, the present invention provides a stress marker for detecting a chronic stress level. The stress marker provided by the present invention (hereinafter also referred to as the marker of the present invention) makes it possible to detect a chronic stress level of an individual subject from which the marker is derived.

[0019] The marker of the present invention may comprise at least one selected from the group consisting of nucleic acids encoding the 197 genes shown in Table 1 below and proteins encoded by the genes. The marker of the present invention may be a nucleic acid encoding a gene shown in Table 1 below (nucleic acid marker), a protein encoded by the gene (protein marker), or a combination thereof, but is preferably a nucleic acid marker.

[0020] [Table 1]

[0021] In one embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding genes shown in Tables 2 and 3 (hereinafter also referred to as nucleic acid markers in Tables 2 and 3, respectively) and proteins encoded by the genes (hereinafter also referred to as protein markers in Tables 2 and 3, respectively). The nucleic acid markers and protein markers in Table 2 may be collectively referred to as markers in Table 2, and the nucleic acid markers and protein markers in Table 3 may be collectively referred to as markers in Table 3. Preferably, the markers in Tables 2 and 3 are nucleic acid markers. As shown in the Examples below, the expression level of 49 genes shown in Table 2 increases according to the chronic stress level of the subject, and the expression level of 24 genes shown in Table 3 decreases according to the chronic stress level of the subject. Therefore, the markers in Table 2 are positive markers whose expression level increases according to the chronic stress level of the subject. On the other hand, the markers in Table 3 are negative markers whose expression level decreases according to the chronic stress level of the subject. In the present invention, either the former positive marker or the latter negative marker may be used, or both may be used in combination.

[0022] In another embodiment, the marker of the present invention includes at least one selected from the group consisting of nucleic acids encoding 140 genes shown in Table 4 (hereinafter also referred to as nucleic acid markers in Table 4) and proteins encoded by the genes (hereinafter also referred to as protein markers in Table 4). The nucleic acid markers and protein markers in Table 4 may be collectively referred to as markers in Table 4. Preferably, the markers in Table 4 are nucleic acid markers. As shown in the examples described later, the genes shown in Table 4 are genes corresponding to the top 140 variable importance based on the Gini coefficient, extracted when extracting feature genes and constructing a prediction model using data on their expression levels in subjects as explanatory variables, chronic stress levels as objective variables, and random forest as a machine learning algorithm.

[0023] In a preferred embodiment, the marker of the present invention comprises at least one selected from the group consisting of nucleic acids encoding the 16 genes shown in Table 5 (hereinafter also referred to as nucleic acid markers of Table 5) and proteins encoded by the genes (hereinafter also referred to as protein markers of Table 5). The nucleic acid markers and protein markers of Table 5 may be collectively referred to as markers of Table 5. Preferably, the markers of Table 5 are nucleic acid markers. The genes shown in Table 5 are genes commonly included in Table 2 or Table 3 and Table 4.

[0024] In a preferred embodiment, the markers of the present invention may further include at least one selected from the group consisting of markers in Table 1 (but other than the markers in Table 5) in addition to at least one selected from the group consisting of markers in Table 5. For example, the markers of the present invention may further include at least one selected from the group consisting of markers in Table 2, markers in Table 3, or markers in Table 4 (but other than the markers in Table 5) in addition to at least one selected from the group consisting of markers in Table 5. For example, the markers of the present invention may further include at least one, preferably all, selected from the group consisting of nucleic acid markers in Table 5 and at least one selected from the group consisting of nucleic acid markers in Table 2, Table 3, or Table 4 (but other than the nucleic acid markers in Table 5). For example, the markers of the present invention may further include at least one, preferably all, selected from the group consisting of protein markers in Table 5 and at least one selected from the group consisting of protein markers in Table 2, Table 3, or Table 4 (but other than the protein markers in Table 5).

[0025] In another preferred embodiment, all of the nucleic acid markers of Table 2 or Table 3, or all of the protein markers of Table 2 or Table 3, in combination, are used as markers of the invention. In another preferred embodiment, all of the nucleic acid markers of Tables 2 and 3, or all of the protein markers of Tables 2 and 3, in combination, are used as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 4 are used in combination as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 5 are used in combination as markers of the present invention. In another preferred embodiment, all of the nucleic acid or protein markers in Table 1 are used in combination as markers of the present invention.

[0026] [Table 2]

[0027] [Table 3]

[0028] [Table 4]

[0029] [Table 5]

[0030] The stress marker of the present invention can be prepared from a biological sample collected from a subject, such as a cell, a stratum corneum, a tissue (biopsy, etc.), a body fluid (blood, etc.), urine, a secretion (saliva, SSL, etc.), etc., according to a conventional method. For example, a commercially available kit can be used to prepare a nucleic acid or a protein from a biological sample. Preferably, the marker of the present invention is a nucleic acid marker, and preferred examples of the nucleic acid prepared from a biological sample include genomic DNA and RNA such as mRNA.

[0031] Examples of subjects from which biological samples containing the markers of the present invention may be obtained include mammals, including humans and non-human mammals. Preferably, the subject is a human or non-human mammal in need of detection of chronic stress levels, or a human desiring to detect chronic stress levels. More preferably, the subject is a human.

[0032] More preferably, the marker of the present invention is a nucleic acid or protein, even more preferably an mRNA, prepared from the SSL of a subject. The site of the skin from which the SSL is collected may be any site of the body, such as the head, face, neck, trunk, limbs, etc., and is preferably a site where sebum is secreted in large amounts, such as the skin of the face.

[0033] Any means used for recovering or removing SSL from the skin can be used to collect SSL from the skin of a subject. Preferably, an SSL absorbent material, an SSL adhesive material, or an instrument for scraping SSL from the skin, as described below, can be used. The SSL absorbent material or 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. More detailed examples of procedures for collecting SSL from the skin include a method of absorbing SSL into a sheet-like material such as oil blotting paper or oil blotting film, a method of adhering SSL to a glass plate, tape, or the like, and a method of scraping SSL off and collecting it with a spatula, scraper, or the like. In order to improve the adsorption of SSL, an SSL absorbent material that has been previously impregnated with a highly lipid-soluble solvent may be used. On the other hand, if the SSL absorbent material contains a highly water-soluble solvent or moisture, the adsorption of SSL is inhibited, so it is preferable that the content of highly water-soluble solvent or moisture is low. It is preferable to use the SSL absorbent material in a dry state.

[0034] The collected SSL may be used immediately in the nucleic acid or protein extraction step described below, or may be stored until it is used in the nucleic acid or protein extraction step. When stored, the SSL is preferably stored under low temperature conditions. The temperature conditions for storing the SSL are preferably 10° C. or lower, more preferably 5° C. or lower, even more preferably 0° C. or lower, even more preferably −20±20° C. to −80±20° C., even more preferably −20±10° C. to −80±10° C., and even more preferably −20±5° C. to −80±5° C. The period for storing the SSL 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, and even more preferably 3 months or less, for example, 3 days or more and 3 months or less.

[0035] For the extraction of nucleic acids or proteins from the collected SSL, a method commonly used for the extraction or purification of nucleic acids or proteins from biological samples can be used. Examples of nucleic acid extraction or purification methods include the phenol / chloroform method, the AGPC (acid guanidinium thiocyanate-phenol-chloroform extraction) method, or a method using columns such as TRIzol (registered trademark), RNeasy (registered trademark), or QIAzol (registered trademark), a method using special magnetic particles coated with silica, a method using Solid Phase Reversible Immobilization magnetic particles, and extraction using a commercially available RNA extraction reagent such as ISOGEN. For the extraction or purification of proteins, a commercially available protein extraction reagent such as QIAzol Lysis Reagent (Qiagen) can be used.

[0036] (2. Chronic stress level detection method) (2.1. Detection of Chronic Stress Levels Based on Marker Expression Levels) In another aspect, the present invention provides a method for detecting a chronic stress level in a subject using the stress marker of the present invention. In the method, the chronic stress level of the subject is detected based on the expression level of the marker of the present invention in the subject. The subject to which the chronic stress level detection method according to the present invention is applied is the same as the subject from which the above-mentioned biological sample containing the stress marker of the present invention is collected.

[0037] In a preferred embodiment, the method for detecting chronic stress level according to the present invention comprises measuring the expression level of the marker of the present invention in a biological sample taken from a subject. The type of the biological sample is as described above, preferably SSL. In one embodiment, the method of the present invention may further comprise taking the SSL of the subject. The procedure for taking the SSL and the procedure for extracting the marker from the SSL are as described above.

[0038] The expression level of the marker of the present invention can be measured according to a method for quantifying nucleic acids or proteins commonly used in the field. For example, the expression level of a nucleic acid marker may be measured according to a procedure for gene expression analysis commonly used in the field. Examples of gene expression analysis techniques include methods for quantifying DNA or its amplification products by real-time PCR, multiplex PCR, microarray, sequencing, chromatography, etc. When the nucleic acid is RNA, the RNA can be converted to cDNA by reverse transcription and then quantified by the above method. The expression level of a protein marker can be measured using a protein quantification method commonly used in the field, such as ELISA, immunostaining, fluorescence, electrophoresis, chromatography, mass spectrometry, etc. The calculated expression level of a marker may be an expression level based on the absolute amount of the target marker in a biological sample, or a relative expression level to the expression level of other standard genes, total nucleic acids, or total proteins.

[0039] Preferably, the marker of the present invention used in the method for detecting chronic stress levels according to the present invention is an SSL-derived mRNA. Preferably, the RNA extracted from the SSL is converted to cDNA by reverse transcription, and then the expression level of the SSL-derived RNA is measured by quantifying the cDNA or its amplification product by the above-mentioned method. For the reverse transcription, a primer targeting a specific RNA to be analyzed may be used, but for more comprehensive storage and analysis of nucleic acids, it is preferable to use a random primer. For the reverse transcription, a general reverse transcriptase or reverse transcription reagent kit can be used. Preferably, a reverse transcriptase or reverse transcription reagent kit with high accuracy and efficiency is used, and examples thereof include M-MLV Reverse Transcriptase and its variants, or commercially available reverse transcriptase or reverse transcription reagent kits, such as PrimeScript (registered trademark) Reverse Transcriptase series (Takara Bio Inc.), SuperScript (registered trademark) Reverse Transcriptase series (Thermo Scientific Co., Ltd.), etc. are preferably used. SuperScript (registered trademark) III Reverse Transcriptase, SuperScript (registered trademark) VILO cDNA Synthesis kit (both Thermo Scientific Co., Ltd.), etc. In PCR of the obtained cDNA, a primer pair targeting a specific DNA to be analyzed may be used to amplify only the specific DNA, or multiple primer pairs may be used to amplify multiple DNAs. Preferably, the PCR is multiplex PCR. Multiplex PCR is a method for simultaneously amplifying multiple gene regions by simultaneously using multiple primer pairs in a PCR reaction system. Multiplex PCR can be performed using a commercially available kit (e.g., Ion AmpliSeq Transcriptome Human Gene Expression Kit; Life Technologies Japan, Inc., etc.).

[0040] By adjusting the reaction conditions of the reverse transcription and PCR, the yield of the PCR reaction product is further improved, and the accuracy of the analysis using the same is further improved. Preferably, in the extension reaction in the reverse transcription, the temperature is preferably adjusted to 42°C ± 1°C, more preferably 42°C ± 0.5°C, and even more preferably 42°C ± 0.25°C, while the reaction time is preferably adjusted to 60 minutes or more, more preferably 80 to 120 minutes. Also preferably, the temperature of the annealing and extension reaction in the PCR is preferably 62°C ± 1°C, more preferably 62°C ± 0.5°C, and even more preferably 62°C ± 0.25°C. Therefore, in the PCR, the 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 of the denaturation reaction in the PCR can be adjusted depending on the DNA to be amplified, but is preferably 95 to 99°C for 10 to 60 seconds. Reverse transcription and PCR at the above-mentioned temperatures and times can be carried out using a thermal cycler generally used for PCR.

[0041] The purification of the reaction product obtained by the PCR is preferably carried out by size separation of the reaction product. By size separation, the target PCR reaction product can be separated from primers and other impurities contained in the PCR reaction solution. Size separation of DNA can be carried out, for example, by a size separation column, a size separation chip, magnetic beads usable for size separation, etc. A preferred example of magnetic beads usable for size separation includes Solid Phase Reversible Immobilization (SPRI) magnetic beads such as Ampure XP.

[0042] The purified PCR reaction product may be subjected to further treatment required for subsequent quantitative analysis. For example, for DNA sequencing, the purified PCR reaction product may be prepared in an appropriate buffer solution, the PCR primer region contained in the PCR-amplified DNA may be cleaved, or an adapter sequence may be further added to the amplified DNA. For example, the purified PCR reaction product may be prepared in a buffer solution, the PCR primer sequence may be removed from the amplified DNA, and adapter ligation may be performed, and the resulting reaction product may be amplified as necessary to prepare a library for quantitative analysis. These operations may be performed, for example, using the 5×VILO RT Reaction Mix included in the SuperScript® VILO cDNA Synthesis kit (Life Technologies Japan, Inc.), and the 5×Ion AmpliSeq HiFi Mix and Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.), according to the protocol included with each kit. For sequencing, a next-generation sequencer (e.g., Ion S5 / XL system, Life Technologies Japan, Inc.) is preferably used. Based on the number of reads generated by sequencing (read count), RNA expression levels can be quantified.

[0043] In one embodiment of the method for detecting chronic stress levels according to the present invention, the expression level of a marker of the present invention in a subject (preferably, the expression level of a marker of the present invention contained in a biological sample collected from the subject) is used as an indicator to detect the chronic stress level of the subject.

[0044] As described above, the nucleic acid markers and protein markers in Table 2 are positive markers whose expression levels increase according to the chronic stress level of the subject, while the nucleic acid markers and protein markers in Table 3 are negative markers whose expression levels decrease according to the chronic stress level of the subject. Thus, if the expression level of the positive marker increases, the subject is detected as being chronically stressed (i.e., the chronic stress level of the subject is high stress), otherwise, the subject is detected as not being chronically stressed (i.e., the chronic stress level of the subject is low stress or healthy). Alternatively, if the expression level of the negative marker decreases, the subject is detected as being chronically stressed, otherwise, the subject is detected as not being chronically stressed. Alternatively, the positive marker and the negative marker may be used in combination in the chronic stress level detection method according to the present invention.

[0045] In one embodiment, the expression level of the marker of the present invention measured from a subject can be compared with a reference value to detect the chronic stress level of the subject. Alternatively, the expression level of the marker of the present invention can be measured from a subject periodically (e.g., monthly) and the fluctuation of the expression level can be tracked to detect the chronic stress level of the subject. The reference value can be a predetermined value, for example, a statistical value (e.g., average value) of the expression level of the marker of the present invention. The reference value may be set for each subject. For example, the reference value may be calculated from the statistical value of the expression level of the marker measured multiple times from the same subject. Alternatively, the standard value can be determined based on a statistical value (e.g., an average value, etc.) of the expression level of the marker of the present invention measured from a reference population (a group of individuals with any chronic stress level), a healthy group (a group of individuals with a chronic stress level in the normal range [e.g., a stress score of 12 to 25 points on the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)]), or a low-stress group (a group of individuals with a chronic stress level in the low-stress range [e.g., a stress score of 23 points or more on the aforementioned simplified version of the Occupational Stress Questionnaire (23 items)]). When multiple types of nucleic acids or proteins are used as the marker of the present invention, it is preferable to determine the standard value for each nucleic acid or protein.

[0046] For example, when the marker of the present invention is a positive marker, if the expression level of the marker is higher than the reference value, the subject can be detected as being chronically stressed, otherwise, the subject can be detected as not being chronically stressed.For example, if the expression level of the positive marker in the subject is statistically significantly higher than the reference value, the subject can be detected as being chronically stressed, otherwise, the subject can be detected as not being chronically stressed.Also, for example, if the expression level of the positive marker in the subject is preferably 110% or more, more preferably 150% or more, even more preferably 200% or more than the reference value, the subject can be detected as being chronically stressed, otherwise, the subject can be detected as not being chronically stressed.

[0047] On the other hand, when the marker of the present invention is a negative marker, if the expression level of the marker is lower than the reference value, the subject can be detected as being chronically stressed, otherwise the subject can be detected as not being chronically stressed.For example, if the expression level of the negative marker in the subject is statistically significantly lower than the reference value, the subject can be detected as being chronically stressed, otherwise the subject can be detected as not being chronically stressed.Also, for example, if the expression level of the negative marker in the subject is preferably 90% or less, more preferably 75% or less, even more preferably 50% or less than the reference value, the subject can be detected as being chronically stressed, otherwise the subject can be detected as not being chronically stressed.

[0048] Alternatively, when multiple nucleic acids or proteins are used in combination as markers, the subject's chronic stress level can be detected based on whether a certain percentage of those nucleic acids or proteins, for example 50% or more, preferably 70% or more, more preferably 90% or more, and even more preferably 100%, meets the above-mentioned expression level criteria.

[0049] (2.2. Detection of Chronic Stress Levels Based on Predictive Models) In another embodiment of the method for detecting a chronic stress level according to the present invention, the chronic stress level of a subject is detected based on a prediction model constructed using data on the expression level of the marker of the present invention (the marker of the present invention contained in a biological sample collected from the subject) (hereinafter referred to as an expression profile). Examples of the expression profile include data on the expression level such as the number of sequencing reads.

[0050] For example, a predictive model (e.g., a discriminant) for determining the chronic stress level in any subject can be constructed by machine learning using the expression profiles of one or more markers (nucleic acids or proteins) obtained from each individual in a teacher sample population (e.g., a population including a high stress group and a low stress group or a healthy group) as explanatory variables, and data on the chronic stress level of each individual in the population (e.g., the presence or absence of high stress) as the objective variable.

[0051] In this specification, the term "feature" is synonymous with "explanatory variable" in machine learning. In this specification, a marker whose expression profile is used as an explanatory variable (feature) in machine learning may be referred to as a "feature marker(s)." In addition, when a feature marker is a nucleic acid marker, it may be referred to as a feature gene.

[0052] The feature marker(s) used in this embodiment may be at least one selected from the group consisting of nucleic acid markers and protein markers in Table 1. The expression profile of the feature marker may be an absolute value or a relative value, or may be normalized. When a plurality of markers are used as the feature marker group, for example, a plurality of markers having a high correlation with the chronic stress level may be selected from the markers of the present invention, and each of the expression profiles thereof may be used as an explanatory variable.

[0053] In a preferred embodiment, all of the nucleic acid markers in Tables 2 and 3, or all of the protein markers in Tables 2 and 3 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 4 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 5 are combined and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 5 are combined with one or more, preferably all of the nucleic acid markers or protein markers in any of Tables 2, 3, or 4 (except for the markers in Table 5) and used as a feature marker group. In another preferred embodiment, all of the nucleic acid markers or protein markers in Table 1 are combined and used as a feature marker group.

[0054] In a preferred embodiment, expression profiles of individuals with high chronic stress levels (high stress group) and individuals with low chronic stress levels (low stress group) are used as teacher samples for machine learning. A discriminant (prediction model) for dividing individuals into high stress group and low stress group is constructed using the teacher samples. The expression profile of the feature marker(s) can be used as an explanatory variable used for constructing the discriminant. For example, a variable indicating whether the individual from which the feature marker(s) originates belongs to the high stress group or the low stress group can be used as an objective variable. A cutoff value for discriminating the high stress group can be determined based on the constructed discriminant. Next, the expression profile of the feature marker(s) derived from the subject whose chronic stress level is to be examined is measured, the measured value obtained is substituted into the discriminant, and the result obtained from the discriminant is compared with the cutoff value to discriminate whether the subject belongs to the high stress group or not.

[0055] For example, a statistically significant difference between two groups (high stress group and low stress group) to be discriminated, for example, a marker whose expression profile varies significantly between the two groups, can be used as a feature marker(s), and the expression profile can be used as an explanatory variable. Alternatively, the feature marker(s) can be extracted using a known algorithm such as a machine learning algorithm. For example, as shown in the following example, they can be extracted based on the importance of variables in a random forest. Alternatively, the feature marker(s) can be extracted using the "Boruta" package of the R language, or the like.

[0056] Alternatively, when using expression profiles of multiple markers to construct a predictive model, the predictive model may be constructed after compressing the data by dimensionality reduction, as necessary. For example, multiple markers are extracted from the nucleic acid markers encoding the gene group shown in Table 1. Then, a principal component analysis is performed on the expression profile of the extracted marker. A predictive model for determining whether a subject is in the high stress group or not can be constructed by machine learning using one or more principal components calculated by the principal component analysis as explanatory variables and a variable indicating whether the individual from which the explanatory variables are derived is in the high stress group or the low stress group as a target variable.

[0057] The algorithm for constructing the prediction model can be a known algorithm such as an algorithm used in machine learning. Examples of machine learning algorithms include, but are not limited to, a linear regression model, a lasso regression, a random forest, a neural network, a linear kernel support vector machine (SVM (linear)), and an rbf kernel support vector machine (SVM (rbf)). The algorithm used in the present invention is preferably a random forest.

[0058] The prediction model thus constructed is input with data for verification to calculate a prediction value, and the model in which the prediction value best matches the actual measurement value, for example, the model with the highest accuracy of the prediction value relative to the actual measurement value, or the model with the smallest root mean square error (RMSE) of the difference between the prediction value and the actual measurement value, can be selected as the optimal model. The expression profile of the marker of the present invention actually measured from a subject can be input to the constructed prediction model to detect the chronic stress level of the subject.

[0059] (3. Evaluation method of chronic stress control agent) In a further aspect, the present invention provides a method for evaluating a chronic stress regulator using the stress marker of the present invention. The method evaluates a chronic stress regulator based on the expression level of the marker of the present invention. Preferably, the method comprises measuring the expression level of the marker of the present invention in a biological sample collected from a subject to which a test substance has been administered. In the method, the type of biological sample used is as described above. Preferably, the biological sample is an SSL, and the marker of the present invention is an SSL-derived mRNA. In one embodiment, the method may further comprise collecting the SSL of the subject. The chronic stress regulator evaluated in the present invention includes a chronic stress suppressor or a chronic stress enhancer, and is preferably a chronic stress suppressor.

[0060] The subject from which the biological sample is collected is a human or a non-human mammal. An appropriate subject can be selected according to the chronic stress control agent to be evaluated. When evaluating a chronic stress suppressant, the subject is preferably a human or a non-human mammal with a high chronic stress level, and when evaluating a chronic stress enhancer, the subject is preferably a human or a non-human mammal with a normal chronic stress level. Preferably, the subject is a human.

[0061] The test substance used in the evaluation method of the present invention is not particularly limited as long as it is a substance that is desired to be used as a chronic stress control agent. The test substance may be a naturally occurring substance, or a substance artificially synthesized by a chemical or biological method, or may be a compound, composition, or mixture. The form of the test substance is not particularly limited and may be any form such as solid, semi-solid, gel, liquid, gas, etc., but gel or liquid is preferable. The test substance may also be a physical stimulus such as light irradiation, heat, or massage. Methods for applying the test substance to the subject include oral administration and parenteral administration such as injection, application, spraying, scattering, dripping, pasting, and irradiation, and are not particularly limited. The dose and application period of the test substance applied to the subject can be appropriately set according to the type and administration form of the test substance.

[0062] The procedures for collecting a biological sample from a subject, extracting a marker from the collected biological sample, and measuring the expression level of the marker are as described above.

[0063] The expression level of the marker of the present invention extracted from the subject increases if it is a positive marker, and decreases if it is a negative marker, depending on the chronic stress level of the subject. Based on the change in the expression level of the marker caused by the test substance, it can be evaluated whether the test substance is a chronic stress control agent. More specifically, a test substance that reduces the expression level of a positive marker or increases the expression level of a negative marker is judged to be a chronic stress suppressor. Conversely, a test substance that increases the expression level of a positive marker or reduces the expression level of a negative marker is judged to be a chronic stress enhancer.

[0064] In one embodiment, the expression level of the marker of the present invention derived from the subject is compared with a control to evaluate the effect of the test substance on chronic stress. More specifically, it is examined whether the expression level of the marker of the present invention under application of the test substance changes compared to the control. Examples of the control include the expression level of the marker of the present invention under non-application of the test substance, the standard value for evaluating the chronic stress level described above, etc.

[0065] In one example, when the expression level of the positive marker under application of the test substance is reduced compared to the control, the test substance can be judged as a chronic stress suppressor. More specifically, when the expression level of the positive marker in the subject or subject group to which the test substance is applied is statistically significantly reduced compared to the control, the test substance can be judged as a chronic stress suppressor. Alternatively, when the expression level of the positive marker in the subject or subject group to which the test substance is applied is preferably reduced to 90% or less, more preferably 75% or less, and even more preferably 50% or less compared to the control, the test substance can be judged as a chronic stress suppressor.

[0066] In another example, if the expression level of the positive marker under application of the test substance increases compared to the control, the test substance can be judged as a chronic stress enhancer. More specifically, if the expression level of the positive marker in the subject or subject group to which the test substance is applied increases statistically significantly compared to the control, the test substance can be judged as a chronic stress enhancer. Alternatively, if the expression level of the positive marker in the subject or subject group to which the test substance is applied increases preferably by 110% or more, more preferably by 150% or more, and even more preferably by 200% or more compared to the control, the test substance can be judged as a chronic stress enhancer.

[0067] In yet another example, when the expression level of the negative marker under application of the test substance increases compared to the control, the test substance can be judged as a chronic stress suppressor. More specifically, when the expression level of the negative marker in the subject or subject group to which the test substance is applied increases statistically significantly compared to the control, the test substance can be judged as a chronic stress suppressor. Alternatively, when the expression level of the negative marker in the subject or subject group to which the test substance is applied increases preferably by 110% or more, more preferably by 150% or more, and even more preferably by 200% or more compared to the control, the test substance can be judged as a chronic stress suppressor.

[0068] In yet another example, when the expression level of the negative marker under application of the test substance is reduced compared to the control, the test substance can be judged as a chronic stress enhancer. More specifically, when the expression level of the negative marker in the subject or subject group to which the test substance is applied is statistically significantly reduced compared to the control, the test substance can be judged as a chronic stress enhancer. Alternatively, when the expression level of the negative marker in the subject or subject group to which the test substance is applied is reduced preferably to 90% or less, more preferably to 75% or less, and even more preferably to 50% or less compared to the control, the test substance can be judged as a chronic stress enhancer.

[0069] (4. Chronic stress level detection kit and chronic stress control agent evaluation kit) In a further aspect, the present invention provides a kit for detecting a chronic stress level for detecting a chronic stress level of a subject according to the method for detecting a chronic stress level according to the present invention, and a kit for evaluating a chronic stress controller for evaluating a chronic stress controller according to the method for evaluating a chronic stress controller according to the present invention. In one embodiment, the kit of the present invention comprises a reagent or instrument for measuring the expression level of the above-mentioned stress marker of the present invention. For example, the kit of the present invention may comprise a reagent for amplifying or quantifying the nucleic acid marker of the present invention (e.g., reverse transcriptase, PCR reagent, primer, probe, adapter sequence for sequencing, etc.), or a reagent for quantifying the protein marker of the present invention (e.g., reagent for immunological measurement, antibody, etc.). Preferably, the kit of the present invention contains an oligonucleotide that specifically hybridizes with the nucleic acid marker of the present invention (e.g., a primer or probe for PCR) or an antibody that recognizes the protein marker of the present invention. Preferably, the kit of the present invention comprises an indicator or guidance for detecting the expression level of the marker of the present invention. For example, the kit of the present invention may include guidance explaining the relationship between an increase or decrease in the expression level of each marker and a chronic stress level, or guidance explaining a reference value of the expression level of each marker for detecting a chronic stress level, or guidance on a discriminant based on a prediction model and a feature marker to be input thereto, etc. The kit of the present invention may further include a biological sample collection device (e.g., the above-mentioned SSL absorbent material or SSL adhesive material), a reagent for extracting the marker of the present invention from a biological sample (e.g., a reagent for nucleic acid purification), a preservative or a storage container for the sample collection device after collection of the biological sample, etc.

[0070] As exemplary embodiments of the present invention, the following substances, manufacturing methods, uses, methods, etc. are further disclosed in this specification, but the present invention is not limited to these embodiments.

[0071] [1] A stress marker comprising at least one selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes. [2] The stress marker described in [1], preferably comprising all of the nucleic acids encoding each of the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, or all of the proteins encoded by each of the genes. [3] The stress marker according to [1] or [2], preferably further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 1 and proteins encoded by the genes. [4] The stress marker according to [1] or [2], further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Tables 2 and 3, and proteins encoded by the genes. [5] The stress marker according to [4], preferably comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 2 and proteins encoded by the genes. [6] The stress marker according to [4], preferably comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 3 and proteins encoded by the genes. [7] The stress marker according to [1] or [2], preferably further comprising at least one selected from the group consisting of nucleic acids encoding the genes shown in Table 4 and proteins encoded by the genes. [8] The stress marker according to any one of [1] to [7], which is preferably a marker for detecting a chronic stress level. [9] The stress marker according to any one of [1] to [8], which is preferably a nucleic acid marker.

[10] The stress marker described in [9], wherein the nucleic acid is preferably mRNA extracted from lipids on the skin surface.

[0072]

[11] A method for detecting a chronic stress level of a subject, comprising measuring the expression level of the stress marker described in any one of [1] to

[10] in a biological sample collected from the subject.

[12] The method described in

[11] , preferably further comprising detecting the chronic stress level of the subject based on the expression level of the stress marker.

[13] The stress marker is Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 2 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 2. And the method comprises: Preferably, the method includes detecting that the chronic stress level of the subject is high stress when the expression level of the stress marker is increased, More preferably, the method includes detecting the chronic stress level of the subject as being high stress when the expression level of the stress marker is higher than a reference value. The method described in

[12] .

[14] The stress marker, Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 3 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 3, And the method comprises: Preferably, the method includes detecting that the chronic stress level of the subject is high stress when the expression level of the stress marker is decreased, More preferably, the method includes detecting that the chronic stress level of the subject is high stress when the expression level of the stress marker is lower than a reference value. The method described in

[12] .

[15] The method described in

[12] , preferably comprising detecting the subject's chronic stress level based on a predictive model constructed using data on the expression level of the stress marker.

[16] Preferably, the predictive model is constructed by machine learning using data on the expression level of the stress marker obtained from each individual in a population including a high stress group and a low stress group or a healthy group as explanatory variables, and the chronic stress level of each individual in the population as a response variable. The method described in

[15] .

[0073]

[17] A method for evaluating a chronic stress controller, comprising measuring the expression level of the stress marker described in any one of [1] to

[10] in a biological sample collected from a subject to which a test substance has been administered.

[18] The method according to

[17] , wherein the chronic stress control agent is preferably a chronic stress suppressor or a chronic stress enhancer, more preferably a chronic stress suppressor.

[19] The method of

[17] or

[18] , preferably further comprising evaluating whether the test substance is a chronic stress regulator based on a change in the expression level of the marker caused by the test substance.

[20] The stress marker, Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 2 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 2. And the method comprises: Preferably, the method includes determining that the test substance is a chronic stress suppressor when the expression level of the stress marker under application of the test substance is reduced compared to a control, and determining that the test substance is a chronic stress enhancer when the expression level of the stress marker under application of the test substance is increased compared to a control. The method described in

[19] .

[21] The stress marker, Preferably, the nucleic acid is at least one selected from the group consisting of a nucleic acid encoding a gene shown in Table 3 and a protein encoded by the gene, More preferably, it is at least one selected from the group consisting of mRNAs derived from skin surface lipids encoding the genes shown in Table 3, And the method comprises: Preferably, the method includes determining that the test substance is a chronic stress suppressor when the expression level of the stress marker under application of the test substance is increased compared to a control, and determining that the test substance is a chronic stress enhancer when the expression level of the stress marker under application of the test substance is decreased compared to a control. The method described in

[19] .

[0074]

[22] A kit for detecting a chronic stress level used in the method according to any one of

[11] to

[16] , or a kit for evaluating a chronic stress regulator used in the method according to any one of

[17] to

[21] , comprising an oligonucleotide that specifically hybridizes with a nucleic acid that is a stress marker according to any one of [1] to

[10] , or an antibody that recognizes a protein that is a stress marker according to any one of [1] to

[10] . EXAMPLES

[0075] The present invention will be described in more detail below based on examples, but the present invention is not limited to these examples.

[0076] Example 1: Construction of a chronic stress level discrimination model using differentially expressed genes 1) Subject selection and SSL collection 175 healthy men and women in their 20s to 60s were asked to answer the simplified version of the Occupational Stress Questionnaire (23 items) (Ministry of Health, Labor and Welfare, 2016), and stress scores were calculated based on the answers. The lower the stress score, the higher the chronic stress. Nineteen subjects with a stress score of 11 points or less, which is the standard for being recognized as having high stress according to the Ministry of Health, Labor and Welfare standard (Revised version of the Stress Check System Implementation Manual Based on the Industrial Safety and Health Act, Ministry of Health, Labor and Welfare, 2016), were selected as subjects in the high stress group. Meanwhile, 20 subjects with a stress score of 23 points or more were selected as subjects in the low stress group. Sebum was collected from the entire face of each of the 39 subjects in both groups using oil blotting film (5 cm x 8 cm, made of polypropylene, 3M). The oil blotting film was transferred to a glass vial and stored at -80°C until it was used for RNA extraction.

[0077] 2) RNA preparation and sequencing The oil blotting film from 1) above was cut to an appropriate size, and RNA was extracted using QIAzol Lysis Reagent (Qiagen) according to the attached protocol. The extracted RNA was reverse transcribed at 42°C for 90 minutes using SuperScript VILO cDNA Synthesis kit (Life Technologies Japan, Inc.) to synthesize cDNA. The 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 was performed using Ion AmpliSeq Transcriptome Human Gene Expression Kit (Life Technologies Japan, Inc.) under the following conditions: [99°C, 2 minutes → (99°C, 15 seconds → 62°C, 16 minutes) × 20 cycles → 4°C, Hold]. The obtained PCR product was purified with Ampure XP (Beckman Coulter, Inc.), followed by buffer reconstitution, digestion of the primer sequence, adapter ligation and 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, Inc.). The genes from which each read sequence was derived were determined by genetic mapping of each read sequence obtained by sequencing using the hg19 AmpliSeq Transcriptome ERCC v1, a reference sequence for the human genome.

[0078] 3) Data analysis and feature gene selection The read counts of each read obtained by sequencing the SSL-derived RNA of the subjects in 2) above were used as the expression level data for each RNA. To correct for differences in total read counts between samples, each read count was converted to an RPM (Reads per million mapped reads) value. However, reads with a read count of less than 10 were treated as missing values. For 3179 genes for which expression level data that was not missing was obtained in 90% or more of the subjects, Student's t test was performed between groups (low stress group and high stress group) based on the expression information, and genes with p<0.05 were extracted. As shown in Table 6, 73 genes with statistically significant changes in expression level between groups were obtained. The 49 genes in Table 6A were markers (positive markers) whose expression levels increase due to chronic stress, as their expression levels were higher in the high stress group. The 24 genes in Table 6B were markers (negative markers) whose expression levels decrease due to chronic stress, as their expression levels were lower in the high stress group. These genes were selected as feature genes to be used in the construction of the following prediction model.

[0079] [Table 6]

[0080] 4) Model construction The expression level data of the feature genes selected in 3) was converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1 to approximate a normal distribution from the RPM values ​​that follow a negative binomial distribution. A prediction model for discriminating chronic stress levels was constructed using the Log2(RPM+1) values ​​of the expression level data of the obtained 73 feature genes as explanatory variables and the chronic stress level (high stress or low stress) as the objective variable. The random forest algorithm was specified as the method in the “caret” package of the R language, and the optimal value of the number of variables (mtry value) used to construct a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 25% in the model when the expression level data of all 73 genes were used as variables, and it was shown that the genes shown in Table 6 can be used as markers for detecting chronic stress levels.

[0081] Example 2: Construction of a chronic stress level discrimination model using genes with high random forest variable importance 1) Usage Data The 3179 types of genes for which non-missing expression level data was obtained for 90% or more of the subjects in Example 1-3) were used in the following analysis. In order to approximate the normal distribution from the RPM values ​​that follow a negative binomial distribution, the gene expression level data was converted to base 2 logarithmic values ​​(Log2(RPM+1) values) by adding an integer 1.

[0082] 2) Selection of feature genes A random forest algorithm was used to select the feature genes to be used in constructing the prediction model. The Log2(RPM+1) values ​​of the expression level data of 3179 genes obtained in 1) were used as explanatory variables, and the chronic stress level (high stress or low stress) was used as the objective variable to construct a prediction model to discriminate the chronic stress level. 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 a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the top 140 genes in terms of variable importance based on the Gini coefficient were calculated (Table 7). These 140 genes were selected as feature genes to be used in constructing the prediction model.

[0083] [Table 7]

[0084] 3) Model construction A prediction model for discriminating chronic stress levels was constructed using the Log2(RPM+1) values ​​of the expression level data of the 140 feature genes selected in 2) as explanatory variables and chronic stress levels (high or low stress) 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 a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 30% in the model when the expression level data of 140 genes were used as variables, and it was shown that the genes shown in Table 7 can be used as markers for detecting chronic stress levels.

[0085] Example 3: Construction of a chronic stress level discrimination model based on the feature genes used in duplicate in Examples 1 and 2 1) Selection of feature genes Among the feature genes used in Examples 1 and 2, the genes that were used in common between the Examples were HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7. These 16 genes were selected as feature genes to be used in constructing a prediction model.

[0086] 2) Model construction A prediction model for discriminating chronic stress levels was constructed using the Log2(RPM+1) values ​​of the expression level data of the 16 feature genes selected in 1) as explanatory variables and chronic stress levels (high or low stress) 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 a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 15% in the model when the expression level data of the 16 genes were used as variables, indicating that the above 16 genes can be used as markers for detecting chronic stress levels.

[0087] Example 4: Construction of a chronic stress level discrimination model based on all the feature genes used in Examples 1 and 2 1) Selection of feature genes All of the feature genes used in either Example 1 or 2 (a total of 197 types of genes shown in Table 1) were selected as feature genes to be used in constructing a prediction model.

[0088] 2) Model construction A prediction model for discriminating chronic stress levels was constructed using the Log2(RPM+1) values ​​of the expression level data of the 197 feature genes selected in 1) as explanatory variables and chronic stress levels (high or low stress) 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 a decision tree at one time was tuned. The random forest algorithm was executed using the mtry value determined by tuning, and the estimated error rate (OOB error rate) was calculated. As a result, the OOB error rate was 20% in the model when the expression level data of the 197 genes were used as variables, indicating that the 197 genes in Table 1 can be used as markers for detecting chronic stress levels.

Claims

1. A method for detecting a chronic stress level in a subject, comprising measuring the expression levels of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes, in a biological sample taken from the subject.

2. The method of claim 1 , wherein the nucleic acid is mRNA extracted from surface lipids of the skin.

3. The method of claim 1 or 2, further comprising detecting a chronic stress level of the subject based on the expression level of the nucleic acid or protein.

4. The method according to claim 3, comprising detecting the chronic stress level of the subject based on a prediction model constructed using data on the expression level of the nucleic acid or protein obtained from each individual in a teacher sample population as an explanatory variable and the chronic stress level of each individual in the teacher sample population as a response variable.

5. A method for evaluating a chronic stress regulator, comprising measuring the expression levels of all of the nucleic acids or all of the proteins selected from the group consisting of nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7, and proteins encoded by the genes, in a biological sample collected from a subject to which a test substance has been administered.

6. A set of oligonucleotides that specifically hybridize to each of the nucleic acids encoding the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7; or A set of antibodies that recognize each of the proteins encoded by the following genes: HERC4, STAG1, IRAK1, MOAP1, CTDNEP1, PTOV1, LY6E, COPS3, FAM135A, ERO1L, CMPK1, CYB5R1, LAPTM4A, EGR2, ITM2B, and TAF7; A kit for detecting a chronic stress level or a kit for evaluating a chronic stress regulator, which is used in the method according to any one of claims 1 to 5, comprising:

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