JP2021175394A - Menstrual cycle marker - Google Patents

Abstract

To provide a method for detecting a menstrual cycle which is convenient and which reduces the burden on a subject.SOLUTION: Provided is a marker that enables detection of a menstrual cycle. Also provided is a method for detecting a menstrual cycle of a subject using the marker.SELECTED DRAWING: None

Description

The present invention relates to menstrual cycle markers.

There is a desire to monitor and understand the menstrual cycle in women's physical condition management. This is important not only to know when you can become pregnant, but also to deal with various changes in your physical condition associated with menstruation (eg, premenstrual syndrome, etc.). As a method of monitoring the menstrual cycle, there have been methods of predicting the time of the next ovulation and menstruation based on the number of days since the previous menstruation and basal body temperature, and methods of determining the state of menstruation based on the amount of female hormone in the blood. Are known. Patent Document 1 describes a method of giving a visual display before the start of menstruation by using a sanitary napkin or a panty liner provided with a pH sensor, a blood sensor, a progesterone hormone sensor, an estrogen hormone sensor and the like. Patent Document 2 describes a method of determining the menstrual cycle of a subject based on the sleeping heart rate data of the subject collected over a plurality of days. Patent Document 3 describes a method of measuring estrogen metabolites and progesterone metabolites in the urine of a subject and determining the ovulation cycle state of the subject based on the ratio of the excretion rates of these metabolites. Patent Document 4 describes that the expression of the antibacterial peptide hBD-3 on the skin surface decreases due to the increase in progesterone with the menstrual cycle.

Techniques for investigating the current and future physiological states of human organisms by analyzing nucleic acids such as DNA and RNA in biological samples have 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 function of the analysis result based on many research reports on monobasic polymorphism and RNA function. It has the advantage of being easy to link. Nucleic acid derived from a living body can be extracted from tissues such as blood, body fluids, secretions and the like. Patent Document 5 states that RNA contained in skin surface lipids (SSL) is used as a sample for analysis of a living body, and marker genes for epidermis, sweat glands, hair follicles, and sebaceous glands are detected in SL. Is described.

Special Table 2002-542846 Japanese Unexamined Patent Publication No. 2006-094969 Special Table 2009-512871 Japanese Unexamined Patent Publication No. 2015-228829 International Publication No. 2018/0083319

The present invention relates to a menstrual cycle marker that enables detection of a menstrual cycle, and a method for detecting a menstrual cycle using the menstrual cycle marker.

The present inventor collected skin surface lipids (SSL) from the skin of a healthy woman and comprehensively analyzed the expression state of RNA contained in the SSL as sequence information. As a result, the present invention found that a specific gene was found. We found that the expression level fluctuates cyclically in conjunction with the menstrual cycle.

That is, the present invention provides a method for detecting a subject's menstrual cycle, which comprises measuring the expression of at least one selected from the group consisting of the genes shown in Table 1 below and the translation products of the genes.
The present invention also provides a menstrual cycle marker comprising nucleic acids derived from the genes shown in Table 1 below and at least one selected from the group consisting of translations of the genes.

The present invention provides a novel menstrual cycle marker. Since the menstrual cycle marker can be collected from SSL, it can be easily and non-invasively obtained. Therefore, according to the present invention, it is possible to detect the menstrual cycle without imposing a burden on the subject.

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

In one aspect, the invention provides a novel menstrual cycle marker. As shown in the examples below, the present inventors have found that the expression of 506 genes shown in Table 1 below fluctuates with the menstrual cycle. Therefore, the nucleic acids derived from the genes shown in Table 1 and the translation products of the genes can be used as markers for detecting the menstrual cycle, respectively.

The names of the genes disclosed herein follow the Official Symbol described in NCBI ([www.ncbi.nlm.nih.gov/]). In the present specification, "nucleic acid derived from a gene" includes mRNA transcribed from the gene, cDNA prepared from the mRNA, reaction product from the cDNA (for example, PCR product, cloned DNA, etc.), and the like. ..

In the present invention, the menstrual cycle of a subject can be detected based on the expression level of each gene shown in Table 1 or its translation product. In the present invention, the expression level of the gene shown in Table 1 or its translation product can be measured by quantifying the nucleic acid derived from the gene or the translation product of the gene. For example, by quantifying the mRNA transcribed from the gene, the cDNA prepared from the mRNA, the reaction product from the cDNA, or the translation product (protein, etc.) obtained by translating the mRNA, the gene or The expression level of the translation product can be measured.

Nucleic acids derived from the genes shown in Table 1 or translation products of the genes can be prepared according to a conventional method from biological samples collected from a subject, such as cells, body fluids, and secretions. Preferably, the nucleic acid is an mRNA prepared from a subject's skin surface lipids (SSL). Also preferably, the translation product is a translation product prepared from the subject's SSL.

In the present invention, "detection" of the menstrual cycle can be paraphrased in terms such as prediction, inspection, measurement, judgment or evaluation support. The terms "detection", "prediction", "examination", "measurement", "judgment" or "evaluation" of the menstrual cycle in the present invention do not include the diagnosis of the menstrual cycle by a doctor.

As used herein, the term "skin surface lipid (SSL)" refers to a fat-soluble fraction present on the surface of the skin and is sometimes referred to as 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 5). 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.

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.

For the extraction of RNA from the collected SSL, a method usually used for extraction or purification of RNA from a biological sample, for example, a phenol / chloroform method, an AGPC (acid guanidinium thiocyanate-phenol-chromoform extraction) method, or TRIzol ( Method using columns such as registered trademark), RNA (registered trademark), QIAzol (registered trademark), method using special magnetic material particles coated with silica, method using Solid Phase Reversible Imaging magnetic material particles, ISOGEN, etc. Extraction with a commercially available RNA extraction reagent or the like can be used. Further, for the extraction of the translation product from the SSL, a method usually used for extracting or purifying a protein from a biological sample, for example, a commercially available protein extraction reagent such as QIAzol Lysis Reagent (Qiagen) can be used.

The menstrual cycle of a subject can be detected by examining the expression of one or more genes or their translation products shown in Table 1 using the nucleic acid or translation product prepared in the above procedure. Accordingly, in another aspect, the invention provides a method of detecting a subject's menstrual cycle, comprising measuring the expression of at least one selected from the group consisting of the genes shown in Table 1 or translations thereof. do.

The subject in the method for detecting the menstrual cycle according to the present invention (hereinafter referred to as the method of the present invention) may be, for example, a female of a human or non-human mammal who needs to detect the menstrual cycle. Preferably, the subject is an animal having SSL on the skin, more preferably a human.

In one embodiment, the method of the present invention comprises measuring the expression of at least one gene (hereinafter, also referred to as a target gene (group)) selected from the group consisting of the genes shown in Table 1 in a subject. In another embodiment, the method of the invention measures the expression of at least one (hereinafter, also referred to as a target product (group)) selected from the group consisting of translation products of the genes shown in Table 1 in a subject. Including that. Alternatively, both the expression of the target gene (group) and the expression of the target product (group) may be measured. Preferably, the expression of the target gene (group) is measured.

Preferably, the expression of the target gene (group) or target product (group) in the method of the present invention is measured using RNA or a translation product contained in the SSL of the subject. In one embodiment, the method of the invention may further comprise collecting the SSL of the subject. In one embodiment, the method of the invention may further comprise extracting RNA or translation product from SSL taken from the subject.

The expression level of the target gene can be measured by quantifying the amount of RNA derived from the target gene contained in the sample derived from the subject, for example, RNA extracted from SSL. The amount of subject-derived RNA may be measured according to the procedure for gene expression analysis using RNA commonly used in the art. As an example of the method of gene expression analysis using RNA, after converting RNA into cDNA by reverse transcription, the cDNA or its amplification product is quantified by real-time PCR, multiplex PCR, microarray, sequencing, chromatography or the like. The method can be mentioned. The expression level of the translation product can be measured by using protein quantification methods commonly used in the art, such as ELISA, immunostaining, fluorescence, electrophoresis, chromatography, mass analysis and the like. The measured expression level may be the absolute amount of expression of the target gene (group) or target product (group) in the biological sample, or relative to the total expression level of other standard substances, genes or translation products. It may be the expression level.

In a preferred embodiment, in the method of the invention, the subject-derived RNA is converted to cDNA by reverse transcription, then the cDNA is subjected to PCR to purify the resulting reaction product. 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. In PCR of the obtained cDNA, only the specific DNA may be amplified using a primer pair targeting the specific DNA to be analyzed, or a plurality of DNAs may be amplified using a plurality of primer pairs. 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.).

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 PCR reaction product is further improved. Preferably, in the extension reaction in the reverse transfer, the temperature is adjusted to preferably 42 ° C. ± 1 ° C., more preferably 42 ° C. ± 0.5 ° C., still more preferably 42 ° C. ± 0.25 ° C. The reaction time is preferably adjusted to 60 minutes or longer, more preferably 80 to 120 minutes. Further, preferably, the temperature of the annealing and extension reactions in the PCR can be appropriately adjusted depending on the primers used, but for example, when the above-mentioned multiplex PCR kit is used, it is preferably 62 ° C. ± 1 ° C., more preferably. Is 62 ° C. ± 0.5 ° 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. A next-generation sequencer (for example, Ion S5 / XL system, Life Technologies Japan Co., Ltd.) is preferably used for sequencing. RNA expression can be quantified based on the number of reads created by sequencing (read count).

Preferably, the method of the invention detects the subject's menstrual cycle based on the expression level of the target gene (group) or the target product (group). For example, the genes shown in Table 1 can be divided into the four groups shown in Tables 2 to 5 according to the pattern of fluctuations in their expression levels during the menstrual cycle (menstrual period, follicular phase, and luteal phase). These fluctuation patterns can be used to detect the subject's menstrual cycle.

The 55 genes shown in Table 2 are genes whose expression levels are lowest in the menstrual period and then increase in the follicular phase and luteal phase. On the other hand, the 49 genes shown in Table 3 are genes whose expression levels are highest in the menstrual period and then decrease in the follicular phase and luteal phase. By using the expression level of any one or more of the genes shown in Tables and Table 3 or their translation products as a reference, whether the subject's menstrual cycle is the menstrual cycle, the follicular phase, or the luteal phase can be determined. Can be detected. The genes shown in Tables 2 and 3 are genes for which no association with menstruation has been reported in the past.

The 180 genes shown in Table 4 are genes whose expression levels are highest in the follicular phase and decreased in the menstrual phase and luteal phase. On the other hand, the 222 genes shown in Table 5 are genes whose expression levels are lowest in the follicular phase and increased in the menstrual phase and luteal phase. Whether or not the subject's menstrual cycle is in the follicular phase can be detected based on the expression level of any one or more of the genes shown in Tables 4 and 5 or their translation products. The genes shown in Tables 4 and 5 are genes for which no association with menstruation has been reported in the past.

In one embodiment of the method of the invention, the expression levels of individual target genes (groups) or target products (groups) are used directly as parameters for detecting the menstrual cycle. For example, the menstrual cycle of a subject can be detected by periodically measuring the expression level of the target gene or target product in the subject and tracking the fluctuation of the expression level. Alternatively, the menstrual cycle of the subject can be detected by comparing the expression level of the target gene or the target product measured from the subject with the reference expression level of the target gene or the target product. As the reference expression level of the target gene or the target product, a predetermined value, for example, a statistical value of the expression level of the target gene or the target product in the entire menstrual cycle (for example, the average expression level) can be used. The reference expression level may be set for each subject. For example, it may be calculated from a statistical value (for example, an average value) of the expression level of a target gene or a target product measured a plurality of times from the same subject in the entire menstrual cycle. Alternatively, the reference expression level may be a statistical value (for example, an average value) of the expression level of the target gene or target product in the entire menstrual cycle measured from the population. When a plurality of genes or translation products are used as the target gene group or target product group, it is preferable to determine the reference expression level for each of the genes or translation products.

For example, if the target gene or target product is the gene shown in Table 2 or its translation product, and the expression level is lower than the reference expression level, the subject can be detected as being in the luteal phase, or the expression level is the reference level. If the expression level is higher than the expression level, the subject can be detected as in the luteal phase. On the other hand, when the target gene or target product is the gene shown in Table 3 or its translation product, if the expression level is higher than the reference expression level, the subject can be detected as being in the luteal phase, or the expression level is the reference level. If the expression level is lower than the expression level, the subject can be detected as in the luteal phase. In the genes of Tables 2 and 3 or their translations, a subject can be detected in the follicular phase if its expression level is similar (neither high nor low) to the reference expression level.

Further, for example, when the target gene or target product is the gene shown in Table 4 or a translation product thereof, the subject can be detected in the follicular phase when the expression level is higher than the reference expression level. On the other hand, when the target gene or target product is the gene shown in Table 5 or a translation product thereof, the subject can be detected in the follicular phase when the expression level is lower than the reference expression level.

Alternatively, two or more of the genes shown in Tables 2 to 5 or their translation products may be combined and used as the target gene or target product. For example, the expression levels of the genes in Tables 2 and 3 or their translation products are similar to the standard expression levels, and the expression levels of the genes shown in Table 4 (or Table 5) or their translation products are higher than the standard expression levels. If (or low), the subject can be detected in the follicular phase. However, the combination of the target gene or the target product in the method of the present invention is not limited to the above example. When a plurality of target gene groups or target product groups are used, a certain percentage, for example, 50% or more, preferably 70% or more, more preferably 90% or more, still more preferably 100% of the gene or its translation product is described above. The subject's menstrual cycle can be detected based on whether or not the expression level criteria are met.

In another embodiment, the subject's menstrual cycle is detected based on a predictive model constructed using expression level data of the target gene (group) or target product (group). As the expression data, data on the expression level of the target gene (group) or target product (group) at each period of the menstrual cycle (follicular phase, luteal phase or menstrual phase) can be used. For example, for each of one or more target genes (groups) or target products (groups), the expression data of each period of the menstrual cycle (each of the follicle stage, the luteal stage, and the menstrual stage) is used as an explanatory variable, and the menstrual cycle (follicles). By machine learning with the objective variable (whether it is the period, the luteal period, or the menstrual period), it is possible to construct an optimal prediction model for detecting the menstrual cycle. For example, from the genes shown in Table 1 or their translation products, a plurality of genes having a larger expression difference can be selected as a target gene group or a target product group, and their expression data can be used as explanatory variables.

Alternatively, when the expression data of a plurality of genes or translation products are used for constructing the prediction model, the prediction model may be constructed after compressing the data by dimension reduction, if necessary. For example, a plurality of genes are selected from the genes shown in Table 1, and those genes or their translation products are extracted as a target gene group or a target product group. Next, a principal component analysis is performed on the expression level (expression level in the follicular phase, luteal phase, and menstrual phase) of the extracted target gene group or target product group in the entire cycle. The menstrual cycle is determined by machine learning with one or more principal components calculated by the principal component analysis as explanatory variables and the menstrual cycle (for example, follicular phase, luteal phase, or menstrual phase) as the objective variable. An optimal prediction model for detection can be constructed.

The dimension and number of the principal components used for constructing the prediction model are not particularly limited, but include components having a large contribution rate, for example, the first principal component, and the total contribution rate is 40% or more, preferably 45% or more. The explanatory variable may be one or more principal components such that preferably 50% or more, more preferably 55% or more.

As an algorithm for constructing a prediction model, a known algorithm such as an algorithm used for machine learning can be used. Examples of machine learning algorithms include algorithms such as Random Forest, Neural net, linear kernel support vector machine (SVM (linear)), and rbf kernel support vector machine (SVM (rbf)). Can be mentioned. Data for verification is input to the constructed prediction model to calculate the prediction value, and the model in which the prediction value best matches the measured value, for example, the accuracy rate (Accuracy) which is a value indicating the degree of agreement between the predicted value and the measured value. ) Can be selected as the optimal prediction model.

By inputting the expression data (or the main component calculated from them) of the target gene (group) or the target product (group) in the subject into the constructed prediction model, the menstrual cycle of the subject is predicted. be able to.

As exemplary embodiments of the present invention, the following substances, manufacturing methods, uses, methods and the like are further disclosed herein. However, the present invention is not limited to these embodiments.

[1] A method for detecting a subject's menstrual cycle, which comprises measuring the expression of at least one selected from the group consisting of the genes shown in Table 1 above and translation products of the genes.
[2] The method according to [1], preferably comprising detecting the menstrual cycle based on the expression level of the gene or translation product.
[3] Preferred includes detecting the menstrual cycle using a predictive model based on the expression level of the gene or translation product.
The prediction model is constructed with the expression level of the gene or translation product in the menstrual cycle or at least one principal component calculated by principal component analysis of the expression level as an explanatory variable and the menstrual cycle as an objective variable. , [2].
[4] Preferably, any one of [1] to [3], wherein the expression level of the gene is measured by quantifying the mRNA of the gene contained in the lipid on the skin surface of the subject. the method of.
[5] Use as at least one menstrual cycle marker selected from the group consisting of nucleic acids derived from the genes shown in Table 1 above and translation products of the genes.
[6] The use according to [5], wherein the nucleic acid derived from the gene is the mRNA of the gene contained in the lipid on the surface of the skin.
[7] A menstrual cycle marker containing at least one selected from the group consisting of nucleic acids derived from the genes shown in Table 1 above and translation products of the genes.
[8] The marker according to [7], wherein the nucleic acid derived from the gene is the mRNA of the gene contained in the lipid on the surface of the skin.

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

Example 1 Identification of menstrual cycle markers 1) SSL collection 38 healthy subjects (women aged 20 to 45 years, BMI 18.5 or more and less than 25.0) were used as subjects. In healthy subjects, it was confirmed in advance that the menstrual cycle was stable every 25 to 30 days. Each period of the menstrual cycle (menstrual period: any day 2 to 4 days after the start of menstruation, follicular phase: any day 10 to 12 days after the start of menstruation, luteal phase: any day 21 to 23 days after the start of menstruation On that day), sebum was collected from the entire face of each subject using a degreasing film (5.0 cm × 8.0 cm, 3M company). The oil removal film was transferred to a glass vial and stored at −80 ° C. for about 1 month until used for RNA extraction.

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. The extracted RNA was reverse transcribed using the SuperScript VILO cDNA Synthesis kit (Life Technologies Japan Co., Ltd.) at 42 ° C. for 90 minutes. The random primer included in the kit was used as the primer for the reverse transcription reaction. The resulting cDNA was amplified 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.). The solution of the obtained purified product is attached to the 5 × VILO RT Reaction Mix attached to the SuperScript VILO cDNA Synthesis kit and the Ion AmpliSeqTranscriptome Human Gene Expression Prime 5 × Hipion Primer (Life Technologies Japan Co., Ltd.). Mix and Ion Ampliseq Transcriptome Human Gene Expression Core Panel were mixed, and then the primer sequences were digested, adapter-ligated and purified, and amplified according to the protocol attached to each kit 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). The gene from which each read sequence was derived was determined by gene mapping each read sequence obtained by sequencing to hg19 AmpliSeq Transcriptome ERCC v1, which is a reference sequence of the human genome.

3) Data analysis The read count of each read obtained by sequencing the SSL-derived RNA of the subject acquired in 2) above was used as the data of the expression level of each RNA. However, reads with a read count of less than 10 were treated as missing values. In order to correct the difference in the total read count between the samples, each read count was converted into an RPM (Reads per million mapped reads) value. We selected 5604 genes for which non-defective expression level data were obtained in 50% or more of the subjects. For these 5604 kinds of genes, genes having a significant relationship with the menstrual cycle were selected based on the expression level data (RPM value). Specifically, using analysis software GeneSpring (Agilent Technologies Co., Ltd.), one-way analysis of variance (one-way analysis of variance) was used to analyze each gene in each cycle (menstrual phase, follicular phase, and luteal phase). The average expression level was calculated, and then multiple comparison analysis by Tukey-Kramer method was performed to extract a group of genes having an average expression level of p <0.05 in any cycle. The missing values were supplemented by Singular Value Decomposition (SVD) imputation. As a result, as shown in Tables 6 to 9, 506 genes were obtained in which the expression changed significantly with the menstrual cycle and the relationship with menstruation was not reported in the previously reported literature. The 55 genes shown in Table 6 had the lowest expression levels during the menstrual period, and then increased in expression levels as they progressed to the follicular phase and the luteal phase. On the other hand, the 49 genes shown in Table 7 had the highest expression level during the menstrual period, and then decreased in the follicular phase and the luteal phase. The 180 genes shown in Table 8 had the highest expression levels in the follicular phase and decreased expression levels in the menstrual phase and the luteal phase. On the other hand, the 222 genes shown in Table 9 had the lowest expression levels in the follicular phase and increased expression levels in the menstrual phase and the luteal phase. These genes can be used as markers for the menstrual cycle.

Example 2 Construction of a menstrual cycle prediction model-1
1) Data set division Of the SSL-derived RNA expression level data set obtained in Example 1, data for 32 subjects, which is 80% of all subjects, was used as training data for the menstrual cycle prediction model, and the remaining 20%, or 6 subjects. The minute data was used as the test data used to evaluate the model accuracy. In order to approximate the RPM value that follows the negative binomial distribution to the normal distribution, the RNA expression level data (read count RPM value) obtained in Example 1 is converted into the logarithmic value of the base 2 (Log ₂ RPM). Value).

2) Selection of feature amounts For the data summarizing the expression level data of the 506 gene groups shown in Tables 6 to 9 in the menstrual phase, follicular phase, and luteal phase, mainly using the tidyverse package of the statistical analysis environment R. Principal component analysis was performed and variable compression was performed. Data having a new dimension (1st to 20th principal components, Table 10) obtained by principal component analysis were selected as features for menstrual cycle detection.

3) Model construction The model construction was performed using the carnet package of the statistical analysis environment R. A prediction model was constructed using the features selected in 2) above as explanatory variables and the menstrual cycle (menstrual phase, follicular phase, or luteal phase) as the objective variable. Four algorithms for each feature: Random Forest, Neural network, linear kernel support vector machine (SVM (linear)), and rbf kernel support vector machine (SVM (rbf)). The prediction model was trained by performing 10-fold cross-validation using. For each algorithm, the main component data of the test data was input to the model after training to calculate the predicted value of the menstrual cycle. The accuracy rate, which is a value indicating the degree of agreement between the predicted value and the measured value, was calculated, and the model with the largest value was selected as the optimum predicted model.

4) Results Table 11 shows the features used to detect the menstrual cycle, the algorithm used with the maximum accuracy, and its accuracy. Based on the main component of the expression level data of the gene group related to the menstrual cycle, the menstrual cycle could be detected with high accuracy. Therefore, it was shown that the menstrual cycle can be detected using the data on the expression level of SSL-derived RNA. Further, since the accuracy obtained in the prediction model based on the first to tenth principal components is higher than that in the prediction model based on the first to fifth principal components, the cumulative contribution rate is 40% or more, preferably 45% or more. It was shown that a highly accurate menstrual cycle prediction model can be constructed by using one or more principal components such as 50% or more, more preferably 55% or more as explanatory variables.

Example 3 Construction of a menstrual cycle prediction model-2
1) Data set division Similar to Example 2, the RNA expression level data of the subjects obtained in Example 1 was divided into training data and test data. RNA expression level data (read count RPM value) was _{converted to Log 2} RPM value.

2) Selection of feature amount 15 genes with the smallest p-value were selected from each of Tables 6-9. Principal component analysis was performed on the data summarizing the expression level data of the selected 60 gene groups in the menstrual phase, follicular phase, and luteal phase, and the 1st to 20th principal components shown in Table 12 were the characteristic quantities for menstrual cycle detection. Selected as.

A menstrual cycle prediction model was constructed in the same procedure as in Example 2 except that the selected features were used as explanatory variables, and the optimum prediction model was selected. Table 13 shows the features used for the detection of the menstrual cycle, the algorithm used with the maximum accuracy, and the accuracy thereof. A higher accuracy was obtained in the prediction model based on the first to second principal components and the first to fifth principal components than in the prediction model based on the first to tenth principal components. From this, it is shown that a highly accurate menstrual cycle prediction model can be constructed even if the cumulative contribution rate is 50% or less by limiting the feature amount to the expression level data that is highly related to the menstrual cycle. Was done.

Claims (6)

A method for detecting a subject's menstrual cycle, which comprises measuring the expression of at least one selected from the group consisting of the genes shown in Table 1 below and the translation products of the genes.

The method of claim 1, comprising detecting the menstrual cycle based on the expression level of the gene or translation product. Includes detecting the menstrual cycle using a predictive model based on the expression level of the gene or translation product.
The prediction model is constructed with the expression level of the gene or translation product in the menstrual cycle or at least one principal component calculated by principal component analysis of the expression level as an explanatory variable and the menstrual cycle as an objective variable. , The method according to claim 2. The method according to any one of claims 1 to 3, wherein the expression level of the gene is measured by quantifying the mRNA of the gene contained in the lipid on the skin surface of the subject. A menstrual cycle marker comprising at least one selected from the group consisting of nucleic acids derived from the genes shown in Table 2 below and translation products of the genes.

The marker according to claim 5, wherein the nucleic acid derived from the gene is the mRNA of the gene contained in the lipid on the surface of the skin.