JP2007228878A - Method for diagnosis of chronic fatigue syndrome - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a new method for easy, objective and accurate estimation of the contraction of a subject to chronic fatigue syndrome. <P>SOLUTION: The contraction of a subject to chronic fatigue syndrome is diagnosed by using a messenger RNA derived from the peripheral blood of the subject and judging the contraction based on the expression analysis result of either one of selected marker genes. The diagnosis of chronic fatigue syndrome is easily performed by the use of a small amount of peripheral blood. The vital functions can be multilaterally grasped from multiple RNA expression levels. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a method for diagnosing chronic fatigue syndrome. More specifically, it is characterized in that the presence or absence of chronic fatigue syndrome in the subject is determined based on the expression analysis result of the selected marker gene using messenger RNA derived from the peripheral blood of the subject. The present invention relates to a method for diagnosing chronic fatigue syndrome.

  Fatigue and fatigue are the feelings that everybody experiences on a daily basis, and the complaint of “both bodily” ranks high in the accused rate in the “National Life Basic Survey” of the Ministry of Health, Labor and Welfare every year. Has been.

  In the national attitude survey on health in 1985, 66.4% recognized fatigue at the time of the survey. 71.7% of respondents replied that “an overnight sleep recovers fatigue.” Fatigue / malaise is short-term, and chronic fatigue seems to be rare. It was. However, in 1998, when the Fatigue Research Group of the Ministry of Health, Labor and Welfare conducted an epidemiological survey of 4,000 men and women aged 15 to 65 in the Toyokawa Public Health Center in Aichi Prefecture, the proportion of those who were aware of fatigue was about 60%. Yes, more than half (35.8% of the total) found chronic fatigue lasting more than half a year, and it was also found that one in five felt a decline in work capacity.

  When “Fatigue” is the main complaint, clinically (1) organic diseases (malignant tumors, infectious diseases, endocrine / metabolic diseases, etc.), (2) non-organic diseases (mood disorders, physical expression disorders) Etc.), (3) Overworked (there is a situation that seems natural due to fatigue), (4) Worried state (I am worried about the illness, but if I guarantee that there is no abnormality, the symptoms disappear without worry), (5) It is divided into chronic fatigue of unknown cause.

  Among these, “chronic fatigue syndrome (CFS)” is included in chronic fatigue of unknown cause. “Chronic fatigue” means any subjective feeling of fatigue that lasts for more than six months, regardless of the degree of fatigue, the presence or absence of symptoms other than fatigue, and whether or not you are ill. Therefore, even if the fatigue level is only slight enough not to interfere with daily life, it is called chronic fatigue if it lasts for more than half a year. On the other hand, “chronic fatigue syndrome” means a disease and is a diagnostic name that meets the diagnostic criteria established by the Ministry of Health, Labor and Welfare and the US Center for Prevention and Control. In other words, the degree of fatigue is severe, and it is diagnosed that a state where the company or school has to be closed for several days a month lasts for more than half a year or is repeated, and no obvious disease is found by the doctor's examination and clinical examination. It becomes a condition.

In addition, it is necessary to satisfy the following 8 items.
1) Slight fever or chills 2) Sore throat 3) Swelling of the lymph nodes on the neck or side 4) Unexplained weakness 5) Pain or discomfort 6) Muscle movement 7 ) Headache (new or newer or worse than before)
8) Joint pain 9) Psychiatric and neurological symptoms (one of dazzling, transient dark spots, forgetfulness, irritability, confusion, reduced thinking ability, reduced concentration, depression)
10) Sleep disturbance (insomnia and oversleep)
11) At the time of onset, these main symptoms appear in a short period (hours to days)
The diseases to be differentiated from CFS include (1) organic diseases, (2) non-organic diseases, (3) fatigue, and (4) anxiety states mentioned above.

  The onset and recurrence of CFS has been estimated to involve stress, infection, and disruption of the neuroendocrine network, but it is not yet clear. In addition, CFS is difficult to differentiate not only from physical disorders but also from mental illnesses such as depression, and since there is no established cure, many patients are walking across multiple facilities seeking diagnosis and treatment. Yes.

  A major problem with conventional diagnostic methods is that they require clinical skills that are skilled in diagnosis. Needless to say, sufficient knowledge and experience regarding chronic fatigue syndrome is necessary, but many diseases exhibit similar symptoms, and differential diagnosis with them is also essential. Therefore, diagnosis must be performed by a specialist who has received sufficient training. However, it is not always easy for primary care physicians to diagnose chronic fatigue syndrome without objective laboratory findings.

  A major factor in the need for proficiency in diagnosis is the absence of a simple and objective pathological evaluation method. A number of testing methods have been tried so far with the aim of objective indicators, but considering the simplicity, the current situation is that application to daily medical care cannot be expected. .

  Incidentally, as a method for evaluating the degree of fatigue: a method of displaying each question evaluation result such as mental fatigue and physical fatigue on the CRT display as numerical data (see Patent Document 1); measuring visual function and auditory function A method for evaluating the degree of fatigue (see Patent Document 2); a method for detecting vibration in a body part and diagnosing the degree of fatigue (see Patent Document 3); a degree of fatigue using the concentration of acylcarcinin in body fluid as an index Have been proposed (see Patent Document 4), but none of them is a method for diagnosing chronic fatigue syndrome.

  On the other hand, a method for predicting the risk of suffering from chronic fatigue syndrome by determining the genotype of 5-HTTLPR in the cell genome (see Patent Document 5), and cell extracts of Rnase L-containing cells such as peripheral blood mononuclear cells Among them, a method (see Patent Document 6) for diagnosing chronic fatigue syndrome by detecting an Rnase L molecule of about 30 kDa has been proposed, but it is not a diagnostic method for messenger RNA.

  Although a method for diagnosing chronic fatigue syndrome targeting messenger RNA has also been reported (see Non-patent documents 1 to 3), a marker gene group different from the marker gene group extracted by the present inventors is used. Specimen processing method is different and it is not practical to use density gradient centrifugation. Furthermore, it has been pointed out that separating leukocytes using density gradient centrifugation alone causes a signal based on stimulation to enter the cell and the gene expression varies (see Non-Patent Document 4).

JP-A-8-164127 JP 2005-168856 A International Publication No. 2002/094091 Pamphlet JP-A-2005-70024 JP 2005-13147 A Special Table 2000-516818 J. Clin. Pathol. 2005; 58; p826-832. Clin. Exp. Allergy, 2003; 33: p1450-1456 Dis Markers, 2002; 18: p193-199 Neuroscience Letters, 2005; 381; p57-62

  An object of the present invention is to provide a novel method for easily, objectively and accurately diagnosing the presence or absence of chronic fatigue syndrome.

  In order to objectively evaluate the pathology of chronic fatigue syndrome, the present inventors have focused on peripheral blood leukocytes that are easily obtained as specimens and that express many receptors for factors related to stress. Then, by comprehensively analyzing and patterning the expression pattern of messenger RNA of about 1500 genes related to stress response, a method that can evaluate the presence or absence of chronic fatigue syndrome was found, and the present invention was completed. It was.

  That is, the present invention uses the messenger RNA derived from the peripheral blood of the subject to determine whether the subject is suffering from chronic fatigue syndrome based on the expression analysis result of the marker gene selected from Table 1 and Table 2. A diagnostic method for determining chronic fatigue syndrome is provided.

  The genes listed in Table 1 are useful marker genes for chronic fatigue syndrome. The marker gene is a gene having a p-value of 0.05 or less by a doitall.pair significant difference test without Bayesian estimation in gene expression analysis between healthy subjects and patients with chronic fatigue syndrome, and is expressed in patients compared to healthy subjects It consists of 10 genes with increased and 2 genes with decreased expression. Table 1 shows the gene symbol of each marker gene, the average value of the expression ratio, the p value, the GenBank number, and the name / annotation. Using a messenger RNA derived from the subject's specimen (peripheral blood), it is determined whether or not the subject suffers from chronic fatigue syndrome by examining the expression profile of the marker gene described in Table 1 above. can do.

  The genes listed in Table 2 are useful chronic fatigue syndrome marker genes selected by another significant difference test. The marker gene is a gene having a p-value of 0.05 or less by pierre.pair significant difference test for Bayesian estimation in gene expression analysis between healthy subjects and patients with chronic fatigue syndrome, and is expressed in patients compared to healthy subjects. It consists of 35 increasing genes. As in Table 1, Table 2 shows the gene symbol, average value of expression ratio, p value, GenBank number, and name / annotation of each marker gene. Similarly to the marker gene described in Table 1, the subject is examined for the expression profile of the marker gene described in Table 2 using a messenger RNA derived from the subject's specimen (peripheral blood). Whether or not the patient is suffering from a syndrome can be determined.

  In the examples of the present specification, RNA was extracted from whole blood obtained from patients and healthy subjects, and the gene expression analysis was performed on the marker genes described in Table 1 or Table 2 using a DNA chip. Here, the DNA chip is one in which a DNA fragment having a base sequence corresponding to a large number of genes is immobilized on a support substrate such as glass, and detects RNA in a sample by hybridization. As long as the gene expression can be comprehensively analyzed, other DNA-immobilized samples (DNA arrays, beads, membrane filters, etc.) and quantitative methods may be used instead of the DNA chip. The patients were untreated patients with chronic fatigue syndrome who gave written informed consent to participate in research for developing this diagnostic method. Diagnosis was in accordance with the CDC criteria for chronic fatigue syndrome. For each patient, healthy controls matched to sex and age were selected and used as comparative controls. The expression level ratio of each gene between a patient sample and a healthy subject sample was determined, and a gene group having a fluorescence intensity of 300 or more in all patient / healthy subject comparison data was used as an analysis target gene. In the patient / healthy person comparison data, genes whose expression level is significantly increased or decreased are extracted by a significant difference test, and genes whose expression level is significantly increased in the patient compared with healthy persons, and significantly decreased. The selected gene was selected as an index for evaluating the presence or absence of chronic fatigue syndrome, ie, “chronic fatigue syndrome marker gene”.

  As a method for determining the presence or absence of chronic fatigue syndrome in a subject, the gene expression profile of the subject, patient, or healthy subject is comparatively analyzed with respect to the marker genes listed in Table 1 and / or Table 2. Can be performed.

  For example, after the subject-derived RNA sample and the healthy subject-derived RNA sample are labeled with fluorescent dyes having different emission wavelengths, the same chronic fatigue syndrome evaluation in which the genes listed in Table 1 and / or Table 2 are mounted Competitive hybridization is performed on the DNA chip. The fluorescence intensity of each probe on the chip indicates the expression intensity ratio of each gene of the subject and the healthy person, and the expression profile is expressed in the patient / healthy person of the genes described in Table 1 and / or Table 2 above. By comparing and analyzing with the original data of the gene expression profile compared, the presence or absence of the subject can be diagnosed. In addition, instead of a healthy person-derived RNA sample, a specific RNA sample (for example, a commercially available universal RNA sample) was used as a control sample, and the subjects, patients, and patients related to the genes listed in Table 1 and / or Table 2 A comparative analysis between healthy persons may be performed. Furthermore, in place of the fluorescence intensity of the universal RNA sample, the so-called one-color method using a data set obtained by normalizing the fluorescence intensity of each color of a subject, a patient, and a healthy person between chips and genes as a reference Can be implemented.

  The gene expression analysis method is not limited to a DNA chip, but a nucleic acid hybridization method using other DNA-immobilized sample such as a DNA array or a membrane filter, a quantitative PCR method such as RT-PCR or real-time PCR, Northern Any analysis method known in the art can be used, such as a blot method, a subtraction method, a differential display method, and a differential hybridization method. In particular, an analysis method using a DNA solid phase sample such as a DNA chip, a DNA array, a membrane filter, and beads is preferable because a large number of genes can be comprehensively analyzed at once.

A probe for detecting a gene can be designed as a sequence complementary to a highly specific region (for example, 3′UTR portion) of each marker gene selected from Table 1 and Table 2 according to a well-known method. A synthetic oligo probe having a length of 25-100 bases or a PCR product having a length of 300-1000 bases can be used. The method for immobilizing the probe on the solid phase is not particularly limited, and the synthesized probe may be spotted on the solid phase or the probe may be synthesized on the solid phase according to a known method. An example of an inspection system using a DNA chip is shown in FIG.

  In an actual clinical test site, a system having a means for comparing and analyzing gene expression data of a subject and gene expression data of healthy subjects and chronic fatigue syndrome patients acquired in advance is prepared. In this system, in addition to gene expression data of subjects, healthy subjects, and patients with chronic fatigue syndrome, clinical analysis such as age and sex is added to perform comparative analysis. For example, if the patient and healthy person's expression data is stored as a database divided by age and gender, data that matches the subject, age, and gender can be extracted from the database, and the data of the subject, patient, and healthy person can be retrieved. A comparative analysis can be performed. The allowable age difference is preferably within 5 years. In addition, the patient's and healthy subjects' expression data is learned in advance by the computer, and the subject's morbidity is determined by making the computer determine whether the expression data of the subject is closer to the expression pattern of the patient or the healthy person. The presence or absence of can also be diagnosed.

  As a data analysis method, in addition to cluster analysis, any analysis method known in the technical field, such as a machine learning algorithm such as a support vector machine, can be used. An overview of the system is shown in FIG.

  According to the present invention, the presence or absence of chronic fatigue syndrome can be easily and objectively and accurately diagnosed by gene expression analysis of RNA obtained from a few cc of peripheral blood of a subject. Thus, diagnosis of chronic fatigue syndrome, which has been difficult in the past, can be easily performed. Since the method of the present invention grasps biological functions from a large number of RNA expression levels from a number of RNA expression levels, compared with the conventional method of measuring limited factors, a method for diagnosing complicated diseases such as chronic fatigue syndrome As such, it is appropriate in principle and has great utility.

  EXAMPLES Hereinafter, although this invention is demonstrated in detail using an Example, this invention is not limited to these.

[Example 1] Selection of marker gene for chronic fatigue syndrome Patients and healthy controls The target patients were those who had not been treated for chronic fatigue syndrome patients who visited the Nagoya University Medical Hospital General Medical Department between February 2000 and May 2004. The person who participated in the study was explained in writing and agreed. This study was approved by the Nagoya University School of Medicine Hospital Ethics Committee. The diagnosis was consistent with the CDC criteria for chronic fatigue syndrome. In addition, for each patient, healthy individuals with matching sex and age were selected and used as healthy controls.

  The 11 patients who obtained the pre-treatment samples were 4 males and 7 females, aged 25 to 55 years (average 35.7 years). Three of the eleven patients showed mild secondary depression or anxiety, but the main symptom is chronic fatigue syndrome.

2. Gene expression analysis From a patient, 5 cc of blood was collected using a PAXgene Blood RNA System (Qiagen), and total RNA was extracted. Blood was collected from 10 am to 1 pm on an empty stomach, and the doctor or nurse collected blood from the cubital vein while resting. Total RNA yield was 5-15 micrograms.

  Next, 5 micrograms of total RNA extracted from each patient was taken out, oligo (dT) 24 primer added with a T7 promoter sequence was annealed, and first strand DNA synthesis was performed first. Next, Second strand DNA having a T7 promoter sequence was synthesized using this First strand DNA as a template. Finally, RNA was synthesized with T7 RNA polymerase using Second strand DNA as a template. 6 micrograms of synthesized RNA was annealed with random hexamers and subjected to reverse transcriptase reaction to synthesize fluorescently labeled cDNA by incorporating Cy5-dCTP into the chain.

  For each patient, 5 cc blood samples were collected from 11 healthy and matched healthy controls, and then total RNA was extracted and cDNA was used in the same manner except that Cy3 was used as the fluorescent label. Was synthesized.

  After mixing equal amounts of two types of cDNAs for comparative analysis, hybridization was performed at 62 ° C. for 12 hours on a DNA chip (Hitachi, Ltd., pharmaceutical response analysis DNA chip). After washing, the fluorescence intensity of each spot was measured with a scanner (ScanArray 5000 manufactured by GSI-Lumonics), and the ratio of the expression level in each gene of the patient sample and the healthy control sample was determined.

3. Data analysis 3.1 Selection of marker genes for chronic fatigue syndrome (I)
The genes to be analyzed were a group of genes (1072) having a fluorescence intensity of 300 or more in all 11 sets of data. First, in the patient / healthy person comparison data, genes whose expression levels were significantly increased or decreased were extracted by a significant difference test (no Bayesian estimation: doitall.pair: p value <0.05). Compared with healthy subjects, there were 10 genes whose expression levels were significantly increased in the patient and 2 genes that were significantly decreased. These genes are shown in Table 1, and the cluster analysis results are shown in FIG. These genes are useful for determining whether or not a subject suffers from chronic fatigue syndrome, that is, as a marker gene for chronic fatigue syndrome.

3.2 Selection of marker gene for chronic fatigue syndrome (II)
The genes to be analyzed were a group of genes (1072) having a fluorescence intensity of 300 or more in all 11 sets of data. First, in the patient / healthy person comparison data, genes whose expression levels were significantly increased or decreased were extracted by a significant difference test (with Bayesian estimation: pierre.pair: p value <0.05). In comparison with healthy subjects, 35 genes whose expression levels were significantly increased in patients were found. These genes are shown in Table 2, and the cluster analysis results are shown in FIG. These genes are useful for determining whether or not a subject suffers from chronic fatigue syndrome, that is, as a marker gene for chronic fatigue syndrome.

4). Confirmation of marker gene of chronic fatigue syndrome by real-time quantitative PCR 4.1 Reverse transcription reaction of standard sample for calibration curve Use Human Blood, Peripheral Leukocyte total RNA (BD Biosciences, catalog number 636580) as standard sample for calibration curve creation Then, reverse transcription reaction with Oligo-dT primer was performed. Starting from 250 ng or 50 ng of the reverse transcript (cDNA), a 5-fold, 5-fold dilution series was prepared for each gene analysis and used as a PCR template.

4.2 Reverse transcription reaction of total RNA samples from patients and healthy subjects Each total RNA sample was subjected to reverse transcription reaction using Oligo-dT primer. As a PCR template, 50 ng or 10 ng of a reverse transcription product (cDNA) was used. The same applies to the endogenous control GAPDH.

4.3 PCR conditions and apparatus The standard samples and each specimen were subjected to PCR for 3 experiments (N = 3). PCR was performed under the conditions of 95 ° C. × 10 min, (95 ° C. × 15 s, 60 ° C. × 1 min) × 40 cycles. The device used was Applied Biosystems PRISM 7900HT.

4.4 Calibration curve creation and data analysis A calibration curve was created using dedicated software SDS2.1 (Applied Biosystems). Based on the measurement value of the standard sample, ΔRn (increase fluorescence amount) is plotted on the vertical axis, and the cycle number is plotted on the horizontal axis, and an amplification curve is automatically created for each gene. Next, the horizontal axis of the threshold line was set in the exponential function amplification region of all the standard samples, and the number of cycles (C _T value) corresponding to the contact point with the amplification curve was determined. Furthermore, C _T value on the vertical axis, a calibration curve obtained by plotting the Quantity on the horizontal axis were automatically created for each gene.

Also the calculation of the Quantity and the average value of each sample, automatically calculates the software amplification curve similar to the above automatically creates, to determine the C _T value, than the calibration line and the C _T value, the Quantity of each sample for each gene did. Then, three experimental average values (± standard deviation (STv)) and CV ((variation coefficient) value of each specimen were calculated for each gene, and it was confirmed that CV value <11%. The mean value for each gene was corrected with the endogenous control gene GAPDH, and the relative value was expressed as the amount of expression change in the patient sample when the healthy sample was set to 1.

4.5 Confirmation of Chronic Fatigue Syndrome Marker Genes by Real-time Quantitative PCR Table 3 shows the results of DNA chip analysis and quantitative PCR analysis for the genes listed in Table 1 using samples from 7 patients and healthy subjects. . The two genes HSPA2 and COX7C were not analyzed here because the primer set purchased from Applied Biosystems was inappropriate for design reasons. The results of the DNA chip and the results of quantitative PCR are in good agreement.

[Example 2] Diagnosis of chronic fatigue syndrome by marker gene Examine patients who attended the Nagoya University Medical Hospital General Medical Department between February 2000 and May 2004 to explain and agree to participate in the research for developing this diagnostic method. The person who got it. This study was approved by the Nagoya University School of Medicine Hospital Ethics Committee. The diagnosis was consistent with the CDC criteria for chronic fatigue syndrome. In addition, for each patient, healthy individuals with matching sex and age were selected and used as healthy controls.

  Eleven patients with chronic fatigue syndrome who obtained pre-treatment samples were 4 males and 7 females, aged from 25 to 55 years (average 35.7 years). Three of the eleven patients showed mild secondary depression or anxiety, but the main symptom is chronic fatigue syndrome. Fifteen patients who were not chronic fatigue syndrome were 3 males, 12 females, and aged from 23 to 61 years (average 37.6 years). Of 15 patients, 2 were withdrawn from disease, 3 were with organic disease, 10 were with mental illness, and 2 were with mild secondary chronic fatigue, but the main was mental illness.

2. Gene expression analysis From the subject, 5 cc of blood was collected from each subject using PAXgene Blood RNA System (Qiagen), and total RNA was extracted. Total RNA yield was 5-10 micrograms. First, first strand DNA synthesis was performed on 5 micrograms of total RNA extracted from each subject by annealing an oligo (dT) 24 primer added with a T7 promoter sequence. Next, Second strand DNA having a T7 promoter sequence was synthesized using this First strand DNA as a template. Finally, RNA was synthesized with T7 RNA polymerase using Second strand DNA as a template. For 6 micrograms of RNA, random hexamers were annealed to carry out reverse transcriptase reaction, and Cy5-dCTP was incorporated into the chain to synthesize fluorescently labeled cDNA.

  As a control, blood was collected from healthy individuals whose sex and age matched each subject, and Cy3-cDNA was synthesized in the same manner as the subject samples. After mixing 6 micrograms of Cy5-cDNA prepared from each subject sample and Cy3-cDNA of the control sample in equal amounts, it is applied to a DNA chip (Hitachi, Ltd., pharmaceutical response analysis DNA chip) for hybridization. Performed at ° C for 12 hours After washing, the fluorescence intensity of each spot is measured with a scanner (GSI-Lumonics ScanArray 5000), and the control software and each subject sample for each gene are measured using numerical software (GSI-Lumonics QuantArray). The expression intensity ratio was determined.

3. 3. Diagnosis of subjects 3.1 Diagnosis of subjects using marker gene (I) of chronic fatigue syndrome Cluster analysis of the above 26 subjects using 12 marker genes (I) of chronic fatigue syndrome shown in Table 1 The results are shown in FIG. Seven were diagnosed with chronic fatigue syndrome, 11 were diagnosed outside chronic fatigue syndrome, and 8 were borderline.

3.2 Diagnosis of Subjects Using Marker Gene (II) for Chronic Fatigue Syndrome Results of clustering analysis of the above 26 subjects with 35 marker genes (II) for chronic fatigue syndrome shown in Table 2 are shown in FIG. Shown in Four were diagnosed with chronic fatigue syndrome, 10 were diagnosed outside chronic fatigue syndrome, and 12 were borderline.

  As described above, the diagnosis of chronic fatigue syndrome by the expression analysis of specific gene groups showed a good agreement with the clinical findings, indicating that the effectiveness of the present invention is high.

  The method of the present invention is useful as an objective diagnostic method for chronic fatigue syndrome in clinical settings.

FIG. 1 is a conceptual diagram of an inspection system using a DNA chip for chronic fatigue syndrome of the present invention. In the figure, F1 represents a DNA chip, F2 represents a probe DNA corresponding to the gene selected in the present invention, F3 represents an excitation light source and a fluorescence detector, and F4 represents a fluorescence detector control computer. FIG. 2 is a conceptual diagram of the evaluation system for chronic fatigue syndrome of the present invention. In the figure, information management such as sex and age is stored in the personal information database. FIG. 3 shows a cluster analysis result of a marker gene group (I) of useful chronic fatigue syndrome. FIG. 4 shows the results of cluster analysis of useful chronic fatigue syndrome marker gene group (II). FIG. 5 shows the result of diagnosis of a subject using a useful marker gene group (I) of chronic fatigue syndrome. FIG. 6 shows the result of diagnosis of a subject using a marker gene group (II) of useful chronic fatigue syndrome.

Claims (9)

  Determine the presence or absence of chronic fatigue syndrome in the subject based on the expression analysis result of one of the marker genes selected from Table 1 and Table 2 using messenger RNA derived from the peripheral blood of the subject A method for diagnosing chronic fatigue syndrome, characterized by   Chronic fatigue characterized by determining the presence or absence of chronic fatigue syndrome in the subject based on the expression analysis results of the marker genes shown in Table 1 using messenger RNA derived from the peripheral blood of the subject How to diagnose the syndrome.   Chronic fatigue characterized by determining the presence or absence of chronic fatigue syndrome in the subject based on the expression analysis results of the marker genes shown in Table 2 using messenger RNA derived from the peripheral blood of the subject How to diagnose the syndrome.   The gene expression analysis is performed by comparatively analyzing a gene expression profile of a subject and a gene expression profile of a chronic fatigue syndrome patient and a healthy person, according to any one of claims 1 to 3. The method for diagnosing chronic fatigue syndrome according to claim 1.   The chronic expression according to any one of claims 1 to 4, wherein the gene expression analysis is performed using any one of a solid-phased DNA sample including a chip, an array, a membrane filter, and beads. Diagnosis of fatigue syndrome.   The gene expression analysis is performed using any method selected from RT-PCR, real-time PCR, Northern blot method, subtraction method, differential display method, and differential hybridization method. Item 5. The method for diagnosing chronic fatigue syndrome according to any one of Items 1 to 4. Each probe specifically hybridizes to the marker gene described in Table 1, and / or each probe for detecting the gene, and / or each hybridizes specifically to the marker gene described in Table 2, and detects the gene An immobilized sample for diagnosing chronic fatigue syndrome, characterized in that each probe is immobilized on a solid phase.   A diagnostic system for chronic fatigue syndrome, comprising means for comparing and analyzing gene expression data of a subject and gene expression data of healthy subjects and chronic fatigue syndrome patients acquired in advance.   9. The system according to claim 8, further comprising a means for comparing and analyzing the respective gene expression data of the subject, the healthy subject, and the chronic fatigue syndrome patient by adding data of each age and sex.

Images