WO2007102402A1 - Method of diagnosing chronic fatigue syndrome - Google Patents

Abstract

A novel method of convenient, objective, highly accurate evaluation as to whether or not a test subject is affected with chronic fatigue syndrome. There is provided a method of diagnosing chronic fatigue syndrome, characterized in that whether or not a test subject is affected with chronic fatigue syndrome is judged on the basis of the analytical result of expression of any of marker genes selected from among those of Table 1 and Table 2, using messenger RNA derived from peripheral blood of the test subject.

Description

 Diagnosis of chronic fatigue syndrome

 Technical field

 [0001] 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.

 Background art

 [0002] Fatigue and fatigue are the feelings that everyone experiences on a daily basis, and the complaint of “both bodily” is the number of complained persons in the “National Life Survey” of the Ministry of Health, Labor and Welfare every year. It is ranked high.

 [0003] In the national attitude survey on health in 1985, 66.4% of the people recognized fatigue at the time of the survey. Seventy-seven percent of respondents replied, “Fatigue is recovered by overnight sleep.” Fatigue fatigue is short-term and chronic fatigue appears to be rare. It has come. However, in 1998, the Fatigue Research Group of the Ministry of Health, Labor and Welfare conducted an epidemiological survey of 4000 men and women aged 15 to 65 in the Toyokawa Public Health Center in Aichi Prefecture. More than half (35.8% of the total) recognized chronic fatigue that lasted for more than half a year, and it was also found that 1 in 5 felt reduced work capacity.

 [0004] When complaining about “malaise”, clinically, (1) organic diseases (malignant tumors, infectious diseases, endocrine disruption, metabolic diseases, etc.), (2) non-organic diseases (mood disorders) (3) Overwork (satisfaction is likely to be a natural situation), (4) Worried state (the power to worry about the illness) ), (5) 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 half a year, 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 extremely mild so as 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" is 1 It 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 intense. It is necessary to leave the company or school for several days in a month. Is a condition for diagnosis.

 [0006] In addition, it is necessary to satisfy the following eight items.

 1) Slight fever or chills

 2) Sore throat

 3) Swollen neck or side lymph nodes

 4) Unexplained weakness

 5) Muscle pain or discomfort

 6) General fatigue that lasts for more than 24 hours

 7) Headache (new appearance, or worse than before)

 8) Joint pain

 9) Psychiatric and neurological symptoms (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.

 [0007] It has not been clarified yet that the onset and recurrence of CFS is presumed to involve stress, infection, and disruption of the neuroendocrine network. In addition, CFS is difficult to distinguish not only physical illness but also mental illness such as depression, and there is no established treatment, so many patients seek diagnosis and treatment across multiple facilities. Walk

 [0008] 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 about chronic fatigue syndrome is necessary, but it is also essential to make a differential diagnosis with many diseases with similar symptoms. Therefore, diagnosis must be performed by a specialist who has received sufficient training. However, for primary 'care physicians, diagnosis of chronic fatigue syndrome is not always easy, without objective laboratory findings. [0009] A major factor in the need for proficiency in diagnosis is the absence of a simple and objective disease state evaluation method. A number of testing methods have been attempted so far with the aim of providing an objective index, but in view of simplicity, application to daily medical care cannot be expected at all. It is.

 [0010] 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); Method of measuring auditory function and assessing its fatigue level (see Patent Document 2); Method of detecting vibrations in body parts and diagnosing fatigue level (see Patent Document 3); Index of issilcalcun concentration in body fluid As a method for evaluating the degree of fatigue (see Patent Document 4) and the like have been proposed, however, the deviation is not a method for diagnosing chronic fatigue syndrome.

 [0011] On the other hand, a method for predicting the risk of chronic fatigue syndrome by determining the genotype of 5-HTTLPR in the cell genome (see Patent Document 5), and Rnase L-containing cells such as peripheral blood mononuclear cells A method for diagnosing chronic fatigue syndrome by detecting an Rnase L molecule of about 30 kDa in a cell extract has been proposed (see Patent Document 6), but it is not a diagnostic method for messenger RNA.

 [0012] Although a method for diagnosing chronic fatigue syndrome targeting messenger RNA has 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. In addition, the sample processing method is different and it is not practical to use density gradient centrifugation. In addition, 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 fluctuates (see Non-Patent Document 4).

 Patent Document 1: JP-A-8-164127

 Patent Document 2: JP-A-2005-168856

 Patent Document 3: International Publication 2002Z094091 Pamphlet

 Patent Document 4: Japanese Patent Laid-Open No. 2005-70024

 Patent Document 5: Japanese Patent Laid-Open No. 2005-13147

 Patent Document 6: Special Table 2000-516818

Patent Literature l: J.Clin.Pathol. 2005; 58; p826 -832 Non-Patent Document 2: Clin. Exp. Allergy, 2003; 33: p 1450— 1456

 Non-Patent Document 3: Dis Markers, 2002; 18: pl93—199

 Non-Patent Document 4: Neuroscience Letters, 2005; 381; p57-62

 Disclosure of the invention

 Problems to be solved by the invention

[0013] 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.

 Means for solving the problem

 [0014] In order to objectively evaluate the pathology of chronic fatigue syndrome, the present inventors have obtained peripheral blood white blood cells that are easily obtained as specimens and that express many of receptors for stress-related factors. Focused on. Then, by comprehensively analyzing and patterning the expression pattern of approximately 1500 messenger RNAs related to stress response, we found a method that can evaluate the presence or absence of chronic fatigue syndrome, leading to the completion of the present invention. It was.

 [0015] That is, the present invention relates to the chronic fatigue syndrome of the subject based on the expression analysis result of the marker gene selected from Table 1 and Table 2 using the messenger RNA derived from the peripheral blood of the subject. A method for diagnosing chronic fatigue syndrome is provided.

 [0016] The genes listed in Table 1 are useful marker genes for chronic fatigue syndrome. The marker gene is a gene with a p-value of 0.05 or less by a doitall.pair significant difference test without Bayesian estimation for gene expression analysis between healthy subjects and patients with chronic fatigue syndrome. It consists of 10 genes whose expression increases in patients and 2 genes whose expression decreases. Table 1 shows the gene symbol, average expression ratio, p-value, GenBank number, and name Z annotation for each marker gene. By using the messenger RNA derived from the subject's specimen (peripheral blood), the expression profile of the marker gene shown in Table 1 above is examined to determine whether the subject is suffering from chronic fatigue syndrome. Can be judged.

[0017] Further, the genes listed in Table 2 are marker genes of useful chronic fatigue syndrome groups selected by another significant difference test. The marker gene is calculated by pierre.pair significant difference test, which performs Bayesian estimation after gene expression analysis between healthy subjects and patients with chronic fatigue syndrome. A gene with a value of 0.05 or less, consisting of 35 genes whose expression is increased in patients compared to healthy individuals. Like Table 1, Table 2 shows the gene symbol, average expression ratio, ρ value, GenBank number, and name Z annotation for each marker gene. Similarly to the marker gene described in Table 1, the subject is tested for susceptibility by examining the expression profile of the marker gene described in Table 2 above using messenger RNA derived from the subject's specimen (peripheral blood). It is possible to determine whether or not the force suffers from fatigue syndrome.

 [0018] In the examples of the present specification, RNA is extracted from whole blood obtained from patients and healthy volunteers, and the gene expression analysis of marker genes shown in Table 1 or Table 2 is performed using a DNA chip. went. Here, a DNA chip is a DNA fragment having a base sequence corresponding to a large number of genes fixed on a support substrate such as glass, and RNA in the sample is detected by means of hybridization. To do. If comprehensive analysis of gene expression is possible, the analysis can be performed using other DNA-immobilized samples (DNA array, beads, membrane filter, etc.) and quantitative methods instead of the above DNA chip. Good. The target patients were those who were not treated with chronic fatigue syndrome and who gave written explanation and consent to participate in the research for developing this diagnostic method. Diagnosis was in accordance with CDC's 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 the patient sample and the healthy subject sample was determined, and the gene group with a fluorescence intensity of 300 or more in all patient Z healthy subject comparison data was used as the analysis target gene. In the comparative data of patient Z healthy subjects, 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 patients compared to normal subjects, and significantly The decreasing gene was selected as an index for evaluating the presence or absence of chronic fatigue syndrome, ie, “chronic fatigue syndrome marker gene”.

 [0019] As a method for determining the presence or absence of chronic fatigue syndrome in a subject, a gene expression profile of the subject, patient, or healthy subject is used for the marker genes listed in Table 1 and Z or Table 2. This can be done by comparative analysis.

[0020] For example, an RNA sample derived from a subject and an RNA sample derived from a healthy subject are labeled with fluorescent dyes having different emission wavelengths, and then the same chronically loaded with the genes listed in Table 1 and Z or Table 2. Competitive hybridization on DNA chip for fatigue syndrome evaluation Yeah. The fluorescence intensity of each probe on the chip indicates the ratio of the expression intensity of each gene of the subject and that of a healthy person, and the expression profile is shown in Table 1 and Z or Table 2 above.

By comparing and analyzing with the original gene expression profile data compared with the expression of healthy Z subjects, the presence or absence of the subject can be diagnosed. In addition, instead of an RNA sample derived from a healthy person, a specific RNA sample (for example, a commercially available universal RNA sample) was used as a control sample, and the genes in Table 1 and Z or Table 2 were examined. A comparative analysis between patients and healthy individuals may be performed. Furthermore, instead of the fluorescence intensity of the universal RNA sample, a data set in which the fluorescence intensity of one color of each of the subject, patient, and healthy person is normalized between chips and between genes is used as a reference. The color method can be implemented.

 [0021] Gene expression analysis methods are not limited to DNA chips, but include nucleic acid hybridization methods using other solid-phased DNA samples such as DNA arrays and membrane filters, RT-PCR, real-time PCR, etc. Any analysis method known in the art can be used, such as quantitative PCR method, Northern blot method, subtraction method, differential 'display method, differential differential method, etc. In particular, analysis methods using solid-phase DNA samples such as DNA chips, DNA arrays, membrane filters, and beads are preferred because many genes can be comprehensively analyzed at once.

 [0022] The probe for detecting the gene is 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. Synthetic oligo probes with a length of 25-100 bases, or PCR products with a length of 300-100 bases can be used. The method for immobilizing the probe on the solid phase is not particularly limited. According to a known method, the synthesized probe may be spotted on the solid phase or the probe may be synthesized on the solid phase. An example of an inspection system using a DNA chip is shown in Fig. 1.

[0023] In an actual clinical laboratory, a system having a means for comparing and analyzing the gene expression data of a subject and the gene expression data of a healthy person and a chronic fatigue syndrome patient who have been acquired in advance. prepare. In this system, in addition to gene expression data for subjects, healthy subjects, and patients with chronic fatigue symptoms, clinical data such as age and sex of each patient Perform comparative analysis by adding For example, if expression data of patients and healthy individuals are stored as a database divided by age and gender, the database power is simply extracted from data that matches the age and sex of the subject. A comparative analysis can be performed. The allowable age difference is preferably within 5 years. In addition, the patient and the healthy person's expression data are trained in advance on the computer, and the computer determines whether the expression data of the subject is closer to the expression pattern of the patient or the healthy person. It is also possible to diagnose the presence or absence of the examiner.

As the 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. Figure 2 shows an overview of the system.

[0025] This specification includes the contents described in the specification and Z or drawings of Japanese Patent Application No. 2006-54414, which is the basis of the priority of the present application.

 The invention's effect

 [0026] According to the present invention, the presence or absence of chronic fatigue syndrome can be easily and objectively determined by analyzing gene expression of RNA obtained from peripheral blood samples of only a few cc of a subject. Diagnosis of chronic fatigue syndrome, which has been difficult in the past, can be easily performed. The method of the present invention is capable of grasping a large number of RNA expression levels and biological functions in many ways, compared with the conventional method of measuring limited factors, and thus a method for diagnosing a complex disease such as chronic fatigue syndrome. As such, it is appropriate in principle and has great utility.

 Brief Description of Drawings

 [0027] 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 is a DNA chip, F2 is a probe DNA corresponding to the gene selected in the present invention, F3 is an excitation light source and a fluorescence detector, and F4 is a fluorescence detector control computer.

FIG. 2 is a conceptual diagram of an 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] Fig. 3 shows the results of cluster analysis of useful marker genes for chronic fatigue syndrome (I). [Fig. 4] Fig. 4 shows the results of cluster analysis of useful chronic fatigue syndrome marker gene cluster (Π).

 [FIG. 5] FIG. 5 shows the results of diagnosis of a subject using a marker gene group (I) of useful chronic fatigue syndrome. Based on cluster analysis of 26 subjects with the marker gene group (I) for chronic fatigue syndrome.

 [FIG. 6] FIG. 6 shows the result of diagnosis of a subject using a marker gene group (II) of useful chronic fatigue syndrome. Based on cluster analysis of 26 subjects with marker genes for chronic fatigue syndrome (Π).

 Example

 Hereinafter, the present invention will be described in more detail with reference to examples, but the present invention is not limited thereto.

 [Example 1] Selection of marker gene for chronic fatigue syndrome

 1. Patients and healthy controls

 Participating patients should participate in research for the development of this diagnostic method among patients with untreated chronic fatigue syndrome who visited Nagoya University Medical Hospital General Medical Department between February 2000 and May 2004. The person who explained the document in writing and obtained consent. This study was approved by the Ethics Committee of Nagoya University Hospital. The diagnosis was consistent with the diagnostic criteria for the CDC chronic fatigue syndrome. In addition, for each patient, healthy individuals with matching sex and age were selected and used as healthy controls.

 [0030] Eleven patients who received 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.

 [0031] 2. Gene expression analysis

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

[0032] Next, 5 micrograms of total RNA extracted from each patient was taken out and T7 promoted. An oligo (dT) 24 primer attached with a sequence was annealed, and first strand DNA synthesis was performed. Next, this first strand DNA was converted into a saddle shape, and S econd strand DNA having a T7 promoter sequence was synthesized. Finally, RNA was synthesized by T7 RNA polymerase using the second strand DNA as a saddle. Six micrograms of synthesized RNA was annealed with random hexamers and subjected to reverse transcriptase reaction, and Cy5-dCTP was incorporated into the chain to synthesize fluorescently labeled cDNA.

 [0033] For each patient, from 11 healthy controls of matching sex and age,

 After collecting 5 cc of blood, total RNA was extracted, and cDNA was synthesized using the same procedure except that Cy3 was used as the fluorescent label.

 [0034] Two types of cDNAs to be subjected to comparative analysis were mixed in equal amounts, and then subjected to hybridization at 62 ° C for 12 hours on a DNA chip (DNA chip for analysis of drug response of Hitachi, Ltd.). After washing, the fluorescence intensity of each spot was measured with a scanner (GSI-Lumonics ScanArray 5000), and the ratio of the expression level of each gene in the patient sample and the healthy control sample was determined.

 [0035] 3. Data analysis

 3.1 Selection of marker genes for chronic fatigue syndrome (I)

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

[table 1] Table Chronic fatigue syndrome marker gene cluster

3.2 Selection of marker genes for chronic fatigue syndrome (II)

 The genes to be analyzed were the gene group (1072) whose fluorescence intensity was 300 or more in all 11 sets of data. First, genes whose expression levels were significantly increased or decreased were extracted by significant difference test (with Bayesian estimation: pierre.pair: p-value <0.05) in the comparison data of healthy patients. Compared with healthy subjects, 35 genes with significantly increased expression levels were found in patients. These genes are shown in Table 2, and cluster analysis results are shown in Figure 4. These genes are useful for determining whether or not a subject is suffering from chronic fatigue syndrome, that is, as a marker gene for chronic fatigue syndrome.

[Table 2]

Table 2 Marker genes for chronic fatigue syndrome (II)

[0037] 4. Real-time quantitative PCR for marker genes of chronic fatigue syndrome Confirmation by PCR

 4.1 Reverse transcription reaction of standard sample for calibration curve creation

 As a standard sample for preparing a calibration curve, human blood and peripheral leukocyte total RNA (BD Biosciences, catalog number 636580) was used, and reverse transcription reaction with Oligo-dT primer was performed. Starting from 250 ng or 50 ng of reverse transcripts (cDNA), a 5-step, 5-fold dilution series was prepared for each gene analysis, and used as a PCR cage.

[0038] 4.2 Reverse transcription reaction of total RNA samples from patients and healthy subjects

Each total RNA sample was subjected to reverse transcription reaction with Oligo-dT primer. As a PCR variant, 50 ng or 10 ng of reverse transcript (cDNA) was used. Endogenous control GAP The same applies to DH.

 [0039] 4.3 PCR conditions and equipment

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

 [0040] 4.4 Calibration curve creation and data analysis

 A calibration curve was created using the dedicated software SDS2.1 (Applied Systems). Based on the measured values of the standard samples, Δ! ¾ (increased fluorescence) was plotted on the vertical axis and the number of cycles on the horizontal axis, and an amplification curve was automatically created for each gene. Next, set the horizontal axis of the Threshold Line in the exponential amplification region of all the standard samples, and set the number of cycles (C value) at the point of contact with the amplification curve.

 T

 Were determined. In addition, a calibration curve with the C value on the vertical axis and Quantity on the horizontal axis is plotted for each gene.

 T

 Created automatically.

 [0041] In the same way as above, the software automatically creates an amplification curve, determines the C value, and automatically calculates the quantity of each sample for each gene from the calibration curve and C value. Calculation

T T

 did. Then, the average of 3 experiments (quantity standard deviation (STv)) and CV ((variation coefficient) value of each specimen were calculated for each gene, and the CV value was confirmed to be 11%. Then, 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.

[0042] 4.5 Real-time quantitative analysis of marker genes for chronic fatigue syndrome Confirmation by PCR

 Table 3 shows the results of DNA chip analysis and quantitative PCR analysis of the genes listed in Table 1 using 7 patients and healthy subjects. For the two genes, HSPA2 and COX7C, the primer set purchased from Applied Biosystems was not suitable for the design reason, so the analysis was conducted here. The DNA chip results and the quantitative PCR results are in good agreement.

[Table 3] Table 3 Real-time quantification of marker genes for chronic fatigue syndrome: PCR

[0043] [Example 2] Diagnosis of chronic fatigue syndrome by marker gene

 1.Subject

 Among the patients who visited the general medical department of Nagoya University Hospital between February 2000 and May 2004, they explained in writing and agreed to participate in research for developing this diagnostic method. It was a person. This study was approved by the Nagoya University School of Medicine Hospital Ethics Committee. Diagnosis was consistent with CDC's diagnostic criteria for chronic fatigue syndrome. In addition, for each patient, healthy individuals with matching sex and age were selected and used as healthy controls.

 [0044] Eleven patients with chronic fatigue syndrome who obtained 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. The 15 patients without chronic fatigue syndrome were 3 males, 12 females, aged between 23 and 61 years (average 37.6 years). Of the 15 patients, 2 were withdrawn from the disease, 3 were with organic disease, 10 were with mental illness, and 2 were with mild, secondary, chronic fatigue, the main being with mental illness.

 [0045] 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 with a T7 promoter sequence attached thereto. Next, second strand DNA having a T7 promoter sequence was synthesized using this first strand DNA as a cage. Finally, RNA synthesis was performed using T7 RNA polymerase using second strand DNA as a saddle. 6 micrograms of RNA were fluorescently labeled by conducting a reverse transcriptase reaction with random hexamer annealing and incorporating Cy5-dCTP into the chain. DNA was synthesized.

 [0046] As a control, blood was collected from healthy subjects whose sex and age matched each subject, and Cy3-cDNA was synthesized in the same manner as the subject sample. 6 micrograms of Cy5-cDNA prepared from each subject sample and Cy3-cDNA of the control sample are mixed in equal amounts, and then applied to a DNA chip (Hitachi, Ltd., DNA chip for pharmaceutical response analysis). The bridging was performed at 62 ° C for 12 hours. After washing, the fluorescence intensity of each spot is measured with a scanner (Scan Array 5000 from GSI-Lumonics), and a control sample and each sample of each subject are sampled using digitization software (Quant Array from GSI-Lumonics). The expression intensity ratio was obtained.

 [0047] 3. Subject diagnosis

 3.1 Diagnosis of subjects using marker gene (I) of chronic fatigue syndrome

 Figure 5 shows the results of clustering analysis of the above 26 subjects with 12 marker genes (I) for chronic fatigue syndrome in Table 1. Seven were diagnosed with chronic fatigue syndrome, 11 were diagnosed with chronic fatigue syndrome, and 8 were borderline.

 [0048] 3.2 Diagnosis of subjects using marker gene (II) of chronic fatigue syndrome

 Figure 6 shows the results of a clustering analysis of the above 26 subjects with 35 marker genes for chronic fatigue syndrome (疲 労) in Table 2. Four were diagnosed with chronic fatigue syndrome, 10 were diagnosed as out of the chronic fatigue syndrome group, and 12 were borderline.

 [0049] As described above, the diagnosis of chronic fatigue syndrome based on the expression analysis of specific gene groups was consistent with the clinical findings, indicating that the effectiveness of the present invention was high.

 Industrial applicability

[0050] The method of the present invention is useful as an objective diagnostic method for chronic fatigue syndrome in clinical settings.

 [0051] All publications, patents and patent applications cited herein are incorporated herein by reference in their entirety.

Claims

The scope of the claims  [1] 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 subject's peripheral blood A method for diagnosing chronic fatigue syndrome, characterized in that  [2] The presence or absence of chronic fatigue syndrome of the subject is determined based on the expression analysis result of the marker gene shown in Table 1 using messenger RNA derived from the subject's peripheral blood. A method for diagnosing chronic fatigue syndrome. [3] The presence or absence of chronic fatigue syndrome in the subject is determined based on the expression analysis result of the marker gene shown in Table 2 using messenger RNA derived from the peripheral blood of the subject. A method for diagnosing chronic fatigue syndrome.  [4] The gene expression analysis is performed by comparing and analyzing a gene expression profile of a subject and a gene expression profile of a chronic fatigue syndrome patient and a healthy subject. The method for diagnosing chronic fatigue syndrome according to any one of -3.  [5] 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. The method for diagnosing chronic fatigue syndrome according to item 1.  [6] The gene expression analysis is performed using any one of the methods selected from RT-PCR, real-time PCR, Northern blotting, subtraction method, differential 'display method, and differential' noisy hybridization method. The diagnostic method for chronic fatigue syndrome according to any one of claims 1 to 4, wherein the method is characterized.  [7] Each probe specifically identified for the marker gene shown in Table 1, each probe for detecting the gene, and Z or  A solid phase for diagnosing chronic fatigue syndrome, characterized in that each of the marker genes listed in Table 2 is specifically noblated and each probe for detecting the gene is immobilized on the solid phase. sample. [8] It has a means to compare and analyze the gene expression data of the subject and the gene expression data of healthy individuals and chronic fatigue syndrome patients who have been acquired in advance. Diagnostic system. 9. The system according to claim 8, further comprising means for performing comparative analysis by adding data of each age and sex to the gene expression data of the subject, the healthy subject, and the chronic fatigue syndrome patient.