Method for acquiring data to classify patients with chronic sinusitis, and its use.

Abstract

To provide methods for getting data to classify patients with chronic sinusitis, to provide kits for classifying patients with chronic sinusitis, to provide arrays for classifying patients with chronic sinusitis, and to provide apparatus for classifying patients with chronic sinusitis.SOLUTION: One embodiment of the present invention uses the expression of at least three genes contained in a gene cluster consisting of a plurality of specific genes as an index.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to a method for acquiring data for classifying patients with chronic rhinosinusitis and its utilization.

Background Art

[0002] Chronic rhinosinusitis is a disease in which chronic inflammation occurs due to infection with a virus or bacteria in one or more of the paranasal sinuses, namely the maxillary sinus, ethmoid sinus, frontal sinus, and sphenoid sinus, and mucus and / or pus accumulates therein.

[0003] The severity of chronic rhinosinusitis is classified based on clinical images (phenotypes) such as the presence or absence of nasal polyps or eosinophilic rhinosinusitis diagnostic criteria (JESREC score), etc., and a treatment method is selected according to the severity (see, for example, Non-Patent Documents 1 to 5).

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

[0005] However, conventional classifications of chronic sinusitis severity do not always correspond to the clinical course, and there is a problem in that the postoperative course and / or response to anti-monoclonal antibody drugs vary from case to case. For this reason, the development of new techniques for classifying chronic sinusitis has been desired.

[0006] The present invention has been made in view of the above-mentioned conventional problems, and aims to provide a method for acquiring data for classifying patients with chronic sinusitis, a kit for classifying patients with chronic sinusitis, an array for classifying patients with chronic sinusitis, and a device for classifying patients with chronic sinusitis. [Means for solving the problem]

[0007] The inventors of this invention have discovered a gene whose expression levels differ among patients suffering from chronic sinusitis, in other words, a gene that can suitably distinguish between patients suffering from chronic sinusitis, and have completed this invention.

[0008] In other words, the present invention includes the following configuration.

[0009] <1> A method for obtaining data to classify patients with chronic sinusitis, comprising a detection step of detecting the expression of at least three genes belonging to the following gene group in a sample taken from a subject: The above gene group consists of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0010] <2> Furthermore, CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, TNFSF13 B The method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9. <1> How to obtain the data described.

[0011] <3> Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CF I, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD74, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSM The detection step includes detecting the expression of at least one gene selected from the gene group consisting of D7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <1> or <2> How to obtain the data described.

[0012] <4> The process further includes a statistical processing step in which the gene expression data obtained in the above detection step is subjected to statistical processing using the Ward method. <1> ~ <3> The method for obtaining the data described in one of the following.

[0013] <5> The gene expression data mentioned above includes the gene expression data of genes included in the following gene group: <4> Method of obtaining the data described above: The above gene group consists of IL32, PLA2G2A, IGHE, IL6, and CCL4.

[0014] <6> The above subjects were administered medication for chronic sinusitis. <1> ~ <5> The method for obtaining the data described in one of the following.

[0015] <7> A method for obtaining data to classify patients with chronic sinusitis, comprising a statistical processing step of performing statistical processing using the Ward method on the expression data of at least three genes contained in a sample taken from a subject.

[0016] <8> The gene expression data mentioned above includes the gene expression data of genes included in the following gene group: <7> Method of obtaining the data described above: The above gene group consists of IL32, PLA2G2A, IGHE, IL6, and CCL4.

[0017] <9> The above subjects were administered medication for chronic sinusitis. <7> or <8> How to obtain the data described.

[0018] <10> A classification kit for patients with chronic sinusitis, comprising an article for detecting the expression of at least three genes from the gene group listed below: The gene group consists of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0019] <11> Furthermore, CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, TNFSF13B The article comprises a method for detecting the expression of at least one gene selected from the gene group consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9. <10> The kit described above.

[0020] <12> Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CFI , CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, C D74, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, S TAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, P TPRC_all, EGR1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD 7. An article for detecting the expression of at least one gene selected from the gene group consisting of STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <10> or <11> The kit described above.

[0021] <13>An array for classifying patients with chronic rhinosinusitis, comprising probes for detecting the expression of at least three or more genes included in the following gene group: The gene group consists of GPR97, AREG, CLC, CSF3, FGF2, IGHG1, IGHG2, IGHG3, IGHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0022] <14>Furthermore, at least one gene selected from the gene group consisting of CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, TNFSF13 B , CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9, and the array according to <13>, comprising probes for detecting the expression of the gene.

[0023] ​<15> Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CFI , CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD 74, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, ST AT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTP RC_all, EGR1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, The system comprises probes for detecting the expression of at least one gene selected from the gene group consisting of STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <13> or <14> The array described above.

[0024] <16> the above <13> ~ <15> A data acquisition device for classifying patients with chronic sinusitis, comprising: a classification array for patients with chronic sinusitis as described in any of the above; and a scanner for detecting signals originating from the above probe. [Effects of the Invention]

[0025] According to one aspect of the present invention, a method for acquiring data for classifying patients with chronic sinusitis, a kit for classifying patients with chronic sinusitis, an array for classifying patients with chronic sinusitis, and a device for classifying patients with chronic sinusitis can be provided. [Brief explanation of the drawing]

[0026] [Figure 1] This graph shows the classification of clusters based on the gene expression results in nasal polyp tissue. [Figure 2] This figure shows the major genes expressed in each cluster and the severity of the symptoms. [Figure 3] This graph shows the gene expression levels in the nasal polyp tissue of each cluster according to an embodiment of the present invention. [Figure 4] This graph shows the classification of clusters based on the gene expression results in nasal polyp tissue. [Figure 5] This table shows the patient conditions, symptoms, and prognosis for each cluster according to embodiments of the present invention. [Modes for carrying out the invention]

[0027] One embodiment of the present invention is described below, but the present invention is not limited thereto. The present invention is not limited to the configurations described below, and various modifications are possible within the scope of the claims, and embodiments and examples obtained by appropriately combining the technical means disclosed in different embodiments and examples are also included in the technical scope of the present invention. Furthermore, all academic and patent documents mentioned herein are incorporated herein by reference. Also, unless otherwise specified herein, "A to B" representing a numerical range is intended to mean "A or greater, B or less".

[0028] In this specification, "chronic sinusitis" refers to a disease in which chronic inflammation (for example, lasting 12 weeks or more) occurs in one or more of the paranasal sinuses: the maxillary sinus, ethmoid sinus, frontal sinus, and sphenoid sinus. More specifically, "chronic sinusitis" refers to a disease in which chronic inflammation (for example, lasting 12 weeks or more) occurs in one or more of the paranasal sinuses: the maxillary sinus, ethmoid sinus, frontal sinus, and sphenoid sinus, and in which nasal polyps are formed. Specific examples of chronic sinusitis include eosinophilic sinusitis and noneosinophilic sinusitis (e.g., nasal polyps associated with aspirin-induced asthma, posterior nasal polyps, and cystic fibrosis).

[0029] [1. How to obtain data] A method for obtaining data to classify patients with chronic sinusitis according to one embodiment of the present invention includes a detection step of detecting the expression of at least three genes included in the following gene group (also called the first gene group) in a sample taken from a subject: The gene group consists of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0030] The genes mentioned above showed very large differences in expression levels when compared among patients with chronic sinusitis (for example, comparing patients with eosinophilic sinusitis to patients with non-eosinophilic sinusitis).

[0031] With the above configuration, it becomes possible to measure the expression levels of genes related to the severity of chronic sinusitis. This allows for detailed classification and diagnosis of chronic sinusitis based on novel criteria. Therefore, this method enables the selection of appropriate treatment methods for chronic sinusitis.

[0032] Traditionally, chronic sinusitis has been classified based on phenotype, and diagnosis and treatment strategies have been considered according to the pathological condition. Currently proposed classification methods include classification based on clinical conditions such as the presence or absence of nasal polyps, detection of eosinophil levels, or the degree of complications, using a severity score. However, in clinical practice, the selected treatment methods have not always been appropriate. On the other hand, there has been no classification of chronic sinusitis based on specific genes. Therefore, it is remarkable that the present invention enables endotype classification of chronic sinusitis based on genes, independent of phenotype.

[0033] The inventors compared gene expression among chronic sinusitis patient groups classified by phenotype and discovered that there are genes (e.g., ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, etc.) whose expression levels differ significantly depending on the patient's symptoms. Based on the expression levels of these genes, it is possible to distinguish between patients with eosinophilic sinusitis and patients with non-eosinophilic sinusitis.

[0034] In one embodiment of the present invention, when patients are classified based on the expression levels of the first gene group described above, the number of clusters into which patients are classified is not particularly limited. For example, the clusters into which patients are classified may be two or more, three or more, four or more, five or more, nine or more, or ten or more. There is no particular upper limit to the number of clusters into which patients are classified, but in reality, it may be 20 or less.

[0035] In one embodiment of the present invention, the subject is a human. In another embodiment of the present invention, the subject is a non-human mammal. Examples of non-human mammals include even-toed ungulates (cattle, wild boars, pigs, sheep, goats, etc.), odd-toed ungulates (horses, etc.), rodents (mice, rats, hamsters, squirrels, etc.), lagomorphs (rabbits, etc.), and carnivores (dogs, cats, ferrets, etc.). The above-mentioned non-human mammals include wild animals in addition to livestock or companion animals.

[0036] In this specification, "sample" refers to all "samples" taken from a subject. Such "samples" include (i) biological samples (e.g., nasal polyps, nasal mucosal tissue, nasal secretions, nasal mucosal swabs, nasal lavage fluid, nasal swab fluid, blood), and pathological specimens prepared by fixing biological samples with a fixative (e.g., formalin), and (ii) solubilized products obtained by solubilizing the materials described in (i) above in a desired solution.

[0037] In one embodiment of the present invention, at least three genes are selected from the first gene group (in other words, genes whose expression is detected in the detection step). The number of genes selected from the first gene group may be four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or twenty-nine or more, or thirty-three or thirty-three. The more genes selected, the more detailed the classification and diagnosis of chronic sinusitis can be.

[0038] The combination of genes selected from the first group of genes described above is not particularly limited and may be any combination.

[0039] From the viewpoint of improving the accuracy of the classification of chronic sinusitis, it is preferable that at least three genes detected in the detection step (genes selected from the first gene group) include at least one gene selected from the group consisting of CLC, IHG1, IHG2, IHG3, IGHM, IL32, IL33, LCP1, ITLN1, PTGDR2, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8, and more preferably at least one gene selected from the group consisting of S100A8, IL32, and IL33.

[0040] In the above method, the total number of gene types detected may be 3 or more, 5 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more, 130 or more, 140 or more, 150 or more, 200 or more, 300 or more, 400 or more, 500 or more, 600 or more, 700 or more, or 800 or more, including at least three of the first gene group described above. From the viewpoint of improving the accuracy of the classification of chronic sinusitis, the more genes detected, the better. The upper limit of the total number of gene types detected is not particularly limited, but may be 2000 or less, 1500 or less, 1000 or less, 900 or less, or 800 or less. With this configuration, not only can the accuracy of the classification of chronic sinusitis be improved, but the detection work can also be simplified.

[0041] The method for detecting the expression of the above gene is not particularly limited, and known methods can be used. For example, mRNA or protein of the gene may be detected. Examples of methods for detecting mRNA include molecular barcoding, PCR, RNA-seq, Northern blotting, and microarray. Examples of methods for detecting protein include molecular barcoding, Western blotting, and ELISA. Among these, molecular barcoding using nCounter® or similar is preferred from the viewpoint of being able to easily measure many targets. These methods can be carried out through known steps.

[0042] The above data acquisition method also applies to CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, and TNFSF13. B The method may include a step of detecting the expression of at least one gene selected from a group of genes consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9 (also referred to as the second gene group).

[0043] The genes mentioned above are those that showed significant differences in expression levels when compared among patients with chronic sinusitis (for example, comparing patients with eosinophilic sinusitis to patients with non-eosinophilic sinusitis).

[0044] With the above configuration, it is possible to further detect genes that are thought to be significantly involved in chronic sinusitis. Therefore, chronic sinusitis can be classified and diagnosed in more detail with greater accuracy.

[0045] The genes selected from the second gene group described above (in other words, the genes whose expression is detected in the detection process) may be two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, or even twenty-one. The more genes selected, the more detailed the classification and diagnosis of chronic sinusitis can be.

[0046] The combination of genes selected from the second group of genes described above is not particularly limited, and any desired combination can be selected.

[0047] From the viewpoint of improving the accuracy of the classification of chronic sinusitis, the gene detected in the detection step (the gene selected from the second gene group) is preferably at least one gene selected from the group consisting of CCL13, CCL24, CCL26, and MARCO, and more preferably at least one gene selected from the group consisting of CCL13, CCL24, and MARCO.

[0048] Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CFI, CI SH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD74, C D59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, STAT3, IT GB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCA The method may include a step of detecting the expression of at least one gene selected from a gene group consisting of P31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B (also called the third gene group).

[0049] The genes mentioned above are those whose expression levels differed when compared among patients with chronic sinusitis (for example, comparing patients with eosinophilic sinusitis to patients with non-eosinophilic sinusitis).

[0050] With the above configuration, it is possible to further detect genes that are thought to be involved in chronic sinusitis. Therefore, chronic sinusitis can be classified and diagnosed in more detail with greater accuracy.

[0051] The genes selected from the third gene group described above (in other words, the genes whose expression is detected in the detection process) may be two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twenty or more, thirty or more, forty or more, fifty or more, sixty or more, seventy or more, eighty or more, ninety or more, one hundred or one hundred or three hundred or three hundred. The more genes selected, the more detailed the classification and diagnosis of chronic sinusitis can be.

[0052] The combination of genes selected from the third group of genes described above is not particularly limited, and any desired combination can be selected.

[0053] From the viewpoint of improving the accuracy of the classification of chronic sinusitis, the gene detected in the detection step (the gene selected from the third gene group) is preferably at least one gene selected from the group consisting of C1QB, STAT6, IL6ST, STAT3, IL13RA1, C3, and C4A / B, and more preferably at least one gene selected from the group consisting of STAT6, IL13RA1, C3, and C4A / B.

[0054] The method for detecting the above data preferably further includes a statistical processing step in which statistical processing is performed using the Ward method on the gene expression data obtained in the above detection step. By including the above statistical processing step, subjects can be classified more accurately based on the obtained data.

[0055] If the data detection method described above includes the statistical processing step described above, it is preferable that the gene expression data includes the gene expression data of genes included in the following gene group (also called the fourth gene group): The gene group consists of IL32, PLA2G2A, IGHE, IL6, and CCL4.

[0056] The gene mentioned above showed a very large difference in expression levels in patients who, despite having severe chronic sinusitis, had a low recurrence rate of chronic sinusitis after treatment.

[0057] With the above configuration, data can be obtained to classify patients based on the expression levels of genes related to the severity and recurrence of chronic sinusitis. Traditionally, it was thought that patients with severe chronic sinusitis also had a high rate of symptom recurrence. However, as mentioned above, patients with large differences in the expression levels of the fourth gene group have a low recurrence rate and a good prognosis. Therefore, by including the expression data of the fourth gene group in the gene expression data, the prognosis of patients with chronic sinusitis can be predicted more accurately.

[0058] The above-mentioned subjects are preferably subjects who have been administered a drug for treating chronic sinusitis. With the above configuration, it is possible to determine whether the classification of the subjects has changed as a result of administering the drug.

[0059] Another embodiment of the present invention provides a method for obtaining data to classify patients with chronic sinusitis, which includes a statistical processing step of performing a Ward's statistical analysis on the expression data of at least three genes contained in a sample taken from a subject. A preferred embodiment of the method for obtaining data to classify patients with chronic sinusitis according to another embodiment of the present invention can be appropriately referenced from the description of the method for obtaining data to classify patients with chronic sinusitis according to the above-described embodiment, and therefore the description is omitted.

[0060] [2. Kit] A kit for classifying patients with chronic sinusitis according to one embodiment of the present invention comprises articles for detecting the expression of at least three genes from the following gene group (also called the first gene group): the gene group consisting of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0061] The following explanations will omit details already covered in section 1, "Data Acquisition Method."

[0062] In this specification, "kit" means any combination of reagents, etc., used for any purpose. This purpose may be medical or experimental. Specifically, this purpose may be diagnostic.

[0063] The above kit may, for example, include an article for detecting mRNA derived from a gene, an article for detecting a protein derived from a gene, or an article for detecting both mRNA and protein derived from a gene.

[0064] Examples of items for detecting mRNA derived from the above gene include a probe that hybridizes to the mRNA of the above gene (e.g., a polynucleotide that hybridizes to mRNA), a primer for detecting the expression of the above gene by PCR, and a library preparation reagent for comprehensively sequencing the mRNA gene (e.g., by RNA-seq).

[0065] Examples of items for detecting proteins derived from the above-mentioned genes include antibodies that specifically bind to the above-mentioned proteins.

[0066] The above article may, for example, (i) emit a detectable signal (e.g., fluorescence) on its own, (ii) bind to another component that emits a detectable signal (e.g., fluorescence), or (iii) bind to mRNA or protein that is bound to another component that emits a detectable signal (e.g., fluorescence).

[0067] In one embodiment, the kit may include reagents and / or auxiliary substances used for diagnosis. In one embodiment, the kit may include one or more storage containers (such as boxes, bottles, or dishes) for storing the reagents and / or auxiliary substances.

[0068] Furthermore, CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, TNFSF13 B The article may include a component for detecting the expression of at least one gene selected from a gene group consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9 (also referred to as a second gene group).

[0069] Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CFI, CIS H, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD74, CD5 9, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, STAT3, ITGB 1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR 1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31 The device may also include an article for detecting the expression of at least one gene selected from a gene group (also called a third gene group) consisting of C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B.

[0070] A classification kit for patients with chronic sinusitis according to one embodiment of the present invention can be used to implement the data acquisition method for classifying patients with chronic sinusitis according to the present embodiment of the present invention described above. Therefore, the number of articles provided in the classification kit for patients with chronic sinusitis according to one embodiment of the present invention (for example, the number of articles for detecting the expression of genes belonging to the first gene group, the number of articles for detecting the expression of genes belonging to the second gene group, the number of articles for detecting the expression of genes belonging to the third gene group, and / or the total number of articles provided in the classification kit) can be set to the same number as described in the section [1. Data Acquisition Method].

[0071] [3. Array] An array for classifying patients with chronic sinusitis according to one embodiment of the present invention comprises probes for detecting the expression of at least one gene included in the following gene group (also called the first gene group): the gene group consisting of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0072] The type of array mentioned above is not particularly limited; examples of usable arrays include microarrays.

[0073] The above array may detect gene expression using mRNA derived from the gene, or it may detect gene expression using a protein derived from the gene. When detecting gene expression using mRNA derived from the gene, the probe may be a probe that hybridizes to the mRNA of the gene (for example, a polynucleotide that hybridizes to the mRNA of the gene), or a primer for detecting gene expression by PCR. When detecting gene expression using a protein derived from the gene, the probe may be an antibody that specifically binds to the protein.

[0074] The above probe may, for example, (i) emit a detectable signal (e.g., fluorescence) on its own, (ii) bind to another structure that emits a detectable signal (e.g., fluorescence), or (iii) bind to mRNA or protein that is bound to another structure that emits a detectable signal (e.g., fluorescence).

[0075] The array may include wells. The size and spacing of the wells can be set as appropriate depending on the type of probe. If the array includes wells, it is preferable that each probe is fixed in a separate well corresponding to each probe.

[0076] The above array further includes CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, and TNFSF13. B The device may also include a probe for detecting the expression of at least one gene selected from a gene group consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9 (also referred to as the second gene group).

[0077] The above arrays further include CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CFI , CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD74, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, STAT3, IT GB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EG R1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31 The device may also include a probe for detecting the expression of at least one gene selected from a gene group consisting of C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B (also called the third gene group).

[0078] An array for classifying patients with chronic sinusitis according to one embodiment of the present invention can be used to carry out the data acquisition method for classifying patients with chronic sinusitis according to the present embodiment of the present invention described above. Therefore, the number of probes provided in the array for classifying patients with chronic sinusitis according to one embodiment of the present invention (for example, the number of probes for detecting the expression of genes belonging to a first gene group, the number of probes for detecting the expression of genes belonging to a second gene group, the number of probes for detecting the expression of genes belonging to a third gene group, and / or the total number of probes provided in the classification kit) can be set to the same number as described in the section [1. Data Acquisition Method].

[0079] [4. Equipment] A data acquisition device for classifying patients with chronic sinusitis according to one embodiment of the present invention comprises a classification array for patients with chronic sinusitis according to one embodiment of the present invention, and a scanner for detecting signals originating from the probe.

[0080] The above probe may, for example, (i) emit a detectable signal (e.g., fluorescence) on its own, (ii) bind to another structure that emits a detectable signal (e.g., fluorescence), or (iii) bind to mRNA or protein that is bound to another structure that emits a detectable signal (e.g., fluorescence).

[0081] The scanner mentioned above can be any scanner capable of detecting the aforementioned signal; any publicly known scanner may be used as appropriate.

[0082] A data acquisition device for classifying patients with chronic sinusitis according to one embodiment of the present invention may include an analysis unit that analyzes signals detected by a scanner based on digital molecular barcode technology. Any known analysis unit may be used as the analysis unit.

[0083] The above configuration may include, for example, a computer that executes instructions for a program, which is software that realizes each function. This computer includes, for example, at least one processor and at least one computer-readable recording medium that stores the program. The object of the present invention is achieved when the processor reads the program from the recording medium and executes it in the computer. For example, a CPU (Central Processing Unit) can be used as the processor. As the recording medium, a "tangible medium that is not temporary," such as ROM (Read Only Memory), can be used, as well as tape, disk, card, semiconductor memory, programmable logic circuit, etc. It may also further include RAM (Random Access Memory) for deploying the program. Furthermore, the program may be supplied to the computer via any transmission medium capable of transmitting the program (such as a communication network or broadcast wave). In one aspect of the present invention, the program can also be realized in the form of a data signal embedded in a carrier wave, which is embodied by electronic transmission.

[0084] [5. Other] One embodiment of the present invention can also be configured as follows:

[0085] <1> A method for diagnosing a patient with chronic sinusitis, comprising a detection step of detecting the expression of at least three genes belonging to the following gene group in a subject: The above gene group consists of GPR97, AREG, CLC, CSF3, FGF2, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, VEGFA, ITLN1, IL32, PLA2G2A, HLA-DRB3, CCL18, CXCL13, and S100A8.

[0086] <2> Furthermore, CCL26, CST1, CXCL10, ALOX15, IGHE, IL6, CCL4, CR2, IL9, CLEC6A, TNFSF13 B The method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of CD44, TGFB1, XBP1, IL32, CXCL9, CCL13, CCL24, MARCO, CXCL1, and S100A9. <1> A method for diagnosing patients with chronic sinusitis as described above.

[0087] <3> Furthermore, CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCND3, CD1A, CD274, CD36, CF I, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, TGFBI, POSTN, B2M, PIGR, LTF, CD74, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, IL6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, CFH, AHR, CTSS, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITAF, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSM The detection step includes detecting the expression of at least one gene selected from the gene group consisting of D7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <1> or <2> A method for diagnosing patients with chronic sinusitis as described above.

[0088] Furthermore, another embodiment of the present invention may be configured as follows.

[0089] <1> A method for obtaining data to classify patients with chronic sinusitis, comprising a detection step of detecting the expression of at least three genes belonging to the following gene group in a sample taken from a subject: The above gene group consists of LAGLS3, JAK3, LTB4R, CD53, CLC, and TRAF6.

[0090] <2> Furthermore, the method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of CD164, CD9, PTK2, TRAF4, CSF1R, IL21R, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, PDCD2, C1R, C1S, NFATC3, STAT5B, CCL13, CCL18, CCL24, CD209, CUL9, MAP4K4, MX1, and SMAD3. <1> How to obtain the data described.

[0091] <3> Furthermore, the method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of GPR97, ALOX15, AREG, CSF3, CST1, FGF2, IGHE, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, POSTN, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, and VEGFA. <1> or <2> How to obtain the data described.

[0092] <4> Furthermore, the method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of BATF3, CARD9, CCL23, CCL26, CCND3, CD1A, CD274, CD36, CFI, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, ITLN1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, and TGFBI. <1> ~ <3> The method for obtaining the data described in one of the following.

[0093] <5> Furthermore, B2M, PIGR, LTF, IL32, CD74, XBP1, HLA-DRB3, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD44, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, S100 A9, 1L6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, S100A8, CFH, AHR, CTSS, CXCL1, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC, EGR1, LITAF, The method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <1> ~ <4> The method for obtaining the data described in one of the following.

[0094] Furthermore, yet another embodiment of the present invention may be configured as follows.

[0095] <1> A method for obtaining data to classify patients with chronic sinusitis, comprising a detection step of detecting the expression of at least three genes belonging to the following gene group in a sample taken from a subject: The above gene group consists of GPR97, ALOX15, AREG, CLC, CSF3, CST1, FGF2, IGHE, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, POSTN, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, and VEGFA.

[0096] <2> Furthermore, the method includes a detection step to detect the expression of at least one gene selected from the gene group consisting of CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CCL13, CCL18, CCL24, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCL26, CCND3, CD1A, CD274, CD36, CFI, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, ITLN1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, and TGFBI. <1> How to obtain the data described.

[0097] <3> Furthermore, B2M, PIGR, LTF, IL32, CD74, XBP1, HLA-DRB3, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD44, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, S100A 9, IL6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, S100A8, CFH, AHR, CTSS, CXCL1, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITA The detection step includes detecting the expression of at least one gene selected from the gene group consisting of F, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <1> or <2> How to obtain the data described.

[0098] <4> A classification kit for patients with chronic sinusitis, comprising items for detecting the expression of at least three genes from the following gene group: The above gene group consists of GPR97, ALOX15, AREG, CLC, CSF3, CST1, FGF2, IGHE, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, POSTN, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, and VEGFA.

[0099] <5> Furthermore, the article comprises a set of genes selected from the gene group consisting of CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CCL13, CCL18, CCL24, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCL26, CCND3, CD1A, CD274, CD36, CFI, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, ITLN1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, and TGFBI, for detecting the expression of at least one gene. <4> The kit described above.

[0100] <6> Furthermore, B2M, PIGR, LTF, IL32, CD74, XBP1, HLA-DRB3, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD44, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, S100A 9, IL6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, S100A8, CFH, AHR, CTSS, CXCL1, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITA The article includes a method for detecting the expression of at least one gene selected from the gene group consisting of F, PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <4> or <5> The kit described above.

[0101] <7> An array for classifying patients with chronic sinusitis, comprising probes for detecting the expression of at least three genes included in the following gene group: The above gene group consists of GPR97, ALOX15, AREG, CLC, CSF3, CST1, FGF2, IGHE, IHG1, IHG2, IHG3, IHG4, IGHM, IL25, IL33, IL5RA, LCP1, MPO, PLAT, POSTN, PTGDR2, RNASE3, SERPINB3, SERPINE1, SIGLEC8, TNFRSF25, TNFRSF6B, TRPV3, TSLP, and VEGFA.

[0102] <8> Furthermore, it includes a probe for detecting the expression of at least one gene selected from the gene group consisting of CD164, CD9, LGALS3, PTK2, TRAF4, CSF1R, IL21R, JAK3, TLR9, TNFRSF1B, CRADD, GP1BB, IRF3, LTB4R, PDCD2, C1R, C1S, CD53, NFATC3, STAT5B, CCL13, CCL18, CCL24, CD209, CUL9, MAP4K4, MX1, SMAD3, TRAF6, BATF3, CARD9, CCL23, CCL26, CCND3, CD1A, CD274, CD36, CFI, CISH, CLEC4A, CTSC, CTSG, DPP4, IL13, IL18R1, ITLN1, KIT, MBP, NOS2, PRKCD, PTAFR, SOCS1, and TGFBI. <7> The array described above.

[0103] <9> Furthermore, B2M, PIGR, LTF, IL32, CD74, XBP1, HLA-DRB3, CD59, HLA-B, HLA-DRA, HLA-C, CD81, APP, CD44, CD99, HLA-A, MCL1, SERPING1, HLA-DPB1, CTNNB1, IFITM1, CD24, S100A9 , IL6ST, STAT3, ITGB1, FN1, CXCR4, NFKBIA, HLA-DPA1, TAPBP, FCGRT, S100A8, CFH, AHR, CTSS, CXCL1, SOCS3, MIF, CD46, PECAM1, ILF3, CXCL12, PTPRC_all, EGR1, LITAF, The device comprises a probe for detecting the expression of at least one gene selected from the gene group consisting of PDGFRB, CXCL2, STAT2, MUC1, TNFSF10, CEBPB, TMEM173, HLA-DQA1, ARHGDIB, PSMB7, BCL6, PSMD7, STAT6, BCAP31, C14orf166, IFNGR1, TGFBR2, CD79A, GPI, TCF4, PSMB8, NOTCH2, SKI, C1QB, IFI16, C1QBP, CSF2RB, IFNAR2, IL13RA1, HLA-DMA, PML, ITGA6, JAK1, C3, CD14, and C4A / B. <7> or <8> The array described above.

[0104] <10> <7> ~ <9> A classification array for patients with chronic sinusitis as described in any one of the following items, A data acquisition device for classifying patients with chronic sinusitis, comprising a scanner that detects signals originating from the above-mentioned probe. [Examples]

[0105] The present invention will be described below based on examples. Note that the following examples are merely illustrative, and the present invention is not intended to be limited to these examples.

[0106] [Example 1] <Testing Method> Nasal polyps were collected from 300 patients with chronic sinusitis who underwent endoscopic sinus surgery at the Department of Otolaryngology-Head and Neck Surgery, Fukui University; Hiroshima University; Tsukuba University; and Matsuwaki Clinic Shinagawa. The genes expressed in the collected nasal polyps and their expression levels were investigated.

[0107] To perform pathological diagnosis, formalin-fixed paraffin-embedded (FFPE) specimens were prepared by fixing the collected nasal polyps in formalin for 6 to 48 hours and embedding them in paraffin. The prepared FFPE specimens were sectioned using a microtome, and RNA was extracted according to the protocol using the RNeasy FFPE Kit (Qiagen). The extracted RNA was quality-checked using a Qubit Fluorometer (Thermo Fisher Scientific) and TapeStation 2100 (Agilent Technology), and samples that met certain values ​​for RNA concentration and RNA degradation were used for the nCounter measurement and analysis described later.

[0108] Using the nCounter® Human Immunology V2 Panel and Panel plus (manufactured by Nanostrings), we prepared arrays immobilized with probes that hybridize to the mRNA of 579 existing genes and probes that hybridize to the mRNA of 30 genes uniquely selected by the inventors.

[0109] The genes included in the array used in this study are as follows: ABCB1、ABL1、ADA、AHR、AICDA、AIRE、ALOX15、APP、AREG、ARG1、ARG2、ARHGDIB、ATG10、ATG12、ATG16L1、ATG5、ATG7、ATM、B2M、B3GAT1、BATF、BATF3、BAX、BCAP31、BCL10、BCL2、BCL2L11、BCL3、BCL6、BID、BLNK、BST1、BST2、BTK、BTLA、C14orf166、C1QA、C1QB、C1QBP、C1R、C1S、C2、C3、C4A / B、C4BPA、C5、C6、C7、C8A、C8B、C8G、C9、CAMP、CARD9、CASP1、CASP10、CASP2、CASP3、CASP8、CCBP2、CCL11、CCL13、CCL15、CCL16、CCL18、CCL19、CCL2、CCL20、CCL22、CCL23、CCL24、CCL26、CCL3、CCL4、CCL5、CCL7、CCL8、CCND3、CCR1、CCR10、CCR2、CCR5、CCR6、CCR7、CCR8、CCRL1、CCRL2、CD14、CD160、CD163、CD164、CD19、CD1A、CD1D、CD2、CD209、CD22、CD24、CD244、CD247、CD27、CD274、CD276、CD28、CD34、CD36、CD3D、CD3E、CD3EAP、CD4、CD40、CD40LG、CD44、CD45R0、CD45RA、CD45RB、CD46、CD48、CD5、CD53、CD55、CD58、CD59、CD6、CD7、CD70、CD74、CD79A、CD79B、CD80、CD81、CD82、CD83、CD86、CD8A、CD8B、CD9、CD96、CD97、CD99、CDH5、CDKN1A、CEACAM1、CEACAM6、CEACAM8、CEBPB、CFB、CFD、CFH、CFI、CFP、CHUK、CIITA、CISH、CLC、CLEC4A、CLEC4E、CLEC5A、CLEC6A、CLEC7A、CLU、CMKLR1、CR1、CR2、CRADD、CSF1、CSF1R、CSF2、CSF2RB、CSF3、CSF3R、CST1、CTLA4-TM、CTLA4_all、CTNNB1、CTSC、CTSG、CTSS、CUL9、CX3CL1、CX3CR1、CXCL1、CXCL10、CXCL11、CXCL12、CXCL13、CXCL2、CXCL9、CXCR1、CXCR2、CXCR3、CXCR4、CXCR6 、CYBB、DEFB1、DEFB103A、DEFB103B、DEFB4A、DPP4、DUSP4、EBI3、EDNRB、EGR1 、EGR2、ENTPD1、EOMES、ETS1、FADD、FAS、FCAR、FCER1A、FCER1G、FCGR1A / B、F CGR2A、FCGR2A / C、FCGR2B、FCGR3A / B、FCGRT、FGF2、FKBP5、FN1、FOXP3、FYN、G ATA3、GBP1、GBP5、GFI1、GNLY、GP1BB、GPI、GPR183、GPR97、GZMA、GZMB、GZMK 、HAMP、HAVCR2、HFE、HLA-A、HLA-B、HLA-C、HLA-DMA、HLA-DMB、HLA-DOB、HLA- DPA1、HLA-DPB1、HLA-DQA1、HLA-DQB1、HLA-DRA、HLA-DRB1、HLA-DRB3、HRAS 、ICAM1、ICAM2、ICAM3、ICAM4、ICAM5、ICOS、ICOSLG、IDO1、IFI16、IFI35、IFI H1, IFIT2, IFITM1, IFNA1 / 13, IFNA2, IFNAR1, IFNAR2, IFNB1, IFNG, IFNGR1, IGF2R, IGHE, IGHG1, IGHG2, IGHG3, IGHG4, IGHM, IKBKAP, IKBKB, IKBKE, IKB KG、IKZF1、IKZF2、IKZF3、IL10、IL10RA、IL11RA、IL12A、IL12B、IL12RB1、IL 13、IL13RA1、IL15、IL16、IL17A、IL17B、IL17F、IL18、IL18R1、IL18RAP、IL19 、IL1A、IL1B、IL1R1、IL1R2、IL1RAP、IL1RL1、IL1RL2、IL1RN、IL2、IL20、IL2 1、IL21R、IL22、IL22RA2、IL23A、IL23R、IL25、IL26、IL27、IL28A、IL28A / B、I L29、IL2RA、IL2RB、IL2RG、IL3、IL32、IL33、IL4、IL4R、IL5、IL5RA、IL6、IL6R 、IL6ST、IL7、IL7R、IL8、IL9、ILF3、IRAK1、IRAK2、IRAK3、IRAK4、IRF1、IRF3、IRF4、IRF5、IRF7、IRF8、IRGM、ITGA2B、ITGA4、ITGA5、ITGA6、ITGAE、ITGAL、ITGAM、ITGAX、ITGB1、ITGB2、ITLN1、ITLN2、JAK1、JAK2、JAK3、KCNJ2、KIR3DL1、KIR3DL2、KIR3DL3、KIR_Activating_Subgroup_1、KIR_Activating_Subgroup_2、KIR_Inhibiting_Subgroup_1、KIR_Inhibiting_Subgroup_2、KIT、KLRAP1、KLRB1、KLRC1、KLRC2、KLRC3、KLRC4、KLRD1、KLRF1、KLRF2、KLRG1、KLRG2、KLRK1、LAG3、LAIR1、LAMP3、LCK、LCP1、LCP2、LEF1、LGALS3、LIF、LILRA1、LILRA2、LILRA3、LILRA4、LILRA5、LILRA6、LILRB1、LILRB2、LILRB3、LILRB4、LILRB5、LITAF、LTA、LTB4R、LTB4R2、LTBR、LTF、LY96、MAF、MALT1、MAP4K1、MAP4K2、MAP4K4、MAPK1、MAPK11、MAPK14、MAPKAPK2、MARCO、MASP1、MASP2、MBL2、MBP、MCL1、MIF、MME、MPO、MR1、MRC1、MS4A1、MSR1、MUC1、MX1、MYD88、NCAM1、NCF4、NCR1、NFATC1、NFATC2、NFATC3、NFIL3、NFKB1、NFKB2、NFKBIA、NFKBIZ、NLRP3、NOD1、NOD2、NOS2、NOTCH1、NOTCH2、NT5E、PAX5、PDCD1、PDCD1LG2、PDCD2、PDGFB、PDGFRB、PECAM1、PIGR、PLA2G2A、PLA2G2E、PLAT、PLAU、PLAUR、PML、POSTN、POU2F2、PPARG、PPBP、PRDM1、PRF1、PRKCD、PSMB10、PSMB5、PSMB7、PSMB8、PSMB9、PSMC2、PSMD7、PTAFR、PTGDR2、PTGER4、PTGS2、PTK2、PTPN2、PTPN22、PTPN6、PTPRC_all、PYCARD、RAF1、RAG1、RAG2、RARRES3、RELA、RELB, RNASE3, RORC, RUNX1, S100A8, S100A9, S1PR1, SELE, SELL, SELPLG, SERPINB3, SERPINE1, SERPING 1, SH2D1A, SIGIRR, SIGLEC8, SKI, SLAMF1, SLAMF6, SLAMF7, SLC2A1, SMAD3, SMAD5, SOCS1, SOCS3, SPP1, S RC, STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B, STAT6, SYK, TAGAP, TAL1, TAP1, TAP2, TAPBP, TBK1, TBX21, TCF4, TCF7, TFRC, TGFB1, TGFBI, TGFBR1, TGFBR2, THY1, TICAM1, TIGIT, TIRAP, TLR1, TLR2, TLR3, TL R4, TLR5, TLR7, TLR8, TLR9, TMEM173, TNF, TNFAIP3, TNFAIP6, TNFRSF10C, TNFRSF11A, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF17, TNFRSF1B, TNFRSF25, TNFRSF4, TNFRSF6B, TNFRSF8, TNFRSF9, TNFSF10, TNF SF11, TNFSF12, TNFSF13B, TNFSF15, TNFSF4, TNFSF8, TOLLIP, TP53, TRAF1, TRAF2, TRAF3, TRAF4, TRAF5 , TRAF6, TRPV3, TSLP, TYK2, UBE2L3, VCAM1, VEGFA, VTN, XBP1, XCL1, XCR1, ZAP70, ZBTB16, ZEB1, sCTLA4. ,

[0110] Following the protocol, extracted RNA was hybridized to probes immobilized on the array described above. Subsequently, gene expression levels in nasal polyp tissue were measured using the nCounter® MAX Analysis System (manufactured by Nanostrings). The measured data was then processed using nSolver. TM Using Data Analysis (Nanostrings), we analyzed the relationship between the phenotype of chronic sinusitis (eosinophilic sinusitis, noneosinophilic sinusitis) and gene expression.

[0111] <Test Results> The analysis was performed using principal component analysis (PCA) based on gene expression. Figure 1 shows the analysis results. In Figure 1, the vertical axis represents PC1 (the element that contributed most to cluster classification), and the horizontal axis represents PC2 (the element that contributed second most to cluster classification). The PCs are assigned in a machine learning manner as PC1, PC2, PC3, etc., in order of their contribution to the analysis results. In other words, the planar diagram composed of PC1 and PC2 contains the most information to explain each cluster. Each PC is formed by a combination of complex items. Figure 1 is a diagram plotting each patient on a plane, compressed into two dimensions to make it easier to recognize the differences between patients resulting from the combination of genes composed in a high dimension, based on the expression levels of 609 genes in each patient. From the results obtained, it was found that the patients in this study could be classified into five clusters.

[0112] Figure 2 shows the major genes that are highly expressed in each cluster. In Figure 2, "non-ECRS" refers to non-eosinophilic sinusitis, and "ECRS" refers to eosinophilic sinusitis. The "CTscore Tissue Eo" on the far right is a graph showing the relative numerical values ​​of the Lund-Mackay CT score, a commonly used clinical evaluation item, and the eosinophil score in the tissue, representing the severity of symptoms in patients in each cluster. Cluster 1 had the lowest numerical values, while Cluster 5 had the highest.

[0113] Figure 3 is a graph plotting the measured gene expression levels for the genes of the first gene group. The vertical axis in Figure 3 represents the expression level of each gene, expressed on a log2 logarithmic scale. The horizontal axis in Figure 3 represents the type of gene. In Figure 3, the data for each gene is arranged on a straight line approximately parallel to the vertical axis. In Figure 3, the genes are plotted in the following order from top right to bottom left: IHG3, IHG1, IHG2, IHG4, ALOX15, IGHM, POSTN, PLAT, IL33, SERPINB3, SERPINE1, VEGFA, LCP1, CST1, TNFRSF25, TNFRSF6B, FGF2, IL5RA, IGHE, AREG, CLC, TSLP, GPR97, IL25, R NA These are SE3, PTGDR2, SIGLEC8, TRPV3, MPO, and CSF3. In Figure 3, the greater the vertical distance, the greater the difference in expression levels between the clusters.

[0114] Figure 3 reveals that there are clear differences in the expression levels of the first gene group among the clusters in nasal polyp tissue collected from patients with eosinophilic sinusitis.

[0115] The results above demonstrate that the types of chronic sinusitis can be more accurately classified by analysis based on gene expression levels.

[0116] Based on the above analysis results, patients with chronic sinusitis were classified into several clusters according to the differences in their respective gene expression levels. The relationship between the five classified clusters and the classification of sinusitis, and their gene expression levels, is described below.

[0117] Cluster 1 Classification: Non-eosinophilic sinusitis Clinical characteristics: Minimal eosinophil infiltration, rare complications, and a good prognosis. Characteristics of expression levels of novel genes: PLAT, IL33, SERPINB 3 All of these genes are highly expressed. The other genes are expressed at relatively low levels. Characteristics of known gene expression levels: IgE, IL-4, IL-5, IL-13, TSLP, IL-17, IL-22, and IFN-γ are all expressed at low levels.

[0118] Cluster 2 Classification: Eosinophilic sinusitis (good prognosis type) Clinical characteristics: Eosinophil infiltration is common, complications are moderate, and the prognosis is good. Characteristics of known gene expression levels: IgE, IL-5, IL-8, MPO, and TSLP are all highly expressed. Characteristics of the new gene expression levels: PLAT, IL33, LCP1, VEGFA, CSF3, TSLP, and MPO are highly expressed. On the other hand, IGG1, IGG2, IGG3, and IGG4 are lowly expressed.

[0119] Cluster 3 Classification: Eosinophilic sinusitis (hyperinflammatory type) Clinical characteristics: Patients are relatively more female, eosinophil infiltration is common, complications are frequent, and the prognosis is poor. Characteristics of known gene expression levels: IL-5, IL-4, IL-13, TSLP, and IFN-γ are all highly expressed. However, ALOX15 is expressed at a low level. Characteristics of novel gene expression levels: IHG1, IHG2, IHG3, IHG4, IL 25 , R NA SE3 and MPO are both highly expressed. On the other hand, TSLP and others are expressed at low levels.

[0120] Cluster 4 Classification: Eosinophilic sinusitis (classic type) Clinical characteristics: Frequent eosinophil infiltration, high incidence of complications, poor prognosis. Characteristics of known gene expression levels: IL-5, POSTN, and ALOX15 are highly expressed. However, IL-4 and IL-13 are low in expression. Characteristics of the new gene expression levels: IHG1, LCP1, CLC, and SIGLEC8 are all highly expressed. On the other hand, PLAT, TRPV3, and CSF3 are low in expression.

[0121] Cluster 5 Classification: Eosinophilic sinusitis (with Type 3 inflammatory type) Clinical characteristics: Patients are mostly elderly men, eosinophil infiltration is common, complications are moderate, and the prognosis is poor. Characteristics of known gene expression levels: In addition to high expression of IgE, TGFβ, IL-5, and IL-4 / IL-13, IL-17 / IL-22 is also highly expressed. Characteristics of novel gene expression levels: IHG2, GPR97, TNFRSF25, R NA SE3 is highly expressed in all of these molecules. On the other hand, IL33, SERPINB3, LCP1, FGF2, TRPV3, and CSF3 are low in expression.

[0122] From the above, we were able to confirm that patients' characteristics (e.g., gender or age) differed for each cluster. Furthermore, when we compared the classification results of these clusters with the clinical characteristics of the patients in question, we found that the classifications and clinical characteristics matched and there were no discrepancies. In addition, it was shown that classification was possible in some areas that had not been clearly classified until now.

[0123] Based on the cluster classification described above, chronic sinusitis can be treated by administering anti-monoclonal antibody drugs according to its classification.

[0124] [Example 2] <Testing Method> Nasal polyps were collected from 434 patients with chronic sinusitis who underwent endoscopic sinus surgery at the Department of Otolaryngology-Head and Neck Surgery, Fukui University; Hiroshima University; Tsukuba University; and Matsuwaki Clinic Shinagawa. Of the collected polyps, the genes expressed within the tissue and their expression levels were investigated in 253 cases.

[0125] FFPE samples were prepared from the collected nasal polyps in the same manner as in Example 1, and RNA was extracted. The extracted RNA was quality-checked using a Qubit Fluorometer (Thermo Fisher Scientific) and a TapeStation 4200 (Agilent Technology), and samples that met certain values ​​for RNA concentration and RNA degradation were used for the nCounter measurement and analysis described later.

[0126] Reagent pack hybridization was performed to prepare each sample for use with nCounter. Expression measurements were performed by digital counting of mRNA using the nCounter® Human Immunology V2 Panel (594 genes) and 30 genes uniquely selected by the inventors. Of the 594 genes, 15 are known as housekeeping genes, which are considered to show no change in any sample. The obtained measurement values ​​were processed using nSolver software (nanostrings) and output to an external source. Furthermore, the output data was analyzed using the statistical software "R".

[0127] <Test Results> The analysis was performed using the Ward method based on gene expression. Figure 4 shows the analysis results. In Figure 4, the vertical axis represents tsne1 (the element that contributed most to cluster classification), and the horizontal axis represents tsne2 (the element that contributed second most to cluster classification). Figure 4 is a diagram plotting each patient on a plane, with the 624 genes compressed into two dimensions to make it easier to recognize the differences between patients resulting from the combination of genes that are composed in a high-dimensional structure. From the obtained analysis results, it was found that the patients in this study could be classified into nine clusters.

[0128] Figure 5 shows the patient conditions, symptoms, and recurrence rates for each cluster. The relationship between the nine classified clusters and the classification of sinusitis, and gene expression levels, is described below.

[0129] Cluster 1 Clinical characteristics: Non-ECRS (non-eosinophilic sinusitis) accounts for half of the patients, and the prognosis is poor. The majority of patients are women. Characteristics of gene expression levels: CXCL10 is highly expressed. On the other hand, ITLN1, CCL26, CST1, and ALOX15 are low in expression.

[0130] Cluster 2 Clinical characteristics: Severe ECRS (severe eosinophilic sinusitis) accounts for a large proportion of patients, but the prognosis is good. Complications include asthma (especially aspirin-induced asthma), and patients have high eosinophil counts in their blood and tissues. Patients are predominantly male. Gene expression characteristics: IL32, IGHE, IL6, and CCL4 are highly expressed. On the other hand, PLA2G2A is lowly expressed.

[0131] Cluster 3 Clinical characteristics: The ratio of patients with ECRS (eosinophilic sinusitis) to those without ECRS is approximately 7:3, but the recurrence rate is relatively low and the prognosis is good. There is little difference in the sex of the patients. Characteristics of gene expression levels: CR2, IL9, CLEC6A, TNFSF13 B It is highly expressed. On the other hand, PLA2G2A is low in expression.

[0132] Cluster 4 Clinical characteristics: A cluster in which no patients developed asthma as a complication. The majority of patients were female. There was a high proportion of patients with Mild-ECRS (mild eosinophilic sinusitis). Characteristics of gene expression levels: HLA-DRB3, CD44, TGFB1, and XBP1 are highly expressed. On the other hand, CCL18 is lowly expressed.

[0133] Cluster 5 Clinical characteristics: The prognosis is poor, and there are many patients with severe-ECRS and mild-ECRS. Many patients develop asthma as a complication. In addition, patients have high eosinophil counts in their blood and tissues. Characteristics of gene expression levels: CST1 is highly expressed. On the other hand, PLA2G2A and CX CL13, IL32, and CXCL9 are expressed at low levels.

[0134] Cluster 6 Clinical characteristics: Half of the patients are non-ECRS. Patients have low eosinophil counts in their tissues and blood. Characteristics of gene expression levels: CCL18, CCL13, CCL24, CCL26, and MARCO are low in expression.

[0135] Cluster 7 Clinical characteristics: High Lund-Mackay score (sinus CT score), frequent complications, and a tendency towards poor prognosis from an early stage. Characteristics of gene expression levels: CXCL13, S100A8, CR2, IL9, and CLEC6A are low in expression.

[0136] Cluster 8 Clinical characteristics: Patients are predominantly male and tend to be younger. Severe-ECRS and moderate-ECRS (moderate eosinophilic sinusitis) are common. Characteristics of gene expression levels: PLA2G2A, CCL26, I GHG 2. IGHE and CR2 are low in expression.

[0137] Cluster 9 Clinical characteristics: The prognosis is relatively good, and half of the patients are non-ECRS. Characteristics of gene expression levels: CXCL10 is highly expressed. On the other hand, ITLN1, CCL26, CST1, and ALOX15 are low in expression.

[0138] From the above, we were able to confirm that clinical characteristics differed for each cluster. Furthermore, when we compared the classification results of these clusters with the clinical characteristics of the patients in question, we found that the classifications and clinical characteristics matched and there were no discrepancies. In addition, it was shown that classification was possible in some areas that had not been clearly classified until now.

[0139] Specifically, among these clusters, clusters 2, 5, and 7 had a high incidence of complications and a large number of severely ill patients. On the other hand, cluster 2 had significantly fewer patients with symptom relapses compared to clusters 5 and 7. Therefore, it was found that patients exhibiting certain gene expression patterns were less likely to experience relapses, even if their condition was severe. [Industrial applicability]

[0140] This invention can be used to classify and diagnose patients with chronic sinusitis.

Claims

1. A detection step involves detecting the expression levels of all genes included in either the following gene group (a) or (b) in a sample taken from a subject, and A method for obtaining data to classify patients with chronic sinusitis, comprising a comparison step of comparing the expression level of the gene with the expression level of the gene in patients with chronic sinusitis: The above gene group is, (a) PLAT, IL33, LCP1, VEGFA, CSF3, TSLP, MPO, IGHG1, IGHG2, IGHG3, IGHG 4, IL25, RNASE3, CLC, SIGLEC8, TRPV3, GPR97, TNFRSF25, SERPINB3, FGF2 (b) CXCL10, ITLN1, CCL26, CST1, ALOX15, IL32, IGHE, IL6, CCL4, PLA2G2A, CR2, IL9, CLEC6A, TNFS F13B, HLA-DRB3, CD44, TGFB1, XBP1, CCL18, CXCL9, CCL13, CCL24, MARCO, CXCL13, S100A8, IGHG2.

2. The expression data of the gene obtained in the above detection step is subjected to statistical processing using Ward's method. The method for obtaining data according to claim 1, further comprising a statistical processing step.

3. The method for obtaining data according to claim 1 or 2, wherein the subject is a subject who has been administered a drug for treating chronic sinusitis.

4. A classification kit for patients with chronic sinusitis, comprising: an article for detecting the expression levels of all genes belonging to either the following gene group (a) or (b) in a sample taken from a subject; and an article for comparing the expression levels of said genes with the expression levels of said genes in patients with chronic sinusitis: The above gene group is, (a) PLAT, IL33, LCP1, VEGFA, CSF3, TSLP, MPO, IGHG1, IGHG2, IGHG3, IGHG 4, IL25, RNASE3, CLC, SIGLEC8, TRPV3, GPR97, TNFRSF25, SERPINB3, FGF2 (b) CXCL10, ITLN1, CCL26, CST1, ALOX15, IL32, IGHE, IL6, CCL4, PLA2G2A, CR2, IL9, CLEC6A, TNFS F13B, HLA-DRB3, CD44, TGFB1, XBP1, CCL18, CXCL9, CCL13, CCL24, MARCO, CXCL13, S100A8, IGHG2.

5. A classification array for patients with chronic sinusitis, comprising probes for detecting the expression levels of all genes belonging to either the following gene group (a) or (b) in a sample taken from a subject, and for comparing the expression levels of said genes with the expression levels of said genes in patients with chronic sinusitis: The above gene group is, (a) PLAT, IL33, LCP1, VEGFA, CSF3, TSLP, MPO, IGHG1, IGHG2, IGHG3, IGHG 4, IL25, RNASE3, CLC, SIGLEC8, TRPV3, GPR97, TNFRSF25, SERPINB3, FGF2 (b) CXCL10, ITLN1, CCL26, CST1, ALOX15, IL32, IGHE, IL6, CCL4, PLA2G2A, CR2, IL9, CLEC6A, TNFS F13B, HLA-DRB3, CD44, TGFB1, XBP1, CCL18, CXCL9, CCL13, CCL24, MARCO, CXCL13, S100A8, IGHG2.

6. The array for classifying patients with chronic sinusitis according to claim 5, A data acquisition device for classifying patients with chronic sinusitis, comprising a scanner that detects signals originating from the above-mentioned probe.

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