Non-invasive biomarkers for liver disease and detection of liver disease using same

Abstract

The present invention provides: a biomarker for non-invasive or low-invasive and high-precision detection of liver diseases; a kit, a method, and a program for non-invasive or low-invasive and high-precision detection of liver disease using the biomarker; also disclosed are a data processing method for detecting a liver disease with high accuracy using the non-invasive marker for the liver disease, and a data processing device for the data processing. More specifically, provided is a biomarker selected from the group consisting of SEQ ID NO: 1-125. Also provided is a liver disease detection kit comprising a nucleic acid capable of specifically binding to a specific miRNA. Furthermore, provided are a method and a program for determining the presence or absence of a liver disease in a subject, the risk or the degree of progress thereof, and / or the degree of success of a treatment to be carried out for treatment, said method comprising the steps of: measuring the level of a biomarker in a biological sample derived from the subject; and comparing the measured value of one or more biomarkers selected from the group consisting of SEQ ID NO: 1-125 with a control value, the biomarkers being one or more types selected from the group consisting of SEQ ID NO: 1-125; also provided are a data processing method for detecting a liver disease with high accuracy using the non-invasive liver disease marker, and a data processing device for the data processing.

Description

Technical Field

[0001] The present invention relates to a non-invasive biomarker for liver disease. The present invention also relates to a kit, method and program for detecting liver disease with high accuracy using the non-invasive biomarker for liver disease, a data processing method for detecting liver disease with high accuracy using the non-invasive biomarker for liver disease, and a data processing device used in the data processing. Background Art

[0002] Liver biopsy is still widely used as a method for analyzing the degree of improvement of fibrosis as a method with high evaluation accuracy. However, patients with cirrhosis have a tendency to bleed, so liver biopsy is likely to cause serious complications. In addition, liver biopsy only collects a part of the liver tissue, so there is always a question of whether the liver biopsy results reflect the overall condition of the liver due to sampling errors.

[0003] It is known that in diseases such as cancer, the function of miRNA is abnormal and may become the cause of the disease. At present, for liver disease, several markers that can be evaluated non-invasively or low-invasively are being used, but markers with high accuracy have not yet been established (e.g., non-patent literature 1). Therefore, it is still strongly desired to develop alternative markers that can accurately diagnose and are non-invasive or low-invasive. The practical application of such high-precision and non-invasive or low-invasive alternative markers not only helps in diagnosis, but also helps to improve the quality of life (QOL) of patients.

[0004] Prior art literature

[0005] Non-patent literature

[0006] Non-patent literature 1: Serum Wisteria floribunda agglutinin-positive Mac-2-binding protein for patients with chronic hepatitis B and C: a comparative study. H Nishikawa, et al. J Viral Hepat. 2016 Dec; 23(12): 977-984. doi: 10.1111 / jvh.12575. Epub 2016 Jul 31. Summary of the invention

[0007] Problem that the invention aims to solve

[0008] The present invention provides a biomarker for non-invasive or low-invasive and high-precision detection of liver disease. In addition, the present invention provides a kit, method and program for non-invasive or low-invasive and high-precision detection of liver disease using the biomarker, and a data processing method for high-precision detection of liver disease using the non-invasive liver disease marker, and a data processing device used in the data processing.

[0009] Solutions for solving problems

[0010] The present inventors conducted intensive studies to solve the above-mentioned problems and found multiple miRNAs that can be used as detection markers for liver disease from blood that can be collected in a low-invasive manner. They also found that liver disease can be significantly detected by using nucleic acids that can specifically bind to them, thereby completing the present invention.

[0011] The present invention includes the following aspects.

[0012] <1-1>

[0013] A liver disease detection kit comprising a nucleic acid capable of specifically binding to one or more polynucleotides selected from the group consisting of the following polynucleotides as biomarkers or the complementary chains of the polynucleotides: miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12 )、miR-223 (SEQ ID NO: 13), miR-378a (SEQ ID NO: 14), miR-34a (SEQ ID NO: 15), miR-320c-1 (SEQ ID NO: 16), miR-320b-1 (SEQ ID NO: 17), miR-192 (SEQ ID NO: 18), miR-148a (SEQ ID NO: 19), miR-100 (SEQ ID NO: 20), miR-144 (SEQ ID NO: 21), miR-320b-2 (SEQ ID NO: 22), miR-451a (SEQ ID NO: 23), miR-548d-3p (SEQ ID NO: 24), miR-320b (SEQ ID NO: 25), miR-1470 (SEQ ID NO: 26), miR-4258 (SEQ ID NO: 27), miR- 4673 (SEQ ID NO: 28), miR-4748 (SEQ ID NO: 29), miR-4787-3p (SEQ ID NO: 30), miR-204-3p (SEQ ID NO: 31), miR-6131 (SEQ ID NO: 32), miR-6752-3p (SEQ ID NO: 33), miR-4648 (SEQ ID NO: 34), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-1231 (SEQ ID NO: 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-6729-3p (SEQ ID NO: 42), miR-6798-3p (SEQ ID NO: 43), miR-379-5p (SEQ ID NO: 44), miR-323a-5p (SEQ ID NO: 45), miR-665 (SEQ ID NO: 46), miR-4279 (SEQ ID NO: 47), miR-3605-5p (SEQ ID NO: 48), miR-4684-3p (SEQ ID NO: 49), miR-365b-5p (SEQ ID NO: 50), miR-6782-5p (SEQ ID NO: 51), miR-6880-3p (SEQ ID NO: 52), miR-6887-3p (SEQ ID NO: 53), miR-4433b-5p (SEQ ID NO: 54), miR-10396a-3p (SEQ ID NO: 55),miR-373-5p (sequence number 56), miR-4539 (sequence number 57), miR-4687-3p (sequence number 58), miR-4763-5p (sequence number 59), miR-4482-3p (sequence number 60), miR-6721-5p (sequence number 61), miR-6812-3p (sequence number 62), miR-6815-5p (sequence number 63), miR-6871-5p (sequence number 64), miR-7975 (sequence number 65), miR-11181-3p (sequence number 66), miR-2278 (sequence number 67), miR-3192-5p (sequence number 68), miR-4722-3 p (SEQ ID NO: 69), miR-4750-3p (SEQ ID NO: 70), miR-6804-5p (SEQ ID NO: 71), miR-6828-5p (SEQ ID NO: 72), miR-614 (SEQ ID NO: 73), miR-320c (SEQ ID NO: 74), miR-2110 (SEQ ID NO: 75), miR-3177-3p (SEQ ID NO: 76), miR-4497 (SEQ ID NO: 77), miR-210-5p (SEQ ID NO: 78), miR-6778-5p (SEQ ID NO: 79), miR-6780a-5p (SEQ ID NO: 80), miR-6801-3p (SEQ ID NO: 81), miR-8059 (SEQ ID NO: 82), miR-657 (SEQ ID NO: 83), miR- 83), miR-921 (SEQ ID NO. 84), miR-4451 (SEQ ID NO. 85), miR-3059-3p (SEQ ID NO. 86), miR-3619-3p (SEQ ID NO. 87), miR-4646-5p (SEQ ID NO. 88), miR-525-5p (SEQ ID NO. 89), miR-4444 (SEQ ID NO. 90), miR-4458 (SEQ ID NO. 91), miR-4746-3p (SEQ ID NO. 92), miR-1292-3p (SEQ ID NO. 93), miR-6133 (SEQ ID NO. 94), miR-6754-3p (SEQ ID NO. 95), miR-365a-3p (SEQ ID NO. 96), miR-365b-3p (SEQ ID NO. 97), 97), miR-6748-3p (SEQ ID NO: 98), miR-6892-3p (SEQ ID NO: 99), miR-8083 (SEQ ID NO: 100), miR-4724-5p (SEQ ID NO: 101), miR-6884-5p (SEQ ID NO: 102), miR-7156-3p (SEQ ID NO: 103), miR-19b-3p (SEQ ID NO: 104), miR-518e-3p (SEQ ID NO: 105), miR-646 (SEQ ID NO: 106), miR-298 (SEQ ID NO: 107), miR-1234-3p (SEQ ID NO: 108), miR-2115-5p (SEQ ID NO: 109), miR-3142 (SEQ ID NO: 110),miR-3179 (SEQ ID NO. 111), miR-3190-5p (SEQ ID NO. 112), miR-3675-3p (SEQ ID NO. 113), miR-4441 (SEQ ID NO. 114), miR-4475 (SEQ ID NO. 115), miR-6718-5p (SEQ ID NO. 116), miR-6743-3p (SEQ ID NO. 117), miR-6894-3p (SEQ ID NO. 118), miR-7112-5p (SEQ ID NO. 119), miR-12116 (SEQ ID NO. 120), miR-518d-3p (SEQ ID NO. 121), miR-145-5p (SEQ ID NO. 122), miR-6801-5p (SEQ ID NO. 123), miR-127-3p (SEQ ID NO. 124), and miR-4731-3p (SEQ ID NO. 125).

[0014] <1-2>

[0015] The liver disease detection kit according to <1-1>, comprising a nucleic acid capable of specifically binding to one or more polynucleotides selected from the group consisting of the following polynucleotides as biomarkers or the complementary chains of the polynucleotides: miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13), miR-378a (SEQ ID NO: 14), miR-34a (SEQ ID NO: 15), miR-320c-1 (SEQ ID NO: 16), miR-320b-1 (SEQ ID NO: 17), miR-192 (SEQ ID NO: 18), miR-148a (SEQ ID NO: 19), miR-100 (SEQ ID NO: 20), miR-144 (SEQ ID NO: 21), miR-320b-2 (SEQ ID NO: 22), miR-451a (SEQ ID NO: 23), miR-548d-3p (SEQ ID NO: 24), miR-320b (SEQ ID NO: 25), miR-1470 (SEQ ID NO: 26), miR- 4258 (SEQ ID NO: 27), miR-4673 (SEQ ID NO: 28), miR-4748 (SEQ ID NO: 29), miR-4787-3p (SEQ ID NO: 30), miR-204-3p (SEQ ID NO: 31), miR-6131 (SEQ ID NO: 32), miR-6752-3p (SEQ ID NO: 33), miR-4648 (SEQ ID NO: 34), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-1231 (SEQ ID NO: 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-6729-3p (SEQ ID NO: 42), miR-6798-3p (SEQ ID NO: 43), miR-379-5p (SEQ ID NO: 44), miR-323a-5p (SEQ ID NO: 45), miR-665 (SEQ ID NO: 46), miR-4279 (SEQ ID NO: 47), miR-3605-5p (SEQ ID NO: 48), miR-4684-3p (SEQ ID NO: 49), miR-365b-5p (SEQ ID NO: 50), miR-6782-5p (SEQ ID NO: 51), miR-6880-3p (SEQ ID NO: 52), miR-6887-3p (SEQ ID NO: 53), miR-4433b-5p (SEQ ID NO: 54),miR-10396a-3p (sequence number 55), miR-373-5p (sequence number 56), miR-4539 (sequence number 57), miR-4687-3p (sequence number 58), miR-4763-5p (sequence number 59), miR-4482-3p (sequence number 60), miR-6721-5p (sequence number 61), miR-6812-3p (sequence number 62), miR-6815-5p (sequence number 63), miR-6871-5p (sequence number 64), miR-7975 (sequence number 65), miR-11181-3p (sequence number 66), miR-2278 (sequence number 67), miR-3192-5p (sequence number 68), miR- 68), miR-4722-3p (SEQ ID NO: 69), miR-4750-3p (SEQ ID NO: 70), miR-6804-5p (SEQ ID NO: 71), miR-6828-5p (SEQ ID NO: 72), miR-614 (SEQ ID NO: 73), miR-320c (SEQ ID NO: 74), miR-2110 (SEQ ID NO: 75), miR-3177-3p (SEQ ID NO: 76), miR-4497 (SEQ ID NO: 77), miR-210-5p (SEQ ID NO: 78), miR-6778-5p (SEQ ID NO: 79), miR-6780a-5p (SEQ ID NO: 80), miR-6801-3p (SEQ ID NO: 81), and miR-8059 (SEQ ID NO: 82).

[0016] <1-3>

[0017] The liver disease detection kit according to <1-2> comprises a nucleic acid capable of specifically binding to a polynucleotide or a complementary chain of the polynucleotide selected from the group consisting of the following polynucleotides serving as biomarkers: miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4).

[0018] <1-4>

[0019] The liver disease detection kit according to <1-3>, wherein the kit further comprises at least one nucleic acid that can specifically bind to a polynucleotide serving as another biomarker.

[0020] <1-5>

[0021] According to the liver disease detection kit described in <1-4>, the other biomarkers are selected from miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-2 One or more of the group consisting of miR-23 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0022] <1-6>

[0023] The liver disease detection kit according to <1-1>, whereinA nucleic acid that can specifically bind to one or more polynucleotides selected from the group consisting of the following polynucleotides as biomarkers, or a complementary strand of the polynucleotide: miR-451a (SEQ ID NO: 23), miR-4787-3p (SEQ ID NO: 30), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-6798-3p (SEQ ID NO: 43), miR-7975 (SEQ ID NO: 65), miR-614 (SEQ ID NO: 73), miR-6801-3p (SEQ ID NO: 81), miR-657 (SEQ ID NO: 83), miR-921 (SEQ ID NO: 84), miR-4451 (SEQ ID NO: 85), miR-305 9-3p (SEQ ID NO: 86), miR-3619-3p (SEQ ID NO: 87), miR-4646-5p (SEQ ID NO: 88), miR-525-5p (SEQ ID NO: 89), miR-4444 (SEQ ID NO: 90), miR-4458 (SEQ ID NO: 91), miR-4746-3p (SEQ ID NO: 92), miR-1292-3p (SEQ ID NO: 93), miR-6133 (SEQ ID NO: 94), miR-6754-3p (SEQ ID NO: 95), miR-365a-3p (SEQ ID NO: 96), miR-365b-3p (SEQ ID NO: 97), miR-6748-3p (SEQ ID NO: 98), miR-6892-3p (SEQ ID NO: 99), )、miR-8083(SEQ ID NO: 100)、miR-4724-5p(SEQ ID NO: 101)、miR-6884-5p(SEQ ID NO: 102)、miR-7156-3p(SEQ ID NO: 103)、miR-19b-3p(SEQ ID NO: 104)、miR-518e-3p(SEQ ID NO: 105)、miR-646(SEQ ID NO: 106)、miR-298(SEQ ID NO: 107)、miR-1234-3p(SEQ ID NO: 108)、miR-2115-5p(SEQ ID NO: 109)、miR-3142(SEQ ID NO: 110)、miR-3179(SEQ ID NO: 111)、miR-3190-5p(SEQ ID NO: 112)、miR -3675-3p (SEQ ID NO. 113), miR-4441 (SEQ ID NO. 114), miR-4475 (SEQ ID NO. 115), miR-6718-5p (SEQ ID NO. 116), miR-6743-3p (SEQ ID NO. 117), miR-6894-3p (SEQ ID NO. 118), miR-7112-5p (SEQ ID NO. 119), miR-12116 (SEQ ID NO. 120), miR-518d-3p (SEQ ID NO. 121), miR-145-5p (SEQ ID NO. 122), miR-6801-5p (SEQ ID NO. 123), miR-127-3p (SEQ ID NO. 124), and miR-4731-3p (SEQ ID NO. 125).

[0024] <1-7>

[0025] The liver disease detection kit according to <1-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0026] <1-8>

[0027] The liver disease detection kit according to <1-1>, wherein the liver disease is liver cirrhosis.

[0028] <1-9>

[0029] The liver disease detection kit according to <1-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0030] <1-10>

[0031] The liver disease detection kit according to <1-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0032] <2-1>

[0033] The liver disease detection kit according to <1-1>, wherein the nucleic acid is selected from the polynucleotide or fragment thereof represented by any one of the following (a) to (c):

[0034] (a)

[0035] (a-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0036] (a-2) a polynucleotide or a fragment thereof containing one or more bases deleted, substituted, added or inserted in the base sequence of the polynucleotide or a fragment thereof of (a-1),

[0037] (a-3) a polynucleotide of (a-1) or (a-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0038] (b) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0039] (c) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (c-1) to (c-4) or a fragment thereof.

[0040] (c-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0041] (c-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (c-1),

[0042] (c-3) a polynucleotide of (c-1) or (c-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0043] (c-4) A polynucleotide comprising a base sequence represented by any of SEQ ID NOs: 1 to 125 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases

[0044] <2-2>

[0045] The liver disease detection kit according to <2-1>, wherein the nucleic acid is selected from the polynucleotide or fragment thereof represented by any one of the following (a) to (c):

[0046] (a)

[0047] (a-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0048] (a-2) a polynucleotide or a fragment thereof containing one or more bases deleted, substituted, added or inserted in the base sequence of the polynucleotide or a fragment thereof of (a-1),

[0049] (a-3) a polynucleotide of (a-1) or (a-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0050] (b) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 4 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0051] (c) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (c-1) to (c-4) or a fragment thereof.

[0052] (c-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0053] (c-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (c-1),

[0054] (c-3) a polynucleotide of (c-1) or (c-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0055] (c-4) A polynucleotide comprising the base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases

[0056] <2-3>

[0057] The liver disease detection kit according to <2-2>, wherein the kit further comprises at least one nucleic acid capable of specifically binding to a polynucleotide serving as another biomarker,

[0058] The nucleic acid capable of specifically binding to a polynucleotide serving as another biomarker is selected from the group consisting of polynucleotides or fragments thereof represented by any of the following (d) to (f):

[0059] (d)

[0060] (d-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0061] (d-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (d-1),

[0062] (d-3) a polynucleotide of (d-1) or (d-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0063] (e) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0064] (f) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (f-1) to (f-4) or a fragment thereof.

[0065] (f-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0066] (f-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (f-1),

[0067] (f-3) a polynucleotide of (f-1) or (f-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0068] (f-4) A polynucleotide comprising a base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases

[0069] <2-4>

[0070] The liver disease detection kit according to <2-1>, wherein the polynucleotide represented by any one of (a) to (c) above is selected from the group consisting of the polynucleotide represented by any one of SEQ ID NOs: 1 to 82.

[0071] <2-5>

[0072] The liver disease detection kit according to <2-1>, wherein the polynucleotide represented by any one of (a) to (c) above is selected from the polynucleotide represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

[0073] <2-6>

[0074] The liver disease detection kit according to <2-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0075] <2-7>

[0076] The liver disease detection kit according to <2-1>, wherein the liver disease is liver cirrhosis.

[0077] <2-8>

[0078] The liver disease detection kit according to <2-4>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0079] <2-9>

[0080] The liver disease detection kit according to <2-5>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0081] <3-1>

[0082] A biomarker selected from the group consisting of the polynucleotides represented by any one of SEQ ID NOs: 1 to 125.

[0083] <3-2>

[0084] The biomarker according to <3-1>, which is selected from the polynucleotide represented by any one of SEQ ID NOs: 1 to 82.

[0085] <3-3>

[0086] The biomarker according to <3-2>, wherein the biomarker is selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0087] <3-4>

[0088] The biomarker according to <3-1>, which is a polynucleotide selected from any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

[0089] <3-5>

[0090] The biomarker according to <3-1>, wherein the biomarker is obtained from a biological sample selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and an organ sample.

[0091] <3-6>

[0092] The biomarker according to <3-1>, which is used for diagnosing the presence or risk of liver disease.

[0093] <3-7>

[0094] The biomarker according to <3-1>, which is used to determine the degree of progression of liver disease.

[0095] <3-8>

[0096] The biomarker according to <3-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0097] <3-9>

[0098] The biomarker according to <3-1>, wherein the liver disease is liver cirrhosis.

[0099] <3-10>

[0100] The biomarker according to <3-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0101] <3-11>

[0102] The biomarker according to <3-4>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0103] <4-1>

[0104] A method for determining the presence or absence of liver disease in a subject, the risk or degree of progression of liver disease, and / or the success of treatment performed for treatment, comprising the following steps:

[0105] determining the level of a biomarker in a biological sample from the subject; and

[0106] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0107] The biomarker is one or more selected from the group consisting of the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0108] <4-2>

[0109] The method according to <4-1>, wherein the biomarker is a polynucleotide selected from any one of SEQ ID NOs: 1 to 82.

[0110] <4-3>

[0111] The method according to <4-2>, wherein the biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0112] <4-4>

[0113] The method according to <4-3> further comprises the following steps:

[0114] measuring the level of an additional biomarker in a biological sample from the subject; and

[0115] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0116] <4-5>

[0117] The method according to <4-4>, wherein the additional biomarker is selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (Sequence number 13), miR-378a (Sequence number 14), miR-34a (Sequence number 15), miR-320c-1 (Sequence number 16), miR-320b-1 (Sequence number 17), miR-192 (Sequence number 18), miR-148a (Sequence number 19), miR-100 (Sequence number 20), miR-144 (Sequence number 21) and miR-320b-2 (Sequence number 22) One or more of the group consisting of.

[0118] <4-6>

[0119] The method according to <4-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0120] <4-7>

[0121] The method according to <4-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0122] <4-8>

[0123] The method according to <4-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0124] <4-9>

[0125] The method according to <4-1>, wherein the liver disease is liver cirrhosis.

[0126] <4-10>

[0127] The method according to <4-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0128] <4-11>

[0129] The method according to <4-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0130] <5-1>

[0131] A method for screening candidate compounds for a therapeutic drug for liver disease in a subject, comprising the following steps:

[0132] measuring the level of the biomarker in a biological sample from the subject to which the candidate compound for the therapeutic drug for liver disease has been administered; and

[0133] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0134] The biomarker is one or more selected from the group consisting of the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0135] <5-2>

[0136] The method according to <5-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 1 to 82.

[0137] <5-3>

[0138] The method according to <5-2>, wherein the biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0139] <5-4>

[0140] The method according to <5-3> further comprises the following steps:

[0141] measuring the level of an additional biomarker in a biological sample from the subject; and

[0142] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0143] <5-5>

[0144] The method according to <5-4>, wherein the additional biomarker is selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (Sequence number 13), miR-378a (Sequence number 14), miR-34a (Sequence number 15), miR-320c-1 (Sequence number 16), miR-320b-1 (Sequence number 17), miR-192 (Sequence number 18), miR-148a (Sequence number 19), miR-100 (Sequence number 20), miR-144 (Sequence number 21) and miR-320b-2 (Sequence number 22) One or more of the group consisting of.

[0145] <5-6>

[0146] The method according to <5-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0147] <5-7>

[0148] The method according to <5-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0149] <5-8>

[0150] The method according to <5-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0151] <5-9>

[0152] The method according to <5-1>, wherein the liver disease is liver cirrhosis.

[0153] <5-10>

[0154] The method according to <5-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0155] <5-11>

[0156] The method according to <5-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0157] <6-1>

[0158] A method of treating liver disease in a subject, comprising the steps of:

[0159] determining the level of a biomarker in a biological sample from the subject; and

[0160] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0161] The biomarker is one or more selected from the group consisting of the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0162] <6-2>

[0163] The method according to <6-1>, wherein the biomarker is a polynucleotide selected from any one of SEQ ID NOs: 1 to 82.

[0164] <6-3>

[0165] The method according to <6-2>, wherein the biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0166] <6-4>

[0167] The method according to <6-3> further comprises the following steps:

[0168] measuring the level of an additional biomarker in a biological sample from the subject; and

[0169] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0170] <6-5>

[0171] The method according to <6-4>, wherein the additional biomarker is selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (Sequence number 13), miR-378a (Sequence number 14), miR-34a (Sequence number 15), miR-320c-1 (Sequence number 16), miR-320b-1 (Sequence number 17), miR-192 (Sequence number 18), miR-148a (Sequence number 19), miR-100 (Sequence number 20), miR-144 (Sequence number 21) and miR-320b-2 (Sequence number 22) One or more of the group consisting of.

[0172] <6-6>

[0173] The method according to <6-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

[0174] <6-7>

[0175] The method according to <6-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0176] <6-8>

[0177] The method according to <6-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0178] <6-9>

[0179] The method according to <6-1>, wherein the liver disease is liver cirrhosis.

[0180] <6-10>

[0181] The method according to <6-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0182] <6-11>

[0183] The method according to <6-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0184] <7-1>

[0185] A method for detecting liver disease with high accuracy, comprising the following steps:

[0186] determining the level of biomarker 1 in a biological sample from the subject;

[0187] Comparing the measured value of the biomarker 1 with the control value to obtain parameter 1;

[0188] measuring the level of at least one additional biomarker N (where N is an integer greater than or equal to 2) in a biological sample from the subject;

[0189] Comparing the measured value of the additional biomarker N with the control value to obtain a parameter N; and

[0190] The result of obtaining a method of at least two parameters in a group consisting of a combination of parameter 1 and one or more parameters N is obtained, wherein,

[0191] The results of at least two parameters in the group indicate the presence or absence of liver disease in the subject, the risk or degree of progression of liver disease, and / or the success of treatment performed for treatment.

[0192] <7-2>

[0193] The method according to <7-1>, wherein the results of the modes of at least two parameters in the group are evaluated using a computer program.

[0194] <7-3>

[0195] The method according to <7-1>, wherein the biomarker 1 and the at least one additional biomarker are selected from the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0196] <7-4>

[0197] The method according to <7-3>, wherein the biomarker 1 and the at least one additional biomarker are selected from the polynucleotides represented by any one of SEQ ID NOs: 1 to 82.

[0198] <7-5>

[0199] According to the method of <7-4>, the biomarker 1 and the at least one additional biomarker are selected from miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3), miR-4521 (sequence number 4), miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), A group consisting of miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0200] <7-6>

[0201] The method according to <7-3>, wherein the biomarker 1 and the at least one additional biomarker are selected from the polynucleotides represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0202] <7-7>

[0203] The method according to <7-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0204] <7-8>

[0205] The method according to <7-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0206] <7-9>

[0207] The method according to <7-1>, wherein the liver disease is liver cirrhosis.

[0208] <7-10>

[0209] The method according to <7-4>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0210] <7-11>

[0211] The method according to <7-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0212] <8-1>

[0213] A liver disease diagnosis auxiliary system, comprising:

[0214] A unit for measuring the level of one or more or at least two biomarkers in a biological sample from a subject,

[0215] A unit for obtaining multiple parameters by comparing the measured values ​​of the above one or more or at least two biomarkers with their control values, and

[0216] A means for calculating the presence or absence of liver disease in the subject, its risk or degree of progression, and / or the degree of success of treatment performed for treatment based on the plurality of parameters.

[0217] <8-2>

[0218] The liver disease diagnosis support system according to <8-1>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by sequence numbers 1 to 125.

[0219] <8-3>

[0220] The liver disease diagnosis support system according to <8-2>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by any of SEQ ID NOs: 1 to 82.

[0221] <8-4>

[0222] According to the liver disease diagnosis support system described in <8-3>, wherein the above-mentioned one or more or at least two biomarkers are selected from miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3), miR-4521 (sequence number 4), miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), m The group consists of iR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0223] <8-5>

[0224] The liver disease diagnosis support system according to <8-2>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0225] <8-6>

[0226] According to the liver disease diagnosis support system described in <8-1>, the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0227] <8-7>

[0228] The liver disease diagnosis support system according to <8-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0229] <8-8>

[0230] The liver disease diagnosis support system according to <8-1>, wherein the liver disease is cirrhosis.

[0231] <8-9>

[0232] According to the liver disease diagnosis support system described in <8-3>, the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0233] <8-10>

[0234] The liver disease diagnosis support system according to <8-5>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0235] <9-1>

[0236] A method for performing data processing for classifying a liver disease of a subject, executed by a computer, the computer comprising:

[0237] Computer / processor, and

[0238] A computer-readable storage medium having software for issuing instructions for classifying the liver disease of the subject,

[0239] The above method comprises the following steps:

[0240] Comparing the measured values ​​of one or more or at least two biomarkers in the biological sample obtained from the subject with their control values, respectively, and storing the data of multiple parameters obtained thereby in a computer-readable storage medium; and

[0241] The computer processes the data of the plurality of parameters by executing instructions using the computer / processor, the computer determines the condition of the liver disease of the subject, and outputs the result of the determination as information obtained by software that issues instructions for classifying the condition of the liver disease of the subject,

[0242] The above disease classification includes one or more selected from the group consisting of liver fibrosis staging, Child-Pugh classification, MELD score, MELD Na score, PELD score, ALBI score, mALBI (modified ALBI) score, FibroScan score, new Inuyama classification and new European classification.

[0243] <9-2>

[0244] The method according to <9-1>, wherein the result of the determination is a combination of results obtained by processing the data of the plurality of parameters and performing determinations on each of the plurality of parameters.

[0245] <9-3>

[0246] The method according to <9-1>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0247] <9-4>

[0248] The method according to <9-3>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by any of SEQ ID NOs: 1 to 82.

[0249] <9-5>

[0250] The method according to <9-4>, wherein the one or more or at least two biomarkers are selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR- The group consists of miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0251] <9-6>

[0252] The method according to <9-3>, wherein the one or more or at least two biomarkers are selected from the polynucleotides represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

[0253] <9-7>

[0254] The method according to <9-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0255] <9-8>

[0256] The method according to <9-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0257] <9-9>

[0258] The method according to <9-1>, wherein the liver disease is liver cirrhosis.

[0259] <9-10>

[0260] The method according to <9-4>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0261] <9-11>

[0262] The method according to <9-6>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0263] <9-12>

[0264] The method according to <9-1>, wherein the subject is a mammalian subject.

[0265] <9-13>

[0266] The method according to <9-11>, wherein the subject is a human subject.

[0267] <10-1>

[0268] A program for causing a computer to execute determination of the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or determination of the degree of success of treatment performed for treatment, the program executing:

[0269] a measurement value obtaining step of obtaining measurement values ​​of the levels of one or more or at least two biomarkers obtained from a biological sample from the subject;

[0270] a parameter data acquisition step of obtaining data of a plurality of parameters by comparing the measured values ​​of the one or more or at least two biomarkers with respective control values; and

[0271] A determination step is performed to process the data of the plurality of parameters to determine the presence or absence of liver disease in the subject or the risk or degree of progression thereof and / or to determine the success of treatment performed for treatment, wherein:

[0272] The above-mentioned biomarker is selected from the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0273] <10-2>

[0274] The program according to <10-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 1 to 82.

[0275] <10-3>

[0276] The method according to <10-2>, wherein the biomarker is selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0277] <10-4>

[0278] The program according to <10-1>, wherein the biomarker is selected from the polynucleotide represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

[0279] <10-5>

[0280] The procedure according to <10-1>, wherein the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0281] <10-6>

[0282] The method according to <10-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0283] <10-7>

[0284] The method according to <10-1>, wherein the liver disease is liver cirrhosis.

[0285] <10-8>

[0286] The method according to <10-2>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0287] <10-9>

[0288] The method according to <10-4>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0289] <11-1>

[0290] A data processing device for performing data processing for detecting liver disease, comprising:

[0291] A missing value supplementation unit, which obtains missing value supplementation data by supplementing the missing values ​​of the miRNA expression data;

[0292] A logarithmic transformation unit, which performs logarithmic transformation on the missing value supplementary data to obtain logarithmic transformation data;

[0293] A regression transformation unit that obtains regression transformation data by substituting the logarithmic transformation data into a regression model and performing regression transformation;

[0294] A training data acquisition unit that acquires training data; and

[0295] The learning model preparation unit uses the above regression transformation data as training data to prepare the learning model, wherein:

[0296] The miRNA is one or more selected from the group consisting of the polynucleotides represented by SEQ ID NOs: 1 to 125.

[0297] <11-2>

[0298] According to the data processing device for performing data processing for detecting liver disease as described in <11-1>, the regression transformation comprises the following steps:

[0299] Multiply by the regression coefficient;

[0300] The logical value indicating the prediction result of the fibrosis stage is obtained by adding the intercept value for each fibrosis stage;

[0301] By performing an inverse transformation of the logistic transformation on the above logic value to transform it into a probability value between 0 and 1, the predicted probability of each fibrosis stage is calculated; and

[0302] Among the above-mentioned prediction probabilities of each fibrosis stage, the fibrosis stage with the highest probability is taken as the predicted value.

[0303] <11-3>

[0304] According to the data processing device for detecting liver disease as described in <11-2>, the above-mentioned regression model is selected from the group consisting of a polynomial LASSO model, a Gaussian LASSO (Least Absolute Shrinkage and Selection Operator) model, and an ordered logistic regression model.

[0305] <11-4>

[0306] According to the data processing device for performing data processing for detecting liver disease as described in <11-1>, the training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

[0307] <11-5>

[0308] According to the data processing device for performing data processing for detecting liver disease described in <11-1>, the data processing for detecting liver disease further includes a training data obtaining step of obtaining training data, and a learning data producing step of producing learning data using the training data.

[0309] The training data is training data indicating the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

[0310] <11-6>

[0311] According to the data processing device for detecting liver disease described in <11-1>, the data processing for detecting liver disease further includes a pre-processing step for obtaining analytical data, and the pre-processing step includes the following steps:

[0312] Listing the low-expressed miRNAs to obtain a low-expressed miRNA list;

[0313] The missing value supplementation data is obtained by supplementing the missing values ​​in the data of the above miRNA list;

[0314] Excluding the low-expression miRNAs included in the low-expression miRNA list from the analysis targets to obtain conversion data; and

[0315] The above transformation data were logarithmically transformed.

[0316] <11-7>

[0317] The data processing device for performing data processing for detecting liver disease according to <11-1>, wherein the biomarker is one or more selected from the group consisting of polynucleotides represented by any one of SEQ ID NOs: 1 to 82.

[0318] <11-8>

[0319] A data processing device for performing data processing for detecting liver disease according to <11-1>, wherein the biomarker is one or more polynucleotides selected from any one of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0320] <11-9>

[0321] According to the data processing device for performing data processing for detecting liver disease as described in <11-1>, the biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

[0322] <11-10>

[0323] The data processing device for performing data processing for detecting liver disease according to <11-1>, wherein the liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

[0324] <11-11>

[0325] The data processing device for performing data processing for detecting liver disease according to <11-1>, wherein the liver disease is liver cirrhosis.

[0326] <11-12>

[0327] The data processing device for performing data processing for detecting liver disease according to <11-7>, wherein the liver disease is a viral liver disease, preferably hepatitis C (HCV) or hepatitis B (HBV).

[0328] <11-13>

[0329] The data processing device for performing data processing for detecting liver disease according to <11-8>, wherein the liver disease is non-alcoholic steatohepatitis (NASH).

[0330] Effects of the Invention

[0331] By using the non-invasive biomarker for liver disease of the present invention, as well as a kit, method and program for using the biomarker, and a data processing method for detecting liver disease with high accuracy using the non-invasive biomarker for liver disease, and a data processing device for use in the data processing, liver disease can be detected non-invasively or low-invasively and with high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0332] Figure 1 This is a graph obtained by analyzing the relationship between the expression levels of various miRNAs and the fibrosis stage for subjects with fibrosis stage 0 and subjects with fibrosis stage 1 to 4.

[0333] Figure 2 This is a graph obtained by analyzing the relationship between the expression levels of various miRNAs and the fibrosis stage (degree of fibrosis) for normal subjects, subjects with fibrosis stage 0, subjects with fibrosis stage 1, and subjects with fibrosis stage 4.

[0334] Figure 3 This is a graph obtained by analyzing the relationship between the expression levels of various miRNAs and the Child-Pugh grade for subjects with a Child-Pugh grade of 0 and subjects with a Child-Pugh grade of 6 or higher.

[0335] Figure 4 This is a graph obtained by analyzing the relationship between the expression level of various miRNAs and the Child-Pugh grade for a subject with a Child-Pugh grade of 0 and a subject with a Child-Pugh grade of 6 to 11.

[0336] Figure 5 is a flowchart showing the flow of data processing for predicting the fibrosis stage of viral liver disease.

[0337] Figure 6 is a flow chart showing the flow of data processing for predicting fibrosis stage of nonalcoholic steatohepatitis (NASH).

[0338] Figure 7 It is a flow chart of the process of data processing for predicting Brunt grade of non-alcoholic steatohepatitis (NASH). DETAILED DESCRIPTION

[0339] The present invention will be described in detail below. However, the following description is not intended to limit the scope of the present invention.

[0340] <Biomarkers>

[0341] In one embodiment of the present invention, a biomarker is provided.

[0342] In the present invention, the biomarker is selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13), miR-378a (SEQ ID NO: 14), miR-34a (SEQ ID NO: 15), miR-3 20c-1 (SEQ ID NO: 16), miR-320b-1 (SEQ ID NO: 17), miR-192 (SEQ ID NO: 18), miR-148a (SEQ ID NO: 19), miR-100 (SEQ ID NO: 20), miR-144 (SEQ ID NO: 21), miR-320b-2 (SEQ ID NO: 22), miR-451a (SEQ ID NO: 23), miR-548d-3p (SEQ ID NO: 24), miR-320b (SEQ ID NO: 25), miR-1470 (SEQ ID NO: 26), miR-4258 (SEQ ID NO: 27), miR-4673 (SEQ ID NO: 28), miR-4748 (SEQ ID NO: 29), miR-4787-3p (SEQ ID NO: 30), miR-20 4-3p (SEQ ID NO: 31), miR-6131 (SEQ ID NO: 32), miR-6752-3p (SEQ ID NO: 33), miR-4648 (SEQ ID NO: 34), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-1231 (SEQ ID NO: 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-6729-3p (SEQ ID NO: 42), miR-6798-3p (SEQ ID NO: 43), miR-379-5p (SEQ ID NO: 44), miR-323a -5p (sequence number 45), miR-665 (sequence number 46), miR-4279 (sequence number 47), miR-3605-5p (sequence number 48), miR-4684-3p (sequence number 49), miR-365b-5p (sequence number 50), miR-6782-5p (sequence number 51), miR-6880-3p (sequence number 52), miR-6887-3p (sequence number 53), miR-4433b-5p (sequence number 54), miR-10396a-3p (sequence number 55), miR-373-5p (sequence number 56), miR-4539 (sequence number 57), miR-4687-3p (sequence number 58),miR-4763-5p (SEQ ID NO: 59), miR-4482-3p (SEQ ID NO: 60), miR-6721-5p (SEQ ID NO: 61), miR-6812-3p (SEQ ID NO: 62), miR-6815-5p (SEQ ID NO: 63), miR-6871-5p (SEQ ID NO: 64), miR-7975 (SEQ ID NO: 65), miR-11181-3p (SEQ ID NO: 66), miR-2278 (SEQ ID NO: 67), miR-3192-5p (SEQ ID NO: 68), miR-4722-3p (SEQ ID NO: 69), miR-4750-3p (SEQ ID NO: 70), miR-6804-5p (SEQ ID NO: 71), miR-682 8-5p (SEQ ID NO: 72), miR-614 (SEQ ID NO: 73), miR-320c (SEQ ID NO: 74), miR-2110 (SEQ ID NO: 75), miR-3177-3p (SEQ ID NO: 76), miR-4497 (SEQ ID NO: 77), miR-210-5p (SEQ ID NO: 78), miR-6778-5p (SEQ ID NO: 79), miR-6780a-5p (SEQ ID NO: 80), miR-6801-3p (SEQ ID NO: 81), miR-8059 (SEQ ID NO: 82), miR-657 (SEQ ID NO: 83), miR-921 (SEQ ID NO: 84), miR-4451 (SEQ ID NO: 85), miR-3059-3p (SEQ ID NO: 86) 、miR-3619-3p (SEQ ID NO: 87), miR-4646-5p (SEQ ID NO: 88), miR-525-5p (SEQ ID NO: 89), miR-4444 (SEQ ID NO: 90), miR-4458 (SEQ ID NO: 91), miR-4746-3p (SEQ ID NO: 92), miR-1292-3p (SEQ ID NO: 93), miR-6133 (SEQ ID NO: 94), miR-6754-3p (SEQ ID NO: 95), miR-365a-3p (SEQ ID NO: 96), miR-365b-3p (SEQ ID NO: 97), miR-6748-3p (SEQ ID NO: 98), miR-6892-3p (SEQ ID NO: 99), miR-8083 (SEQ ID NO: 100), miR-4724-5p (SEQ ID NO. 101), miR-6884-5p (SEQ ID NO. 102), miR-7156-3p (SEQ ID NO. 103), miR-19b-3p (SEQ ID NO. 104), miR-518e-3p (SEQ ID NO. 105), miR-646 (SEQ ID NO. 106), miR-298 (SEQ ID NO. 107), miR-1234-3p (SEQ ID NO. 108), miR-2115-5p (SEQ ID NO. 109), miR-3142 (SEQ ID NO. 110), miR-3179 (SEQ ID NO. 111), miR-3190-5p (SEQ ID NO. 112), miR-3675-3p (SEQ ID NO. 113),The group consists of miR-4441 (SEQ ID NO. 114), miR-4475 (SEQ ID NO. 115), miR-6718-5p (SEQ ID NO. 116), miR-6743-3p (SEQ ID NO. 117), miR-6894-3p (SEQ ID NO. 118), miR-7112-5p (SEQ ID NO. 119), miR-12116 (SEQ ID NO. 120), miR-518d-3p (SEQ ID NO. 121), miR-145-5p (SEQ ID NO. 122), miR-6801-5p (SEQ ID NO. 123), miR-127-3p (SEQ ID NO. 124), and miR-4731-3p (SEQ ID NO. 125).

[0343] In one embodiment of the present invention, the biomarker is selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13), miR-378a (SEQ ID NO: 14), miR-34a (SEQ ID NO: 15), No. 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21), miR-320b-2 (sequence number 22), miR-451a (sequence number 23), miR-548d-3p (sequence number 24), miR-320b (sequence number 25), miR-1470 (sequence number 26), miR-4258 (sequence number 27), miR-4673 (sequence number 28), miR-4748 (sequence number 29), miR-4787-3p (sequence number 30), miR-4787-3p (sequence number 31), miR-4787-3p (sequence number 32), miR-4787-3p (sequence number 33), miR-4787-3p (sequence number 34), miR-4787-3p (sequence number 35), miR-4787-3p (sequence number 36), miR-4787-3p (sequence number 37), miR-4787-3p (sequence number 38), miR-4787-3p (sequence number 39), miR-4787-3p (sequence number 40), miR-4787-3p (sequence number 41), miR-4787-3p (sequence number 42), miR-4787-3p (sequence number 43), miR-4787-3 SEQ ID NO: 30), miR-204-3p (SEQ ID NO: 31), miR-6131 (SEQ ID NO: 32), miR-6752-3p (SEQ ID NO: 33), miR-4648 (SEQ ID NO: 34), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-1231 (SEQ ID NO: 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-6729-3p (SEQ ID NO: 42), miR-6798-3p (SEQ ID NO: 43), miR-379-5p (SEQ ID NO: 44), miR-323a-5p (SEQ ID NO: 45), miR-665 (SEQ ID NO: 46), miR-4279 (SEQ ID NO: 47), miR-3605-5p (SEQ ID NO: 48), miR-4684-3p (SEQ ID NO: 49), miR-365b-5p (SEQ ID NO: 50), miR-6782-5p (SEQ ID NO: 51), miR-6880-3p (SEQ ID NO: 52), miR-6887-3p (SEQ ID NO: 53), miR-4433b-5p (SEQ ID NO: 54), miR-10396a-3p (SEQ ID NO: 55), miR-373-5p (SEQ ID NO: 56), miR-4539 (SEQ ID NO: 57),miR-4687-3p (sequence number 58), miR-4763-5p (sequence number 59), miR-4482-3p (sequence number 60), miR-6721-5p (sequence number 61), miR-6812-3p (sequence number 62), miR-6815-5p (sequence number 63), miR-6871-5p (sequence number 64), miR-7975 (sequence number 65), miR-11181-3p (sequence number 66), miR-2278 (sequence number 67), miR-3192-5p (sequence number 68), miR-4722-3p (sequence number 69), miR-47 The group consists of miR-50-3p (sequence number 70), miR-6804-5p (sequence number 71), miR-6828-5p (sequence number 72), miR-614 (sequence number 73), miR-320c (sequence number 74), miR-2110 (sequence number 75), miR-3177-3p (sequence number 76), miR-4497 (sequence number 77), miR-210-5p (sequence number 78), miR-6778-5p (sequence number 79), miR-6780a-5p (sequence number 80), miR-6801-3p (sequence number 81) and miR-8059 (sequence number 82).

[0344] In one embodiment of the present invention, the biomarkers include miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but are not limited to these. In one embodiment of the present invention, the biomarker is selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), The group consists of miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0345] In one embodiment of the present invention, the biomarker may be a biomarker selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4), but is not limited to these. In an embodiment of the present invention, a biomarker selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4) is provided. In a preferred embodiment of the present invention, the biomarker is one or more selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4). In one embodiment of the present invention, the biomarker is miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), or miR-4521 (SEQ ID NO: 4), or a combination thereof.

[0346] In an embodiment of the present invention, as a biomarker, in addition to the biomarker selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4), a biomarker selected from the group consisting of miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-1 One or more additional biomarkers in the group consisting of miR-94-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0347] miR-4454 (SEQ ID NO. 1), miR-3138 (SEQ ID NO. 2), miR-3679 (SEQ ID NO. 3), miR-4521 (SEQ ID NO. 4), miR-320A (SEQ ID NO. 5), miR-483 (SEQ ID NO. 6), miR-122 (SEQ ID NO. 7), miR-125B-1 (SEQ ID NO. 8), miR-125B-2 (SEQ ID NO. 9), miR-194-1 (SEQ ID NO. 10), miR-194-2 (SEQ ID NO. 11), miR-9 9a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22) are the following sequences.

[0348]

Table 1-1

[0349]

[0350] miR-451a (sequence number 23), miR-548d-3p (sequence number 24), miR-320b (sequence number 25), miR-1470 (sequence number 26), miR-4258 (sequence number 27), miR-4673 (sequence number 28), miR-4748 (sequence number 29), miR-4787-3p (sequence number 30), miR-204-3p (sequence number 31), miR-6131 (sequence number 32), miR-6752-3p (sequence number 33), miR-4648 (sequence number 34), miR-6126 (sequence number 35), miR-1228-3p (sequence number 36), miR-1231 (sequence number 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-6729-3p (SEQ ID NO: 42), miR-6798-3p (SEQ ID NO: 43), miR-379-5p (SEQ ID NO: 44), miR-323a-5p (SEQ ID NO: 45), miR-665 (SEQ ID NO: 46), miR-4279 (SEQ ID NO: 47), miR-3605-5p (SEQ ID NO: 48), miR-4684-3p (SEQ ID NO: 49), miR-365b-5p (SEQ ID NO: 50), miR-6782-5p (SEQ ID NO: 51), miR-6880-3p (SEQ ID NO: 52), miR-6887-3p (SEQ ID NO: 53), miR-4433b-5p (SEQ ID NO: 54), miR-10396a-3p (SEQ ID NO: 55), miR-373-5p (SEQ ID NO: 56), miR-4539 (SEQ ID NO: 57), miR-4687-3p (SEQ ID NO: 58), miR-4763-5p (SEQ ID NO: 59), miR-4482-3p (SEQ ID NO: 60), miR-6721-5p (SEQ ID NO: 61), miR-6812-3p (SEQ ID NO: 62), miR-6815-5p (SEQ ID NO: 63), miR-6871-5p (SEQ ID NO: 64), miR-7975 (SEQ ID NO. 65), miR-11181-3p (SEQ ID NO. 66), miR-2278 (SEQ ID NO. 67), miR-3192-5p (SEQ ID NO. 68), miR-4722-3p (SEQ ID NO. 69), miR-4750-3p (SEQ ID NO. 70), miR-6804-5p (SEQ ID NO. 71), miR-6828-5p (SEQ ID NO. 72), miR-614 (SEQ ID NO. 73), miR-320c (SEQ ID NO. 74), miR-2110 (SEQ ID NO. 75), miR-3177-3p (SEQ ID NO. 76), miR-4497 (SEQ ID NO. 77), miR-210-5p (SEQ ID NO. 78),miR-6778-5p (SEQ ID NO: 79), miR-6780a-5p (SEQ ID NO: 80), miR-6801-3p (SEQ ID NO: 81), and miR-8059 (SEQ ID NO: 82) have the following sequences.

[0351]

Table 1-2

[0352]

[0353]

[0354]

[0355] In one embodiment of the present invention, the biomarker is selected from miR-451a (SEQ ID NO: 23), miR-4787-3p (SEQ ID NO: 30), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-6798-3p (SEQ ID NO: 43), miR-7975 (SEQ ID NO: 65), miR-614 (SEQ ID NO: 73), miR-6801-3p (SEQ ID NO: 81), miR-657 (SEQ ID NO: 83), miR-921 (SEQ ID NO: 84), miR-4451 (SEQ ID NO: 85), miR-3059-3p (SEQ ID NO: 86), miR-3619-3p (SEQ ID NO: 87), miR- 87), miR-4646-5p (SEQ ID NO: 88), miR-525-5p (SEQ ID NO: 89), miR-4444 (SEQ ID NO: 90), miR-4458 (SEQ ID NO: 91), miR-4746-3p (SEQ ID NO: 92), miR-1292-3p (SEQ ID NO: 93), miR-6133 (SEQ ID NO: 94), miR-6754-3p (SEQ ID NO: 95), miR-365a-3p (SEQ ID NO: 96), miR-365b-3p (SEQ ID NO: 97), miR-6748-3p (SEQ ID NO: 98), miR-6892-3p (SEQ ID NO: 99), miR-8083 (SEQ ID NO: 100), miR-4724-5p (SEQ ID NO. 101), miR-6884-5p (SEQ ID NO. 102), miR-7156-3p (SEQ ID NO. 103), miR-19b-3p (SEQ ID NO. 104), miR-518e-3p (SEQ ID NO. 105), miR-646 (SEQ ID NO. 106), miR-298 (SEQ ID NO. 107), miR-1234-3p (SEQ ID NO. 108), miR-2115-5p (SEQ ID NO. 109), miR-3142 (SEQ ID NO. 110), miR-3179 (SEQ ID NO. 111), miR-3190-5p (SEQ ID NO. 112), miR-3675-3p (SEQ ID NO. 113), The group consists of miR-4731-3p (sequence number 125), miR-518d-3p (sequence number 126), miR-145-5p (sequence number 127), miR-6801-5p (sequence number 128), miR-127-3p (sequence number 129), and miR-4731-3p (sequence number 129).

[0356] miR-451a (SEQ ID NO: 23), miR-4787-3p (SEQ ID NO: 30), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-6798-3p (SEQ ID NO: 43), miR-7975 (SEQ ID NO: 65), miR-614 (SEQ ID NO: 73), miR-6801-3p (SEQ ID NO: 81), miR-657 (SEQ ID NO: 83), miR-921 (SEQ ID NO: 84), miR-4451 (SEQ ID NO: 85), miR-3059-3p (SEQ ID NO: 86), miR-3619-3p (SEQ ID NO: 87), miR-4646-5p (SEQ ID NO: 88), miR-525-5p (SEQ ID NO: 89), miR-4444 (SEQ ID NO: 90), miR-4458 (SEQ ID NO: 91), miR-4746-3p (SEQ ID NO: 92), miR-1292-3p (SEQ ID NO: 93), miR-6133 (SEQ ID NO: 94), miR-6754-3p (SEQ ID NO: 95), miR-365a-3p (SEQ ID NO: 96), miR-365b-3p (SEQ ID NO: 97), miR-6748-3p (SEQ ID NO: 98), miR-6892-3p (SEQ ID NO: 99), miR-8083 (SEQ ID NO: 100), miR-4724-5p (SEQ ID NO: 101), miR-6884-5p (SEQ ID NO: 102), miR-7156-3p (SEQ ID NO: 103), miR-19b-3p (SEQ ID NO: 104), miR-518e-3p (SEQ ID NO: 105), miR-646 (SEQ ID NO: 106), miR-298 (SEQ ID NO: 107), miR-1234-3p (SEQ ID NO: 108), miR-2115-5p (SEQ ID NO: 109), miR-3142 (SEQ ID NO: 110), miR-3179 (SEQ ID NO: 111), miR-3190-5p (SEQ ID NO: 112), miR-3675-3p (SEQ ID NO: 113), m iR-4441 (sequence number 114), miR-4475 (sequence number 115), miR-6718-5p (sequence number 116), miR-6743-3p (sequence number 117), miR-6894-3p (sequence number 118), miR-7112-5p (sequence number 119), miR-12116 (sequence number 120), miR-518d-3p (sequence number 121), miR-145-5p (sequence number 122), miR-6801-5p (sequence number 123), miR-127-3p (sequence number 124) and miR-4731-3p (sequence number 125) are the following sequences.

[0357]

Table 1-3

[0358]

[0359]

[0360]

[0361] Sometimes miRNA has isoforms or variants. For example, sometimes a part of the bases in the sequence of the biomarker exemplified above, such as more than 1, 1 to 5, 1 to 3, 1 to 2 or 1 base, is replaced or deleted, or a base (such as more than 1, 1 to 5, 1 to 3, 1 to 2 or 1 base) not contained in the biomarker is added or inserted. In addition, sometimes there is more than 90%, more than 91%, more than 92%, more than 93%, more than 94%, more than 95%, more than 96%, more than 97%, more than 98% or about 99% consistency with the biomarker. Consistency can be determined, for example, by BLAST, etc. In particular, except for the unreasonable understanding of this, the biomarkers recorded in this specification may include these mutants and variants.

[0362] In the present invention, the biomarkers exemplified above can be obtained from biological samples of a subject or object. Examples of biological samples for obtaining the biomarkers of the present invention include, but are not limited to, blood, serum, plasma, saliva, sweat, tears, stool, urine, organ specimens, skin, tissue leaching fluid, and hair. In one embodiment of the present invention, the biological sample for obtaining the biomarkers of the present invention is selected from blood, serum, plasma, saliva, sweat, tears, stool, urine, and organ specimens. In an embodiment of the present invention, a biomarker obtained from a biological sample selected from the group consisting of blood, serum, plasma, whole blood, hemolyzed blood cells, fractionated or processed blood, saliva, sweat, tears, stool, urine, and organ specimens is provided. In one embodiment of the present invention, the biological sample is blood. In one embodiment of the present invention, the biological sample is serum. In one embodiment of the present invention, the biological sample is plasma. In one embodiment of the present invention, the biological sample is saliva. In one embodiment of the present invention, the biological sample is sweat. In one embodiment of the present invention, the biological sample is tears. In one embodiment of the present invention, the biological sample is stool. In one embodiment of the present invention, the biological sample is urine. In one embodiment of the present invention, the biological sample is an organ to be examined.

[0363] In an embodiment of the present invention, the above-mentioned biomarker is a biomarker for liver disease. In one embodiment of the present invention, the above-mentioned biomarker can be used for the detection of liver disease, for example, it can be used for the diagnosis of the presence or absence of liver disease or its risk, the determination or monitoring of the degree of progression of liver disease, and / or the determination of the success of the treatment implemented for treatment or the monitoring of the therapeutic effect, etc., but it is not limited to these. In one embodiment of the present invention, the above-mentioned biomarker is used for the detection of liver disease. In a preferred embodiment of the present invention, the above-mentioned biomarker is used for the diagnosis of the presence or absence of liver disease or its risk. In a preferred embodiment of the present invention, the above-mentioned biomarker is used for the determination of the degree of progression of liver disease. In a preferred embodiment of the present invention, the above-mentioned biomarker is used for the determination of the success of the treatment implemented for treatment. The determination of the success of the treatment implemented for treatment includes, for example, whether the candidate compound of the therapeutic drug has exerted a preferred therapeutic effect, the presence or absence of a therapeutic response, and the determination of tolerance, but it is not limited to these.

[0364] In one embodiment of the present invention, a biomarker for detecting liver disease is provided. In a preferred embodiment of the present invention, a biomarker for diagnosing the presence or risk of liver disease is provided. In a preferred embodiment of the present invention, a biomarker for determining the degree of progression of liver disease is provided. In a preferred embodiment of the present invention, a biomarker for determining the success of treatment implemented for treatment is provided. Disease classification related to the degree of progression of liver disease will be described later.

[0365] As liver diseases targeted by the non-invasive biomarkers for liver diseases of the present invention, hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer can be listed, but are not limited to these. In one embodiment of the present invention, the liver diseases targeted by the non-invasive biomarkers for liver diseases are selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer. In a preferred embodiment of the present invention, the liver diseases targeted by the non-invasive biomarkers for liver diseases are cirrhosis. In a preferred embodiment of the present invention, the liver diseases targeted by the non-invasive biomarkers for liver diseases are viral liver diseases, such as hepatitis C and hepatitis B.

[0366] In another preferred embodiment of the present invention, the liver disease targeted by the non-invasive biomarker for liver disease is non-alcoholic steatohepatitis (NASH).

[0367] In one embodiment of the present invention, a biomarker is provided, wherein the liver disease targeted by the non-invasive biomarker for liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer. In a preferred embodiment of the present invention, a biomarker is provided, wherein the liver disease targeted by the non-invasive biomarker for liver disease is cirrhosis. In a preferred embodiment of the present invention, a biomarker is provided, wherein the liver disease targeted by the non-invasive biomarker for liver disease is viral liver disease, such as hepatitis C and hepatitis B.

[0368] In another preferred embodiment of the present invention, a biomarker is provided, wherein the liver disease targeted by the non-invasive biomarker for liver disease is non-alcoholic steatohepatitis (NASH).

[0369] <Methods for detecting liver disease>

[0370] The present invention provides a method for detecting liver disease, comprising the steps of: measuring the level of a biomarker in a biological sample from a subject; and, comparing the measured value of the biomarker with a control value. In one embodiment of the present invention, the method for detecting liver disease includes, for example, diagnosis of the presence or absence of liver disease or its risk, determination of the degree of progression of liver disease, and / or determination of the success of treatment implemented for treatment, but is not limited to these. In one embodiment of the present invention, the method for detecting liver disease is used for diagnosis of the presence or absence of liver disease or its risk, determination of the degree of progression of liver disease, and / or determination of the success of treatment implemented for treatment. In one embodiment of the present invention, the method for detecting liver disease is used for diagnosis of the presence or absence of liver disease or its risk. In one embodiment of the present invention, the method for detecting liver disease is used for determination of the degree of progression of liver disease. In one embodiment of the present invention, the method for detecting liver disease is used for determination of the success of treatment implemented for treatment. The determination of the success of treatment implemented for treatment includes, for example, determination of whether the candidate compound of the therapeutic drug exerts a preferred therapeutic effect, the presence or absence of a therapeutic response, and tolerance, but is not limited to these.

[0371] In one embodiment of the present invention, the biomarker used in the above-mentioned method for detecting liver disease is one or more selected from the group consisting of sequence numbers 1 to 125. In one embodiment of the present invention, a method for determining the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or the success of treatment performed for treatment is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of sequence numbers 1 to 125.

[0372] In one embodiment of the present invention, the biomarker is one or more selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, a method for determining the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or the success of treatment for treatment is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of sequence numbers 1 to 82.

[0373] In one embodiment of the present invention, the above-mentioned biomarker can be selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4), but is not limited to these. In a preferred embodiment of the present invention, the biomarker used in the above-mentioned method for detecting liver disease is one or more selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4). In one embodiment of the present invention, the biomarker used in the above-mentioned method for detecting liver disease is miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) or miR-4521 (sequence number 4) or a combination of these.

[0374] In one embodiment of the present invention, there is provided:

[0375] A method for determining the presence or absence of liver disease in a subject, the risk or degree of progression of liver disease, and / or the success of treatment performed for treatment, comprising the following steps:

[0376] determining the level of a biomarker in a biological sample from the subject; and

[0377] The measured values ​​of the above biomarkers were compared with the control values.

[0378] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0379] In an embodiment of the present invention, as a biomarker, in addition to one or more selected from the group consisting of miR-4454 (SEQ ID NO. 1), miR-3138 (SEQ ID NO. 2), miR-3679 (SEQ ID NO. 3) and miR-4521 (SEQ ID NO. 4), additional biomarkers may also be used. In an embodiment of the present invention, the above-mentioned method for detecting liver disease may further include the following steps: measuring the level of the additional biomarker in the biological sample from the subject; and comparing the measured value of the above-mentioned additional biomarker with the control value.

[0380] In one embodiment of the present invention, there is provided:

[0381] A method for determining the presence or absence of liver disease in a subject, the risk or degree of progression of liver disease, and / or the success of treatment performed for treatment, comprising the following steps:

[0382] determining the level of a biomarker in a biological sample from the subject; and

[0383] The measured values ​​of the above biomarkers were compared with the control values.

[0384] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4),

[0385] The method further comprises the steps of:

[0386] measuring the level of an additional biomarker in a biological sample from the subject; and

[0387] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0388] In the above method, the additional biomarker can be selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13) , miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, the method is provided, which uses a miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13 ), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22) The present invention further comprises one or more additional biomarkers from the group consisting of miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0389] In another embodiment of the present invention, the biomarker used in the above method is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, a method for determining the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or the success of treatment performed for treatment is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 125.

[0390] The present invention further provides a method for detecting liver disease with high accuracy, comprising the following steps:

[0391] determining the level of biomarker 1 in a biological sample from the subject;

[0392] Comparing the measured value of the biomarker 1 with the control value to obtain parameter 1;

[0393] measuring the level of at least one additional biomarker N (where N is an integer greater than or equal to 2) in a biological sample from the subject;

[0394] Comparing the measured value of the additional biomarker N with the control value to obtain a parameter N; and

[0395] The result of obtaining a method of at least two parameters in a group consisting of a combination of parameter 1 and one or more parameters N is obtained, wherein,

[0396] The results of at least two parameters in the group indicate the presence or absence of liver disease in the subject, the risk or degree of progression of liver disease, and / or the success of treatment performed for treatment.

[0397] In the above-mentioned method for detecting liver disease with high accuracy, the results of the method for at least two parameters in the above-mentioned group can be evaluated using a computer program. In one embodiment of the present invention, a method for detecting liver disease with high accuracy is provided, which comprises the following steps:

[0398] determining the level of biomarker 1 in a biological sample from the subject;

[0399] Comparing the measured value of the biomarker 1 with the control value to obtain parameter 1;

[0400] measuring the level of at least one additional biomarker N (where N is an integer greater than or equal to 2) in a biological sample from the subject;

[0401] Comparing the measured value of the additional biomarker N with the control value to obtain a parameter N; and

[0402] The result of obtaining a method in which at least two parameters are in a group consisting of a combination of parameter 1 and one or more parameters N is obtained.

[0403] The results of at least two parameters in the group indicate the presence or absence of liver disease in the subject, its risk or progression, and / or the success of treatment for treatment, wherein:

[0404] The results of the method for at least two parameters in the above group are evaluated using a computer program.

[0405] In the above-mentioned method for detecting liver disease with high accuracy, the above-mentioned biomarker 1 and the above-mentioned at least one additional biomarker are one or more selected from the group consisting of sequence numbers 1 to 125. In one embodiment of the present invention, there is provided a method for detecting liver disease with high accuracy, wherein the above-mentioned biomarker 1 and the above-mentioned at least one additional biomarker are one or more selected from the group consisting of sequence numbers 1 to 125.

[0406] In the above-mentioned method for detecting liver disease with high accuracy, the above-mentioned biomarker 1 and the above-mentioned at least one additional biomarker are one or more selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, there is provided a method for detecting liver disease with high accuracy according to the above-mentioned method, wherein the above-mentioned biomarker 1 and the above-mentioned at least one additional biomarker are one or more selected from the group consisting of sequence numbers 1 to 82.

[0407] In the above-mentioned method for detecting liver disease with high accuracy, the above-mentioned biomarker 1 and the above-mentioned at least one additional biomarker can be selected from miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3), miR-4521 (sequence number 4), miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), miR -194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, a method for detecting liver disease with high accuracy is provided, wherein the biomarker 1 and the at least one additional biomarker are selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194- The group consists of miR-1 (sequence number 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0408] In another embodiment of the present invention, the biomarker used in the above method is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, a method for detecting liver disease with high accuracy is provided, wherein the biomarker 1 and the at least one additional biomarker are one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0409] In one embodiment of the present invention, software for implementing the above-mentioned method of detecting liver disease with high accuracy and a device equipped with the software are provided.

[0410] Each biomarker, biological sample, and liver disease are as described above.

[0411] In the above-mentioned method for detecting liver disease, the measured value refers to the measured value of the biomarker of the subject at the time of measurement, and the control value refers to the value of the biomarker of a subject without liver disease or the past measured value of a subject with liver disease.

[0412] The present inventors found that the expression of miR-320A and miR-483 increased significantly with the progression of fibrosis. Regarding miR-122 (miR-99a, miR-194-1 also showed the same pattern) and miR-125B-1 (miR-125B-2 also showed the same pattern), a significant increase in expression was observed when the fibrosis stage was 0. Regarding miR-223-3P, a significant decrease in expression was observed when compared with normal subjects, and regarding miR-4454, a linear decrease in miRNA expression was observed. Therefore, in the judgment of the method for detecting liver disease, regarding miRNAs that significantly increase with fibrosis (miR-34A, miR-194-1, miR-99a, miR-3679, miR-3138, miR-320c-1, miR-320b-1, miR-483, miR-192, miR-122, miR-378a, miR-148a, miR-100, miR-194-2, miR-125B-2, miR-125B-1, miR-320A), when the biomarker level in the biological sample is higher than the control value, it can be judged that the possibility of liver disease is high, or when the biomarker level in the biological sample is not higher than the control value (i.e., the same or lower), it can be judged that the possibility of not having liver disease is high. On the contrary, in the method for detecting liver disease, regarding miRNA (miR-144, miR-4521, miR-4454, miR-223-3p) that is significantly reduced with fibrosis, when the biomarker level in the biological sample is lower than the control value, it can be judged that the possibility of liver disease is high, or when the biomarker level in the biological sample is not lower than the control value (i.e., the same or higher), it can be judged that the possibility of not having liver disease is high.

[0413] In addition, regarding miRNAs (miR-99a, miR-483, miR-122, miR-125B-2, miR-320b-2, miR-125B-1, miR-320A) that significantly increase with an increase in the score based on the Child-Pugh classification, when the biomarker level in the biological sample is higher than the control value, it can be judged that the possibility of liver disease is high, or when the biomarker level in the biological sample is not higher than the control value (i.e., the same or lower), it can be judged that the possibility of not having liver disease is high. On the contrary, in the method for detecting liver disease, when the biomarker level in the biological sample is lower than the control value for miRNA (miR-144, miR-194-1, miR-4454, miR-223-3p) that is significantly reduced with fibrosis, it can be judged that the possibility of liver disease is high, or when the biomarker level in the biological sample is not lower than the control value (i.e., the same or higher), it can be judged that the possibility of not having liver disease is high.

[0414] The present inventors have discovered a number of biomarkers that show a clear correlation with the disease classification of liver disease conditions, and have developed a method for detecting liver disease by measuring the values ​​of the biomarkers and their changes. The disease classification related to the degree of progression of liver disease will be described later.

[0415] <Method for screening candidate compounds for therapeutic drugs>

[0416] The present invention provides a method for screening candidate compounds for therapeutic drugs for liver disease in a subject, comprising the steps of: measuring the level of a biomarker in a biological sample from the subject to which the candidate compound for therapeutic drugs for liver disease has been administered; and comparing the measured value of the biomarker with a control value.

[0417] In one embodiment of the present invention, the biomarker used in the method for screening candidate compounds for therapeutic drugs for liver diseases is one or more selected from the group consisting of sequence numbers 1 to 125. In one embodiment of the present invention, a method for screening candidate compounds for therapeutic drugs for liver diseases of a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample of the subject to which the candidate compound for therapeutic drugs for liver diseases is administered; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of sequence numbers 1 to 125.

[0418] In one embodiment of the present invention, the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 82. In one embodiment of the present invention, a method for screening a candidate compound for a therapeutic drug for liver disease in a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample of the subject to which the candidate compound for a therapeutic drug for liver disease has been administered; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 82.

[0419] In one embodiment of the present invention, the biomarker used in the screening method for candidate compounds of the above-mentioned therapeutic drug can be selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4), but is not limited to these. In a preferred embodiment of the present invention, the biomarker used in the screening method for candidate compounds of the above-mentioned therapeutic drug is one or more selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4). In one embodiment of the present invention, the biomarker used in the screening method for candidate compounds of the above-mentioned therapeutic drug is miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) or miR-4521 (sequence number 4) or a combination of these.

[0420] In one embodiment of the present invention, a method for screening candidate compounds for a therapeutic drug for liver disease in a subject is provided, comprising the following steps:

[0421] measuring the level of the biomarker in a biological sample from the subject to which the candidate compound for the therapeutic drug for liver disease has been administered; and

[0422] The measured values ​​of the above biomarkers were compared with the control values.

[0423] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0424] In another embodiment of the present invention, as a biomarker, in addition to one or more selected from the group consisting of miR-4454 (SEQ ID NO. 1), miR-3138 (SEQ ID NO. 2), miR-3679 (SEQ ID NO. 3) and miR-4521 (SEQ ID NO. 4), additional biomarkers may also be used. In an embodiment of the present invention, the screening method for candidate compounds of the above-mentioned therapeutic drug may further include the following steps: measuring the level of the additional biomarker in the biological sample from the object; and comparing the measured value of the above-mentioned additional biomarker with the control value.

[0425] In one embodiment of the present invention, a method for screening candidate compounds for a therapeutic drug for liver disease in a subject is provided, comprising the following steps:

[0426] measuring the level of a biomarker in a biological sample from a subject to whom a candidate compound for a therapeutic agent for liver disease has been administered; and

[0427] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0428] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4),

[0429] The method further comprises the steps of:

[0430] measuring the level of an additional biomarker in a biological sample from the subject; and

[0431] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0432] In the above method, the additional biomarker can be selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13) , miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, the method is provided, which uses a miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13 ), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22) The present invention further comprises one or more additional biomarkers from the group consisting of miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0433] In another embodiment of the present invention, the biomarker used in the above method is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, a method for screening a candidate compound for a therapeutic drug for liver disease in a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample of the subject to which the candidate compound for a therapeutic drug for liver disease is administered; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0434] Each biomarker, biological sample, and liver disease are as described above.

[0435] In the above-mentioned method for screening candidate compounds for therapeutic drugs, the measured value refers to the measured value of the biomarker of the subject at the time of measurement, and the control value refers to the value of the biomarker in a subject without liver disease or the past measured value of a subject with liver disease.

[0436] The present inventors have discovered multiple biomarkers that show a clear correlation with the disease classification of liver disease conditions, and developed a screening method for candidate compounds of therapeutic drugs by observing and studying the measured values ​​and changes of the biomarkers. The disease classification related to the degree of progression of liver disease will be described later.

[0437] <Treatment methods for liver disease>

[0438] The present invention provides the above-mentioned method for treating liver disease in a subject, comprising the steps of: measuring the level of a biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value.

[0439] In one embodiment of the present invention, the biomarker used in the method for treating liver disease is one or more selected from the group consisting of SEQ ID NOs: 1 to 125. In one embodiment of the present invention, a method for treating liver disease in a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 125.

[0440] In one embodiment of the present invention, the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 82. In one embodiment of the present invention, a method for treating liver disease in a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 1 to 125.

[0441] In one embodiment of the present invention, the biomarker used in the above-mentioned method for treating liver disease can be selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4), but is not limited to these. In a preferred embodiment of the present invention, the biomarker used in the above-mentioned method for treating liver disease is one or more selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4). In one embodiment of the present invention, the biomarker used in the above-mentioned method for treating liver disease is miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) or miR-4521 (sequence number 4) or a combination of these.

[0442] In one embodiment of the present invention, there is provided a method for treating liver disease in a subject, comprising the steps of:

[0443] determining the level of the biomarker in a biological sample from the subject; and

[0444] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0445] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR3138 (SEQ ID NO: 2), miR3679 (SEQ ID NO: 3), and miR4521 (SEQ ID NO: 4).

[0446] In an embodiment of the present invention, as a biomarker, in addition to one or more selected from the group consisting of miR-4454 (SEQ ID NO. 1), miR-3138 (SEQ ID NO. 2), miR-3679 (SEQ ID NO. 3) and miR-4521 (SEQ ID NO. 4), additional biomarkers may also be used. In an embodiment of the present invention, the above-mentioned method for treating liver disease may further include the following steps: measuring the level of the additional biomarker in the biological sample from the subject; and comparing the measured value of the additional biomarker with the control value.

[0447] In one embodiment of the present invention, there is provided a method for treating liver disease in a subject, comprising the steps of:

[0448] determining the level of the biomarker in a biological sample from the subject; and

[0449] The measured values ​​of the above biomarkers are compared with the control values, wherein:

[0450] The biomarker is one or more selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR3138 (SEQ ID NO: 2), miR3679 (SEQ ID NO: 3), and miR4521 (SEQ ID NO: 4),

[0451] The method further comprises the steps of:

[0452] measuring the level of an additional biomarker in a biological sample from the subject; and

[0453] The measured values ​​of the above-mentioned additional biomarkers were compared with the control values.

[0454] In the above method, the additional biomarker can be selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13) , miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, the method is provided, which uses a miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13 ), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22) The present invention further comprises one or more additional biomarkers from the group consisting of miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0455] In another embodiment of the present invention, the biomarker used in the above method is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, a method for treating liver disease in a subject is provided, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject; and comparing the measured value of the biomarker with a control value, wherein the biomarker is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0456] Each biomarker, biological sample, and liver disease are as described above.

[0457] In the above-mentioned method for treating liver disease, the measured value refers to the measured value of the biomarker of the subject at the time of measurement, and the control value refers to the value of the biomarker of a subject without liver disease or the past measured value of a subject with liver disease.

[0458] The above method may include the following steps: before determining the level of the biomarker, exosomes are prepared from the biological sample as needed. In this case, the level of the biomarker in the biological sample is measured in the form of the level of the biomarker in the prepared exosomes. The following methods can be used to recover exosomes from the biological sample: ultracentrifugation separation method (for example, Thery C., Curr. Protoc. Cell Biol. (2006) Chapter 3: Unit 3.22.), polymer precipitation method, immunoprecipitation method, FACS method, ultrafiltration method, gel filtration method, HPLC method and a method of adsorbing to a carrier such as beads using an antibody or lectin. In addition, a commercially available exosome isolation kit can also be used to recover exosomes.

[0459] The recovery of exosomes or confirmation of the physical properties of exosomes can be carried out according to known methods, for example, visual confirmation can be carried out using an electron microscope or the particle size and number of exosomes can be measured using NTA (Nano Tracking Analysis) technology. Alternatively, the presence of exosomes can be confirmed by confirming the expression of proteins and / or genes that can serve as exosome markers.

[0460] Regarding the determination of the level of biomarkers, there is no particular limitation as long as the method can determine the RNA level, and it is usually carried out by using a method that uses a probe, primer, or other substance that can specifically bind to an RNA biomarker. "Nucleic acid" in this specification includes natural nucleic acids such as DNA and RNA, or artificially developed nucleic acids such as cross-linked nucleic acids such as PNA and locked nucleic acids (2', 4'-BNA), or a combination thereof. A substance that can specifically bind to RNA may contain an artificially designed sequence (e.g., a sequence for labeling, tagging, etc.) in at least a portion.

[0461] The determination of biomarker levels can be carried out by measuring the binding level of the substance that can specifically bind to the biomarker by measuring the binding level of the substance that can specifically bind to the biomarker. "Binding level" can be set to binding amount, binding number or binding ratio or a numerical value representing them (for example, the measured values ​​such as the measured fluorescence intensity itself). In this case, a labeled substance can be used as a substance that can specifically bind to the marker RNA, or the marker RNA is labeled and used. In addition, the standard sample is usually measured at the same time, and a standard curve or calibration curve is prepared based on the standard sample, and the value calculated by the measured value of the sample or the numerical value standardized with the standard sample level as an index is determined, and it is used as the binding level. As a method for labeling, for example, radioisotope (RI) labeling, fluorescent labeling and enzyme labeling can be cited.

[0462] For example, the above method may include the following (a) to (c):

[0463] (a) contacting at least one probe with a biological sample;

[0464] (b) measuring the binding level of the biomarker in the biological sample that is bound to the probe; and

[0465] (c) determining the level of the biomarker in the biological sample based on the determined level of binding.

[0466] Alternatively, RNA levels can be measured using a method using PCR, for example, qPCR, ARMS (Amplification Refractory Mutation System), RT-PCR (Reverse transcriptase-PCR) or nested PCR can be performed using primers. Alternatively, the Invader (registered trademark) method can be used. For example, a method using the GenoExplorer™ miRNA qRT-PCR Kit (GenoSensor Corporation) together with appropriate primers can be used.

[0467] Amplification of all or part of the biomarker in a biological sample can be performed by performing a PCR reaction using the biological sample as a template, etc. The level of the amplified nucleic acid can be measured by a dot / blot / hybridization method, a surface plasmon resonance method (SPR method), a PCR-RFLP method, an in situ RT-PCR method, a PCR-SSO (sequence specific Oligonucleotide) method, a PCR-SSP method, an AMPFLP (Amplifiable fragment length polymorphism) method, a MVR-PCR method, and a PCR-SSCP (single strand conformation polymorphism) method.

[0468] For example, RNA levels can be measured by the following steps:

[0469] (a) Using a biological sample as a template, amplifying all or part of the biomarkers in the biological sample using primers:

[0470] (b) determining the level of the amplified nucleic acid molecule; and

[0471] (c) determining the level of the biomarker in the biological sample based on the level of the amplified nucleic acid molecule.

[0472] In this specification, "level" refers to a numerically related index related to the amount present, for example, including concentration, amount or an index (preferably a numerical index) that can be used instead of them. Therefore, the level can be the measured value itself, or it can be a value converted into a concentration or an absolute amount. In addition, the level can be an absolute numerical value (amount, amount per unit area or volume, etc.), or it can also be a relative numerical value obtained by comparing with a comparison control (such as an internal control, etc.) set as needed.

[0473] The present inventors have discovered a number of biomarkers that show a clear correlation with the disease classification of liver disease conditions, and developed a treatment method for liver disease by observing and studying the measured values ​​and changes of the biomarkers. The disease classification related to the degree of progression of liver disease will be described later.

[0474] <Liver disease diagnosis support system>

[0475] The present invention provides a liver disease diagnosis auxiliary system, which has:

[0476] A unit for measuring the level of one or more or at least two biomarkers in a biological sample from a subject,

[0477] A unit for obtaining multiple parameters by comparing the measured values ​​of the above one or more or at least two biomarkers with their control values, and

[0478] A means for calculating the presence or absence of liver disease in the subject, its risk or degree of progression, and / or the degree of success of treatment performed for treatment based on the plurality of parameters.

[0479] In one embodiment of the present invention, the biomarker used in the liver disease diagnosis support system is one or more selected from the group consisting of sequence numbers 1 to 125. In one embodiment of the present invention, there is provided a liver disease diagnosis support system according to the above, wherein the biomarker is one or more selected from the group consisting of sequence numbers 1 to 125.

[0480] In one embodiment of the present invention, the biomarker used in the liver disease diagnosis support system is one or more selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, there is provided a liver disease diagnosis support system according to the above, wherein the biomarker is one or more selected from the group consisting of sequence numbers 1 to 82.

[0481] In one embodiment of the present invention, the one or more or at least two biomarkers can be selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, a liver disease diagnosis support system is provided, wherein the one or more or at least two biomarkers are selected from miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3), miR-4521 (sequence number 4), miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), miR-194-2 (sequence number 11), miR-194-3 (sequence number 12), miR-194-4 (sequence number 13), miR-194-5 (sequence number 14), miR-194-6 (sequence number 15), miR-194-7 (sequence number 16), miR-194-8 (sequence number 17), miR-194-9 (sequence number 18), miR-194-10 (sequence number 19), miR-194-11 (sequence number 11), miR-194-2 The present invention relates to a group consisting of miR-10 (sequence number 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0482] In another embodiment of the present invention, the biomarker used in the liver disease diagnosis support system is one or more selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, there is provided a liver disease diagnosis support system, wherein the biomarker is one or more selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0483] Each biomarker, biological sample, and liver disease are as described above.

[0484] In the above-mentioned diagnosis support system, the measured value refers to the measured value of the biomarker of the subject at the time of measurement, and the control value refers to the value of the biomarker of a subject without liver disease or the past measured value of a subject with liver disease.

[0485] The inventors have discovered a number of biomarkers that show a clear correlation with the disease classification of liver disease, and developed a diagnostic support system by observing and studying the measured values ​​and changes of the biomarkers. The disease classification related to the degree of progression of liver disease will be described later.

[0486] In one embodiment of the present invention, there is provided a device equipped with the above-mentioned liver disease diagnosis support system.

[0487] <Liver disease test kit>

[0488] In one embodiment of the present invention, a liver disease detection kit and a detection device are provided, which contain a nucleic acid that can specifically bind to a specific miRNA. In the present invention, the nucleic acid that can specifically bind to a specific miRNA can be selected from a polynucleotide that is a biomarker for liver disease and a nucleic acid that can specifically bind to a complementary strand of the polynucleotide.

[0489] In one embodiment of the present invention, the biomarker used in the above-mentioned liver disease detection kit is one or more selected from the group consisting of SEQ ID NOs. 1 to 125. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned, wherein the above-mentioned biomarker is one or more selected from the group consisting of SEQ ID NOs. 1 to 125.

[0490] In one embodiment of the present invention, the biomarker used in the above-mentioned liver disease detection kit is one or more selected from the group consisting of SEQ ID NOs. 1 to 82. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned, wherein the above-mentioned biomarker is one or more selected from the group consisting of SEQ ID NOs. 1 to 82.

[0491] The biomarker for liver disease used in the present invention is, for example, a polynucleotide selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4), but is not limited thereto. In one embodiment of the present invention, the biomarker for liver disease is a polynucleotide selected from the group consisting of miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), and miR-4521 (SEQ ID NO: 4).

[0492] In one embodiment of the present invention, a liver disease detection kit is provided, which comprises a nucleic acid capable of specifically binding to a polynucleotide selected from the group consisting of miR-4454 (sequence number 1), miR-3138 (sequence number 2), miR-3679 (sequence number 3) and miR-4521 (sequence number 4) as biomarkers or a complementary chain of the polynucleotide.

[0493] The above-mentioned liver disease detection kit may further comprise at least one nucleic acid that can specifically bind to a polynucleotide that is another biomarker. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned method, which further comprises at least one nucleic acid that can specifically bind to a polynucleotide that is another biomarker.

[0494] In the above-mentioned liver disease detection kit, other biomarkers can be selected from miR-320A (sequence number 5), miR-483 (sequence number 6), miR-122 (sequence number 7), miR-125B-1 (sequence number 8), miR-125B-2 (sequence number 9), miR-194-1 (sequence number 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-224 (sequence number 14), miR-225B-1 (sequence number 15), miR-225B-2 (sequence number 16), miR-225B-2 (sequence number 17), miR-225B-1 (sequence number 18), miR-225B-2 (sequence number 19), miR-320A (sequence number 5), miR-320B-2 (sequence number 6), miR-320B-1 (sequence number 8), miR-325B-2 (sequence number 9), miR-320A (sequence number 1 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but are not limited to these. In one embodiment of the present invention, a liver disease detection kit is provided, which uses a protein selected from miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99a (SEQ ID NO: 12), miR-223 (SEQ ID NO: 13), miR-194-2 (SEQ ID NO: 14), miR-223 (SEQ ID NO: 15), miR-194-1 (SEQ ID NO: 16), miR-194-2 (SEQ ID NO: 17), miR-223 (SEQ ID NO: 18), miR-194-2 (SEQ ID NO: 19), miR-194-2 (SEQ ID NO: 20), miR-223 (SEQ ID NO: 21), miR-223 (SEQ ID NO: 22), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-223 (SEQ ID NO: 23), miR-194-2 (SEQ ID NO: 24), miR-194-2 (SEQ ID NO: 25), miR-223 (SEQ ID NO: 26), miR-194-1 (SEQ ID NO: 11), miR-194-2 (SEQ ID NO: 12), miR-223 (SEQ ID NO: 26), miR-194-2 (SEQ ID NO: 13), miR-194-2 (SEQ ID NO: 14), miR-223 (SEQ ID NO: 26), miR-194- One or more other biomarkers in the group consisting of miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0495] In another embodiment of the present invention, the biomarker used in the above-mentioned liver disease detection kit is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned, wherein the above-mentioned biomarker is one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0496] In one embodiment of the present invention, there is provided the above-mentioned liver disease detection kit, wherein the above-mentioned nucleic acid is selected from the polynucleotide or a fragment thereof shown in any one of the following (a) to (c).

[0497] (a)

[0498] (a-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0499] (a-2) a polynucleotide or a fragment thereof containing one or more bases deleted, substituted, added or inserted in the base sequence of the polynucleotide or a fragment thereof of (a-1),

[0500] (a-3) a polynucleotide of (a-1) or (a-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0501] (b) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0502] (c) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (c-1) to (c-4) or a fragment thereof.

[0503] (c-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0504] (c-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (c-1),

[0505] (c-3) a polynucleotide of (c-1) or (c-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0506] (c-4) A polynucleotide comprising the base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases.

[0507] In one embodiment of the present invention, the polynucleotide represented by any one of (a) to (c) used in the above-mentioned liver disease detection kit is selected from the polynucleotide represented by any one of sequence numbers 1 to 82. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned, wherein the polynucleotide represented by any one of (a) to (c) is selected from the polynucleotide represented by any one of sequence numbers 1 to 82.

[0508] In one embodiment of the present invention, there is provided the above-mentioned liver disease detection kit, wherein the above-mentioned nucleic acid is selected from the polynucleotide or a fragment thereof shown in any one of the following (a) to (c).

[0509] (a)

[0510] (a-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0511] (a-2) a polynucleotide or a fragment thereof containing one or more bases deleted, substituted, added or inserted in the base sequence of the polynucleotide or a fragment thereof of (a-1),

[0512] (a-3) a polynucleotide of (a-1) or (a-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0513] (b) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 4 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0514] (c) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (c-1) to (c-4) or a fragment thereof.

[0515] (c-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0516] (c-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (c-1),

[0517] (c-3) a polynucleotide of (c-1) or (c-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0518] (c-4) A polynucleotide comprising the base sequence represented by any of SEQ ID NOs: 1 to 4, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases.

[0519] In one embodiment of the present invention, there is provided the above-mentioned detection kit, wherein the nucleic acid capable of specifically binding to a polynucleotide serving as another biomarker is selected from the polynucleotide or a fragment thereof shown in any one of the following (d) to (f).

[0520] (d)

[0521] (d-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0522] (d-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (d-1),

[0523] (d-3) a polynucleotide of (d-1) or (d-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0524] (e) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and

[0525] (f) A polynucleotide that hybridizes under stringent conditions to a polynucleotide represented by any of the following (f-1) to (f-4) or a fragment thereof.

[0526] (f-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases,

[0527] (f-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (f-1),

[0528] (f-3) a polynucleotide of (f-1) or (f-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide,

[0529] (f-4) A polynucleotide comprising a base sequence represented by any of SEQ ID NOs: 5 to 21, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases

[0530] In another embodiment of the present invention, the polynucleotide represented by any one of (a) to (c) used in the above-mentioned liver disease detection kit is one or more selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, there is provided a liver disease detection kit according to the above-mentioned, wherein the polynucleotide represented by any one of (a) to (c) is one or more selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0531] In this specification, "polynucleotide" is used for nucleic acids including any one of RNA, DNA and RNA / DNA (chimeric). It should be noted that the above-mentioned DNA includes any one of cDNA, genomic DNA and synthetic DNA. In addition, the above-mentioned RNA includes any one of total RNA, mRNA, rRNA, miRNA, siRNA, snoRNA, snRNA, non-coding RNA and synthetic RNA. In this specification, "synthetic DNA" and "synthetic RNA" refer to DNA and RNA artificially made based on a specified base sequence (which can be any one of a natural sequence or a non-natural sequence.) For example, an automatic nucleic acid synthesizer is used. In this specification, "non-natural sequence" is intended to be used in a broad sense, including sequences different from natural sequences, such as substitutions, deletions, insertions and / or additions of more than one nucleotide (i.e., mutant sequences), sequences containing more than one modified nucleotide (i.e., modified sequences), etc. In addition, in this specification, polynucleotides can be used interchangeably with nucleic acids.

[0532] In the present specification, a "fragment" refers to a polynucleotide (including an oligonucleotide) having a continuous partial base sequence of a polynucleotide, and desirably has a length of 15 bases or more, preferably 17 bases or more, and more preferably 19 bases or more.

[0533] In this specification, "microRNA (miRNA)" is used to refer to a 15-25 base non-coding RNA that is transcribed in the form of a hairpin-like RNA precursor, cleaved by a dsRNA cleavage enzyme having RNase III cleavage activity, incorporated into a protein complex called RISC, and participates in the translation inhibition of mRNA unless otherwise specified. In addition, the "miRNA" used in this specification includes not only the "miRNA" shown in a specific base sequence (or sequence number), but also the precursor (pre-miRNA, pri-miRNA) of the "miRNA", miRNA with the same biological function as these, such as homologs (i.e., homologs (homolog) or orthologs (ortholog)), mutants and derivatives such as gene polymorphisms. As the precursor, homolog, mutant or derivative, specifically, it can be identified using miRBase release 20 (http: / / www.mirbase.org / ), and examples include "miRNA" having a base sequence that hybridizes with a complementary sequence of any specific base sequence shown in any of sequence numbers 1 to 22 under the stringent conditions described below. Furthermore, the "miRNA" used in this specification can also be the gene product of the miR gene, such gene product includes mature miRNA (for example, the non-coding RNA of 15 to 25 bases or 19 to 25 bases involved in the translation inhibition of mRNA as mentioned above) or miRNA precursor (for example, the pre-miRNA or pri-miRNA as mentioned above).

[0534] In the present specification, the "probe" includes a polynucleotide for specifically detecting RNA produced by gene expression or a polynucleotide derived from the RNA and / or a complementary polynucleotide thereof.

[0535] In this specification, "primer" includes a polynucleotide and / or its complementary polynucleotide for specifically identifying and amplifying RNA produced by gene expression or a polynucleotide derived from the RNA. Here, "complementary polynucleotide (complementary chain, negative chain)" refers to: a polynucleotide having a base complementary relationship based on the base pair relationship of A:T(U) and G:C relative to the full-length sequence or partial sequence thereof (here, for convenience, it is referred to as the positive chain) of a polynucleotide consisting of a base sequence defined by any one of sequence numbers 1 to 22 or a base sequence in which u is t in the base sequence. Among them, the complementary chain is not limited to the case where it forms a completely complementary sequence with the base sequence of the positive chain as the object, and it can also be a case where it has a complementary relationship to the extent that it can hybridize with the positive chain as the object under stringent conditions.

[0536] In this specification, "stringent conditions" refer to conditions under which a nucleic acid probe hybridizes to its target sequence at a greater degree than to other sequences (e.g., the average of the background measurement value + the standard error of the background measurement value × 2 or more measurement value). Stringent conditions are sequence-dependent and vary depending on the environment in which hybridization is performed. By controlling the stringency of hybridization and / or washing conditions, a target sequence that is 100% complementary to the nucleic acid probe can be identified.

[0537] In this specification, the "nucleic acid" that can specifically bind to the polynucleotide selected from the above-mentioned miRNA as a biomarker is a synthetic or prepared nucleic acid, specifically, including a "nucleic acid probe" or "primer", which can be used directly or indirectly to detect the presence of liver disease in the subject, or to diagnose the presence or absence of liver disease, the degree of liver disease, the presence or absence of improvement of liver disease or the degree of improvement, sensitivity to liver disease treatment, or to screen for candidate substances useful for the prevention, improvement or treatment of liver disease. Among these, nucleotides, oligonucleotides and polynucleotides that can specifically recognize and bind to the following substances are included: the transcription product or cDNA synthetic nucleic acid shown in any of sequence numbers 1 to 22 that is related to the occurrence of liver disease and is in the body, especially in the body fluid waiting for the specimen such as blood and urine. Based on the above properties, these nucleotides, oligonucleotides and polynucleotides can be effectively used as probes for detecting the above-mentioned genes expressed in the body, tissue, cell, etc., and can also be effectively used as primers for amplifying the above-mentioned genes expressed in the body.

[0538] In the present specification, “capable of specifically binding” means that the nucleic acid probe or primer used in the present invention binds to a specific target nucleic acid and does not substantially bind to other nucleic acids.

[0539] The term "detection" used in this specification can be replaced with the terms such as inspection, determination, detection or determination assistance. In addition, the term "evaluation" in this specification is used in the following meaning: based on the inspection results or measurement results, diagnosis or evaluation is assisted.

[0540] The biomarkers and liver diseases are as described above.

[0541] The present inventors have discovered a plurality of biomarkers that show a clear correlation with the disease classification of liver disease conditions, and have developed a liver disease detection kit and a detection device by using the biomarkers.

[0542] <Data processing method for classifying liver disease>

[0543] The present invention provides:

[0544] A method for performing data processing for classifying a liver disease of a subject, executed by a computer, the computer comprising:

[0545] Computer / processor, and

[0546] A computer-readable storage medium having software for issuing instructions for classifying the liver disease of the subject,

[0547] The above method comprises the following steps:

[0548] Comparing the measured values ​​of one or more or at least two biomarkers in the biological sample obtained from the subject with their control values, respectively, and storing the data of multiple parameters obtained thereby in a computer-readable storage medium; and

[0549] The computer processes the data of the plurality of parameters by executing instructions using the computer / processor, and the computer determines the condition of the subject's liver disease and outputs the result of the determination as information obtained by software that issues instructions for classifying the condition of the subject's liver disease.

[0550] In one embodiment of the present invention, there is provided:

[0551] A method for performing data processing for classifying a liver disease of a subject, executed by a computer, the computer comprising:

[0552] Computer / processor, and

[0553] A computer-readable storage medium having software for issuing instructions for classifying the liver disease of the subject,

[0554] The above method comprises the following steps:

[0555] Comparing the measured values ​​of one or more or at least two biomarkers in the biological sample obtained from the subject with their control values, respectively, and storing the data of multiple parameters obtained thereby in a computer-readable storage medium; and

[0556] The computer processes the data of the plurality of parameters by executing instructions using the computer / processor, the computer determines the condition of the liver disease of the subject, and outputs the result of the determination as information obtained by software that issues instructions for classifying the condition of the liver disease of the subject,

[0557] The above disease classification includes one or more selected from the group consisting of liver fibrosis staging, Child-Pugh classification, MELD score, MELD Na score, PELD score, ALBI score, mALBI (modified ALBI) score, FibroScan score, new Inuyama classification, new European classification and Brunt classification.

[0558] In the above method, the result of the determination may be a combination of results obtained by processing the data from the above multiple parameters. In one embodiment of the present invention, there is provided a method according to the above method, wherein the result of the determination is a combination of results obtained by processing the data from the above multiple parameters.

[0559] In one embodiment of the present invention, the one or more or at least two biomarkers used in the method for performing the above-mentioned data processing are selected from the group consisting of SEQ ID NOs: 1 to 125. In one embodiment of the present invention, a method for performing the above-mentioned data processing is provided, wherein the one or more or at least two biomarkers are selected from the group consisting of SEQ ID NOs: 1 to 125.

[0560] In one embodiment of the present invention, the one or more or at least two biomarkers used in the method for performing the above-mentioned data processing are selected from the group consisting of SEQ ID NOs: 1 to 82. In one embodiment of the present invention, a method for performing the above-mentioned data processing is provided, wherein the one or more or at least two biomarkers are selected from the group consisting of SEQ ID NOs: 1 to 82.

[0561] In one embodiment of the present invention, the one or more or at least two biomarkers can be selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22), but is not limited to these. In one embodiment of the present invention, a method for performing the above-mentioned data processing is provided, wherein the above-mentioned one or more or at least two biomarkers are selected from miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-194-3 (SEQ ID NO: 12), miR-194-4 (SEQ ID NO: 13), miR-194-5 (SEQ ID NO: 14), miR-194-6 (SEQ ID NO: 15), miR-194-7 (SEQ ID NO: 16), miR-194-8 (SEQ ID NO: 17), miR-194-9 (SEQ ID NO: 18), miR-194-10 (SEQ ID NO: 19), miR-194-11 (SEQ ID NO: 19), miR-194-2 0), miR-194-2 (sequence number 11), miR-99a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21) and miR-320b-2 (sequence number 22).

[0562] In another embodiment of the present invention, the one or more or at least two biomarkers used in the method for performing the above data processing are one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, a method for performing the above data processing is provided, wherein the one or more or at least two biomarkers are one or more selected from the group consisting of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0563] Each biomarker, biological sample, and liver disease are as described above.

[0564] In the above-mentioned diagnosis support system, the measured value refers to the measured value of the biomarker of the subject at the time of measurement, and the control value refers to the value of the biomarker of a subject without liver disease or the past measured value of a subject with liver disease.

[0565] The present inventors have discovered a number of biomarkers that show a clear correlation with the disease classification of liver disease conditions, and developed a data processing method for disease classification of the liver disease conditions of the subject by observing and studying the measured values ​​and changes of the biomarkers. The disease classification related to the degree of progression of liver disease will be described below.

[0566] As a disease classification of liver disease conditions, for example, liver fibrosis staging, Child-Pugh classification, MELD score, MELD Na score, PELD score, ALBI score, mALBI (improved ALBI) score, FibroScan score, new dog mountain classification, new European classification and Brunt classification can be listed, but are not limited to these. These disease classifications can be used alone or in combination. In one embodiment of the present invention, the above-mentioned disease classification includes one or more selected from the group consisting of liver fibrosis staging, Child-Pugh classification, MELD score, MELD Na score, PELD score, ALBI score, mALBI (improved ALBI) score, FibroScan score, new dog mountain classification, new European classification and Brunt classification.

[0567] · Liver fibrosis staging

[0568] The disease is divided into five stages: F0 (no fibrosis), F1 (mild), F2 (moderate), F3 (severe), and F4 (cirrhosis), starting from when fibrosis is no longer observed and gradually increases to cirrhosis.

[0569] Child-Pugh classification

[0570] It is an index showing the degree of liver damage. As shown in Table 1 below, the presence or absence of hepatic encephalopathy, the presence or absence of ascites, serum bilirubin concentration (Bil), serum albumin concentration (Alb), and prothrombin activity value (PT-INR) are evaluated with 1 to 3 points, and the total score is classified into 3 stages from A to C. The lowest degree of damage is A (compensated), and as the degree of damage increases, it becomes B (non-compensated) and C (non-compensated).

[0571]

Table 2-1

[0572]

[0573] MELD score

[0574] The scoring system uses total bilirubin (T-Bil), prothrombin activity (PT-INR), creatinine (Cr), and the presence or absence of dialysis treatment. It is an indicator of liver reserve capacity for diagnosing cirrhosis and liver transplant registrants aged 12 years and older.

[0575] MELD Na score

[0576] This scoring system adds serum sodium concentration (Na) to the same blood biochemical test data (T-Bil, PT-INR, Cr) as the conventional MELD score. It is an indicator of liver reserve capacity used for diagnosing acute hepatitis / cirrhosis and liver transplant registrants aged 12 years and older.

[0577] PELD score

[0578] The scoring system uses blood biochemical test data (Bil, PT-INR, Alb) and age and growth level to diagnose liver reserve capacity in cirrhosis and liver transplant registrants under the age of 12.

[0579] ALBI score

[0580] This is a scoring system that uses blood biochemical test data (T-Bil, Alb) and is an indicator used when selecting a treatment method for hepatocellular carcinoma. Generally, the score can be divided into three levels (1, 2, 3) called ALBI levels.

[0581] mALBI (modified ALBI) scoring

[0582] This is a scoring system that uses the same blood biochemical test data (T-Bil, Alb) as the conventional ALBI score, and is an indicator used when selecting a treatment method for hepatocellular carcinoma. By dividing it into four levels (1, 2a, 2b, 3), it is expected to have better evaluation capabilities.

[0583] FibroScan score

[0584] The liver stiffness is measured by placing a special "probe" that transmits vibrations and ultrasound waves against the right flank surface, and the vibrations and ultrasound waves are transmitted in a numerical manner. The more fibrotic and stiff the liver is, the faster the vibrations are transmitted, so the degree of fibrosis can be understood from the numerical value. In addition, it is used not only to evaluate fibrosis, but also to predict cancer and evaluate the degree of portal hypertension.

[0585] New Inuyama Classification

[0586] The condition of chronic hepatitis was graded into four categories: periportal, intralobular, portal inflammation, and fibrosis, and scored semi-quantitatively.

[0587] New European Classification

[0588] The causes are classified into hepatitis viral, autoimmune, drug-induced, and unknown causes, and recorded in 4 stages (grading) and 5 stages (staging).

[0589] Brunt classification

[0590] It is an index for evaluating / grading the pathological findings of NASH by the degree of activity (grading) and staging (staging). Grading is evaluated in 3 stages, with (1) fat deposition, (2) hepatocyte ballooning, and (3) the degree of inflammatory cell infiltration in the lobule and portal vein area as evaluation items for comprehensive evaluation. Staging is evaluated in 4 stages, with stage 1 being centrilobular fibrosis, stage 2 being stage 1 + portal vein fibrosis, stage 3 being the addition of bridging fibrosis, and stage 4 being cirrhosis.

[0591]

Table 2-2

[0592]

[0593] In one embodiment of the present invention, there are provided software for implementing the above-mentioned method of performing data processing for classifying the condition of liver disease of a subject and a device equipped with the software.

[0594] In an embodiment of the present invention, the subject or object is a mammalian subject, more specifically, a mammal including primates such as humans and chimpanzees, pet animals such as dogs and cats, livestock animals such as cows, horses, sheep, and goats, and rodents such as mice and rats. In a preferred embodiment of the present invention, the subject or object is a human subject.

[0595] <Program for causing a computer to execute determination of liver disease and medium recording the program>

[0596] In one embodiment of the present invention, there is provided:

[0597] A program for causing a computer to execute determination of the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or determination of the degree of success of treatment performed for treatment, the program executing:

[0598] a measurement value obtaining step of obtaining measurement values ​​of the levels of one or more or at least two biomarkers obtained from a biological sample from the subject;

[0599] a parameter data acquisition step of obtaining data of a plurality of parameters by comparing the measured values ​​of the one or more or at least two biomarkers with respective control values; and

[0600] A determination step of determining the presence or absence of liver disease in the subject or its risk or degree of progression and / or determining the success of treatment performed for treatment based on the data of the plurality of parameters, wherein:

[0601] The biomarker is selected from the group consisting of SEQ ID NOs: 1 to 125. In one embodiment of the present invention, the program may be recorded on a medium. In one embodiment of the present invention, a recording medium recording the program is provided.

[0602] In one embodiment of the present invention, in the above-mentioned program, the above-mentioned biomarker is selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, there is provided a program according to the above-mentioned, wherein the above-mentioned biomarker is selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, the program can be recorded on a medium. In one embodiment of the present invention, there is provided a recording medium having the program recorded thereon.

[0603] In one embodiment of the present invention, in the above program, the biomarker is selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, there is provided a program according to the above, wherein the biomarker is selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, the program may be recorded on a medium. In one embodiment of the present invention, there is provided a recording medium having the program recorded thereon.

[0604] Each biomarker, biological sample, and liver disease are as described above.

[0605] <Data processing device for performing data processing for detecting liver disease>

[0606] In one embodiment of the present invention, there is provided:

[0607] A data processing device for performing data processing for detecting liver disease, comprising:

[0608] A missing value supplementation unit, which obtains missing value supplementation data by supplementing the missing values ​​of the miRNA expression data;

[0609] A logarithmic transformation unit, which performs logarithmic transformation on the missing value supplementary data to obtain logarithmic transformation data;

[0610] A regression transformation unit that obtains regression transformation data by substituting the logarithmic transformation data into a regression model and performing regression transformation;

[0611] A training data acquisition unit that acquires training data; and

[0612] The learned model preparation unit prepares a learned model using the regression transformed data as training data.

[0613] In one embodiment of the present invention, the missing value supplementation unit supplements the missing value for each miRNA by supplementing the missing value for the data of the expression amount of each miRNA, thereby obtaining missing value supplementation data for each miRNA. In the present invention, as the data of miRNA expression, the miRNA expression data obtained by the analysis method using the high-precision microarray DNA chip 3D-Gene (registered trademark) of Toray Industries, Ltd. is used. The miRNA expression data includes three kinds of data: "Raw data", "BG subtraction" and "globalnormalization". In the present invention, "global normalization" is used. However, the "globalnormalization" data contains missing values, so the missing values ​​are supplemented with the minimum value of each miRNA sample. Thus, the "global normalization" data contains a specified number of data. Missing values ​​can be understood in a general sense, referring to the value of the position where the data is missing for some reason. In addition, since the distribution of the above-mentioned miRNA expression data is a log-normal distribution, the above-mentioned missing values ​​are supplemented and the logarithmic transformation described later is performed before the statistical analysis process.

[0614] In one embodiment of the present invention, the logarithmic transformation unit performs logarithmic transformation on the missing value supplementation data that has been supplemented with missing values ​​as described above to obtain logarithmic transformation data. The logarithmic transformation can be performed by, for example, log e 、log 2 、log 10 In the present invention, a logarithmic transformation with a base of 2 (based on log 2 logarithmic transformation of ).

[0615] In one embodiment of the present invention, the regression transformation unit obtains the regression transformation data by substituting the above-mentioned logarithmic transformation data into the regression model and performing regression transformation. Regression transformation can be understood in a general sense, which means analyzing the relationship between two variables x and y by using a method such as regression analysis, thereby:

[0616] y=f(x) (1)

[0617] Functions such as t(x) and t(x) formulate the relationship between two variables.

[0618] In one embodiment of the present invention, the above regression transformation includes the following steps:

[0619] Multiply by the regression coefficient;

[0620] The logical value indicating the prediction result of the fibrosis stage is obtained by adding the intercept value for each fibrosis stage;

[0621] By performing an inverse transformation of the logical transformation on the above logical value to transform it into a probability value between 0 and 1, the predicted probability of each fibrosis stage is calculated; and

[0622] The fibrosis stage with the highest probability among the above-mentioned prediction probabilities of each fibrosis stage is used as the prediction value. In one embodiment of the present invention, a data processing device for performing the above-mentioned data processing for detecting liver disease is provided.

[0623] The above regression transformation includes the following steps:

[0624] Multiply by the regression coefficient;

[0625] The logical value indicating the prediction result of the fibrosis stage is obtained by adding the intercept value for each fibrosis stage;

[0626] By performing an inverse transformation of the logical transformation on the above logical value to transform it into a probability value between 0 and 1, the predicted probability of each fibrosis stage is calculated; and

[0627] Among the above-mentioned prediction probabilities of each fibrosis stage, the fibrosis stage with the highest probability is taken as the predicted value.

[0628] The above regression coefficient can be understood in a general sense, which refers to the slope of the straight line representing the regression formula (regression model) on the coordinate plane in the regression analysis, and refers to the coefficient α when the average relationship between the cause variable x (explanatory variable) and the result variable y (target variable) is represented by the linear equation y=αx+β. β refers to the intercept. In the present invention, the regression coefficient generated by the regression model is used to calculate the predicted value of the fibrosis stage.

[0629] In the present invention, the above-mentioned fibrosis staging refers to the fibrosis staging of the liver. Specifically, the above-mentioned fibrosis staging refers to the following: regarding the liver fibrosis staging classification, as described above, no fibrosis is observed to cirrhosis, which is divided into 5 stages: F0 (no fibrosis is observed), F1 (mild), F2 (moderate), F3 (severe), and F4 (cirrhosis).

[0630] The above-mentioned logical (logit) value can be understood in a general sense as a value calculated from a probability p ranging from 0 to 1. When the probability is 0.5, the logical value becomes 0, and changes toward infinity when the probability increases from 0.5, and changes toward negative infinity when the probability decreases from 0.5. The logical value logit(p) for probability p is defined by the following formula (2). The following formula (2) is also called a logical function.

[0631] logit(p)= ln(p / (1-p)) (2)

[0632] In the formula, p is the probability variable and ln is the natural logarithm function. p / (1-p) is called the odds. That is, the logarithm of the odds is the logical value.

[0633] Logical transformation can be understood in a general sense, and refers to the process of obtaining the logic from the probability p through the above formula (2). In addition, the inverse transformation of logical transformation can be understood in a general sense, and refers to the process of obtaining the probability value from the logical value a, as shown in the following formula (3). The following formula (3) is also called a logical function.

[0634] expit(a)= 1 / (1+e -a ) (3)

[0635] In the formula, a is a logical value, and expit(a) is a probability value. In the present invention, the probability value is used as the predicted probability of each fibrosis stage. In addition, the fibrosis stage with the highest probability among the predicted probabilities of each fibrosis stage is used as the predicted value.

[0636] The above-mentioned regression model is also called a regression formula, which refers to a formula derived by a statistical method in a formula representing the relationship between two variables. In one embodiment of the present invention, the above-mentioned regression model can be selected from a group consisting of a polynomial LASSO model, a Gaussian LASSO (Least Absolute Shrinkage and Selection Operator) model, and an ordered logistic regression model. In one embodiment of the present invention, by using a data processing device for data processing for detecting liver disease according to the above-mentioned method, wherein the above-mentioned regression model is selected from a group consisting of a polynomial LASSO model, a Gaussian LASSO (Least Absolute Shrinkage and Selection Operator) model, and an ordered logistic regression model.

[0637] The multinomial LASSO model refers to a multinomial logistic regression model that performs variable selection and predicts more than 3 categories. The multinomial logistic regression model will produce a 2-item logistic regression model based on 1 category in more than 3 categories according to the number of combinations. The predicted probability of each category is calculated using the produced 2-item logistic regression model, and the category with the highest predicted probability is used as the predicted value. In the present invention, when using the multinomial LASSO model, the above-mentioned 5-stage fibrosis stage is analyzed as a category. It should be noted that, in order to select variables, a cross-validation (Cross-Validation, hereinafter referred to as LOO-CV) based on the Leave One Out method was performed. The process of the statistical analysis process using the multinomial LASSO is shown below.

[0638]

Table 3

[0639] Flow of statistical analysis using multinomial LASSO

[0640] start

[0641] Using the analysis matrix as the object, variable selection is performed by LOO-CV LASSO

[0642] Plotting of residual curves and solution paths using models created using LOO-CV

[0643] Save the lambda min regression coefficient of the model created using LOO-CV

[0644] Prediction of fibrosis stage by the model developed using LOO-CV

[0645] △End

[0646] The Gaussian LASSO (Least Absolute Shrinkage and Selection Operator) model is a linear regression model that performs variable selection and predicts the target variable. The linear regression model is shown in the following formula (4).

[0647] y=α 1 x 1 +α 2 x 2 +...+β (4)

[0648] This formula represents the average relationship between the cause variable x (explanatory variable) and the variable y (target variable) as the result quantity, where α is the coefficient and β represents the intercept. In this case, a linear score is calculated. In the present invention, when using the Gaussian LASSO model, the above-mentioned 5-stage fibrosis stage is analyzed as the target variable, and the fibrosis stage is predicted using the cutoff value by the calculated prediction score. The process of statistical analysis processing using Gaussian LASSO is shown below.

[0649]

Table 4

[0650] Flow of statistical analysis using Gaussian LASSO

[0651] start

[0652] Using the analysis matrix as the object, variable selection is performed by LOO-CV LASSO

[0653] Plotting of residual curves and solution paths using models created using LOO-CV

[0654] Save the lambda min regression coefficient of the model created using LOO-CV

[0655] Predicted linear scores from models made using LOO-CV

[0656] The cutoff value of the linear score was calculated from the ROC curve

[0657] Prediction of fibrosis grade using calculated cutoff values

[0658] △End

[0659] The ordered logistic regression model refers to a regression model that predicts more than 3 categories with a sequential relationship. The ordered logistic regression model compares the entire category group and uses the category with the highest prediction probability as the predicted value. In the present invention, when using the ordered logistic regression model, the above-mentioned 5-stage fibrosis stage is analyzed as a category with a sequential relationship, and the miRNA selected by polynomial LASSO and Gaussian LASSO is used as an explanatory variable. The process of statistical analysis processing using ordered logistic regression is shown below.

[0660]

Table 5

[0661] Flow of statistical analysis using ordinal logistic regression

[0662] start

[0663] Using the analysis matrix as the object, a model is created through ordered logistic regression

[0664] Save the regression coefficients of the model created

[0665] Prediction of fibrosis stage by the constructed model

[0666] △End

[0667] In the present invention, when the "Ordered Logistic Regression Model 1" is used, the common miRNA selected by both the polynomial LASSO and the Gaussian LASSO is used as an explanatory variable. In addition, in the present invention, when the "Ordered Logistic Regression Model 2" is used, the miRNA selected by the polynomial LASSO and / or the Gaussian LASSO is used as an explanatory variable.

[0668] In one embodiment of the present invention, the training data acquisition unit acquires training data. In the present invention, the training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood. In one embodiment of the present invention, a data processing device for performing data processing for detecting liver disease according to the above is provided, wherein the training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

[0669] In one embodiment of the present invention, the learned model preparation unit uses the above-mentioned regression transformation data as training data to prepare the learned model. In the present invention, the above-mentioned training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood. The data processing device of the present invention for performing data processing for detecting liver disease uses the learned model obtained in this way to perform trained data processing.

[0670] In one embodiment of the present invention, the data processing for detecting liver disease also includes a training data acquisition step for obtaining training data, and a learning data production step for producing learning data using the above training data. In this embodiment, the above training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood. In one embodiment of the present invention, a data processing device for performing data processing for detecting liver disease according to the above is provided, wherein the data processing for detecting liver disease also includes a training data acquisition step for obtaining training data, and a learning data production step for producing learning data using the above training data, and the above training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

[0671] In one embodiment of the present invention,

[0672] The data processing for detecting liver disease also includes a pre-processing step to obtain the data for analysis, and the pre-processing step includes:

[0673] Listing the low-expressed miRNAs to obtain a low-expressed miRNA list;

[0674] The missing value supplementation data is obtained by supplementing the missing values ​​in the data of the above miRNA list;

[0675] Excluding the low-expression miRNAs included in the low-expression miRNA list from the analysis targets to obtain conversion data; and

[0676] The data for transformation is logarithmically transformed. In one embodiment of the present invention, a data processing device for performing data processing for detecting liver disease is provided, wherein:

[0677] The data processing for detecting liver disease also includes a pre-processing step to obtain the data for analysis, and the pre-processing step includes:

[0678] Listing the low-expressed miRNAs to obtain a low-expressed miRNA list;

[0679] The missing value supplementation data is obtained by supplementing the missing values ​​in the data of the above miRNA list;

[0680] Excluding the low-expression miRNAs included in the low-expression miRNA list from the analysis targets to obtain conversion data; and

[0681] The above transformation data were logarithmically transformed.

[0682] The low-expression miRNA is a miRNA with a low expression level. By listing the low-expression miRNA to obtain a low-expression miRNA list, the low-expression miRNA included in the low-expression miRNA list can be excluded from the analysis target from the conversion data.

[0683] Regarding the logarithmic transformation, as described above.

[0684] In one embodiment of the present invention, in the above-mentioned data processing device for performing data processing for detecting liver disease, the above-mentioned miRNA is selected from the group consisting of sequence numbers 1 to 125. In one embodiment of the present invention, there is provided a data processing device for performing data processing for detecting liver disease, wherein the above-mentioned miRNA is selected from the group consisting of sequence numbers 1 to 125.

[0685] In one embodiment of the present invention, in the above-mentioned data processing device for performing data processing for detecting liver disease, the above-mentioned miRNA is selected from the group consisting of sequence numbers 1 to 82. In one embodiment of the present invention, there is provided a data processing device for performing data processing for detecting liver disease, wherein the above-mentioned miRNA is selected from the group consisting of sequence numbers 1 to 82.

[0686] In one embodiment of the present invention, in the above-mentioned data processing device for performing data processing for detecting liver disease, the above-mentioned miRNA is selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125. In one embodiment of the present invention, there is provided a data processing device for performing data processing for detecting liver disease, wherein the above-mentioned miRNA is selected from the group consisting of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

[0687] Each miRNA, biological sample, and liver disease are as described above.

[0688] Example

[0689] The present invention will be specifically described below by way of examples, but these are not intended to limit the scope of the present invention.

[0690] Example 1. miRNA analysis using RNA seq

[0691] method:

[0692] (1) Preparation of exosome fraction

[0693] Serum obtained from 17 serum samples from patients with different fibrosis stages and Child-Pugh scores and 3 normal serum samples (Table 3) was subjected to ultracentrifugation to purify the exosome fraction. The serum was first centrifuged at 2,000g for 10 minutes, the obtained supernatant was further centrifuged at 12,000g for 30 minutes, and the supernatant thus obtained was centrifuged at 110,000g for 70 minutes, and the final precipitate fraction was used as the exosome fraction.

[0694]

Table 6

[0695]

[0696] (2) miRNA analysis

[0697] RNA was purified and extracted from the above exosome fractions, and miRNA analysis was performed by RNA sequencing (RNA-seq). In addition, for these miRNAs, the correlation between changes in conditions such as non-infection, HBV infection, HCV infection, and fibrosis stage, Child-Pugh score, and changes in miRNA expression was analyzed.

[0698] result:

[0699] 1) Comparison of miRNA expression levels in subjects with fibrosis stage 0 and 1 to 4

[0700] For subjects with fibrosis stage 0 and subjects with fibrosis stage 1 to 4, the relationship between the expression level of various miRNAs and fibrosis stage was analyzed, and the results are shown in Figure 1 . Figure 1 Among the points shown, the points surrounded by black circles correspond to miRNAs that show significant expression changes. Figure 1 The points surrounded by the thick black rectangle shown in the upper part are the points where the expression is significantly increased. Figure 1The points surrounded by the thin black rectangles shown in the lower half are points where the expression significantly decreases. Among the points with significantly increased expression, MIR122 (log2 5.637 times) and MIR483 (Log2 5.093 times) were the highest, followed by MIR3679 (log2 5.050 times), MIR3138 (log2 4.854 times), MIR99A (log2 4.740 times), MIR194-1 (log2 4.740 times), MIR192 (log2 4.672 times), MIR34A (log2 4.164 times), MIR125B2 (log2 3.897 times), MIR125B1 (log2 3.561 times), MIR100 (log2 3.490 times), MIR320C1 (log2 3.443 times), MIR320B1 (log2 3.389 times), MIR194-2 (log2 The expression of MIR4521 (log2-1.545 times) and MIR144 (log2-2.709 times) decreased significantly.

[0701] (2) Correlation between the degree of fibrosis and miRNA expression levels

[0702] The relationship between the expression levels of various miRNAs and the fibrosis stage (degree of fibrosis) was analyzed for normal subjects, subjects with fibrosis stage 0, subjects with fibrosis stage 1, and subjects with fibrosis stage 4, and the results are shown in Figure 2 .like Figure 2 As shown, the expression of MIR320A and MIR483 increased significantly with the progression of fibrosis. Regarding MIR122 (MIR99A, MIR194-1 also showed the same pattern) and MIR125B1 (MIR125B2 as well), the expression increased significantly when the fibrosis stage was 0. Regarding MIR223-3P, it was found that the expression level was significantly decreased compared with normal subjects, and regarding MIR4454, the miRNA expression level was found to decrease linearly. As described above, by using a comprehensive analysis of serum samples with different fibrosis stages, miRNAs that can serve as biomarkers can be selected.

[0703] (3) Comparison of miRNA expression levels in subjects with Child-Pugh scores of 0 and 6 or higher

[0704] For subjects with a Child-Pugh score (also referred to as "CP score", "CP", or "CPF") of 0 and subjects with a CP score of 6 or more, the relationship between various miRNAs and CP scores was analyzed, as shown in Figure 3 . Figure 3Among the points shown, the points surrounded by black circles correspond to miRNAs that show significant expression changes. In addition, the points surrounded by thick black rectangles are points with significantly increased expression, and the points surrounded by thin black rectangles are points with significantly decreased expression. Figure 3 As shown, the expression of MIR122 (log2 5.256 times), MIR483 (log2 5.157 times), MIR99A (log2 4.903 times), MIR194-1 (log2 3.943 times), MIR125B2 (log2 3.810 times), MIR125B1 (log2 3.539 times), MIR320B2 (log2 3.262 times), and MIR320A (log2 2.142 times) increased significantly, and the expression of MIR144 decreased significantly.

[0705] (4) Correlation with Child-Pugh score

[0706] The relationship between the expression level of various miRNAs and the CP score for the subjects with a CP score of 0 and the subjects with a CP score of 6 to 11 is shown in Figure 4 .like Figure 4 As shown, for MIR194-1, it was observed that the miRNA expression decreased as the CP score decreased. For MIR320A, although no linearity was observed, an upward trend in the miRNA expression level was observed until the CP score was 10. For MIR99A, a downward trend in the miRNA expression level was observed, and for MIR4454 and MIR223-3P, a significant decrease in expression level was observed compared to normal subjects. As described above, by using a comprehensive analysis of serum samples with different CP scores, miRNAs that can serve as biomarkers can be selected.

[0707] Example 2. miRNA analysis using 3D-Gene (registered trademark)

[0708] method:

[0709] (1) Preparation of exosome fraction

[0710] Similarly to the above, 300 μL of serum obtained from 17 serum samples from patients with different fibrosis stages and CP scores and 2 normal serum samples were used to purify the exosome fraction using ultracentrifugation separation technology in the same manner as in Example 1.

[0711]

Table 7

[0712]

[0713] (2) Analysis of miRNA

[0714] MiRNA was purified from the exosome fraction obtained as the precipitate fraction, and the quality of RNA was confirmed using Bioanalyzer (Agilent, 2100), and hybridization was performed using the 3D-Gene (registered trademark) base plate of Toray Industries, Inc. The expression changes of miRNA were analyzed using the 3D-Gene (registered trademark) analysis method. In addition, for these miRNAs, the correlation between the changes in the condition of non-infection, HBV infection, HCV infection, and the changes in fibrosis stage and CP score and the changes in miRNA expression was analyzed.

[0715] result:

[0716] A variety of miRNAs that change with fibrosis stage and CP score were obtained, and representative examples include miR-223 and miR-4454, whose miRNA expression levels decrease with the progression of fibrosis stage and increase of CP score.

[0717] Example 3. Correlation analysis between liver disease and miRNA

[0718] Using miRNA samples from patients with viral liver diseases (HCV, HBV), and using the miRNA expression data measured by the high-precision microarray DNA chip "3D-Gene" (registered trademark) developed by Toray as described above, the missing values ​​of the miRNA expression data were supplemented with the minimum value of each sample to obtain missing value supplemented data, and the missing value supplemented data were logarithmically transformed with base 2 to obtain logarithmic transformed data. The predicted value of fibrosis stage was calculated for the logarithmic transformed data using the regression coefficients calculated using the four regression models, and the correlation analysis between liver disease condition and miRNA was performed ( Figure 5 ).

[0719] (1) Correlation analysis using the multinomial LASSO model

[0720] For each stage, the logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 3-1 were substituted into the following formula (5) to obtain a logical value.

[0721] η=α 1 x 1 +α 2 x 2 +...+β (5)

[0722] In the formula, α represents a coefficient, β represents an intercept, x represents each miRNA, and η represents a logical value. The logical value is transformed into a probability value between 0 and 1 by performing an inverse transformation of the logical transformation, and the predicted probability is calculated, and the fibrosis stage with the highest probability is used as the predicted value.

[0723]

Table 8

[0724] Table 3-1 Regression coefficients of the multinomial LASSO model

[0725]

[0726]

[0727] (2) Correlation analysis using Gaussian LASSO model

[0728] Logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 3-2 were substituted into the following formula (6) to obtain a logical value.

[0729] η=α 1 x 1 +α 2 x 2 +...+β (6)

[0730] In the formula, α represents a coefficient, β represents an intercept, x represents each miRNA, and η represents a logical value. For the logical value and the intercept value of each stage in Table 3-3, the following conditions are matched and the fibrosis stage matching the conditions is used as a predicted value.

[0731] If the logical value is less than the cutoff value of stage 0|1, the fibrosis stage is 0.

[0732] When the logical value is greater than the cutoff value of stage 0|1 and less than the cutoff value of stage 1|2, the fibrosis stage is 1.

[0733] When the logical value is greater than the cutoff value of stage 1|2 and less than the cutoff value of stage 2|3, the fibrosis stage is 2.

[0734] When the logical value is greater than the cutoff value of stage 2|3 and less than the cutoff value of stage 3|4, the fibrosis stage is 3.

[0735] When the logical value is greater than the cutoff value of stage 3|4, the fibrosis stage is 4.

[0736]

Table 9

[0737] Table 3-2 Regression coefficients of Gaussian LASSO model

[0738]

[0739]

Table 10

[0740] Table 3-3 Cutoff values ​​of Gaussian LASSO

[0741]

[0742] (3) Correlation analysis using ordered logistic regression model 1

[0743] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in Table 3-4 below, and the intercept shown in Table 3-5 below were substituted into the following formula (7) to obtain a logical value.

[0744] η=-α 1 x 1 -α 2 x 2 -...+β (7)

[0745] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the fibrosis stage with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0746] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0747] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0748] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0749] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0750] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0751]

Table 11

[0752] Table 3-4 Regression coefficients of ordered logistic regression model 1

[0753]

[0754]

Table 12

[0755] Table 3-5 Intercept values ​​of ordered logistic regression model 1

[0756]

[0757] (4) Correlation analysis using ordered logistic regression model 2

[0758] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in Table 3-6 below, and the intercept (Intercept) shown in Table 3-7 below were substituted into the following formula (8) to obtain a logical value.

[0759] η=-α 1 x 1 -α 2 x 2 -...+β (8)

[0760] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the fibrosis stage with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0761] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0762] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0763] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0764] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0765] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0766]

Table 13

[0767] Table 3-6 Regression coefficients of ordered logistic regression model 2

[0768]

[0769]

[0770]

Table 14

[0771] Table 3-7 Intercept values ​​of ordered logistic regression model 2

[0772]

[0773] ( 5) Judgement Special performance

[0774] ( 5-1) Accuracy

[0775] A contingency table was made using the actual group (healthy (Normal), F1: stage 1, F2: stage 2, F3: stage 3, F4: stage 4) and the predicted group, and the consistency rate of the diagonal components was calculated. Furthermore, the accuracy of each group was calculated from the contingency table. The accuracy when using the above 4 regression models is shown in the following table. It can be seen that any model shows a sufficiently high accuracy.

[0776]

Table 15

[0777]

[0778] (5-2) Kappa coefficient

[0779] Then calculate the kappa coefficient (kappa statistic). The kappa coefficient is an indicator for evaluating the consistency of categorical variables with an ordinal relationship, and is also called Cohen's coefficient of agreement. Usually the kappa coefficient takes a value of 0 to 1, and the larger the value, the higher the consistency. Generally, if the kappa coefficient exceeds 0.6, it can be said that sufficiently high performance is shown. In addition, the kappa coefficient that takes into account the weight of the order is called the weighted kappa coefficient. When investigating the consistency of ordinal scales such as the degree of symptoms, it is appropriate to use a weighted kappa coefficient instead of the kappa coefficient. The weighted kappa coefficient is used in the present invention. The weighted kappa coefficients when using the above-mentioned 4 regression models are shown in the following table. It shows that any model has sufficiently high performance.

[0780]

Table 16

[0781]

[0782] The present inventors have found that retrospective analysis using miRNA of patients with viral hepatitis can be used to predict and determine the fibrosis stage non-invasively. As described above, it is shown that the fibrosis stage can be predicted by measuring the miRNA of patients with viral hepatitis using the present invention and substituting it into the prediction model formula. The present inventors have constructed 4 prediction models, and these prediction models are used separately as needed, so that the prediction results can be obtained with high accuracy.

[0783] Example 4. Correlation analysis between NASH and miRNA (1)

[0784] Using miRNA samples from NASH patients, the miRNA expression data measured by the high-precision microarray DNA chip "3D-Gene" (registered trademark) developed by Toray as described above were used, and the missing values ​​of the miRNA expression data were supplemented with the minimum value of each sample to obtain missing value supplement data, and the missing value supplement data were logarithmically transformed with base 2 to obtain logarithmic transformed data. The predicted value of NASH fibrosis stage was calculated using the regression coefficients calculated using the four regression models for the logarithmic transformed data ( Figure 6 ).

[0785] (1) Correlation analysis using the multinomial LASSO model

[0786] For each stage, the logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 4-1 were substituted into the following formula (9) to obtain a logical value.

[0787] η=α 1 x 1 +α 2 x 2 +...+β (9)

[0788] In the formula, α represents a coefficient, β represents an intercept, x represents each miRNA, and η represents a logical value. The logical value is transformed into a probability value between 0 and 1 by performing an inverse transformation of the logical transformation, and the predicted probability is calculated, and the fibrosis stage with the highest probability is used as the predicted value.

[0789]

Table 17

[0790] Table 4-1 Regression coefficients of the multinomial LASSO model

[0791]

[0792] (2) Correlation analysis using Gaussian LASSO model

[0793] The logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 4-2 were substituted into the following formula (10) to obtain a logical value.

[0794] η=α 1 x 1 +α 2 x 2 +...+β (10)

[0795] In the formula, α represents a coefficient, β represents an intercept, x represents each miRNA, and η represents a logical value. The logical value and the cutoff value of each stage in Table 4-3 are matched with the following conditions, and the fibrosis stage matching the conditions is used as a predicted value.

[0796] When the logical value is less than the cutoff value of stage 0|1, the fibrosis stage is 0.

[0797] When the logical value is greater than the cutoff value of stage 0|1 and less than the cutoff value of stage 1|2, the fibrosis stage is 1.

[0798] When the logical value is greater than the cutoff value of stage 1|2 and less than the cutoff value of stage 2|3, the fibrosis stage is 2.

[0799] When the logical value is greater than the cutoff value of stage 2|3 and less than the cutoff value of stage 3|4, the fibrosis stage is 3.

[0800] When the logical value is greater than the cutoff value of stage 3|4, the fibrosis stage is 4.

[0801]

Table 18

[0802] Table 4-2 Regression coefficients of Gaussian LASSO model

[0803]

[0804]

Table 19

[0805] Table 4-3 Cutoff values ​​of Gaussian LASSO

[0806]

[0807] (3) Correlation analysis using ordered logistic regression model 1

[0808] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in Table 4-4 below, and the intercept (Intercept) shown in Table 4-5 below were substituted into the following formula (11) to obtain a logical value.

[0809] η=-α 1 x 1 -α 2 x 2 -...+β (11)

[0810] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the fibrosis stage with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0811] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0812] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0813] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0814] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0815] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0816]

Table 20

[0817] Table 4-4 Regression coefficients of ordered logistic regression model 1

[0818] Variable regression coefficient

[0819] hsa-miR-6798-3p 4.36204

[0820]

Table 21

[0821] Table 4-5 Intercept values ​​of ordered logistic regression model 1

[0822]

[0823] (4) Correlation analysis using ordered logistic regression model 2

[0824] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in Table 4-6 below, and the intercept (Intercept) shown in Table 4-7 below were substituted into the following formula (12) to obtain a logical value.

[0825] η=-α 1 x 1 -α 2 x 2 -...+β (12)

[0826] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the fibrosis stage with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0827] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0828] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0829] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0830] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0831] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0832]

Table 22

[0833] Table 4-6 Regression coefficients of ordered logistic regression model 2

[0834]

[0835]

Table 23

[0836] Table 4-7 Intercept values ​​of ordered logistic regression model 2

[0837]

[0838] (5) Discrimination performance

[0839] (5-1) Accuracy

[0840] A contingency table was made using the actual group (0: healthy, 1: stage 1, 2: stage 2, 3: stage 3, 4: stage 4) and the predicted group, and the consistency rate of the diagonal components was calculated. Furthermore, the accuracy of each group was calculated from the contingency table. The accuracy when using the above four regression models is shown in the following table. It can be seen that any model shows a sufficiently high accuracy.

[0841]

Table 24

[0842]

[0843] (5-2) Kappa coefficient

[0844] The weighted Kappa coefficients when using the above four regression models are shown in the following table. It is shown that all the models have sufficiently high performance.

[0845]

Table 25

[0846]

[0847] The present inventors have found that retrospective analysis using miRNA of NASH patients can be used to predict and determine the fibrosis stage non-invasively. As described above, it is shown that the fibrosis stage can be predicted by measuring the miRNA of NASH patients using the present invention and substituting it into the prediction model formula. The present inventors have constructed 4 prediction models, and these prediction models are used separately as needed, so that the prediction results can be obtained with high accuracy.

[0848] Example 5. Correlation analysis between NASH and miRNA (2)

[0849] Using miRNA samples from NASH patients, the miRNA expression data measured by the high-precision microarray DNA chip "3D-Gene" (registered trademark) developed by Toray as described above were used to supplement the missing values ​​of the miRNA expression data with the minimum value of each sample to obtain missing value supplement data, and the missing value supplement data were logarithmically transformed with base 2 to obtain logarithmic transformed data. The predicted value of Brunt grade of NASH was calculated using the regression coefficients calculated using the four regression models for the logarithmic transformed data ( Figure 7 ).

[0850] (1) Correlation analysis using the multinomial LASSO model

[0851] For each stage, the logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 5-1 were substituted into the following formula (13) to obtain a logical value.

[0852] η=α 1 x 1 +α 2 x 2 +...+β (13)

[0853] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. The logical value is transformed into a probability value between 0 and 1 by performing the inverse transformation of the logical transformation, and the predicted probability is calculated, and the Brunt grade with the highest probability is used as the predicted value.

[0854]

Table 26

[0855] Table 5-1 Regression coefficients of the multinomial LASSO model

[0856]

[0857] (2) Correlation analysis using Gaussian LASSO model

[0858] The logarithmic transformation data corresponding to each miRNA and the regression coefficient and intercept (Intercept) shown in the following Table 5-2 were substituted into the following formula (14) to obtain a logical value.

[0859] η=α 1 x 1 +α 2 x 2 +...+β (14)

[0860] In the formula, α represents a coefficient, β represents an intercept, x represents each miRNA, and η represents a logical value. The logical value and the cutoff value of each stage in Table 5-3 are matched with the following conditions, and the Brunt grade matching the conditions is used as a predicted value.

[0861] When the logical value is less than the cutoff value of stage 0|1, the Brunt grade is 0.

[0862] When the logical value is greater than the cutoff value of stage 0|1 and less than the cutoff value of stage 1|2, the Brunt grade is 1.

[0863] When the logical value is greater than the cutoff value of stage 1|2 and less than the cutoff value of stage 2|3, the Brunt grade is 2.

[0864] When the logical value is greater than the cutoff value of stage 2|3 and less than the cutoff value of stage 3|4, the Brunt grade is 3.

[0865] When the logical value is higher than the cutoff value of stage 3|4, the Brunt grade is 4.

[0866]

Table 27

[0867] Table 5-2 Regression coefficients of the multinomial LASSO model

[0868]

[0869]

Table 28

[0870] Table 5-3 Cutoff values ​​of Gaussian LASSO

[0871]

[0872] (3) Correlation analysis using ordered logistic regression model 1

[0873] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in Table 5-4 below, and the intercept shown in Table 5-5 below were substituted into the following formula (15) to obtain a logical value.

[0874] η=-α 1 x 1 -α 2 x 2 -...+β (15)

[0875] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the Brunt grade with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0876] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0877] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0878] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0879] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0880] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0881]

Table 29

[0882] Table 5-4 Regression coefficients of ordered logistic regression model 1

[0883]

[0884]

Table 30

[0885] Table 5-5 Intercept values ​​of ordered logistic regression model 1

[0886]

[0887] (4) Correlation analysis using ordered logistic regression model 2

[0888] For each stage, the logarithmic transformation data corresponding to each miRNA, the regression coefficient shown in the following Table 5-6, and the intercept (Intercept) shown in the following Table 5-7 were substituted into the following formula (16) to obtain a logical value.

[0889] η=-α 1 x 1 -α 2 x 2 -...+β (16)

[0890] In the formula, α represents the coefficient, β represents the intercept, x represents each miRNA, and η represents the logical value. For the logical value, the inverse transformation of the logical transformation is performed to transform it into a probability value between 0 and 1, and the predicted probability is calculated. Furthermore, the difference in predicted probability is calculated for each stage, and the fibrosis stage with the highest probability is used as the predicted value. The calculation method of the difference in predicted probability for each stage is as follows.

[0891] The difference in predicted probability of stage 0 = 1-predicted probability of stage 0|1

[0892] The difference in predicted probability of stage 1 = predicted probability of stage 0|1 - predicted probability of stage 1|2

[0893] The difference in predicted probabilities for stage 2 = predicted probabilities for stage 1|2 - predicted probabilities for stage 2|3

[0894] The difference in predicted probabilities for stage 3 = predicted probabilities for stages 2|3 - predicted probabilities for stages 3|4

[0895] The difference in predicted probabilities for stage 4 = predicted probabilities for stages 3 | 4

[0896]

Table 31

[0897] Table 5-6 Regression coefficients of ordered logistic regression model 2

[0898]

[0899]

[0900]

Table 32

[0901] Table 5-7 Intercept values ​​of ordered logistic regression model 2

[0902]

[0903] (5) Discrimination performance

[0904] (5-1) Accuracy

[0905] A contingency table was made using the actual group (0: healthy, 1: stage 1, 2: stage 2, 3: stage 3, 4: stage 4) and the predicted group, and the consistency rate of the diagonal components was calculated. Furthermore, the accuracy of each group was calculated from the contingency table. The accuracy when using the above four regression models is shown in the following table. It can be seen that any model shows a sufficiently high accuracy.

[0906]

Table 33

[0907]

[0908] (5-2) Kappa coefficient

[0909] The weighted Kappa coefficients when using the above four regression models are shown in the following table. It is shown that all the models have sufficiently high performance.

[0910]

Table 34

[0911]

[0912] The present inventors have found that retrospective analysis using miRNA of NASH patients can be used to predict and determine Brunt grade non-invasively by using miRNA. As described above, it is shown that Brunt grade can be predicted by measuring miRNA of NASH patients using the present invention and substituting it into the prediction model formula. The present inventors have constructed 4 prediction models, and these prediction models are used separately as needed, so that prediction results can be obtained with high accuracy.

[0913] Industrial Applicability

[0914] By using the non-invasive biomarker for liver disease of the present invention, as well as a kit, method and program for using the biomarker, and a data processing method for detecting liver disease with high accuracy using the non-invasive biomarker for liver disease, and a data processing device for the data processing, liver disease can be detected non-invasively or low-invasively and with high accuracy.

Claims

1. A liver disease detection kit comprising a nucleic acid capable of specifically binding to one or more polynucleotides selected from the group consisting of the following polynucleotides as biomarkers or the complementary strands of the polynucleotides: miR-4454 (SEQ ID NO: 1), miR-3138 (SEQ ID NO: 2), miR-3679 (SEQ ID NO: 3), miR-4521 (SEQ ID NO: 4), miR-320A (SEQ ID NO: 5), miR-483 (SEQ ID NO: 6), miR-122 (SEQ ID NO: 7), miR-125B-1 (SEQ ID NO: 8), miR-125B-2 (SEQ ID NO: 9), miR-194-1 (SEQ ID NO: 10), miR-194-2 (SEQ ID NO: 11), miR-99 a (sequence number 12), miR-223 (sequence number 13), miR-378a (sequence number 14), miR-34a (sequence number 15), miR-320c-1 (sequence number 16), miR-320b-1 (sequence number 17), miR-192 (sequence number 18), miR-148a (sequence number 19), miR-100 (sequence number 20), miR-144 (sequence number 21), miR-320b-2 (sequence number 22), miR-451a (sequence number 23), miR-548d-3p (sequence number 24), miR-320b (sequence number 25), miR-1470 (sequence number 26), miR-4258 (SEQ ID NO: 27), miR-4673 (SEQ ID NO: 28), miR-4748 (SEQ ID NO: 29), miR-4787-3p (SEQ ID NO: 30), miR-204-3p (SEQ ID NO: 31), miR-6131 (SEQ ID NO: 32), miR-6752-3p (SEQ ID NO: 33), miR-4648 (SEQ ID NO: 34), miR-6126 (SEQ ID NO: 35), miR-1228-3p (SEQ ID NO: 36), miR-1231 (SEQ ID NO: 37), miR-4286 (SEQ ID NO: 38), miR-3935 (SEQ ID NO: 39), miR-2467-3p (SEQ ID NO: 40), miR-1185-2-3p (SEQ ID NO: 41), miR-1185-2-3p (SEQ ID NO: 42), miR-1185-2-3p (SEQ ID NO: 43), miR-1185-2-3p (SEQ ID NO: 44), miR-1185-2-3p (SEQ ID NO: 45), miR-1185-2-3p (SEQ ID NO: 46), miR-1185-2-3p (SEQ ID NO: 47), miR-1185-2-3p (SEQ ID NO: 48), miR-1185-2-3p (SEQ ID NO: 49), miR-1185-2-3p (SEQ ID NO: 50), miR-1 41), miR-6729-3p (sequence number 42), miR-6798-3p (sequence number 43), miR-379-5p (sequence number 44), miR-323a-5p (sequence number 45), miR-665 (sequence number 46), miR-4279 (sequence number 47), miR-3605-5p (sequence number 48), miR-4684-3p (sequence number 49), miR-365b-5p (sequence number 50), miR-6782-5p (sequence number 51), miR-6880-3p (sequence number 52), miR-6887-3p (sequence number 53), miR-4433b-5p (sequence number 54),miR-10396a-3p (SEQ ID NO: 55), miR-373-5p (SEQ ID NO: 56), miR-4539 (SEQ ID NO: 57), miR-4687-3p (SEQ ID NO: 58), miR-4763-5p (SEQ ID NO: 59), miR-4482-3p (SEQ ID NO: 60), miR-6721-5p (SEQ ID NO: 61), miR-6812-3p (SEQ ID NO: 62), miR-6815-5p (SEQ ID NO: 63), miR-6871-5p (SEQ ID NO: 64), miR-7975 (SEQ ID NO: 65), miR-11181-3p (SEQ ID NO: 66), miR-2278 (SEQ ID NO: 67), miR-3192- 5p (SEQ ID NO: 68), miR-4722-3p (SEQ ID NO: 69), miR-4750-3p (SEQ ID NO: 70), miR-6804-5p (SEQ ID NO: 71), miR-6828-5p (SEQ ID NO: 72), miR-614 (SEQ ID NO: 73), miR-320c (SEQ ID NO: 74), miR-2110 (SEQ ID NO: 75), miR-3177-3p (SEQ ID NO: 76), miR-4497 (SEQ ID NO: 77), miR-210-5p (SEQ ID NO: 78), miR-6778-5p (SEQ ID NO: 79), miR-6780a-5p (SEQ ID NO: 80), miR-6801-3p (SEQ ID NO: 81), miR-80 59 (SEQ ID NO: 82), miR-657 (SEQ ID NO: 83), miR-921 (SEQ ID NO: 84), miR-4451 (SEQ ID NO: 85), miR-3059-3p (SEQ ID NO: 86), miR-3619-3p (SEQ ID NO: 87), miR-4646-5p (SEQ ID NO: 88), miR-525-5p (SEQ ID NO: 89), miR-4444 (SEQ ID NO: 90), miR-4458 (SEQ ID NO: 91), miR-4746-3p (SEQ ID NO: 92), miR-1292-3p (SEQ ID NO: 93), miR-6133 (SEQ ID NO: 94), miR-6754-3p (SEQ ID NO: 95), miR-365a-3p (SEQ ID NO: 96), miR-365a-3p (SEQ ID NO: 97), miR-365b-3p (SEQ ID NO: 98), miR-365c-3p (SEQ ID NO: 99), miR-365d-3p (SEQ ID NO: 100), miR-365d-3p (SEQ ID NO: 101), miR-365d-3p (SEQ ID NO: 102), miR-365d-3p (SEQ ID NO: 103), miR-365d-3p (SEQ ID NO: 104), miR-365d-3p (SEQ ID NO: 105), miR-365d-3p (SEQ ID NO: 106), miR-365d-3p (SEQ ID NO: 107), miR-365d-3p (SEQ ID NO: 108), miR-365d-3p (SEQ ID NO: 1 No. 96), miR-365b-3p (SEQ ID NO. 97), miR-6748-3p (SEQ ID NO. 98), miR-6892-3p (SEQ ID NO. 99), miR-8083 (SEQ ID NO. 100), miR-4724-5p (SEQ ID NO. 101), miR-6884-5p (SEQ ID NO. 102), miR-7156-3p (SEQ ID NO. 103), miR-19b-3p (SEQ ID NO. 104), miR-518e-3p (SEQ ID NO. 105), miR-646 (SEQ ID NO. 106), miR-298 (SEQ ID NO. 107), miR-1234-3p (SEQ ID NO. 108), miR-2115-5p (SEQ ID NO. 109),miR-3142 (SEQ ID NO: 110), miR-3179 (SEQ ID NO: 111), miR-3190-5p (SEQ ID NO: 112), miR-3675-3p (SEQ ID NO: 113), miR-4441 (SEQ ID NO: 114), miR-4475 (SEQ ID NO: 115), miR-6718-5p (SEQ ID NO: 116), miR-6743-3p (SEQ ID NO: 117), miR- 6894-3p (sequence number 118), miR-7112-5p (sequence number 119), miR-12116 (sequence number 120), miR-518d-3p (sequence number 121), miR-145-5p (sequence number 122), miR-6801-5p (sequence number 123), miR-127-3p (sequence number 124) and miR-4731-3p (sequence number 125).

2. The liver disease detection kit according to claim 1, wherein: The present invention comprises a nucleic acid capable of specifically binding to one or more polynucleotides selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 82 as biomarkers, or to complementary chains of the polynucleotides.

3. The liver disease detection kit according to claim 2, wherein: The present invention comprises a nucleic acid capable of specifically binding to a polynucleotide selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 4 as biomarkers or a complementary strand of the polynucleotide.

4. The detection kit according to claim 3, wherein The kit further comprises at least one nucleic acid capable of specifically binding to a polynucleotide serving as another biomarker.

5. The detection kit according to claim 4, wherein The other biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 5 to 22.

6. The liver disease detection kit according to claim 1, wherein: The present invention comprises a nucleic acid capable of specifically binding to one or more polynucleotides selected from the group consisting of polynucleotides of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125 as biomarkers or to complementary chains of the polynucleotides.

7. The liver disease detection kit according to claim 1, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

8. The liver disease detection kit according to claim 2, wherein: The liver disease is viral liver disease.

9. The liver disease detection kit according to claim 6, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

10. The liver disease detection kit according to claim 1, wherein: The nucleic acid is selected from the polynucleotide or a fragment thereof represented by any of the following (a) to (c): (a) (a-1) a polynucleotide consisting of a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases, (a-2) a polynucleotide or a fragment thereof containing one or more bases deleted, substituted, added or inserted in the base sequence of the polynucleotide or a fragment thereof of (a-1), (a-3) a polynucleotide of (a-1) or (a-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide, (b) a polynucleotide comprising a base sequence complementary to the base sequence represented by any of SEQ ID NOs: 1 to 125 or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases, and (c) A polynucleotide that hybridizes under stringent conditions with a polynucleotide represented by any of the following (c-1) to (c-4) or a fragment thereof: (c-1) a polynucleotide consisting of a base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof containing 15 or more consecutive bases, (c-2) a polynucleotide or a fragment thereof containing one or more base deletions, substitutions, additions or insertions in the base sequence of the polynucleotide or a fragment thereof of (c-1), (c-3) a polynucleotide of (c-1) or (c-2) or a fragment thereof comprising a modified nucleic acid and / or a modified nucleotide, (c-4) A polynucleotide comprising the base sequence represented by any of SEQ ID NOs: 1 to 125, or a base sequence in which u is t in the base sequence, or a fragment thereof comprising 15 or more consecutive bases.

11. The liver disease detection kit according to claim 10, wherein: The polynucleotide represented by any one of (a) to (c) is selected from the group consisting of the polynucleotides represented by any one of SEQ ID NOs: 1 to 82.

12. The liver disease detection kit according to claim 10, wherein: The polynucleotide represented by any one of (a) to (c) is selected from the group consisting of the polynucleotides represented by any one of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

13. The liver disease detection kit according to claim 10, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

14. The liver disease detection kit according to claim 11, wherein: The liver disease is viral liver disease.

15. The liver disease detection kit according to claim 12, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

16. A biomarker selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 125. The biomarker according to claim 16 , which is selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82. The biomarker according to claim 17 , which is selected from the group consisting of the polynucleotides of SEQ ID NOs. 1 to 4.

19. The biomarker according to claim 16, which is selected from the group consisting of polynucleotides of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

20. The biomarker according to claim 16, wherein The biomarker is obtained from a biological sample selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and an organ sample.

21. The biomarker according to claim 16, which is used for diagnosing the presence or risk of liver disease.

22. The biomarker according to claim 16, which is used to determine the degree of progression of liver disease.

23. The biomarker according to claim 16, wherein The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

24. The biomarker according to claim 17, wherein The liver disease is viral liver disease.

25. The biomarker according to claim 19, wherein The liver disease is non-alcoholic steatohepatitis (NASH).

26. A method for determining the presence or absence of liver disease in a subject, the risk or degree of progression of liver disease, and / or the success of treatment for treatment, comprising the following steps: determining the level of a biomarker in a biological sample from the subject; and The measured value of the biomarker is compared with a control value, wherein: The biomarker is one or more selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

27. The method according to claim 26, wherein: The biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

28. The method according to claim 27, wherein: The biomarker is selected from the group consisting of the polynucleotides of SEQ ID NOs. 1 to 4.

29. The method according to claim 28, wherein: The following steps are also included: measuring the level of an additional biomarker in a biological sample from the subject; and The additional biomarker measured values ​​are compared to the control values.

30. The method of claim 29, wherein: The additional biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 5 to 22.

31. The method of claim 26, wherein: The biomarker is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

32. The method of claim 26, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

33. The method of claim 26, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

34. The method of claim 27, wherein: The liver disease is viral liver disease.

35. The method of claim 31, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

36. A method for screening candidate compounds for a therapeutic drug for liver disease in a subject, comprising the steps of: measuring the level of the biomarker in a biological sample from the subject to which the candidate compound for the treatment of liver disease has been administered; and The measured value of the biomarker is compared with a control value, wherein: The biomarker is one or more selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

37. The method of claim 36, wherein: The biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

38. The method of claim 37, wherein: The biomarker is selected from the group consisting of the polynucleotides of SEQ ID NOs. 1 to 4.

39. The method of claim 38, wherein: The following steps are also included: measuring the level of an additional biomarker in a biological sample from the subject; and The additional biomarker measured values ​​are compared to the control values.

40. The method of claim 39, wherein: The additional biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 5 to 22.

41. The method of claim 36, wherein: The biomarker is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

42. The method of claim 36, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

43. The method of claim 36, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

44. The method of claim 37, wherein: The liver disease is viral liver disease.

45. The method of claim 41, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

46. ​​A method of treating liver disease in a subject, comprising the steps of: determining the level of a biomarker in a biological sample from the subject; and The measured value of the biomarker is compared with a control value, wherein: The biomarker is one or more selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

47. The method of claim 46, wherein: The biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

48. The method of claim 47, wherein: The biomarker is selected from the group consisting of the polynucleotides of SEQ ID NOs. 1 to 4.

49. The method of claim 48, wherein: The following steps are also included: measuring the level of an additional biomarker in a biological sample from the subject; and The additional biomarker measured values ​​are compared to the control values.

50. The method of claim 49, wherein: The additional biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 5 to 22.

51. The method of claim 46, wherein: The biomarker is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

52. The method of claim 46, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

53. The method of claim 46, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

54. The method of claim 47, wherein: The liver disease is viral liver disease.

55. The method of claim 51, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

56. A method for detecting liver disease with high accuracy, comprising the following steps: determining the level of biomarker 1 in a biological sample from the subject; Comparing the measured value of the biomarker 1 with the control value to obtain parameter 1; measuring the level of at least one additional biomarker N in a biological sample from the subject, wherein N is an integer greater than or equal to 2; Comparing the measured value of the additional biomarker N with the control value to obtain a parameter N; and The result of obtaining a method in which at least two parameters are in a group consisting of a combination of parameter 1 and one or more parameters N is obtained. The results of the method of at least two parameters in the group indicate the presence or absence of liver disease in the subject, or the risk or degree of progression thereof, and / or the success of treatment performed for treatment.

57. The method of claim 56, wherein: The results of the approach for at least 2 parameters in the set are evaluated using a computer program.

58. The method of claim 56, wherein: The biomarker 1 and the at least one additional biomarker are selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

59. The method of claim 58, wherein: The biomarker 1 and the at least one additional biomarker are at least one selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

60. The method of claim 58, wherein: The biomarker 1 and the at least one additional biomarker are selected from the group consisting of polynucleotides of SEQ ID NOs: 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

61. The method of claim 56, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

62. The method of claim 56, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

63. The method of claim 59, wherein: The liver disease is viral liver disease.

64. The method of claim 60, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

65. A liver disease diagnosis auxiliary system, comprising: A unit for measuring the level of one or more biomarkers in a biological sample from a subject, A unit for obtaining a plurality of parameters by comparing the measured values ​​of the one or more biomarkers with their control values, and A unit for calculating the presence or absence of liver disease in the subject, its risk or degree of progression, and / or the success rate of treatment performed for treatment based on the plurality of parameters.

66. The liver disease diagnosis auxiliary system according to claim 65, wherein: The one or more biomarkers are selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

67. The liver disease diagnosis auxiliary system according to claim 66, wherein: The biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

68. The liver disease diagnosis auxiliary system according to claim 66, wherein: The biomarker is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

69. The liver disease diagnosis auxiliary system according to claim 65, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

70. The liver disease diagnosis auxiliary system according to claim 65, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

71. The liver disease diagnosis auxiliary system according to claim 67, wherein: The liver disease is viral liver disease.

72. The liver disease diagnosis auxiliary system according to claim 68, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

73. A method for data processing for classifying a liver disease of a subject, executed by a computer, the computer comprising: Computer / processor, and A computer-readable storage medium having software for issuing instructions for classifying the liver disease of the subject, The method comprises the following steps: Comparing the measured values ​​of one or more biomarkers in the biological sample obtained from the subject with their control values, thereby obtaining data of multiple parameters; and The computer processes the data of the plurality of parameters by executing instructions using the computer / processor, the computer determines the condition of the liver disease of the subject, and outputs the result of the determination as information obtained by software that issues instructions for classifying the condition of the liver disease of the subject, wherein: The disease classification includes one or more selected from the group consisting of liver fibrosis staging, Child-Pugh classification, MELD score, MELD Na score, PELD score, ALBI score, mALBI (modified ALBI) score, FibroScan score, new Inuyama classification, new European classification and Brunt classification.

74. The method of claim 73, wherein: The result of the determination is a combination of results obtained by processing the data of the plurality of parameters and performing determinations on each of the plurality of parameters.

75. The method of claim 73, wherein: The one or more biomarkers are selected from the group consisting of polynucleotides of SEQ ID NOs: 1 to 125.

76. The method of claim 75, wherein: The biomarker is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

77. The method of claim 75, wherein: The biomarker is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, 83 to 125.

78. The method of claim 73, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

79. The method of claim 73, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

80. The method of claim 76, wherein: The liver disease is viral liver disease.

81. The method of claim 77, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

82. The method of claim 73, wherein: The subject is a mammalian subject.

83. The method of claim 82, wherein: The subject is a human subject.

84. A program for causing a computer to execute determination of the presence or absence of liver disease in a subject, or the risk or degree of progression thereof, and / or determination of the success of treatment performed for treatment, the program executing: a measurement value obtaining step of obtaining measurement values ​​of the levels of one or more biomarkers obtained from a biological sample from the subject; a parameter data obtaining step of obtaining data of a plurality of parameters by comparing the measured values ​​of the one or more biomarkers with respective control values; and A determination step of determining the presence or absence of liver disease in the subject or its risk or degree of progression and / or determining the success of treatment performed for treatment based on the data of the plurality of parameters, wherein: The biomarker is selected from the group consisting of polynucleotides of SEQ ID NOs. 1 to 125.

85. A data processing device for performing data processing for detecting liver disease, comprising: A missing value supplementation unit, which obtains missing value supplementation data by supplementing the missing values ​​of the miRNA expression data; A logarithmic transformation unit, which obtains logarithmic transformation data by performing logarithmic transformation on the missing value supplementary data; A regression transformation unit that obtains regression transformation data by substituting the logarithmic transformation data into a regression model and performing regression transformation; A training data acquisition unit, which acquires training data; and The learned model preparation unit uses the regression transformation data as training data to prepare the learned model, wherein: The miRNA is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 125.

86. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The regression transformation comprises the following steps: Multiply by the regression coefficient; The logical value indicating the prediction result of the fibrosis stage is obtained by adding the intercept value for each fibrosis stage; Calculating the predicted probability of each fibrosis stage by performing an inverse transformation of the logical value to transform the logical value into a probability value between 0 and 1; and Among the predicted probabilities of the various fibrosis stages, the fibrosis stage with the highest probability is taken as the predicted value.

87. The data processing device for performing data processing for detecting liver disease according to claim 86, wherein: The regression model is selected from the group consisting of a multinomial LASSO model, a Gaussian LASSO (least absolute shrinkage and selection algorithm) model, and an ordered logistic regression model.

88. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

89. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The data processing for detecting liver disease further includes a training data obtaining step of obtaining training data and a learning data producing step of producing learning data using the training data. The training data is training data representing the relationship between the miRNA concentration in the learning blood and the fibrosis stage corresponding to the miRNA concentration in the learning blood.

90. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The data processing for detecting liver disease also includes a pre-processing step to obtain the data for analysis, and the pre-processing step includes the following steps: Listing the low-expressed miRNAs to obtain a low-expressed miRNA list; Obtaining missing value supplementation data by supplementing missing values ​​in the data of the miRNA list; Excluding the low-expression miRNAs included in the low-expression miRNA list from the analysis targets to obtain conversion data; and The transformation data is logarithmically transformed.

91. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The miRNA is one or more selected from the group consisting of the polynucleotides of SEQ ID NOs: 1 to 82.

92. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The miRNA is selected from the group consisting of polynucleotides of sequence numbers 23, 30, 35, 36, 43, 65, 73, 81, and 83 to 125.

93. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The biological sample is selected from the group consisting of blood, serum, plasma, saliva, sweat, tears, stool, urine and organ specimens.

94. The data processing device for performing data processing for detecting liver disease according to claim 85, wherein: The liver disease is selected from the group consisting of hepatitis, liver fibrosis, fatty liver, cirrhosis and liver cancer.

95. The data processing device for performing data processing for detecting liver disease according to claim 91, wherein: The liver disease is viral liver disease.

96. The data processing device for performing data processing for detecting liver disease according to claim 92, wherein: The liver disease is non-alcoholic steatohepatitis (NASH).

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