Sample identification methods and biomarkers

Abstract

To provide a method for identifying samples derived from diseased subjects with high accuracy.SOLUTION: A sample identification method according to an embodiment identifies whether a sample is derived from a diseased subject. The method includes: setting a first reference sequence which is composed of a first sequence including a single nucleotide polymorphism site that is suggested to be related to a disease based on a criterion and in which the single nucleotide polymorphism site is a first base, and a corresponding second reference sequence; outputting the number of first short-chain nucleic acids including the first reference sequence (number of ID-2) and the number of second short-chain nucleic acids including the second reference sequence (number of ID-1) for a group of short-chain nucleic acids in the sample; calculating the ratio R=(number of ID-1 / (number of ID-1+number of ID-2)); comparing the ratio R with a threshold value to determine the magnitude relationship; and determining whether the sample is derived from a subject who has a disease or is at risk of developing a disease based on the number of sequences with a ratio R greater than the threshold value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] Embodiments of the present invention relate to sample identification methods and biomarkers. [Background technology]

[0002] There is a known test method that distinguishes between cancer patients and healthy individuals by analyzing nucleic acids isolated from easily collected body fluids. For example, a discrimination system using miRNA (microRNA) has been widely verified. miRNA is a single-stranded nucleic acid of about 17 to 25 bases, and it has been revealed that it has the function of regulating gene expression. It has also been reported that the type and expression level of various diseases change from the early stages. For example, various miRNAs are used as cancer markers in cancer patients. This is based on the knowledge that the amount of these miRNAs present in cancer patients is increased or decreased compared to healthy individuals.

[0003] Tests using miRNA quantity or miRNA concentration as an index tend to produce differences in results and trends between samples or data sets, resulting in large variability between data and results, and unstable test performance. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2017-514519 Summary of the Invention [Problem to be solved by the invention]

[0005] The problem to be solved by the present invention is to provide a method for identifying samples derived from diseased individuals with high accuracy. [Means for solving the problem]

[0006] According to an embodiment, the method of sample identification is a method of identifying a sample as being from a diseased subject or not. The method comprises: determining one or more sets of a first reference sequence, the first reference sequence consisting of a first sequence having a single nucleotide polymorphism site suggested to be associated with a disease of interest based on a defined criterion, the single nucleotide polymorphism site being a first base, and a corresponding second reference sequence consisting of the first sequence, the single nucleotide substitution site including a second base different from the first base; Regarding the short-chain nucleic acid group contained in the sample, the number of first short-chain nucleic acids (the number of ID-2) that contain a sequence of 10 bases or more in length, including the single nucleotide polymorphism site, so as to match the first reference sequence from its 5' end forward; and The number of second short-stranded nucleic acids (the number of ID-1) that contain a sequence of 10 bases or more in length, including the single nucleotide polymorphism site, so that the second reference sequence matches the first 5' end of the second short-stranded nucleic acid. output; Calculating the ratio R=(number of ID-1 / (number of ID-1+number of ID-2)) for each corresponding sequence; Comparing the value of the ratio R with a predetermined threshold value to determine the magnitude relationship therebetween; and determining whether the sample is derived from a subject having or at risk of developing a disease based on the number of sequences having a ratio R greater than the threshold value; Includes. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic diagram showing a first embodiment. [Diagram 2] FIG. 2 is an image diagram of a result obtained by the first embodiment. [Diagram 3] FIG. 4 is a schematic diagram showing a second embodiment. [Figure 4] 13 is an image diagram of the results obtained by the second and third embodiments. [Diagram 5] FIG. 1 is a schematic diagram showing the procedure of Example 1. [Figure 6] Graph showing the results of Example 2. [Figure 7] Graph showing the results of Example 2. [Figure 8] Graph showing the results of Example 2. [Figure 9] FIG. 13 shows the results of Example 3. [Figure 10] FIG. 13 shows the results of Example 4. [Figure 11] FIG. 13 shows the results of Example 5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, embodiments will be described with reference to the accompanying drawings. In each embodiment, substantially the same components are denoted by the same reference numerals, and the description thereof may be partially omitted. The drawings are schematic, and the relationship between the thickness of each part and the planar dimensions, the thickness ratio of each part, etc. may differ from the actual ones.

[0009] (First embodiment) In the first embodiment, the sample identification method according to the embodiment is a method for identifying a sample as to whether it is derived from a subject suffering from a disease. The method is as shown in FIG. 1. In S11, a reference sequence set is set. The reference sequence set includes a first reference sequence and a second reference sequence corresponding thereto. The first reference sequence is represented by a first sequence having a single nucleotide polymorphism site that suggests a relationship with a target disease based on a determined criterion. The second reference sequence is a sequence corresponding to the first reference sequence. That is, the second reference sequence is the same as the first sequence except for the type of base at the single nucleotide polymorphism site. In other words, the second reference sequence shares the first sequence with the first reference sequence except for the single nucleotide polymorphism site. Here, the reference sequence set may be one set or multiple sets may be set.

[0010] In S12, the number of first short-chain nucleic acids (number of ID-2) and the number of second short-chain nucleic acids (number of ID-1) are output for the group of short-chain nucleic acids contained in the sample. The first short-chain nucleic acids are a group of short-chain nucleic acids having the full length of the first reference sequence and / or a sequence in which a base on the 3' end of the first reference sequence is deleted at its 5' end. The first reference sequence in which a base on the 3' end is deleted may be a sequence of 8 bases or more, for example, 9 bases or more, for example, 10 bases or more, or a sequence of at least 10 bases, including the single-nucleotide polymorphism site so as to match from the 5' end. The sequence in which a base on the 3' end of the first reference sequence is deleted may be any sequence of 8 bases or more, 9 bases or more, or 10 bases or more obtained by deleting one base at a time from the full length sequence at the 3' end. The first short-chain nucleic acid includes the full length of the first reference sequence and / or a sequence in which the bases on the 3'-end side of the first reference sequence are deleted. In addition, when the first short-chain nucleic acid includes a sequence in which the bases on the 3'-end side of the first reference sequence are deleted, the first short-chain nucleic acid may include all sequences that differ in the number of deleted bases, or may include at least one type of sequence. The first short-chain nucleic acid may include any sequence that is longer than the length of the deleted bases. The same applies to the second short-chain nucleic acid. Here, if the length of the reference sequence is too short, the identification accuracy may decrease. For example, in the case of miRNA, it is preferable that the base length is longer than the seed sequence. The length of the short-chain nucleic acid detected in the sample is not particularly limited.

[0011] Next, in S13, the ratio R = (number of ID-1 / (number of ID-1+number of ID-2)) is calculated for each corresponding sequence. For each corresponding sequence, calculation is performed for each reference sequence set, each type of corresponding sequence, and each length. In S14, the value of the ratio R is compared with a determined threshold value to determine the magnitude relationship between them. Next, in S15, based on the number of sequences for which the value of ratio R is greater than the threshold value, it is determined whether the sample is derived from a subject who has a disease or is at risk of developing a disease.

[0012] A "sample" may be a cell, tissue, and / or liquid collected from a subject, or a mixture thereof, or a processed product obtained by appropriately processing the cell, tissue, and / or liquid. A "sample" may be a bodily fluid taken out of a subject. When the sample is a bodily fluid, it may be, for example, serum or plasma, or other bodily fluids such as blood, leukocyte interstitial fluid, urine, stool, sweat, saliva, oral mucosa, nasal mucosa, nasal mucosa, pharyngeal mucosa, sputum, digestive fluid, gastric juice, lymphatic fluid, cerebrospinal fluid, tears, breast milk, amniotic fluid, semen, vaginal fluid, or a mixture thereof. For example, the sample may be one immediately after being collected from a subject, one cultured, one preserved by a desired procedure, or the supernatant obtained after maintaining the sample in a desired liquid. For example, it is preferable to use bodily fluids such as blood, serum, and plasma as samples because they are easy to collect.

[0013] The method of the embodiment analyzes a group of short nucleic acids contained in a sample as described above. The "group of short nucleic acids" may be, for example, RNA, mRNA, ncRNA (non-coding RNA), housekeeping ncRNA, tRNA, small ncRNA, miRNA, piRNA, tsRNA, IncRNA, etc. For example, ncRNA (non-coding RNA), housekeeping ncRNA, tRNA, small ncRNA, miRNA, piRNA, tsRNA, IncRNA, etc. are preferred. For example, a more preferred example is miRNA.

[0014] "Identification" of a sample can refer to identifying whether the subject from which the sample being tested is derived is suffering from a disease, or is at risk of developing a disease, or is at high risk of developing a disease.

[0015] A "subject" may be an animal or plant from which a sample is to be collected. For example, the subject may be a mammal, including a human, or a non-human mammal. For example, the subject may be a human, a primate such as a monkey, a rodent such as a mouse, a rat, or a guinea pig, a companion animal such as a dog, a cat, or a rabbit, a livestock animal such as a horse, a cow, or a pig, or an animal in exhibition.

[0016] For example, such a subject can be tested for a disease state by utilizing the sample identification method. In other words, the sample identification method can be utilized to diagnose the subject. The results obtained by the sample identification method can assist a doctor in making a diagnosis. Alternatively, the sample identification method can be used to assist in screening for a disease, such as a medical checkup.

[0017] In this method, information about RNA in a sample can be obtained after extracting RNA from the sample. The RNA extraction method may be performed by a method known per se, for example, a commercially available kit may be used.

[0018] Here, the term "disease" refers to any disease to be detected, and may be any disease that is predicted to have some degree of relationship with the reference sequence or that shows a tendency to be related. For example, such a disease is cancer. Here, cancer includes any stage of the disease, for example, a state in which the cancer remains in the organ where it originated, a state in which the cancer has spread to surrounding tissues, a state in which the cancer has metastasized to lymph nodes, and a state in which the cancer has metastasized to further distant organs. For example, the cancer may be at least one type of cancer selected from the group consisting of breast cancer, colon cancer, lung cancer, stomach cancer, pancreatic cancer, cervical cancer, uterine cancer, ovarian cancer, sarcoma, prostate cancer, bile duct cancer, bladder cancer, esophageal cancer, liver cancer, brain tumor, and kidney cancer.

[0019] An example of a "first reference sequence consisting of a first sequence having a single nucleotide polymorphism site suggested to be associated with a target disease based on a defined criterion, the single nucleotide polymorphism site being a first base" is shown in Table 1.

[0020] [Table 1]

[0021] The example shown in Table 1 is an example of a short chain sequence that suggests a relationship between the base type at the polymorphic site and pancreatic cancer. Called "hsa-miR-1-3p," the full length of hsa-miR-1-3p is 22 bases long, and is an RNA sequence consisting of a sequence shown in the 5' to 3' direction as TGGAATGTAA(A / G)GAAGTATGTAT. The 11th base from the 5' end is a single-base polymorphic site. Here, the two types of bases (A and G) that can be included in the polymorphic site are written together in parentheses. The wild-type base is adenine (A). That is, the wild-type sequence is TGGAATGTAA(A)GAAGTATGTAT (SEQ ID NO: 1).

[0022] In the second reference sequence corresponding to such a first reference sequence, the base at the single nucleotide polymorphism site is a second base, guanine (G), different from A, which is the wild type. That is, the second reference sequence is, for example, TGGAATGTAA(G)GAAGTATGTAT (SEQ ID NO: 2). The first reference sequence and the second reference sequence share a sequence other than the single nucleotide polymorphism site. In addition, as shown in Table 2, the further second reference sequence corresponding to the first reference sequence may be a sequence having a base other than adenine (A), that is, cytosine (C) and thymine (T), at the single nucleotide polymorphism site in the first reference sequence. These further second reference sequences share sequences other than the single nucleotide polymorphism with the first reference sequence and the previous second reference sequence, and are mutually equivalent sequences.

[0023] [Table 2]

[0024] As shown in Table 2, the first short-chain nucleic acid (ID-2) contains a sequence of 10 bases or more in length, including the single nucleotide polymorphism site, so as to match the first reference sequence from its 5'-end forward. Here, SEQ ID NO: 1 is the full-length sequence of the first reference sequence. That is, in the case of this sequence, the deletion at the 3'-end is 0. In SEQ ID NO: 1, the single nucleotide polymorphism site is located at the 11th base from the 5'-end and at the 12th base from the 3'-end. Here, "containing a sequence of at least 10 bases in length, including the single nucleotide polymorphism site, so as to match the first nucleotide polymorphism site from its 5'-end forward" can be read as "containing any position from the single nucleotide polymorphism site to the 3'-end."

[0025] That is, the full-length sequence of the first reference sequence is deleted one base at a time from the 3'-end to the base immediately before the single-nucleotide polymorphism site. The first short-chain nucleic acid (ID-2) includes, as a part of sequences of various lengths, sequences including any of full-length sequence 0, full-length sequence-1, full-length sequence-2, full-length sequence-3, ..., full-length sequence-n, so as to match from the 5'-end forward. Here, n is the number of bases from the 3'-end of the first reference sequence to the position immediately before the single-nucleotide polymorphism site. By the way, although this is not the case, in other cases, for example, when the single-nucleotide polymorphism site is located less than 10 bases from the 5'-end, n may be the maximum integer such that the length of the reference sequence shortened by deleting the 3'-end is 10 bases or more. Referring to Table 2, for example, when the first reference sequence is the sequence shown in SEQ ID NO: 1, n=11. That is, in this case, the first short-chain nucleic acid (ID-2) may include, as a single short-chain nucleic acid, a nucleic acid consisting of full-length sequence 0, full-length sequence-1, full-length sequence-2, full-length sequence-3, full-length sequence-4, full-length sequence-5, full-length sequence-6, full-length sequence-7, full-length sequence-8, full-length sequence-9, full-length sequence-10 or full-length sequence-11, and, as a part of a single short-chain nucleic acid, a nucleic acid including full-length sequence 0, full-length sequence-1, full-length sequence-2, full-length sequence-3, full-length sequence-4, full-length sequence-5, full-length sequence-6, full-length sequence-7, full-length sequence-8, full-length sequence-9, full-length sequence-10 or full-length sequence-11 on the 5' end side. In other words, the first short-chain nucleic acid (ID-2) may include all sequences that can be detected using full-length sequence 0, full-length sequence-1, full-length sequence-2, full-length sequence-3, full-length sequence-4, full-length sequence-5, full-length sequence-6, full-length sequence-7, full-length sequence-8, full-length sequence-9, full-length sequence-10, or full-length sequence-11 as an index. The same is true for the second short-chain nucleic acid (ID-1). What is output in (S12) is the number of such first short-chain nucleic acids (ID-2) in the sample and the number of the corresponding second short-chain nucleic acids (ID-1).

[0026] Here, the "output" may take any form as long as it enables the ratio R=(number of ID-1 / (number of ID-1+number of ID-2)) in the following (S13) to be calculated. For example, the number of ID-1 and the number of ID-2 may be determined by a person who implements the method through manual experimentation, or may be derived automatically by a reaction and / or analysis device, or a combination of these may be used. Furthermore, these numbers may be actually output to an output unit such as a display, or may be temporarily stored in a CPU or a calculation unit of a computer, for example.

[0027] The "predetermined criterion" may be, for example, AUC. For example, the criterion may be a specific AUC value, for example, an AUC value of 0.7 or more. Here, AUC is an index used to indicate the accuracy of a test. For example, it can be obtained using a graph as follows. That is, the sensitivity is plotted on the vertical axis, and (1-specificity) is plotted on the horizontal axis as 0 to 1, and the threshold is continuously changed. The AUC is shown as the area under the curve when an ROC curve is created when the threshold is changed continuously. Here, "specificity" is a numerical value between 0 and 1 that represents the proportion of test values ​​of a healthy group that are below a threshold when a test value exceeds a certain threshold and is judged to be positive. The closer the AUC is to 1, i.e., the closer the curve is to the upper left corner, the higher the diagnostic ability of the diagnostic method is indicated, and the closer the AUC is to 0.5, i.e., the closer the curve is to the diagonal, the lower the diagnostic ability of the diagnostic method is indicated.

[0028] Here, the "healthy group" may be data obtained from healthy individuals. The "healthy individual" refers to an individual not suffering from cancer, and preferably an individual not suffering from a malignant neoplasm, including cancer or a malignant tumor, that is, a "non-cancer individual." The "predetermined criteria" may be determined in advance based on past data prior to the implementation of the method, or may be set simultaneously with the implementation of the method based on the data. For example, the criteria may be derived using a sample of known origin, such as whether it is from a non-cancer individual or a cancer patient, or a control sample.

[0029] In (S13), the ratio R=(number of ID-1 / (number of ID-1+number of ID-2)) is calculated for each corresponding sequence. Using the magnitude of this ratio R as an index, it is possible to distinguish between samples derived from non-cancer individuals and samples derived from cancer patients. In order to distinguish between samples derived from non-cancer individuals and samples derived from cancer patients, a determined threshold can be used using a control sample whose origin is clear. If the value of the ratio R of a sample to be distinguished is larger than the determined threshold, the sample can be determined to be derived from a subject who has a disease or is at risk of developing a disease. An example of a graph showing the relationship between the ratio R and samples derived from non-cancer individuals and cancer patients, as well as the threshold, is shown in FIG. 2.

[0030] Figure 2 shows an example of the results obtained using nine basic sequence sets as markers for five different sample groups, as a distribution of the ratio R. Figure 2(a) shows one of the graphs created for each of the nine markers. The graph shows the ratio R on the vertical axis and the sample groups H(2021) and H(2022) derived from two different non-cancer subject groups, and the sample groups PC(2020), PC(2021), and PC(2022) derived from three different pancreatic cancer patient groups on the horizontal axis. Data from each sample group was spotted as one point per sample in a violin plot showing the distribution of data. Figure 2(b) shows the results of plotting the number of markers that exceeded the threshold on the vertical axis from the data obtained for each of the nine markers used.

[0031] This step corresponds to the step of counting the number of sequences with a ratio R greater than the threshold value. This step makes it possible to more clearly visualize the difference in the trends of the data from the sample group derived from non-cancer subjects and the sample group derived from the cancer patient by using a threshold value. Figure 2(c) is a plot of the data in Figure 2(b). It is a graph of the ROC curve between the sample groups H(2021) and H(2022) derived from the non-cancer subject group and the sample groups PC(2020), PC(2021) and PC(2022) derived from the pancreatic cancer patient group. The vertical axis of the graph is the true positive rate, and the horizontal axis is the false positive rate. In the case of this data, it is an example of a graph showing that high accuracy discrimination is possible with AUC = 0.935. To determine "from the number of sequences with a ratio R greater than the threshold value", it is sufficient to count the number of sequences with a large ratio R value, compare that number with a threshold set for the number of markers used, and determine whether the sample is derived from a subject who has a disease or is at risk of developing the disease. For example, in FIG. 2(b), if the ratio R is large for five or more of the nine markers, the sample can be determined to be derived from a subject who has a disease or is at risk of developing a disease.

[0032] According to the first embodiment, a method is provided that can identify samples derived from diseased individuals with higher accuracy.

[0033] Second embodiment The second embodiment is a further method for identifying a sample as to whether it is derived from a diseased subject. This method will be described with reference to FIG. 3 (FIG. 3). First, in S31, RNA is extracted from a sample derived from a subject, and its sequence is decoded. Next, in S32, the sequence of the set sequence set is compared with the sequence group decoded in S31, and the number of sequences with perfectly matching 5' ends is output. Next, in S33, the ratio R (number of ID-1 / (number of ID-1+number of ID-2)) is calculated for each set sequence set. After that, in S34, the magnitude relationship between the ratio R and the threshold is determined by comparing each sequence set with a set threshold. In S35, the sample is identified as to whether or not the disease is present, based on the number of sequence sets that show a ratio R greater than the threshold.

[0034] Here, the RNA can be decoded by using a sequencing method such as a next-generation sequencer (NGS), a nanopore sequencer, the Sanger method (electrophoresis, capillary sequencer), the Maxam-Gilbert method, etc. Alternatively, it may be performed by using at least two of these methods in combination.

[0035] According to the second embodiment, a method is provided that can identify samples derived from diseased individuals with higher accuracy.

[0036] (Third embodiment) The third embodiment is a candidate sequence of a biomarker for identifying a sample derived from a cancer patient. Here, the biomarker is also simply referred to as a marker. Examples of candidate sequences of the biomarker are shown in Table 3.

[0037] [Table 3]

[0038] The candidate sequences of the markers shown in Table 3 can be used as a sequence set including the first standard sequence and the second standard sequence. The underlined parts are single nucleotide polymorphism sites. Each sequence is the full length sequence of the first standard sequence and the second standard sequence, respectively. The standard sequence to be counted as the first short-chain nucleic acid (ID-2) can be the full length sequence or any sequence obtained by deleting the 3' end from the full length sequence, or any sequence of any length obtained by deleting the full length sequence and the 3' end in order, that is, all sequences of different lengths. The sequence with the 3' end deleted is a sequence that completely matches the full length sequence from the 5' end side, contains a single nucleotide polymorphism site, and is at least 10 bases long. The same applies to the second short-chain nucleic acid (ID-1). Table 4 shows an example of a sequence group in which nine types of sequences for use as biomarkers are combined and used as standard sequences.

[0039] [Table 4]

[0040] The IDs assigned to each set of one example of a sequence group are "ID-2" for the first standard sequence, "ID-1" for the second standard sequence, and for each set, the "serial number during the experiment," "type of base at the polymorphic site," and "number of deleted bases at the 3' end." For example, the first standard sequence of set 1 is "10_C_-9," which means the serial number during the experiment is No. 10, the base at the polymorphic site is cytosine (C), and the number of deleted bases at the 3' end is -9.

[0041] The type of base at the single nucleotide polymorphism site in the second reference sequence may be one corresponding type of base, or may be all three corresponding types as shown in set 9. In this case, the sequence counted as (number of ID-1 + number of ID-2) is the target for all four types of bases. If desired, all three types may be used for sets 1 to 8. Alternatively, any one base may be selected for all sets.

[0042] FIG. 4 shows an example of data movement when the above procedure is used in the method using the second embodiment using nine types of marker sets. This is an example of outputting data obtained in each step as a table. While referring to the sequences of the marker set numbers 1 to 9 used, the "molecule sequence" and the "paired sequence" are output from the NGS analysis data obtained for the nucleic acid present in the sample in S31. In S32, the number of the first short-chain nucleic acid (the number of ID-2) and the number of the second short-chain nucleic acid (the number of ID-1) are counted and output to the respective "count" columns. In S33, the ratio R is calculated for each corresponding sequence and output to the "ratio" column. The "threshold" is determined and input for each marker, or is output to each column based on a test performed in advance. In S34, the markers whose ratio is greater than the threshold are checked as a result of comparing each ratio with the threshold. In S35, the number of markers whose ratio is determined to be greater than the threshold in S34 is output. Comparison of the number of markers with a pre-determined threshold distinguishes between samples with respect to the presence or absence of disease or the magnitude of risk.

[0043] The reference sequences of set numbers 1 to 9 shown in Table 4 can be used as markers to identify samples from cancer patients. In addition, by simultaneously using all nine types of markers as a marker group in the sample identification method, it is possible to further improve the accuracy.

[0044] (Fourth embodiment) The fourth embodiment is a biomarker for identifying a sample derived from a cancer patient. Examples of biomarkers are shown in Tables 5 to 13. Tables 5 to 13 list sequences that serve as indicators for all sequences to be counted as the first short-chain nucleic acid and the second short-chain nucleic acid as marker sets selected from the marker candidate sets 1 to 9 shown in Table 3, respectively. The sequences included in each table are sequences that can identify pancreatic cancer with AUC>0.7 from the sequence group in which the 3' side of the marker candidate sequences shown in Table 3 has been deleted. Each table is listed in order of shortening by one base from the top row downward. The marker candidates shown in Table 3 are full-length sequences from which the respective reference sequences are derived. The number of deletions shown in each of Tables 5 to 13 indicates the number of deletions from the sequence shown in Table 3.

[0045] For the bases at the single nucleotide polymorphism site to be used for calculating the ratio R, please refer to the "Formula" column. "All" is calculated using the wild-type base and the other three types of bases. The AUC column lists the year in which the sample was analyzed. In each table, all sequences that can be used as the second reference sequence are listed in order of shortening by one base from the top row downwards. The first reference sequence is a sequence in which the base number listed in the position column from the 5' end of the second reference sequence (ID-1) is replaced with the base listed in the WT column. In the table, "WT" indicates the wild type, and "mu" indicates the mutant type base. Here, in the marker set 2, only the sequences that are -13 bases removed from the full-length sequence from which the reference sequence shown in Table 3 is derived are shown. At least one of the sequences listed in Tables 5 to 13 may be used as a standard sequence or index sequence to be counted as the second short-chain nucleic acid (ID-1). In addition, sequences listed in any one or more of Tables 5 to 13 may be combined and used simultaneously, or one or more sequences listed in any one or more of Tables may be combined and used simultaneously, or one or more sequences listed in all of Tables 5 to 13 may be combined and used simultaneously. Alternatively, all of the sequences listed in all of Tables 5 to 13 may be combined and used simultaneously. In addition, as a marker set, one short chain sequence set selected from the sequence sets shown in Tables 5 to 13, or one sequence selected from each of the sequence sets shown in Tables 5 to 13 may be combined and used in combination of at least two or more types. This makes it possible to identify samples derived from cancer patients with high accuracy. The cancer identified here is, for example, pancreatic cancer.

[0046] [Table 5]

[0047] [Table 6]

[0048] [Table 7]

[0049] [Table 8]

[0050] [Table 9]

[0051] [Table 10]

[0052] [Table 11]

[0053] [Table 12]

[0054] [Table 13]

[0055] According to the fourth embodiment, samples derived from cancer patients can be identified with high accuracy. Furthermore, it is expected that the method will be significantly effective in cancer treatment and research.

[0056] Experimental section Example 1 The samples shown in Table 14 were used to establish standard sequences.

[0057] [Table 14]

[0058] The obtained specimen samples were 120 in total, including 24 samples from pancreatic cancer patients analyzed in 2020, 24 samples from non-cancer subjects and 24 samples from pancreatic cancer patients analyzed in 2021, and 24 samples from non-cancer subjects and 24 samples from pancreatic cancer patients analyzed in 2022. Three stages of selection were performed to set the reference sequence. The procedure is shown in Figure 5.

[0059] First, in selection 1), a list of single-base mutation candidates (1,084 types) was output using REDITools from the 2021 NGS alignment output file. Candidate sequences of up to three different lengths and their pair sequences were prepared (total of 2,454 pairs). The sequences that matched perfectly between them were output, and mu / (WT+mu) was calculated. Data from 2021 was used. Candidates that could distinguish non-cancer from pancreatic cancer with an AUC > 0.7 were selected. 54 types were obtained.

[0060] Next, in selection 2), sequences were prepared by cutting 1 bp at a time from the full length of the 54 pairs obtained to the position of the mutation or until the sequence length was 10 bp (total of 2,256 pairs). In this case, the bases at the 1-base system site were all A, G, C, and T, and 2,256 x 4 = 9,024 sequences were prepared. The sequences that matched perfectly were output and the division of all combinations was calculated. At this time, data from 2020 and 2021 was used as the data. In addition, in the study, the sum of all denominators was also considered. From the results obtained, candidates that could distinguish non-cancer from pancreatic cancer with AUC > 0.7 were selected. 17 types were obtained.

[0061] Finally, selection 3) was performed. Candidates whose results could be reproduced were selected from the NGS data obtained in fiscal year 2022. As a result, there were nine types. For these nine types, pairs were obtained that could distinguish non-cancer from pancreatic cancer with AUC > 0.7 when the length was changed. The obtained sequences were 93 pairs. The sequences and each data are shown in Tables 5 to 13.

[0062] As shown in Tables 5 to 13, all sequences can be used as biomarkers capable of identifying pancreatic cancer with high accuracy with AUC > 0.7.

[0063] Example 2 The ratios R of each marker set shown in Tables 5 to 13 were plotted on graphs and shown in Figures 6 to 8. For each marker set, the thresholds for discriminating between non-cancer and pancreatic cancer were 0.96, 0.025, 0.46, 0.25, 0.4, 0.5, 0.14, 0.57, and 0.8, respectively, for marker sets 1 to 9. A different tendency was observed in the ratio R between non-cancer and pancreatic cancer.

[0064] Example 3 From the results obtained for each of the marker sets 1 to 9, the number of markers whose ratio R exceeded the threshold was counted. The results are shown in Figure 9. The number of markers that exceeded the threshold for all 9 markers is shown by type and age of the subject from which the sample was derived. As shown in Figure 9(a), samples from non-cancer subjects and samples from pancreatic cancer subjects tended to be more clearly different. As shown in Figure 9(b), it was revealed that there was little variation in the data analyzed in any year, making it possible to distinguish samples. As shown in Figure 9(c), the AUC for the entire sample was 0.935, making it clear that high accuracy could be obtained.

[0065] Example 4 The results using six markers are shown in Figure 10. The six markers used were marker sets 3, 4, 5, 7, 8, and 9. This is a marker group excluding markers that have a low threshold in Example 2 and may be prone to erroneous judgment, and markers that, when used as a reference sequence, share a sequence with let7-based miRNAs, which have many highly homologous miRNAs.

[0066] The results of counting the number of markers that exceeded the threshold for these six markers are shown. Samples from non-cancer subjects and samples from pancreatic cancer subjects tended to differ more clearly. As shown in Figure 10(a), samples from non-cancer subjects and samples from pancreatic cancer subjects tended to differ more clearly. As shown in Figure 10(b), it was clear that there was little variability in the data analyzed in any year, making it possible to distinguish samples. As shown in Figure 10(c), the AUC for all samples was 0.924, demonstrating high accuracy.

[0067] Example 5 Figure 11 shows an image comparing the selection of short nucleic acids to be analyzed when classified by miRNA name and when classified using a sequence that matches the 5' end of the sequence present in the sample. Figure 11(a) shows an image of the results of NGS analysis classified by miRNA name. In order to search for mutations, alignment is performed with two mismatches allowed. Performing alignment with mismatches allowed is a common method for analyzing sequences that have diversity, such as miRNA. In this case, a sequence selected from a database is used as a marker. Although miRNA sequences are registered in the database under different names, they may actually differ by only a few bases. miR-206 and miR-1-3p, shown as examples in Figure 11(a), differ only in the three underlined bases and have the rest of the sequence in common. When the NGS analysis results are aligned to such a marker, sequences classified as both miR-206 and miR-1-3p, as enclosed in a yellow dotted line frame, are generated. As a result, even though the NGS analysis of the sample actually yielded only 94 sequences, the results showed 102 sequences due to the presence of sequences classified in both categories, which resulted in ambiguity in the results and reduced reproducibility. In contrast, the results shown in FIG. 11(b) show the results of NGS analysis performed by the method according to this embodiment. Since only sequences that match the 5'-end sequence from the sample nucleic acid are extracted, it is possible to exclude diversity on the 3'-end side from the analysis target. Therefore, it is possible to clearly classify similar sequences, enabling more accurate identification of samples.

[0068] Example 6 A perfectly matched sequence from the 5' end is used as a marker, and we investigated how many bp the perfectly matched sequence marker can be shortened to. When the sequences of 2,656 miRNAs registered in miRBase (Release 22) were truncated from the 3' end, we counted how many other miRNAs had perfectly matched sequences from the 5' end, and confirmed the sequence length that matched 10 or more miRNAs.

[0069] [Table 15]

[0070] As a result, it was revealed that when the reference sequence length was 8 bp, the number of other miRNAs with a perfect match was less than 1%, but when it was shortened to 6 bp, the sequence was identical to more than 10% of miRNAs. An increase in the number of miRNAs with identical sequences means that the amount of sequences with that sequence at the 5' end changes due to more factors, making stable quantification difficult.

[0071] These results provide a method for identifying samples derived from diseased individuals with high accuracy.

[0072] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims.

[0073] The sequence numbers and corresponding base sequences are listed below: SEQ ID NO: 1 TGGAATGTAAAGAAGTATGTAT SEQ ID NO: 2 TGGAATGTAAGGAAGTATGTAT SEQ ID NO: 3 TGGAATGTAAAGAAGTATGTA SEQ ID NO: 4 TGGAATGTAAAGAAGTATGT SEQ ID NO:5 TGGAATGTAAA SEQ ID NO: 6 ATGACCTATGAATTGACAGAC SEQ ID NO: 7 CAAAGTGCTTACAGTGCAGGTAG SEQ ID NO:8 TAGCACCATCTGAAATCGGTTA SEQ ID NO: 9 TAGCAGCACATCATGGTTTACA SEQ ID NO: 10 TGAGGTAGTAGGTTGTATGGTT SEQ ID NO: 11 CTTTCAGTCGGATGTTTACAGC SEQ ID NO: 12 TTCACAGTGGCTAAGTTCCGC SEQ ID NO: 13 TGGAATGTAAAGAAGTATGTAT SEQ ID NO: 14 CTGACCTATGAATTGACAGAC SEQ ID NO: 15 CAAAGTGCTCACAGTGCAGGTAG SEQ ID NO: 16 TAGCACCATTTGAAATCGGTTA SEQ ID NO: 17 TAGCAGCACATAATGGTTTACA SEQ ID NO: 18 TGAGGTAGTAGGTTGTGTGGTT SEQ ID NO: 19 CTTTCAGTCGGATGTTTGCAGC SEQ ID NO: 20 TTCACAGTGGCTAAGTTCTGC SEQ ID NO: 21 TGGAATGTAAAGAAGTATGAAT SEQ ID NO: 22 TGGAATGTAAAGAAGTATGGAT SEQ ID NO: 23 TGGAATGTAAAGAAGTATGCAT SEQ ID NO:24 TGGAATGTAAAGAAGTA SEQ ID NO: 25 TGGAATGTAAGGAAGTA SEQ ID NO: 26 TGGAATGTAAG SEQ ID NO: 27 CTGACCTATGAA SEQ ID NO:28 CAAAGTGCTC SEQ ID NO:29 TAGCACCATT SEQ ID NO: 30 TGGAATGTAAGGAAGT SEQ ID NO: 31 TAGCAGCACATAATGGTTT SEQ ID NO: 32 TGAGGTAGTAGGTTGTG SEQ ID NO: 33 CTTTCAGTCGGATGTTTG SEQ ID NO: 34 TTCACAGTGGCTAAGTTCT SEQ ID NO: 35 TGGAATGTAAAGAAGTATG SEQ ID NO: 36 ATGACCTATGAA SEQ ID NO: 37 CAAAGTGCT(A+G+T) SEQ ID NO:38 TAGCACCATC SEQ ID NO:39 TGGAATGTAAAGAAGT SEQ ID NO: 40 TAGCAGCACATCATGGTTT SEQ ID NO: 41 TGAGGTAGTAGGTTGTA SEQ ID NO: 42 CTTTCAGTCGGATGTTTA SEQ ID NO: 43 TTCACAGTGGCTAAGTTCC SEQ ID NO: 44 TGGAATGTAAAGAAGTTTG SEQ ID NO: 45 CTGACCTATGAATTGACAG SEQ ID NO: 46 CTGACCTATGAATTGACA SEQ ID NO: 47 CTGACCTATGAATTGAC SEQ ID NO:48 CTGACCTATGAATTGA SEQ ID NO:49 CTGACCTATGAATTG SEQ ID NO: 50 CTGACCTATGAATT SEQ ID NO:51 CTGACCTATGAAT SEQ ID NO:52 CTGACCTATGA SEQ ID NO:53 CTGACCTATG SEQ ID NO:54 TAGCACCATTTGAAATCGGTT SEQ ID NO: 55 TAGCACCATTTGAAATCGGT SEQ ID NO:56 TAGCACCATTTGAAATCGG SEQ ID NO:57 TAGCACCATTTGAAATCG SEQ ID NO:58 TAGCACCATTTGAAATC SEQ ID NO:59 TAGCACCATTTGAAAT SEQ ID NO:60 TAGCACCATTTGAAA SEQ ID NO:61 TAGCACCATTTGAA SEQ ID NO:62 TAGCACCATTTGA SEQ ID NO:63 TAGCACCATTTG SEQ ID NO:64 TAGCACCATTT SEQ ID NO: 65 TGGAATGTAAGGAAG SEQ ID NO:66 TGGAATGTAAGGAA SEQ ID NO:67 TGGAATGTAAGGA SEQ ID NO:68 TGGAATGTAAGG SEQ ID NO: 69 TAGCAGCACATAATGGTT SEQ ID NO: 70 TAGCAGCACATAATGGT SEQ ID NO:71 TAGCAGCACATAATGG SEQ ID NO:72 TAGCAGCACATAATG SEQ ID NO:73 TAGCAGCACATAAT SEQ ID NO:74 TAGCAGCACATAA SEQ ID NO:75 TAGCAGCACATA SEQ ID NO: 76 TGAGGTAGTAGGTTGTGT SEQ ID NO:77 CTTTCAGTCGGATGTTTGC SEQ ID NO:78 TTCACAGTGGCTAAGTTCTG SEQ ID NO:79 TGGAATGTAAAGAAGTAT Sequence number 80 TGGAATGTAAAGAAGTA.

Claims

1. 1. A method for identifying a sample, comprising: determining one or more sets of a first reference sequence, the first reference sequence being made up of a first sequence having a single nucleotide polymorphism site suggested to be associated with a disease of interest based on a defined criterion, the single nucleotide polymorphism site being a first base, and a corresponding second reference sequence being made up of the first sequence, the single nucleotide substitution site being a second base different from the first base; Regarding the short-chain nucleic acid group contained in the sample, the number of first short-chain nucleic acids (the number of ID-2) that contain a sequence of 10 bases or more in length, including the single nucleotide polymorphism site, so as to match the first reference sequence from its 5' end forward; and The number of second short-chain nucleic acids (the number of ID-1) that contain a sequence of 10 bases or more in length, including the single nucleotide polymorphism site, so that the second reference sequence matches the first 5' end of the second short-chain nucleic acid. output; Calculate the ratio R=(number of ID-1 / (number of ID-1+number of ID-2)) for each corresponding sequence; Comparing the value of the ratio R with a defined threshold value to determine the magnitude relationship therebetween; and determining whether the sample is derived from a subject having or at risk of developing the disease based on the number of sequences having a ratio R greater than the threshold value; The method includes:

2. The method according to claim 1, wherein the first reference sequence is one sequence set selected from the sequence sets shown in Tables 5 to 13, or at least two or more sequence sets selected from the sequence sets shown in Tables 5 to 13 are used in combination.

3. The method of claim 1 , wherein the disease is cancer.

4. The method of claim 1 , wherein the disease is pancreatic cancer.

5. The method according to claim 1 , wherein the short nucleic acid is miRNA.

6. The method of claim 1 , wherein the sample is serum or plasma.

7. The method according to claim 1, wherein the method is carried out using a sequencing technique such as a next-generation sequencer, a nanopore sequencer, the Sanger method (electrophoresis, capillary sequencer) or the Maxam-Gilbert method.

8. A biomarker for identifying a sample as being derived from a diseased subject or not, the biomarker being one of a set of sequences selected from the set of sequences shown in Tables 5 to 13, or a combination of at least two or more sequences selected from the set of sequences shown in Tables 5 to 13.

9. The marker according to claim 8 , wherein the disease suffered by the subject is cancer.

10. The marker according to claim 8 , wherein the disease suffered by the subject is pancreatic cancer.

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