Analysis method, marker, and kit

Abstract

To provide an analysis method, a marker, and a kit for simply determining the presence or absence of affliction with pancreatic cancer in a test subject.SOLUTION: According to embodiments, an analysis method, a marker, and a kit for determining the presence or absence of affliction with pancreatic cancer in a test subject are provided, comprising quantifying three or more miRNAs selected from the group consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p, and hsa-miR-885-5p, and a corrective miRNA in a sample derived from the test subject.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to an analysis method, a marker, and a kit.

Background Art

[0002] In recent years, the relationship between microRNA (miRNA) and diseases has attracted attention. miRNA has a function of regulating gene expression, and it has been reported that the type and expression level thereof change from the initial stage in various diseases. That is, in patients with a certain disease, the amount of a specific miRNA is increased or decreased compared with that in healthy subjects. Therefore, examining the amount of the miRNA in a sample collected from a subject is a means of knowing whether the patient has the disease.

Summary of the Invention

Problems to be Solved by the Invention

[0003] The problem to be solved by the present invention is to provide an analysis method, a marker, and a kit that can simply determine whether a subject has pancreatic cancer.

Means for Solving the Problems

[0004] According to an embodiment, there is provided an analysis method for determining the presence or absence of pancreatic cancer, including quantifying three or more miRNAs selected from a group of miRNAs consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p, and hsa-miR-885-5p in a sample derived from an analysis target and a correction miRNA.

Brief Description of the Drawings

[0005]

Figure 1

Figure 2

Figure 3

MODE FOR CARRYING OUT THE INVENTION

[0006] Hereinafter, the markers and kits of the embodiment will be described with reference to the drawings.

[0007] ·First Embodiment (Analysis Method) The analysis method according to the first embodiment is a method for determining the presence or absence of pancreatic cancer in an analysis target, including quantifying three or more miRNAs selected from the miRNA group consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p and hsa-miR-885-5p and a correction miRNA in a sample derived from the analysis target.

[0008] The above-mentioned total of seven miRNA groups are also referred to as "target miRNA groups" in the following description. Each individual miRNA constituting the target miRNA group is also referred to as a "target miRNA". Each target miRNA contains the nucleotide sequences of SEQ ID NOs: 1 to 7 described in Table 1 below. In this specification, the notation "T" in the sequence listing corresponding to each SEQ ID NO means "U".

[0009]

Table 1

[0010] The target miRNA to be quantified in this embodiment may be a combination of three or more target miRNAs. For example, combinations of three target miRNAs, combinations of four target miRNAs, or combinations of five target miRNAs can be mentioned. However, it is preferably a combination that has been investigated and confirmed for the sensitivity and specificity of the discrimination performance of pancreatic cancer in advance under specific conditions (such as the type of analysis target, sample, detection and quantification method, etc.). Specifically, it is preferably the combinations shown in Tables 2-1 to 2-3 below (Combination Nos. 1 to 85). As will be described in the examples below, it has been confirmed that the combinations of target miRNAs of Combination Nos. 1 to 3, Nos. 31 to 40, and Nos. 65 to 73 among Combination Nos. 1 to 85 exhibit particularly excellent pancreatic cancer discrimination performance. Therefore, it is more preferable that the target miRNA to be quantified is a combination of target miRNAs of Combination Nos. 1 to 3, Nos. 31 to 40, and Nos. 65 to 73.

[0011]

Table 2-1

[0012]

Table 2-2

[0013]

Table 2-3

[0014] The miRNA for correction is an miRNA that has been previously confirmed to commonly exist at a certain abundance in samples derived from pancreatic cancer patients and samples derived from individuals other than pancreatic cancer patients. As an example of the miRNA for correction, hsa-miR-486-5p (SEQ ID NO: 8: UCCUGUACUGAGCUGCCCCGAG), which is known to have a high expression level in common among pancreatic cancer patients, healthy individuals, and patients with various cancers other than pancreatic cancer, can be mentioned.

[0015] The analysis target is the animal to be analyzed in this method, that is, the animal that provides the sample. The analysis target may be an animal having some disease or a healthy animal. For example, the analysis target may be an animal that may have cancer or an animal that has had cancer in the past, and in particular, an animal that may have pancreatic cancer or an animal that has had pancreatic cancer in the past. The analysis target is preferably a human.

[0016] Alternatively, the analysis target may be another animal. The other animal is, for example, a mammal, and includes, for example, primates such as monkeys, rodents such as mice, rats, or guinea pigs, companion animals such as dogs, cats, or rabbits, livestock animals such as horses, cows, or pigs, or animals belonging to exhibition animals, etc. When it is an animal other than a human, the target miRNA is the miRNA corresponding to the above miRNA that exists in that animal.

[0017] The sample derived from the analysis target includes a sample collected from the analysis target or a sample appropriately processed therefrom. The sample is preferably serum or plasma. The sample may be other body fluids, such as blood, interstitial fluid of white blood cells, urine, feces, sweat, saliva, oral mucosa, nasal mucosa, nasal discharge, pharyngeal mucosa, sputum, digestive fluid, gastric juice, lymph fluid, cerebrospinal fluid, tear fluid, breast milk, amniotic fluid, semen, or vaginal fluid. Alternatively, the sample may be a tissue or cells, etc., and may be a tissue or cells collected from the analysis target, cultured, or the supernatant thereof.

[0018] In this specification, various "cancers" include those at any stage. For example, they include the state of remaining within the organ of the origin, the state of spreading to surrounding tissues, the state of metastasis to lymph nodes, and the state of metastasis to distant organs, etc. In this specification, pancreatic cancer refers to a malignant tumor (neoplasm) formed in pancreatic tissue. For example, pancreatic cancer generally includes those referred to as "pancreatic carcinoma", "pancreatic cancer", "pancreatic carcinoma", "pancreatic cancer", "pancreatic ductal carcinoma", or "invasive pancreatic ductal carcinoma". Also, various cancers in this specification include, for example, epithelial tumors, non-epithelial tumors, or tumors composed of both epithelial and non-epithelial components.

[0019] Hereinafter, an example of the procedure of the method of the first embodiment will be described with reference to FIGS. 1(a), 1(b), and 1(c).

[0020] As shown in FIG. 1(a), the analysis method includes, for example preparing a sample derived from the analysis target (preparation step (S11)), quantifying three or more target miRNAs and a correction miRNA among the target miRNA group in the sample derived from the analysis target (quantification step (S12)), and including.

[0021] First, a sample derived from the analysis target is prepared (preparation step (S11)). The sample can be collected using a general method according to its type. The sample may be used as it is after collection, or may be processed so as not to inhibit the reaction for nucleic acid quantification or to be in a state suitable for the reaction. The processing is, for example, mincing, homogenizing, centrifuging, precipitating, extracting, and / or separating, etc., and can be performed by any known means.

[0022] For example, extraction may be performed using a commercially available nucleic acid extraction kit. As the nucleic acid extraction kit, for example, NucleoSpin (registered trademark) miRNA Plasma (manufactured by Macherey-Nagel), Quick-cfRNA Serum & Plasma Kit (manufactured by Zymo Research), miRNeasy Serum / Plasma kit (manufactured by Qiagen), miRVana PARIS isolation kit (manufactured by Thermo Fisher), PureLinkTM Total RNA Blood Kit (manufactured by Thermo Fisher), Plasma / Serum RNA Purification Kit (manufactured by Norgen Biotek), microRNA Extractor (registered trademark) SP Kit (manufactured by Fujifilm Wako Pure Chemical Corporation), High Pure miRNA Isolation Kit (manufactured by Sigma-Aldrich), etc. can be used. Alternatively, without using a commercially available kit, for example, the sample may be treated with a protein denaturing agent or the like, an organic solvent or a buffer solution may be used, and extraction may be performed by centrifugation or nucleic acid precipitation.

[0023] Next, three or more target miRNAs and correction miRNAs contained in a sample derived from an analysis target are quantified (quantification step (S12)). The quantification step (S12) can be performed using a general method for quantifying RNA, particularly short-chain RNAs such as miRNAs. As a general method, for example, a method of reverse-transcribing miRNA to generate cDNA, amplifying the obtained cDNA, and detecting and quantifying the amplification product can be mentioned. When the RNA is short-chain, in order to facilitate amplification, it is also generally practiced to add an artificial sequence to the short-chain RNA end or to extend the cDNA obtained by reverse transcription so as to add an artificial sequence to the end. Further, as techniques for directly amplifying the RNA in the sample without going through reverse transcription and detecting and quantifying the amplification product, the 1Step RT-qPCR method and the rolling circle amplification method are known. Furthermore, when the concentration of miRNA in the sample is relatively high or when an apparatus capable of highly sensitive measurement can be used, directly detecting miRNA (or its cDNA) without amplifying the miRNA is also one of the general methods. As an apparatus capable of direct detection, for example, a microarray equipped with a nucleic acid probe that hybridizes to and specifically binds to miRNA can be mentioned.

[0024] For amplification, for example, the PCR method (including the qPCR method) or the LAMP method can be used. Detection and quantification may be performed after amplification or may be performed over time during amplification. For detection and quantification, for example, a measurement method using a signal based on turbidity or absorbance, a measurement method using an optical signal, a measurement method using an electrochemical signal, or a combination thereof can be used. For example, miRNA can be quantified from the intensity or change amount of the above signal obtained according to the amount of amplification product, or the time until the signal reaches a threshold value (rise time) or the number of rise cycles when using the PCR method. Further, for detection and quantification, for example, the results of next-generation sequencing (NGS) method may be used. In that case, relative quantification of miRNA can be performed from detection results such as the number of reads aligned to miRNA.

[0025] The quantitative value of miRNA may be determined using a calibration curve representing the relationship between the detection result of the above signal and the abundance of miRNA. The calibration curve can be created by detecting signals for a plurality of standard samples containing miRNA at different known concentrations. By comparing this calibration curve with the detection result of the signal obtained for the sample derived from the analysis target, the abundance of miRNA in the sample can be calculated. The abundance of miRNA in the sample may be calculated, for example, as the copy number of miRNA per unit amount of the sample.

[0026] The quantification in the quantification step (S12) may be performed, for example, using a commercially available kit. Examples of commercially available kits include TaqMan (registered trademark) Advanced miRNA cDNA Synthesis Kit (manufactured by Thermo Fisher, catalog No. A28007), TaqMan (registered trademark) Advanced miRNA Assays (manufactured by Thermo Fisher, catalog No. A25576), TaqMan (registered trademark) Fast Advanced Master Mix (manufactured by Thermo Fisher, catalog No. 4444964), miRCURY LNA (registered trademark) RT Kit (manufactured by Qiagen, catalog No. 339340), miRCURY LNA (registered trademark) miRNA PCR Assays (manufactured by Qiagen, catalog No. 339306, SYBR (registered trademark) Green qPCR microRNA detection system (manufactured by Origene Technologies), etc., and can be used together with primers and probes that specifically amplify miRNA.

[0027] For the quantification of three or more target miRNAs and miRNAs for correction, separate reaction systems may be prepared for each type of miRNA, and quantification may be performed by performing reverse transcription, extension, amplification, and / or detection for each reaction system. Alternatively, for example, by using a flow channel chip or the like capable of simultaneously detecting a plurality of nucleic acids, a plurality of types of miRNAs may be detected and quantified in the same reaction system. Alternatively, for example, by using a probe or the like capable of simultaneously detecting a plurality of nucleic acids, a plurality of types of miRNAs may be detected and quantified in the same reaction system. Further, for example, by using the NGS method, it is also possible to perform amplification, detection, and quantification of a plurality of types of miRNAs in a super-parallel manner in the same reaction system.

[0028] Considering that the quantification values of each target miRNA may vary depending on the individual in terms of the overall activity level of miRNAs, and that the yields of extraction and amplification reactions may vary depending on the type of cells or specimens in the specimen treatment, it is preferable to perform correction to normalize the quantification values of each target miRNA using the quantification value of the miRNA for correction. Specifically, it is preferable to perform correction to normalize the quantification values of each target miRNA using its quantification value in a sample derived from the analysis target, with hsa-miR-486-5p as the miRNA for correction (for example, converting it to a ratio with the miRNA for correction, etc.).

[0029] The data related to the detection of three or more target miRNAs and miRNAs for correction obtained in the quantification step (S12) can be used for determining the presence or absence of pancreatic cancer in the analysis target. The analysis method of the first embodiment may further include a determination step (S13) of the presence or absence of pancreatic cancer in the analysis target, which is performed after the quantification step (S12), as shown in FIG. 1(b).

[0030] In the determination step (S13), it is determined whether pancreatic cancer is included in the sample derived from the analysis target by comparing a certain criterion with the data obtained in the quantification step (S12). That is, the determination step (S13) can provide information that can assist in the determination that the analysis target has pancreatic cancer. Note that the determination of "having the disease" also includes the determination that the possibility of having the disease is high. Conversely, the determination of "not having the disease" also includes the determination that the possibility of having the disease is low.

[0031] The criterion in the determination step (S13) may be set with reference to the result of the quantification step (S12), or may be a preset threshold value (for example, a value determined from known literature or past findings). Alternatively, after constructing a determination algorithm for calculating a "determination score" related to the incidence of pancreatic cancer in advance, a threshold value that can significantly discriminate the presence or absence of pancreatic cancer among the determination scores calculated using the determination algorithm is determined and may be used as the criterion.

[0032] That is, the determination step (S13) of the method of the first embodiment may include a step of constructing a determination algorithm for calculating a determination score. The determination algorithm for pancreatic cancer incidence can be constructed based on the data of the quantitative results obtained by subjecting samples derived from individuals known to have or not have pancreatic cancer to quantitative analysis as learning samples. The quantitative analysis of the learning samples may be performed before the quantification step (S12) or in parallel with the quantification step (S12).

[0033] For example, the step of constructing the determination algorithm may include: (S21) preparing learning samples; (S22) quantifying the correction miRNA and three or more target miRNAs in the prepared learning samples; (S23) constructing a determination algorithm for determining the presence or absence of pancreatic cancer by referring to the quantitative values of the correction miRNA and three or more target miRNAs in the learning samples and information about the individuals from which the learning samples are derived. The information about the individual includes information related to the presence or absence of pancreatic cancer, and may further include personal data such as medical history, gender, BMI, and smoking rate.

[0034] Here, the determination algorithm is a calculation formula and / or a program that executes the calculation formula, and performs various operations (such as arithmetic operations and exponential logarithmic operations, etc.) on a combination of values obtained by normalizing the quantitative values of three or more target miRNAs with a correction miRNA, and ultimately calculates a determination score that serves as an indicator of pancreatic cancer susceptibility. In other words, the determination score is an indicator in which the possibility that the analysis target has pancreatic cancer is quantified based on a combination of the quantitative values of three or more target miRNAs and the quantitative value of the correction miRNA, and the calculation method thereof varies depending on the determination algorithm adopted.

[0035] As described above, the learning sample is derived from an individual whose pancreatic cancer status is known. More specifically, the learning sample is a specimen collected from an individual with pancreatic cancer, a specimen collected from an individual other than an individual with pancreatic cancer, and cultured cells of pancreatic cancer, etc. Here, an individual other than an individual with pancreatic cancer is, for example, a healthy individual and an individual with cancer other than pancreatic cancer, etc. In the present specification, a healthy individual refers to an individual who has at least not contracted cancer. It is preferable that the healthy individual is a healthy individual without diseases or abnormalities. Also, an individual with cancer other than pancreatic cancer is, for example, an individual with breast cancer, lung cancer, gastric cancer, and colorectal cancer.

[0036] The individual from which the learning sample is derived may be an individual different from the individual to be analyzed by this method, but it is preferably an individual belonging to the same species, that is, a human if the analysis target is a human. Also, the physical conditions such as age, gender, height, and weight of the control or the number of people are not particularly limited, but it is more preferable that the physical conditions are the same as or similar to those of the analysis target of this analysis method.

[0037] As one type of determination algorithm, a trained model obtained by performing machine learning so as to be able to determine the presence of pancreatic cancer may be constructed. Such a model can be created using learning data prepared in advance. The learning data includes the quantitative values of the target miRNA and the correction miRNA in the learning sample.

[0038] In order to create a trained model with higher determination accuracy, it is preferable that the individuals from which the learning samples are derived are more diverse and plural. Therefore, as learning data, for example, quantitative analysis results for each specimen collected from a plurality of individuals suffering from cancers other than pancreatic cancer, and / or quantitative analysis results for each specimen collected from a plurality of healthy individuals are preferably prepared. Further, such learning data includes information on each individual, including the presence or absence of pancreatic cancer, but may also include personal data such as medical history, gender, BMI, smoking rate, etc. in addition to that.

[0039] When the trained model obtained using machine learning is, for example, a neural network model, in the learning of the neural network model, the neural network model may be configured such that when training data is input, a determination result of disease onset is obtained as an output. Further, the model of machine learning is not limited to this method, and other models such as a linear model, a non-linear model, a Bayesian model, a support vector machine model, a random forest model, a boosting model, etc. may also be used.

[0040] The determination algorithm may be constructed to calculate a determination score with different weightings for each target miRNA. For example, a determination score may be calculated by constructing an algorithm in which the absolute value of the weighting coefficient is set high for a target miRNA with a high relevance to pancreatic cancer and the absolute value of the weighting coefficient is set low for a target miRNA with a low relevance.

[0041] The degree of association between each target miRNA and pancreatic cancer may be estimated by a binary logistic regression model or a multinomial logistic regression model. Examples of estimation methods in the binary logistic regression model or multinomial logistic regression model include regularization estimation (such as Ridge) and sparse estimation (such as Lasso, SCAD, MCP, etc.). In addition, not limited to the binary logistic regression model or multinomial logistic regression model, for example, an estimation method based on statistical methods such as ANOVA analysis or Kruskal-Wallis analysis, or a machine learning method may be used to estimate the degree of association between each target miRNA and pancreatic cancer.

[0042] In the step of constructing the determination algorithm (S23), a value serving as a criterion for pancreatic cancer susceptibility is also determined. For example, referring to the determination scores calculated for pancreatic cancer patients among the learning samples and the determination scores calculated for individuals without pancreatic cancer among the learning samples, a threshold of the determination score that can significantly distinguish between the two may be determined as the criterion. Whether the discrimination between the two is significant can be determined by whether a probability value (such as sensitivity or specificity, etc.) regarding discrimination performance, which can be calculated by statistical processing, meets a certain level.

[0043] The threshold of the determination score may be set in advance for each analysis target. For example, in an opportunity such as a regular health check, if the determination score is calculated from the quantitative value of the target miRNA in the healthy state of the analysis target, the determination score in the healthy state can be used as the threshold. Furthermore, when the determination score obtained from the analysis target after a certain period has elapsed from the healthy state is higher or lower than the threshold, an alarm can be issued assuming that the analysis target may have pancreatic cancer. The threshold may be a different value for each individual.

[0044] Furthermore, in the determination step (S13) of the method of the first embodiment, the quantitative result obtained in the quantification step (S12) is applied to the determination algorithm constructed in the construction step (S23) to calculate the determination score of the analysis target (calculation step (S31)), and the presence or absence of pancreatic cancer may be determined by comparing the determination score of the analysis target with a threshold value (comparison step (S32)). In the comparison step (S32), the probability that the analysis target has pancreatic cancer may be calculated according to the magnitude of the difference between the determination score of the analysis target and the threshold value. For example, it may be determined that the greater the difference between the determination score of the analysis target and the threshold value, the higher the probability of having pancreatic cancer.

[0045] According to the analysis method of the first embodiment described above, three or more target miRNAs and correction miRNAs in a sample derived from the analysis target are quantified, and by comparing the obtained determination score with a reference, it is possible to simply determine the presence or absence of pancreatic cancer in the analysis target. In other words, according to this method, it is possible to easily distinguish between an individual with pancreatic cancer and an individual without pancreatic cancer.

[0046] Since the method of this embodiment can use serum or plasma that can be easily collected in health examinations and the like, pancreatic cancer can be detected early. By using serum or plasma, etc., the physical and economic burden on the analysis target can be greatly reduced compared to cytology and the like, and since the procedure is easy, the burden on the examiner is also small. In addition, since the miRNA concentration contained in serum or plasma is stable, more accurate tests can be performed.

[0047] According to a further embodiment, determining that a subject has pancreatic cancer also includes determining the prognosis or recurrence of pancreatic cancer in the subject to be analyzed. For example, as shown in FIG. 1(c), the analysis method includes, after the quantification step (S12), a prognosis / recurrence determination step (S14) of determining the presence or absence of the prognosis or recurrence of pancreatic cancer in the subject to be analyzed from the quantification results. In the prognosis / recurrence determination step (S14), for example, by comparing a determination score with a threshold value, it is possible to determine that the prognosis of pancreatic cancer in the subject to be analyzed is poor, or that pancreatic cancer has recurred or is highly likely to recur. In some cases, it may be preferable to use a threshold value determined for each subject to be analyzed.

[0048] Also, after the determination step (S13) and / or the prognosis / recurrence determination step (S14), it is also possible to select and assist in selecting the type of treatment method or drug type to be applied to the subject to be analyzed according to the determination result. For example, as shown in FIG. 1(d), the analysis method includes a selection step (S15) of selecting the type of treatment method or drug type to be applied to the subject to be analyzed from the determination results after the determination step (S13) and / or the prognosis / recurrence determination step (S14). Here, the treatment method or drug is for the treatment of pancreatic cancer. The type of treatment method or drug type includes the dosage, timing, or duration of use of the treatment method or drug.

[0049] According to a further embodiment, there is also provided an analysis method for assisting in determining the presence or absence of pancreatic cancer in a subject to be analyzed, which includes quantifying three or more target miRNAs and correction miRNAs in a sample derived from the subject to be analyzed (quantification step (S12)). "Assisting in the determination" includes, for example, obtaining information regarding the possibility that the subject to be analyzed has pancreatic cancer. "Information" is, for example, information regarding the analysis result of the sample and may be, for example, a determination score. According to this method, it is possible to obtain more accurate information for determining the presence or absence of pancreatic cancer, prognosis determination, recurrence determination, or selection of a treatment method or drug applied to the subject to be analyzed in the subject to be analyzed.

[0050] According to a further embodiment, the present analysis method can also be used for detecting pancreatic cancer cells in a sample not derived from the analysis target. For example, when artificially producing pancreatic cancer cells, it can also be used to confirm whether the cells are present in the produced cell-containing solution.

[0051] (Marker) According to the first embodiment, a marker for detecting pancreatic cancer of the analysis target, which contains three or more types of target miRNAs, is provided.

[0052] Here, the "marker" refers to a substance that can determine whether the sample and / or the analysis target from which it is derived is in a specific state by detecting its presence or concentration in the sample.

[0053] The marker for detecting pancreatic cancer according to the first embodiment can, for example, measure its abundance (quantitative value) in a sample derived from the analysis target, and as described above, determine the presence or absence of pancreatic cancer in the analysis target, the prognosis or the presence or absence of recurrence, or select a treatment method or drug applied to the analysis target.

[0054] (Kit) According to the first embodiment, a kit for detecting pancreatic cancer is provided.

[0055] The kit contains nucleic acids that can specifically bind to three or more miRNAs selected from the group of miRNAs consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p, and hsa-miR-885-5p (i.e., hybridize with the target miRNA), and nucleic acids that can specifically bind to the corrective miRNA. That is, the kit may contain nucleic acids that can specifically bind to the target miRNAs of any one combination among combination Nos. 1 to 85 of the target miRNA combinations in Tables 2-1 to 2-3, and nucleic acids that can specifically bind to the corrective miRNA. Although it will be described in the examples below, among combination Nos. 1 to 85, the combinations of target miRNAs of combination Nos. 1 to 3, Nos. 31 to 40, and Nos. 65 to 73 exhibit particularly excellent pancreatic cancer discrimination performance. Therefore, it is preferable that the kit contains nucleic acids that can specifically bind to each of the combinations of target miRNAs of combination Nos. 1 to 3, Nos. 31 to 40, and Nos. 65 to 73, and nucleic acids that can specifically bind to the corrective miRNA. The kit may further contain reagents that can be used in common methods for quantifying short-chain RNAs such as RNA, particularly miRNAs.

[0056] When using the qPCR method for detecting each miRNA, the nucleic acid that can specifically bind to each miRNA may be a reverse transcription (RT) primer for reverse transcribing each miRNA, an elongation (EL) primer for elongating the miRNA, or a primer set for amplifying each miRNA. In addition, it may contain a probe nucleic acid for detecting each miRNA.

[0057] The RT primer is a primer for obtaining the cDNA of each miRNA. The RT primer contains a sequence complementary to at least a part of the sequence of each miRNA or the sequence added to each miRNA. The RT primer may further contain an artificial sequence added to the cDNA to facilitate amplification of the cDNA of each miRNA.

[0058] The EL primer is a primer for adding an artificial sequence to cDNA in order to facilitate the amplification of the cDNA of each miRNA. The EL primer may include a sequence complementary to at least a part of the sequence of the cDNA of each miRNA and a sequence added for the extension of each cDNA. Also, in order to facilitate the amplification of the cDNA of each miRNA, an adapter sequence can be added to the cDNA.

[0059] The amplification primer set includes, for example, at least a forward primer and a reverse primer if it is for the PCR method. Or, the amplification primer set may be for the LAMP method. For example, the amplification primer set may include primers of sequences corresponding to the base sequences of each miRNA, which are used in a general LAMP method. Or, the amplification primer set may be for the NGS method, for example, and may include a forward primer containing an artificial adapter sequence and a reverse primer containing its complementary sequence. The amplification primer set for the NGS method may include multiple combinations of a forward primer and a reverse primer containing different barcode sequences in order to analyze multiple samples simultaneously. When the amplification primer set is used in the rolling cycle amplification method, the kit further includes circular single-stranded DNA to which the amplification primer hybridizes and serves as a template for amplification.

[0060] Each primer included in the amplification primer set may be designed to bind to the cDNA of each miRNA or its complementary sequence, or may be designed to bind to an artificial sequence added by the RT primer, the EL primer, and / or the adapter sequence.

[0061] Furthermore, when each miRNA in the sample is directly detected by a microarray, the nucleic acid that can specifically bind to each miRNA is a nucleic acid probe provided in the microarray. The nucleic acid probe may have at least a part of the sequence of each miRNA, its cDNA, or its amplification product, or its complementary sequence.

[0062] The above nucleic acids included in the kit may be individually or in combination and provided in a container together with a suitable carrier. Suitable carriers include, for example, water, physiological solutions or buffers, etc. Or, it may be provided in a dry state. The container may be, for example, a tube or a microtiter plate, etc. Alternatively, these nucleic acids may be provided immobilized on a solid phase such as a microfluidic chip.

[0063] The above nucleic acids included in the kit may include modified nucleic acids such as LNA, BNA, methylated nucleic acids such as 2'-OMe, and cross-linked nucleic acids. Also, they may be nucleic acids modified with molecules such as MGB, FITC, DIG, biotin, etc. Also, they may be labeled with substances used for detecting optical signals, for example, fluorescent substances such as FAM, ROX, JOE, VIC, HEX, Cy3, Cy5, AlexaFluor, ATTO, IRDye, etc., and quenching substances such as TAMRA, Iowa Black, BHQ, Zen, Eclipse, etc.

[0064] In addition to the above nucleic acids, the kit may include reagents used for reverse transcription, extension or amplification, for example, enzymes, substrates and / or labeling substances that generate optical signals or electrochemical signals used for detection. Labeling substances are, for example, fluorescent dyes such as SYBR Green, EvaGreen®, SYTO® 82, etc., and indicators such as metal complexes such as ruthenium hexamine when detecting current.

[0065] The kit can be used, for example, for determining the presence or absence of pancreatic cancer, prognosis determination, recurrence determination, selection of treatment methods or drug types, etc. in the object to be analyzed as described above.

[0066] According to a further embodiment, a kit for detecting pancreatic cancer is provided as a composition or diagnostic agent for diagnosing pancreatic cancer. Also, according to the embodiment, the use of the above nucleic acids in the manufacture of a composition for diagnosing pancreatic cancer or a diagnostic agent for pancreatic cancer is also provided.

[0067] [Example] The experiments conducted to obtain the markers of the present embodiment and the verification by the discrimination algorithm constructed using the markers of the present embodiment will be described below.

[0068] Example 1. Comprehensive search for pancreatic cancer patient markers miRNA markers that can distinguish pancreatic cancer patients from healthy individuals and cancer patients other than pancreatic cancer were searched for as follows.

[0069] · Preparation of specimens As specimens, about 1000 specimens of breast cancer patient serum, pancreatic cancer patient serum, lung cancer patient serum, colorectal cancer patient serum, and healthy person serum were prepared (see Table 3). For the creation of the determination model described later, the specimen group was randomly divided into two groups, Group A (about 600 specimens) and Group B (about 400 specimens).

[0070]

Table 3

[0071] · Specimen processing and miRNA quantification miRNA was extracted from 300 μL of all sera using Nucleospin (registered trademark) miRNA Plasma (manufactured by Macherey-Nagel).

[0072] For cDNA synthesis, TaqMan miRNA cDNA Synthesis Kit (Applied Biosystems, Cat. A28007) was used. The TaqMan miRNA cDNA Synthesis Kit is a kit characterized by generally reverse-transcribing all mature RNAs present in a sample, rather than being target-specific, by adding a poly(A) chain to the 3' end of mature RNA and ligating an adapter sequence to the 5' end. Therefore, cDNA corresponding to all mature RNAs including miRNA in each specimen was obtained.

[0073] Among the obtained cDNA, miRNAs were quantified by performing qPCR using TaqMan Fast Advanced Master Mix (manufactured by Applied Biosystems) and TaqMan Advanced miRNA Assays (manufactured by Applied Biosystems) according to the protocol included. The TaqMan PCR method that utilizes 5'-nuclease activity is an amplification method characterized by excellent detection accuracy by using a primer for amplifying a target and a TaqMan probe in which fluorescence resonance energy transfer (FRET) occurs within the molecule and that specifically binds to the target in combination. The quantitative value of the miRNA contained in the specimen obtained by applying this TaqMan PCR method was, for example, by using a target miRNA of a known concentration, creating a calibration curve of the cycle number (Ct value) until the cDNA corresponding to each probe and primer reaches the detection standard and the corresponding concentration, and determining the concentration from the Ct value for the specimen of unknown concentration using the calibration curve.

[0074] Note that the quantitative value of each miRNA was corrected using the quantitative value of hsa-miR-486-5p (SEQ ID NO: 8), which is a miRNA for correction, to obtain the expression level ratio.

[0075] · Construction of discrimination algorithm Using the expression level ratios obtained for each of Group A and Group B, a discrimination algorithm was constructed as follows.

[0076] Specifically, by using the quantitative value data obtained from Group A as learning data for constructing a discrimination algorithm, a pancreatic cancer discrimination algorithm A was constructed, and the algorithm A was verified using the quantitative values obtained from Group B as test data. Furthermore, by using the quantitative value data obtained from Group B as learning data for constructing a discrimination algorithm, a discrimination algorithm B was constructed, and the algorithm B was verified using the quantitative values obtained from Group A as test data. By thus dividing the specimen group and individually performing learning, and by verifying each other using the test data, it is possible to extract and select a combination of miRNAs that function as pancreatic cancer markers with higher robustness.

[0077] ·Extraction of miRNA combinations and verification results of discrimination performance For all combinations of miRNAs that could be detected and quantified, the Area Under the Curve (AUC) value of the Receiver Operating Characteristic (ROC) curve was calculated using the constructed pancreatic cancer discrimination algorithm. The closer the AUC value is to 1.0, the higher the detection accuracy of pancreatic cancer. Here, when extracting miRNA combinations with an AUC value of 0.73 or higher, 85 combinations of miRNAs shown in Tables 4-1 to 4-3 below were obtained. The AUC values described in Tables 4-1 to 4-3 are the average values of the AUC values calculated for each of the discrimination algorithms A and B.

[0078]

Table 4-1

[0079]

Table 4-2

[0080]

Table 4-3

[0081] The miRNAs that make up all combinations (NO.1 - 85) of miRNAs shown in Table 4 - 1 to Table 4 - 3 are found to be any 3 or more of hsa - miR - 205 - 5p, hsa - miR - 223 - 5p, hsa - miR - 29c - 3p, hsa - miR - 324 - 3p, hsa - miR - 34a - 5p, hsa - miR - 483 - 5p, and hsa - miR - 885 - 5p. Therefore, any combination of 3 or more of hsa - miR - 205 - 5p, hsa - miR - 223 - 5p, hsa - miR - 29c - 3p, hsa - miR - 324 - 3p, hsa - miR - 34a - 5p, hsa - miR - 483 - 5p, and hsa - miR - 885 - 5p, that is, any combination of 3 or more selected from the target miRNA groups described in Table 1, can distinguish pancreatic cancer patients from healthy individuals and other cancer patients, indicating that the pancreatic cancer - discriminating performance of the combination is excellent.

[0082] In particular, the combination of miRNAs selected from the miRNA group is more preferably the following combinations in which an AUC value of 0.79 or more was observed: the combination consisting of hsa-miR-205-5p, hsa-miR-324-3p and hsa-miR-483-5p (Combination No. 1), the combination consisting of hsa-miR-205-5p, hsa-miR-223-5p and hsa-miR-483-5p (Combination No. 2), the combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p and hsa-miR-483-5p (Combination No. 3), the combination consisting of hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 31), the combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p and hsa-miR-483-5p (Combination No. 32), the combination consisting of hsa-miR-205-5p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 33), the combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 34), the combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p and hsa-miR-483-5p (Combination No. 35), the combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p and hsa-miR-483-5p (Combination No. 36), the combination consisting of hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 37), the combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 38), the combination consisting of hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No.39) A combination consisting of hsa-miR-205-5p, hsa-miR-324-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 40), a combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 65), a combination consisting of hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 66), a combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 67), a combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 68), a combination consisting of hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p (Combination No. 69), a combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p and hsa-miR-483-5p (Combination No. 70), a combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 71), a combination consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 72), and a combination consisting of hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p and hsa-miR-885-5p (Combination No. 73).

[0083] Example 2. Discrimination performance by target miRNA alone and combinations of miRNAs other than the target miRNA · Discrimination performance of pancreatic cancer by target miRNA alone As a comparative example, regarding the discrimination performance when using the target miRNAs (SEQ ID NOs: 1 to 7) described in Table 1 alone as pancreatic cancer markers, similar to Example 1, the AUC value was obtained using a discrimination algorithm for verification.

[0084] · Verification results The verification results are shown in Table 5. Comparing with the AUC values of Combinations 1 to 85 described in Example 1, it can be seen that the target miRNA alone does not have sufficient discrimination performance for pancreatic cancer.

[0085]

Table 5

[0086] · Discrimination performance of pancreatic cancer by miRNAs other than the target miRNA As a further comparative example, the discrimination performance of pancreatic cancer by combinations of miRNAs other than the target miRNA was also verified. Specifically, for the combinations of the miRNAs of SEQ ID NOs: 9 to 14 described in Table 6 below, the AUC value was obtained using a discrimination algorithm in the same manner as in Example 1 for verification. Each miRNA described in Table 6 has been found to be a marker for primary screening of cancer and can distinguish between healthy subjects and subjects with breast cancer, pancreatic cancer, lung cancer, gastric cancer, and colorectal cancer. However, it has also been found that each miRNA described in Table 6 only reveals the difference from healthy subjects as a primary screening marker for cancer and does not identify the cancer type (i.e., distinguish a pancreatic cancer patient, for example, from a group of samples of subjects with breast cancer, pancreatic cancer, lung cancer, gastric cancer, and colorectal cancer).

[0087]

Table 6

[0088] · Verification results The verification results are shown in Table 7. In Table 7, for example, hsa-miR-106b-5p is described by the abbreviation "106b-5p", and other miRNAs are also described in the same abbreviated manner.

[0089] When compared with the AUC values of the combinations (NO. 1 to 85) of the target miRNAs of this embodiment described in Example 1, it can be seen that the AUC values of the combinations (NO. 86 to 91) of the miRNAs described in Table 6 are low. Therefore, it was shown that the combination of the target miRNAs of this embodiment exhibits superior pancreatic cancer discrimination performance compared to the combination of miRNAs other than the target miRNAs.

[0090]

Table 7

[0091] Although several 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, replacements, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the invention described in the claims and its equivalent scope.

Claims

**Claim 1** An analysis method for determining the presence or absence of pancreatic cancer in the subject to be analyzed, comprising quantifying three or more target miRNAs selected from the miRNA group consisting of miRNAs for correction and hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p and hsa-miR-885-5p in a sample derived from the subject to be analyzed. **Claim 2** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-324-3p and hsa-miR-483-5p. **Claim 3** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-223-5p and hsa-miR-483-5p. **Claim 4** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-29c-3p and hsa-miR-483-5p. **Claim 5** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p. **Claim 6** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p and hsa-miR-483-5p. **Claim 7** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-324-3p, hsa-miR-34a-5p and hsa-miR-483-5p. **Claim 8** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-34a-5p and hsa-miR-483-5p. **Claim 9** The method according to claim 1, wherein the miRNA selected from the miRNA group is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p and hsa-miR-483-5p. **Claim 10** The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, and hsa-miR-483-5p.

11. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

12. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

13. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-34a-5p, and hsa-miR-483-5p.

14. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-324-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

15. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, and hsa-miR-483-5p.

16. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

17. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-34a-5p, and hsa-miR-483-5p.

18. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-34a-5p, and hsa-miR-483-5p.

19. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, and hsa-miR-483-5p.

20. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, and hsa-miR-483-5p.

21. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

22. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-324-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

23. The method according to claim 1, wherein the miRNA selected from the group of miRNAs is hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-483-5p, and hsa-miR-885-5p.

24. The method according to claim 1, wherein the quantitative values of the three or more target miRNAs are normalized by the quantitative value of the miRNA for correction.

25. The method according to claim 24, wherein the miRNA for correction is hsa-miR-486-5p.

26. (S21) preparing a learning sample from an individual whose pancreatic cancer status is known; (S22) quantifying the miRNA for correction and the three or more target miRNAs in the learning sample; and (S23) constructing a determination algorithm for determining the presence or absence of pancreatic cancer based on the information about the individual, the quantitative value of the miRNA for correction, and the quantitative values of the three or more target miRNAs in the learning sample further comprising The method according to claim 1, wherein the presence or absence of pancreatic cancer in the subject to be analyzed is determined by applying the quantitative values of the miRNA for correction and the quantitative values of the three or more target miRNAs in the sample derived from the subject to be analyzed to the determination algorithm.

27. The method according to claim 26, wherein the sample is from an individual who is known to be healthy or suffering from cancer other than pancreatic cancer.

28. The method according to claim 27, wherein the cancers other than pancreatic cancer are breast cancer, lung cancer, gastric cancer and colorectal cancer.

29. The method according to claim 26, wherein the information about the individual includes information about the presence or absence of pancreatic cancer in the individual.

30. The method according to claim 26, wherein the determination algorithm is constructed by binary logistic regression, multinomial logistic regression, statistical methods or modeling by machine learning.

31. The binary logistic regression and the multinomial logistic regression are regularized estimation or sparse estimation, The method according to claim 30, wherein the modeling by machine learning is modeling using a linear model, a non-linear model, a Bayesian model, a support vector machine model, a random forest model, a boosting model or a neural network model.

32. The method according to claim 1, wherein the quantification is performed using the PCR method, the LAMP method, the next-generation sequencing method, or the microarray method.

33. The method according to claim 1, wherein the sample is serum or plasma.

34. A kit for detecting pancreatic cancer, comprising a nucleic acid capable of specifically binding to three or more miRNAs selected from the miRNA group consisting of hsa-miR-205-5p, hsa-miR-223-5p, hsa-miR-29c-3p, hsa-miR-324-3p, hsa-miR-34a-5p, hsa-miR-483-5p and hsa-miR-885-5p, and a nucleic acid capable of specifically binding to hsa-miR-486-5p.

35. The kit according to claim 34, wherein the nucleic acid capable of specifically binding to the miRNA is a nucleic acid for reverse transcription for reverse transcribing the miRNA, a nucleic acid for extension for extending the miRNA, a set of nucleic acids for amplification for amplifying the miRNA, or a nucleic acid probe or a set of nucleic acid primers for detecting the miRNA.

36. The kit according to claim 34, wherein the miRNA selected from the group of miRNAs is hsa-miR-205-5p, hsa-miR-324-3p, and hsa-miR-483-5p.

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