Gene identification device, gene identification method and use thereof

Abstract

To provide a new gene identification method capable of identifying genes involved in disease, a gene identification device and use thereof.SOLUTION: A gene identification method comprises an extraction step (S1) for extracting a plurality of pieces of data of interest that satisfies a predetermined condition from expression level data on an expression level of each gene contained in a biometric sample collected from each of a plurality of subjects each suffering from disease, a selection step (S2) for carrying out at least two or more steps capable of selecting a candidate gene, and an identification step (S3) for identifying the candidate gene selected in any of the two or more steps as a gene involved in the disease.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a gene identification device, a gene identification method, and uses thereof. [Background technology]

[0002] Various genes are known to be involved in various diseases, and identification of such genes is important for the treatment, prevention, diagnosis, and the like of diseases.

[0003] 2. Description of the Related Art Conventionally, methods for identifying genes involved in diseases and the like based on the magnitude of gene variation have been known (see, for example, Non-Patent Documents 1 to 3). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Colo Trapnell et al., Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions, Nat Biotechnol., 32, 381-386, (2014) [Non-Patent Document 2] Sumit Mukherjee et al., Molecular estimation of neurodegeneration pseudotime in order brains, Nat Commun., 11, 5781, (2020) [Non-Patent Document 3] Kieran R Campbell et al., Nat Commun., 9, 2442, (2018) Summary of the Invention [Problem to be solved by the invention]

[0005] However, there is room for improvement in the above-mentioned conventional techniques in terms of identifying genes involved in diseases, and there is a demand for the development of new techniques for identifying genes involved in diseases.

[0006] For example, there is a large degree of individual variation in human disease tissue samples, making it extremely difficult to identify the true causative gene of a disease from these samples.

[0007] An object of one aspect of the present invention is to provide a novel gene identifying device and method capable of identifying genes involved in a disease, and uses thereof. [Means for solving the problem]

[0008] The present inventors have found that it is possible to identify genes involved in a disease by dividing patients into groups according to the progression of the disease and analyzing data focusing on a specific group (e.g., a group in the early stage of the disease), and have thus completed the present invention.

[0009] [1] A gene identification method comprising: an extraction step of extracting a plurality of pieces of data of interest that satisfy a predetermined condition from expression level data in which the expression level of each of a plurality of genes contained in a biological sample collected from each of a plurality of subjects suffering from a disease is associated with a severity level of a symptom of the disease for each of the plurality of subjects; a selection step of executing at least any two or more steps of: (1) a step A of selecting a predetermined number of candidate genes involved in the aggravation of the symptoms based on a result of a principal component analysis of the plurality of pieces of data of interest; (2) a step B of selecting a predetermined number of candidate genes involved in the aggravation from the plurality of pieces of data of interest using a predetermined machine learning model; and (3) a step C of selecting a predetermined number of candidate genes involved in the aggravation based on a minimum spanning tree generated based on a result of the principal component analysis of the plurality of pieces of data of interest; and an identification step of identifying the candidate genes selected in any of the two or more steps as genes involved in the disease.

[0010] [2] In the extraction step, coordinates of the expression data of each of the plurality of subjects are determined in a two-dimensional coordinate system in which a first principal component and a second principal component obtained by principal component analysis of the expression data are set as X and Y coordinates, respectively, the expression data of each of the plurality of subjects is classified into a plurality of sets corresponding to the severity levels, center coordinates are calculated for each of the plurality of sets, and a reference value indicating a first principal component value obtained by principal component analysis of a distance matrix indicating the distance between the center coordinates of the plurality of sets and a reference value indicating the severity level of the set are calculated. and, if a magnitude relationship between the reference value of the set with the lowest severity level and the reference value of the set with the second lowest severity level is different from a magnitude relationship between the reference value of the set with the nth lowest severity level and the reference value of the set with the n+1th lowest severity level, extracting the expression amount data included in the sets with the lowest severity level to the nth lowest severity level as the plurality of data of interest (n is an integer of 2 or more).

[0011] [3] The gene identification method according to [1] or [2], wherein in the step A, the plurality of data of interest are classified into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of data of interest, a virtual permutation of each of the groups based on the magnitude of the determined first principal component value is determined, and a predetermined number of genes whose expression levels are increased or decreased according to the virtual permutation are selected as the candidate genes.

[0012] [4] The gene identification method according to any one of [1] to [3], wherein in the step B, the plurality of pieces of data of interest are divided into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of pieces of data of interest, a virtual permutation of each of the predetermined number of groups is determined, the virtual permutation being based on a magnitude of the determined first principal component value, the plurality of pieces of data of interest to which a virtual label indicating the virtual permutation has been assigned is input into the predetermined machine learning model, and a predetermined number of genes whose expression levels linearly increase or decrease according to the virtual permutation are selected as the candidate genes.

[0013] [5] The method for identifying genes according to any one of [1] to [4], wherein in the step C, a minimum spanning tree is generated using a group of points obtained by plotting points corresponding to each of the plurality of data of interest in a two-dimensional coordinate system in which a first principal component value and a second principal component value determined by principal component analysis of the plurality of data of interest are the X-coordinate and the Y-coordinate, respectively, and a point having the lowest severity level among the end points included in the minimum spanning tree is selected as a starting point; and a linear regression analysis is performed on the correlation between (1) a moving distance when moving along the minimum spanning tree from the starting point to another point included in the minimum spanning tree, and (2) an amount of change between the expression level of each of the plurality of genes corresponding to the starting point and the expression level of each of the plurality of genes corresponding to the other point; and a predetermined number of the candidate genes involved in the aggravation are selected based on the absolute value of the slope of the regression line obtained by the linear regression analysis from among genes having a coefficient of determination of the linear regression analysis equal to or greater than a predetermined threshold value.

[0014] [6] [7] The method for identifying a gene according to [5], wherein, in the step C, if a tendency of increase or decrease in the first principal component value when moving from a central coordinate of the point group having the lowest severity level to a central coordinate of the point group having the second lowest severity level is different from a tendency of increase or decrease in the first principal component value when moving from a central coordinate of the point group having the nth lowest severity level to a central coordinate of the point group having the n+1th lowest severity level, the method generates the minimum spanning tree by deleting points corresponding to data from the point group having the n+1th lowest severity level to the point group having the highest severity level from the point group obtained by plotting the expression amount data on the two-dimensional coordinate system, wherein n is an integer equal to or greater than 2.

[0015] [7] a selection unit that executes at least any two or more steps of: (1) step A of selecting a predetermined number of candidate genes involved in the aggravation of the symptoms based on a result of a principal component analysis of the plurality of data of interest; (2) step B of selecting a predetermined number of candidate genes involved in the aggravation from the plurality of data of interest using a predetermined machine learning model; and (3) step C of selecting a predetermined number of candidate genes involved in the aggravation based on a minimum spanning tree generated based on a result of the principal component analysis of the plurality of data of interest; and an identification unit that identifies the candidate genes selected in any of the two or more steps as genes involved in the disease.

[0016] [8] A control program for causing a computer to function as the gene identification device described in [7], the control program causing a computer to function as the extraction unit, the selection unit, and the identification unit.

[0017] [9] A method for obtaining data for diagnosing cataracts, comprising a step of measuring the expression level of at least one gene selected from the group consisting of the CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene in a biological sample collected from a subject.

[0018]

[10] An administration step of administering a candidate substance for a therapeutic or preventive agent for cataract to a non-human model organism; a measuring step of measuring the expression level of at least one gene selected from the group consisting of CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene in the non-human model organism after the administration step; A screening method for a therapeutic or preventive agent for cataracts, comprising a determination step of determining whether the candidate substance is a therapeutic or preventive agent for cataracts by comparing the expression level of each gene measured in the measurement step with a control expression level.

[0019]

[11] A marker for diagnosing cataracts, comprising at least one gene selected from the group consisting of the CDC42EP1 gene, the ARTN gene, the XKR8 gene, the ACTG2 gene, the RPS6KA1 gene, the SLC16A11 gene, the PLEKHG6 gene, the TMBIM1 gene, the AIFM3 gene, the LDLRAD2 gene, the COL1A1 gene, the CARD10 gene, the ACHE gene, the NUDT8 gene, the LRRC30 gene, the ADAMTS13 gene, the KRTAP10-6 gene, the BZRAP1 gene, the IQCF5 gene, the GGT6 gene, the PLEKHA4 gene, the OBP2B gene, the LILRA5 gene, the SH3TC1 gene, the HHAT gene, and the RP11-9L18.3 gene. Effect of the Invention

[0020] According to one aspect of the present invention, it is possible to provide a new gene identifying device and method capable of identifying genes involved in a disease, and uses thereof. [Brief description of the drawings]

[0021] [Figure 1] FIG. 1 is a diagram showing an example of a schematic configuration of a gene identification system. [Diagram 2] FIG. 13 is a diagram showing another example of the schematic configuration of a gene identification system. [Diagram 3] FIG. 1 is a functional block diagram showing an example of the configuration of a gene identification device. [Figure 4] 1 is a flowchart showing an example of a flow of processing performed by a gene identification device. [Diagram 5] 13 is a flowchart showing an example of a flow of a process performed by an extraction unit. [Figure 6] FIG. 1 is a diagram showing an example of a plot of expression level data in a two-dimensional coordinate system in which a first principal component and a second principal component obtained by principal component analysis of expression level data for each of a plurality of subjects are set as the X coordinate and the Y coordinate, respectively. [Figure 7] FIG. 7 is a diagram showing an example of a state in which the central coordinates of each of a plurality of sets are plotted in the same two-dimensional coordinate system as in FIG. [Figure 8]FIG. 13 is a diagram showing an example of a plot of a first principal component obtained by principal component analysis of a distance matrix indicating the distance between the center coordinates of multiple clusters and the LOCS grade in a two-dimensional coordinate system with the X coordinate and the Y coordinate, respectively. [Figure 9] 7 is a diagram showing an example of a state in which a plurality of extracted data of interest are plotted in the same two-dimensional coordinate system as in FIG. 6. FIG. [Figure 10] 1 is a flowchart showing an example of the processing flow in step A. [Figure 11] FIG. 13 is a diagram showing an example of a state in which a plurality of pieces of data of interest are plotted in a two-dimensional coordinate system in which a first principal component and a second principal component obtained by principal component analysis of the plurality of pieces of data of interest are set as the X coordinate and the Y coordinate, respectively. [Figure 12] FIG. 1 is a diagram illustrating the characteristics of genes selected as candidate genes by the selection unit. [Figure 13] FIG. 2 shows an example of a candidate gene selected by step A. [Figure 14] 13 is a flowchart showing an example of the processing flow in step B. [Figure 15] FIG. 1 shows an example of a candidate gene selected in step B. [Figure 16] 13 is a flowchart showing an example of the processing flow in step C. [Figure 17] FIG. 13 is a diagram showing an example of a state in which a plurality of pieces of data of interest are plotted in a two-dimensional coordinate system in which a first principal component and a second principal component obtained by principal component analysis of the plurality of pieces of data of interest are set as the X coordinate and the Y coordinate, respectively. [Figure 18] FIG. 18 is a diagram showing an example of a minimal decision tree generated using the point group shown in FIG. 17. [Figure 19] FIG. 13 is a diagram showing an example of a result of a linear regression analysis. [Figure 20] FIG. 1 shows a breakdown of the severity levels of disease symptoms in expression level data used in the examples of the present invention. [Figure 21]FIG. 1 is a Venn diagram showing the overlap between candidate genes selected in step A, candidate genes selected in step B, and candidate genes selected in step C. [Figure 22] 1 is a graph showing that the expression level of a specific gene changes with the progression of cataract in an example of the present invention. [Figure 23] 1 is a graph showing that the expression level of a specific gene changes with the progression of cataract in an example of the present invention. [Figure 24] 1 is a graph showing that the expression level of a specific gene changes with the progression of cataract in an example of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] An embodiment of the present invention will be described below, but the present invention is not limited thereto. The present invention is not limited to each configuration described below, and various modifications are possible within the scope of the claims, and embodiments and examples obtained by appropriately combining the technical means disclosed in different embodiments and examples are also included in the technical scope of the present invention. In addition, all academic literature and patent documents described in this specification are incorporated herein by reference. In addition, unless otherwise specified in this specification, "A to B" representing a numerical range means "A or more, B or less."

[0023] The present invention can also contribute to achieving Goal 3 of the Sustainable Development Goals (SDGs) advocated by the United Nations, which is to "Ensure good health and promote well-being for all."

[0024] 1. Gene Identification System 100 (Overview of gene identification system 100) First, an overview of a gene identification system 100 according to one embodiment of the present invention will be described. The gene identification system 100 is a system including a gene identification device 1, which is a computer that identifies genes involved in a disease from expression level data of each of a plurality of genes.

[0025] Here, the "expression level data" refers to data in which the expression level of each of a plurality of genes contained in a biological sample collected from each of a plurality of subjects suffering from a disease is associated with the severity level of the disease symptoms of each of the plurality of subjects. The "severity level" is information for evaluating the progression of a disease and the severity of symptoms, and can be defined for each disease. In the case of human diseases, the "severity level" is a clinical index determined based on the diagnosis of a doctor.

[0026] The gene identification device 1 extracts a plurality of pieces of data of interest that satisfy a predetermined condition from the expression level data, executes at least two or more steps capable of selecting a candidate gene, and selects a candidate gene in each of the executed steps. The gene identification device 1 then identifies the candidate gene selected in each of the executed two or more steps as a gene involved in a disease.

[0027] The gene identification system 100 is applicable to any disease in which the expression level of a gene may be associated with the onset and aggravation of the disease. For example, the "subject" may be a mammal, including humans, dogs, and cats, or may be a bird, fish, amphibian, reptile, or insect. Furthermore, the "subject" may be a plant, such as rice, soybean, or vegetable. For example, when the "subject" is a human, the "disease" may be cataract or retinopathy. Furthermore, the "disease" may be cancer, respiratory disease, infectious disease, skin disease, or the like. When the "subject" is a plant, the "disease" may be an infectious disease caused by fungi, viruses, or the like.

[0028] The "gene" may be DNA or RNA. The expression level of the gene may be measured using a microarray. The microarray can comprehensively measure the expression levels of many kinds of genes. Alternatively, the expression level of the gene may be measured using a measurement method using a next-generation sequencer. For example, it is possible to extract mRNA from a tissue containing a large number of cells, and comprehensively measure the abundance of these by a next-generation sequencer.

[0029] (Configuration of gene identification system 100) Next, the configuration of the gene identification system 100 will be described with reference to FIG. 1 and FIG. 2. FIG. 1 is a diagram showing an example of the schematic configuration of the gene identification system 100. FIG. 2 is a diagram showing another example of the schematic configuration of the gene identification system 100. The gene identification system 100 may include a gene identification device 1 and an expression level analysis device 2. The expression level analysis device 2 is a device installed in a medical facility H, and is a device that outputs expression level data including an analysis result obtained by analyzing the expression level of each of a plurality of genes contained in a biological sample collected from each of a plurality of subjects suffering from a disease. The expression level analysis device 2 may be a computer used by a medical professional belonging to the medical facility H. Here, the "medical facility H" may be a hospital that diagnoses and treats the disease of a subject, or may be a testing company that analyzes the sequence of a gene extracted from a biological sample collected from a subject and provides the analysis result to a hospital or the like.

[0030] FIG. 1 shows a case where a gene identification system 100 is applied in a medical facility H by providing a gene identification device 1. In the gene identification system 100 shown in FIG. 1, the gene identification device 1 and an expression level analysis device 2 are connected via a network capable of transmitting information. A network such as a LAN (Local Area Network) or the Internet may be used in the gene identification system 100. The gene identification system 100 may be equipped with one or more expression level analysis devices 2. In the medical facility H to which the gene identification system 100 is applied, genes involved in diseases can be identified using expression level data including analysis results analyzed in the medical facility H.

[0031] In the gene identification system 100, the expression level analysis device 2 may have the functions of the gene identification device 1. For example, a computer program that causes a computer to execute the processes performed by the gene identification device 1 may be installed in the expression level analysis device 2, thereby realizing a single computer that has the functions of the expression level analysis device 2 and the functions of the gene identification device 1.

[0032] Fig. 2 shows a gene identification system 100 including a gene identification device 1 capable of acquiring expression level data from each of a plurality of medical facilities H in which an expression level analysis device 2 is installed. In the gene identification system 100 shown in Fig. 2, the gene identification device 1 and the expression level analysis devices 2 of each medical facility H are connected via a network capable of transmitting information. The gene identification system 100 shown in Fig. 2 can acquire expression level data including the analysis results obtained in each medical facility H, identify genes involved in a disease, and notify each medical institution H that is the provider of the expression level data of the identification results.

[0033] (Configuration of gene identification device 1) Next, the configuration of the gene identifying device 1 will be described with reference to Fig. 3. Fig. 3 is a functional block diagram showing an example of the configuration of the gene identifying device 1.

[0034] As shown in FIG. 3, the gene identification device 1 includes a control unit 10, a storage unit 20, and a communication unit 30 that communicates with the expression level analysis device 2.

[0035] The control unit 10 may be a CPU (Central Processing Unit). The control unit 10 reads a control program, which is software stored in the storage unit 20, and deploys it in a memory such as a RAM (Random Access Memory) to execute various functions. The control unit 10 includes an acquisition unit 11, an extraction unit 12, a selection unit 13, and an identification unit 14.

[0036] The storage unit 20 may store expression level data 21 acquired by the acquisition unit 11 (for example, provided by a medical facility H). The storage unit 20 may also store candidate genes selected by the selection unit 13 as selection results 22. For the sake of simplicity, the control program is omitted from the storage unit 20 shown in FIG. 3.

[0037] The acquisition unit 11 may acquire the expression amount data 21, for example, from the expression amount analysis device 2 of the medical facility H via the communication unit 30, and store it in the storage unit 20. Note that the expression amount data 21 may already be stored in the storage unit 20, and in this case, the acquisition unit 11 is not an essential component.

[0038] The extraction unit 12 extracts a plurality of pieces of interesting data that satisfy a predetermined condition from the expression level data 21. The process performed by the extraction unit 12 will be described later with a specific example.

[0039] The selection unit 13 selects candidate genes by executing at least two or more steps among the following steps A, B, and C. The processing performed by the selection unit 13 will be described later with a specific example. Step A: A step of selecting a predetermined number of candidate genes involved in the aggravation of symptoms based on the results of principal component analysis of multiple pieces of data of interest. Step B: A step of selecting a predetermined number of candidate genes involved in the progression of the disease from multiple pieces of data of interest using a predetermined machine learning model. Step C: A step of selecting a predetermined number of candidate genes involved in the aggravation based on a minimum spanning tree generated based on the results of principal component analysis of multiple pieces of data of interest.

[0040] The selection unit 13 may store information about the candidate genes selected in each executed step in the storage unit 20 as a selection result 22.

[0041] The identification unit 14 identifies the candidate genes selected in any of the two or more steps as genes involved in the disease. The identification unit 14 may provide the identification result to the expression level analysis device 2 via the communication unit 30. The process performed by the identification unit 14 will be described later with a specific example.

[0042] 2. Gene identification method (Processing performed by gene identification device 1) Next, the flow of the process (gene identification method) performed by the gene identification device 1 will be described with reference to Fig. 4. Fig. 4 is a flow chart showing an example of the flow of the process performed by the gene identification device 1. Note that the following description will be given taking as an example a case where the "subject" is a human and the "disease" is cataract.

[0043] The severity (and progression) of cataract can be evaluated based on the degree of cortical cataract (COR), nuclear cataract (NUC), posterior subcapsular cataract (PSC), etc. In the following, the case where the LOCSIII (Lens Opacities Classification System III) classification is applied as the "severity level" will be described as an example, but is not limited thereto. For example, the WHO classification may be applied as the severity level of cataract.

[0044] First, the extraction unit 12 extracts a plurality of pieces of interesting data whose severity levels satisfy a predetermined condition from the expression amount data 21 (step S1: extraction step). The expression amount data 21 is associated with the expression amount of each of a plurality of genes contained in a biological sample collected from each of a plurality of subjects suffering from a disease, and the LOCSIII classification of the symptoms of the disease of each of the subjects. In the LOCSIII classification, the least severe grade is C1, and the most severe grade is C5. Hereinafter, the grades in the LOCSIII classification will be referred to as "LOCS grades".

[0045] Next, the selection unit 13 selects candidate genes involved in the aggravation of the disease by performing at least two or more steps among the above-mentioned steps A to C (step S2: selection step).

[0046] Next, the identifying unit 14 identifies the candidate genes selected in any of the two or more steps executed by the selecting unit 13 as genes that may be involved in the onset of the disease (Step S3: Identifying step). The selection unit 13 may execute a known step D in addition to at least two steps among steps A to C. Step D: A step of selecting, as candidate genes, genes whose expression levels change significantly in accordance with changes in the severity of the disease in the expression level data 21.

[0047] According to the above configuration, the gene identification device 1 executes two or more steps capable of selecting candidate genes based on different principles, and identifies the candidate genes selected in any of the two or more executed steps as genes that may be involved in the onset of a disease. This allows the gene identification device 1 to accurately identify genes that may be involved in the onset of a disease.

[0048] <Processing performed by extraction unit 12> Next, the process performed by the extraction unit 12 in step S1 in Fig. 4 will be described using Fig. 5 with reference to Figs. 6 to 9. Fig. 5 is a flowchart showing an example of the flow of the process performed by the extraction unit 12.

[0049] The extraction unit 12 first determines the coordinates of the expression level data 21 of each of the multiple subjects in a two-dimensional coordinate system in which the first and second principal components obtained by principal component analysis of the expression level data 21 are the X and Y coordinates, respectively (step S11).

[0050] Fig. 6 is a diagram showing an example of a plot of expression level data 21 in a two-dimensional coordinate system in which the first and second principal components obtained by principal component analysis of the expression level data 21 of each of a plurality of subjects are set as the X and Y coordinates, respectively. Fig. 6 shows a point group with LOCS grades of C1 to C5.

[0051] Next, the extraction unit 12 classifies the expression level data 21 of each of the multiple subjects into multiple groups corresponding to the severity levels, and calculates the center coordinates for each of the multiple groups (step S12). Figure 7 is a diagram showing an example of the state in which the center coordinates of each of the multiple groups are plotted in the same two-dimensional coordinate system as in Figure 6.

[0052] Next, the extraction unit 12 analyzes the correlation between the first principal component value (reference value) obtained by principal component analysis of the distance matrix indicating the distance between the center coordinates of the multiple sets and the severity level of the set (step S13). Then, the extraction unit 12 analyzes (1) the magnitude relationship between the first principal component value of the set with the lowest severity level and the first principal component value of the set with the second lowest severity level, and (2) the magnitude relationship between the first principal component value of the set with the nth lowest severity level (n is an integer of 2 or more) and the first principal component value of the set with the n+1th lowest severity level (n is an integer of 2 or more). Then, when the magnitude relationship between the two is different, the extraction unit 12 extracts the expression amount data included in the sets from the lowest severity level to the nth lowest severity level as multiple pieces of interest data.

[0053] 8 is a diagram showing an example of a state in which the first principal component and the LOCS grade obtained by principal component analysis of the distance matrix indicating the distance between the center coordinates of a plurality of sets are plotted in a two-dimensional coordinate system with the X coordinate and the Y coordinate, respectively. In this case, the extraction unit 12 analyzes the following (i) to (iv) and compares the magnitude relationship in (i) with the magnitude relationship in each of (ii) to (iv).

[0054] (i) The magnitude relationship between the first principal component value of the set whose LOCS grade is C1 and the first principal component value of the set whose LOCS grade is C2.

[0055] (ii) The magnitude relationship between the first principal component value of the set whose LOCS grade is C2 and the first principal component value of the set whose LOCS grade is C3.

[0056] (iii) The magnitude relationship between the first principal component value of the set whose LOCS grade is C3 and the first principal component value of the set whose LOCS grade is C4.

[0057] (iv) The magnitude relationship between the first principal component value of the set whose LOCS grade is C4 and the first principal component value of the set whose LOCS grade is C5.

[0058] In the example shown in FIG. 8, in (i), the first principal component value is decreasing (i.e., C1 > C2), whereas in (ii), the first principal component value is increasing (i.e., C2 < C3). Therefore, the extraction unit 12 extracts, as a plurality of target data, the set with the LOCS grade of C1 and the set with the LOCS grade of C2 combined. That is, the extraction unit 12 extracts, as a plurality of target data satisfying a predetermined condition, only the set in which the first principal component value monotonically increases or decreases from the first principal component value of the set with the LOCS grade of C1. The plurality of target data extracted in this way includes the expression level data 21 with the LOCS grades of C1 and C2, and satisfies a predetermined condition that the first principal component value monotonically increases or decreases. FIG. 9 is a diagram showing an example of a state in which the extracted plurality of target data is plotted in the same two-dimensional coordinate system as in FIG. 6.

[0059] According to the above configuration, the gene identification device 1 extracts, as target data, the expression level data in which the first principal component value monotonically increases or decreases as the symptoms of the disease worsen after the onset of the disease. Thereby, the gene identification device 1 can accurately select candidate genes involved in the aggravation of the disease by subjecting only the extracted target data to the next selection step.

[0060] <Step A> Here, step A will be described with reference to FIGS. 11 to 13 and using FIG. 10. FIG. 10 is a flowchart showing an example of the processing flow in step A.

[0061] When executing the step A, the selection unit 13 first classifies a plurality of pieces of attention data into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of pieces of attention data (step S201). FIG. 11 is a diagram showing an example of a state in which a plurality of pieces of attention data are plotted in a two-dimensional coordinate system in which the first and second principal components obtained by a principal component analysis of a plurality of pieces of attention data having LOCS grades of C1 to C2 are set as the X and Y coordinates, respectively. In FIG. 11, the pieces of attention data having a LOCS grade of C1 are classified into three groups, "C1-1" to "C1-3", and the pieces of attention data having a LOCS grade of C2 are classified into three groups, "C2-1" to "C2-3". As shown in FIG. 11, "C1-3" and "C2-1" have a high degree of overlap, so they may be regarded as one group, "C1-3+C2-1".

[0062] Next, the selection unit 13 determines the virtual permutation of each of the groups based on the magnitude of the determined first principal component value (step S202). In the example shown in Fig. 11, the first principal component value increases in the order of "C1-1", "C1-2", "C1-3+C2-1", "C2-2", and "C2-3". In this case, the selection unit 13 determines "C1-1" as the first virtual permutation, "C1-2" as the second virtual permutation, "C1-3+C2-1" as the third virtual permutation, "C2-2" as the fourth virtual permutation, and "C2-3" as the fifth virtual permutation.

[0063] Next, the selection unit 13 selects a predetermined number of genes whose expression levels increase or decrease according to the virtual permutation as candidate genes (step S203). Fig. 12 is a diagram explaining the characteristics of genes selected as candidate genes by the selection unit 13. As shown in Fig. 12, the selection unit 13 selects genes whose expression levels increase monotonically in accordance with the virtual permutation and genes whose expression levels decrease monotonically in accordance with the virtual permutation as candidate genes. Fig. 13 is a diagram showing an example of candidate genes selected by step A.

[0064] In step S203, a predetermined number of genes whose expression levels increase or decrease according to the hypothetical permutation and whose factor loadings obtained by principal component analysis of multiple data of interest are equal to or greater than a predetermined value may be selected as candidate genes.

[0065] Thus, in step A, data with no original difference in LOCS grade is considered to be an intrinsic progression of severity, although not reflected in the LOCS grade, and classified into a predetermined number of groups, and a virtual permutation of each group is determined. In other words, the virtual permutation can be considered to mean the virtual progression of severity, and genes whose expression level monotonically increases or decreases according to the virtual permutation are genes whose expression level changes according to the virtual progression of severity.

[0066] <Process B> Here, the process B will be described using Fig. 14 while referring to Fig. 15. Fig. 14 is a flowchart showing an example of the processing flow in the process B.

[0067] When executing the step B, the selection unit 13 first classifies the plurality of pieces of data of interest into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of pieces of data of interest (step S204).

[0068] Next, the selection unit 13 determines a virtual permutation of each of the groups based on the magnitude of the determined first principal component value (step S205). The processing in steps S204 to S205 may be the same as the processing in steps S201 to S202 in FIG.

[0069] Next, the selection unit 13 inputs the plurality of pieces of attention data to which the virtual labels indicating the virtual permutations are added to a predetermined classification model, and classifies the plurality of pieces of attention data into a predetermined number of groups (step S206). Here, the predetermined classification model may be a known classifier, for example, a support vector machine (SVM). Note that the virtual label indicating the virtual permutation may be the same as the virtual permutation determined in step S202 of process A.

[0070] Next, the selection unit 13 selects a predetermined number of genes whose expression levels linearly increase or decrease according to the virtual permutation as candidate genes (step S207). Fig. 15 is a diagram showing an example of the candidate genes selected by the step B.

[0071] Step B is the same as step A in that it classifies data with the same LOCS grade into a predetermined number of groups and determines a virtual permutation of each group, on the assumption that even data with no original difference in LOCS grade contains data with inherently different severity. However, it differs from step A in that a classification model is used to classify multiple pieces of data of interest into a predetermined number of groups. Therefore, in step B, candidate genes different from those selected in step A may be selected.

[0072] Here, the selection unit 13 selects candidate genes using a classification model that classifies a plurality of pieces of attention data to which virtual labels indicating virtual permutations are assigned into a predetermined number of groups, but the present invention is not limited to this configuration. For example, the selection unit 13 may select candidate genes from a plurality of pieces of attention data to which virtual labels indicating virtual permutations are assigned using a learned regression model (for example, based on the magnitude of the regression error) so as to minimize the regression error. That is, the selection unit 13 inputs a plurality of pieces of attention data to which virtual labels indicating virtual permutations are assigned into a predetermined machine learning model, and selects a predetermined number of genes whose expression levels linearly increase or decrease according to the virtual permutation as candidate genes.

[0073] <Process C> Here, the process C will be described using Fig. 16 while referring to Figs. 17 to 19. Fig. 16 is a flow chart showing an example of the processing flow in the process C.

[0074] When executing step C, the selection unit 13 first generates a minimum spanning tree using a point cloud obtained by plotting points corresponding to each of the plurality of pieces of attention data in a two-dimensional coordinate system in which the first principal component value and the second principal component value determined by the principal component analysis of the plurality of pieces of attention data are the X coordinate and the Y coordinate, respectively (step S208). Fig. 17 is a diagram showing an example of a state in which the plurality of pieces of attention data are plotted in a two-dimensional coordinate system in which the first principal component and the second principal component obtained by the principal component analysis of the plurality of pieces of attention data are the X coordinate and the Y coordinate, respectively. Fig. 18 is a diagram showing an example of a minimum decision tree generated using the point cloud shown in Fig. 17.

[0075] In step S208, the selection unit 13 analyzes the following (1) and (2).

[0076] (1) The tendency for the first principal component value to increase or decrease when moving from the central coordinate of the point cloud with the lowest severity level to the central coordinate of the point cloud with the second lowest severity level.

[0077] (2) The tendency of the first principal component value to increase or decrease when moving from the central coordinate of a point group having the nth (n is an integer of 2 or more) lowest severity level to the central coordinate of a point group having the n+1th (n is an integer of 2 or more) lowest severity level. Then, if the two tendencies of increase and decrease are different, the selection unit 13 may generate a minimum spanning tree by deleting point groups corresponding to data ranging from the point group with the lowest severity level (n+1) (n is an integer of 2 or greater) to the point group with the highest severity level from the point group obtained by plotting the expression level data in a two-dimensional coordinate system.

[0078] Next, the selection unit 13 selects, as a starting point, a point having the lowest severity level among the end points included in the minimum spanning tree. For example, the selection unit 13 selects, as a starting point, the end point P1 in the minimum decision tree shown in FIG. 18 (step S209).

[0079] Next, the selection unit 13 performs a linear regression analysis on the correlation between the following (1) and (2) (step S210).

[0080] (1) The distance traveled when moving along the minimum spanning tree from the starting point to another point included in the minimum spanning tree.

[0081] (2) The amount of change between the expression level of each of a plurality of genes corresponding to the starting point and the expression level of each of a plurality of genes corresponding to another point. Fig. 19 is a diagram showing an example of the results of linear regression analysis, in which the horizontal axis represents the migration distance and the vertical axis represents the expression level (after standardization transformation).

[0082] Next, the selection unit 13 selects a predetermined number of candidate genes involved in the progression of the disease based on the absolute value of the slope of the regression line obtained by the linear regression analysis from among genes whose coefficient of determination in the linear regression analysis is equal to or greater than a predetermined threshold value (step S211).

[0083] In step C, the degree to which the symptoms corresponding to the starting point (i.e., the symptoms are considered to be the worst) have become severe to symptoms corresponding to other points is quantified as the distance traveled from the starting point to the other points, and genes whose expression levels change according to the distance traveled are selected as candidate genes. Thus, since step C is a step different from steps A and B, candidate genes different from those selected in steps A and B may be selected.

[0084] The values ​​of all genes in the expression level data (and the data of interest) may be standardized and transformed, and the standardized values ​​may be used for the principal component analysis. As an example, the standardization is performed according to the following procedure.

[0085] (i) Calculate the logarithm of each value of the expression level data. The logarithm may be a natural logarithm or a common logarithm.

[0086] (ii) For each gene, the calculated logarithm was transformed so that the mean value was “0” and the variance was “1”.

[0087] The expression level data values ​​may differ greatly in mean and variance for each gene. If such expression level data is used as is to compare the expression levels of each gene, the expression level data values ​​cannot be compared. By using standardized values ​​as expression levels, the mean and variance of each expression level data value can be made uniform, making it easier to compare the expression levels of genes. The "expression level" in the graph shown in Figure 19 is the standardized expression level data value.

[0088] 3. Method for obtaining data for diagnosing cataracts, and marker for diagnosing cataracts A method for acquiring data for diagnosing cataracts according to one embodiment of the present invention includes: detecting, in a biological sample collected from a subject, a number of genes that are involved in the diagnosis of cataracts, including a CDC42EP1 (official name: CDC42 effector protein 1) gene, an ARTN (official name: Artemin) gene, an XKR8 (official name: XK related 8) gene, an ACTG2 (official name: Actin Gamma 2) gene, an RPS6KA1 (official name: Ribosomal protein S6 kinase A1) gene, an SLC16A11 (official name: Solute carrier family 16 member 11) gene, a PLEKHG6 (official name: Pleckstrin homology and RhoGEF domain containing G6) gene, a TMBIM1 (official name: Transmembrane BAX inhibitor motif containing 1) gene, an AIFM3 (official name: Apoptosis inducing factor mitochondria associated 3) gene, a LDLRAD2 (official name: Low density lipoprotein receptor class A domain containing 2) gene, COL1A1 (formal name: Collagen type I alpha 1 chain) gene, CARD10 (formal name: Caspase recruitment domain family member 10) gene, ACHE (formal name: Acetylcholinesterase) gene, NUDT8 (formal name: Nudix hydrolase 8) gene, LRRC30 (formal name: Leucine rich repeat containing 30) gene, ADAMTS13 (formal name: ADAM metallopeptidase with thrombospondin type 1 motif 13) gene, KRTAP10-6 (formal name: Keratin associated protein 10-6) gene, BZRAP1 (formal name: Benzodiazapine Receptor Peripheral-Associated Protein 1) gene, IQCF5 (formal name: IQ motifcontaining F5) gene, GGT6 (official name: Gamma-glutamyltransferase 6) gene, PLEKHA4 (official name: Pleckstrin homology domain containing A1) gene, OBP2B (official name: Odorant binding protein 2B) gene, LILRA5 (official name: Leukocyte immunoglobulin like receptor A5) gene, SH3TC1 (official name: SH3 domain and tetratricopeptide repeats 1) gene, HHAT (official name: Hedgehog The method includes a step of measuring the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26) selected from the group consisting of the RP11-9L18.3 gene, the RP11-9L18.3 gene, and the RP11-9L18.3 gene.

[0089] As described below, the above genes are genes that have been identified as genes involved in the progression of cataracts (in other words, genes whose expression levels change with the progression of cataracts). Therefore, according to the above configuration, data for diagnosing cataracts can be obtained.

[0090] From the standpoint of obtaining more accurate data for diagnosing cataracts, in the step of measuring the expression level of a gene, it is preferable to measure the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) selected from the group consisting of the XKR8 gene, ACTG2 gene, SLC16A11 gene, PLEKHG6 gene, LRRC30 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, and LILRA5 gene.

[0091] In this specification, the term "cataract" refers to a disease that generally causes opacity in the crystalline lens. The opacity caused by cataract in the crystalline lens includes, for example, cortical opacity, nuclear opacity, posterior subcapsular opacity, cortical spoke-like opacity, anterior subcapsular opacity, fibrous folds, aqueous spaces, perinuclear punctate opacity under transillumination, blisters, punctate opacity, and coronal opacity.

[0092] The above-mentioned cataract is not limited, and examples thereof include age-related cataract, juvenile cataract, diabetic cataract, congenital cataract, traumatic cataract, and atopic cataract. More specifically, the above-mentioned cataract is, for example, cortical cataract, posterior subcapsular cataract, anterior subcapsular cataract, and nuclear cataract.

[0093] Generally, in most cases of age-related cataract, cortical cataract occurs in the early stage, and then progresses to nuclear cataract. According to the method for acquiring data for diagnosing cataract according to one embodiment of the present invention, data for diagnosing cortical cataract, which is the early stage, can be acquired. The data can be extremely useful for early diagnosis and early treatment of cataract.

[0094] The biological sample is not limited to, but may include, for example, a lens (more specifically, the cortex, the anterior capsule, the posterior capsule, the nucleus, or a mixture thereof).

[0095] If the cortex and / or tissue near the cortex of the crystalline lens is used as the biological sample, data that allows for a highly accurate diagnosis of cortical cataract can be obtained. If the posterior capsule and / or tissue near the posterior capsule of the crystalline lens is used as the biological sample, data that allows for a highly accurate diagnosis of posterior subcapsular cataract can be obtained. If the anterior capsule and / or tissue near the anterior capsule of the crystalline lens is used as the biological sample, data that allows for a highly accurate diagnosis of anterior subcapsular cataract can be obtained. If the nucleus and / or tissue near the nucleus of the crystalline lens is used as the biological sample, data that allows for a highly accurate diagnosis of nuclear cataract can be obtained.

[0096] The subject may include, but is not limited to, humans and non-human animals (e.g., livestock, pets, and laboratory animals). Non-human animals may include, for example, monkeys, chimpanzees, cows, pigs, sheep, goats, horses, dogs, cats, rabbits, mice, and rats.

[0097] In the measuring step, the expression level of at least one gene selected from the 26 genes is measured.

[0098] In the measurement step, the expression level of each gene may be measured based on the expression level of mRNA, or may be measured based on the expression level of protein. When the expression level of each gene is measured based on the expression level of mRNA, the measurement step can be performed based on a known method such as RT-PCR or Northern blotting. When the expression level of each gene is measured based on the expression level of protein, the measurement step can be performed based on a known method such as ELISA or Western blotting.

[0099] The expression levels of certain genes among the above genes (specifically, CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene) increase with the progression of cataract. Therefore, by comparing the expression level of each gene measured in the above measurement step with the control expression level, cataract (e.g., the progression of cataract) can be determined.

[0100] The control expression level can be, for example, the expression level of each gene previously measured in patients with cataracts and / or the expression level of each gene previously measured in healthy individuals without cataracts.

[0101] For example, the expression level of each gene measured in the measurement step is "A", and the expression level of each gene previously measured in a healthy individual without cataract is "B". When the value of "A / B" is, for example, 1.5 or more, 2.0 or more, 3.0 or more, 5.0 or more, or 10.0 or more, it can be determined that the subject suffers from cataract (or that cataract is progressing in the subject). The upper limit of the value of "A / B" is not limited, and may be, for example, 10.0 or 20.0.

[0102] For example, the expression level of each gene measured in the measurement step is "A", and the expression level of each gene previously measured in the patient with cataract is "C". When the "A / C" value is, for example, 1.0 or more, it can be determined that the subject suffers from cataract (or that cataract is progressing in the subject). The upper limit of the "A / C" value is not limited, and may be, for example, 5.0 or 10.0.

[0103] The expression level of a specific gene (specifically, OBP2 gene) among the above genes decreases with the progression of cataract. Therefore, by comparing the expression level of each gene measured in the above measurement step with the control expression level, cataract (e.g., the progression of cataract) can be determined.

[0104] The control expression level can be, for example, the expression level of each gene previously measured in patients with cataracts and / or the expression level of each gene previously measured in healthy individuals without cataracts.

[0105] For example, the expression level of each gene measured in the measurement step is "A'", and the expression level of each gene previously measured in a healthy individual without cataract is "B'". When the value of "A' / B'" is, for example, less than 1.0, 0.8 or less, 0.6 or less, 0.4 or less, or 0.2 or less, it can be determined that the subject suffers from cataract (or that cataract is progressing in the subject). The lower limit of the value of "A' / B'" is not limited and may be, for example, 0, 0.1, or 0.01.

[0106] For example, the expression level of each gene measured in the measurement step is "A'", and the expression level of each gene previously measured in the patient who has developed cataract is "C'". When the value of "A' / C'" is, for example, 1.0 or less, it can be determined that the subject suffers from cataract (or that cataract is progressing in the subject). The lower limit of the value of "A' / C'" is not limited, and may be, for example, 0.01, 0.1, or 0.5.

[0107] As described above, in the method for acquiring data for diagnosing cataract according to one embodiment of the present invention, cataract can be diagnosed based on the expression levels of 26 types of genes. In other words, the above 26 types of genes can be used as markers for diagnosing cataract.

[0108] Therefore, a marker for diagnosing cataracts in one embodiment of the present invention includes at least one gene (e.g., at least 3, at least 5, at least 7, at least 10, at least 13, at least 15, at least 17, at least 20, at least 23, at least 25, or 26) selected from the group consisting of the CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene.

[0109] In one embodiment of the present invention, the marker for diagnosing cataracts preferably includes at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) selected from the group consisting of the XKR8 gene, ACTG2 gene, SLC16A11 gene, PLEKHG6 gene, LRRC30 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, and LILRA5 gene.

[0110] 4. Screening method for therapeutic or preventive agents for cataracts In the following, the description of the configurations described in the above section [2. Method of acquiring data for diagnosing cataracts, and markers for diagnosing cataracts] will be omitted.

[0111] A method for screening a therapeutic or preventive agent for cataract according to one embodiment of the present invention includes the steps of: administering a candidate substance for a therapeutic or preventive agent for cataract to a non-human model organism; and detecting a candidate substance for a therapeutic or preventive agent for cataract in the non-human model organism after the administration step, the candidate substance for a therapeutic or preventive agent for cataract, the CDC42EP1 gene, the ARTN gene, the XKR8 gene, the ACTG2 gene, the RPS6KA1 gene, the SLC16A11 gene, the PLEKHG6 gene, the TMBIM1 gene, the AIFM3 gene, the LDLRAD2 gene, the COL1A1 gene, the CARD10 gene, the ACHE gene, the NUDT8 gene, the LRRC30 gene, the ADAMTS13 gene, the KRTAP10-6 gene, the BZRAP1 gene, the IQCF5 gene, the GGT The method includes a measuring step of measuring the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26) selected from the group consisting of the C11-9L18.6 gene, the C11-9L18.3 gene, the C11-9L18.3 gene, the C11-9L18.4 gene, the C11-9L18.5 gene, the C11-9L18.6 ...3 gene, and a determining step of determining whether or not the candidate substance is a therapeutic or preventive agent for cataracts by comparing the expression level of each gene measured in the measuring step with a control expression level.

[0112] As described below, the above genes are genes that have been identified as genes involved in the progression of cataracts (in other words, genes whose expression levels change with the progression of cataracts). Therefore, according to the above configuration, it is possible to screen for therapeutic or preventive agents for cataracts.

[0113] From the viewpoint of screening therapeutic or preventive agents for cataracts with greater accuracy, in the measurement step of measuring the expression level of a gene, it is preferable to measure the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) selected from the group consisting of the XKR8 gene, ACTG2 gene, SLC16A11 gene, PLEKHG6 gene, LRRC30 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, and LILRA5 gene.

[0114] As used herein, the term "therapeutic agent" refers to a drug that provides a therapeutic effect. The above-mentioned therapeutic effect refers to, but is not limited to, the following examples: (1) Reducing the severity of one or more symptoms associated with a disease compared to when the therapeutic agent is not administered; (2) Preventing the increase in severity or progression of one or more symptoms associated with a disease, as compared to when the therapeutic agent is not administered; (3) Reducing the rate of increase in severity or progression of one or more symptoms associated with a disease, as compared to when the therapeutic agent is not administered. The one or more symptoms associated with the disease may be systemic or local.

[0115] In the present specification, the term "prophylactic agent" refers to a drug that provides a prophylactic effect. The above-mentioned prophylactic effect refers to the effects exemplified below, but is not limited thereto. (1) Preventing the onset of one or more symptoms related to a disease or reducing the risk compared to when the prophylactic agent is not administered; (2) Preventing the recurrence of one or more symptoms related to a disease or reducing the risk compared to when the prophylactic agent is not administered; (3) Preventing the onset of one or more symptoms related to a disease or reducing the risk compared to when the prophylactic agent is not administered. The one or more symptoms related to the disease may be systemic or local.

[0116] The non-human model organism is not limited to, and examples thereof include induced cataract model organisms (e.g., UV-A induced cataract model organisms, selenium induced cataract model organisms, L-buthionine-(S,R)-sulfoximine induced cataract model organisms, and hyperbaric oxygen induced cataract model organisms) and hereditary cataract models (e.g., Shumiya Cataract Rat (SRC), Ihara Cataract Rat (ICR)). The non-human model organism may be the organism itself, or tissues or cells collected from the organism.

[0117] The candidate substance for the therapeutic or preventive agent for cataract is not limited, and may be, for example, an organic compound or an inorganic compound.

[0118] In the administration step, the candidate substance is administered to the non-human model organism. The specific configuration of the administration step is not limited, and for example, (i) the candidate substance may be administered to the non-human model organism itself (e.g., parenteral administration (e.g., ophthalmic administration, intraconjunctival administration, intravitreal administration, subconjunctival administration, sub-Tenon administration, intraperitoneal administration), oral administration, transdermal administration, transmucosal administration, intravenous administration), or (ii) the candidate substance may be added to a medium in which tissues or cells collected from the non-human model organism are cultured.

[0119] In the measurement step, the expression level of each gene may be measured based on the expression level of mRNA, or may be measured based on the expression level of protein. When the expression level of each gene is measured based on the expression level of mRNA, the measurement step can be performed based on a known method such as RT-PCR or Northern blotting. When the expression level of each gene is measured based on the expression level of protein, the measurement step can be performed based on a known method such as ELISA or Western blotting.

[0120] In the determination step, the expression level of each gene measured in the measurement step is compared with a control expression level to determine whether or not the candidate substance is a therapeutic or preventive agent for cataract.

[0121] The control expression level can be, for example, the expression level of each gene previously measured in a non-human model organism that has developed cataracts, and / or the expression level of each gene previously measured in a non-human model organism that has not developed cataracts.

[0122] The expression levels of certain genes among the above genes (specifically, CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene) increase with the progression of cataract. Therefore, in the determination step, the expression levels of each gene measured in the measurement step are compared with the control expression levels to determine whether the candidate substance is a therapeutic or preventive agent for cataract.

[0123] The control expression level can be, for example, the expression level of each gene previously measured in a non-human model organism (in other words, a non-human model organism that has developed cataracts) and / or the expression level of each gene previously measured in a wild-type non-human model organism (in other words, a non-human model organism that has not developed cataracts).

[0124] For example, the expression level of each gene measured in the measurement step is designated as "D," and the expression level of each gene previously measured in a wild-type non-human model organism is designated as "E." When the value of "D / E" is, for example, 0.8 to 1.2, 0.9 to 1.1, or approximately 1.0, the candidate substance can be determined to be a therapeutic or preventive agent for cataracts.

[0125] For example, the expression level of each gene measured in the measurement step is "D", and the expression level of each gene previously measured in the non-human model organism (in other words, a non-human model organism that has developed cataract) is "F". When the value of "D / F" is, for example, less than 1.0, 0.8 or less, 0.6 or less, 0.4 or less, or 0.2 or less, it can be determined that the candidate substance is a therapeutic or preventive agent for cataract. The lower limit of "D / F" is not limited, and may be, for example, 0, 0.1, or 0.01.

[0126] The expression level of a specific gene (specifically, OBP2 gene) among the above genes decreases with the progression of cataract. Therefore, in the determination step, the expression level of each gene measured in the measurement step is compared with the control expression level, thereby determining whether the candidate substance is a therapeutic or preventive agent for cataract.

[0127] The control expression level can be, for example, the expression level of each gene previously measured in a non-human model organism (in other words, a non-human model organism that has developed cataracts) and / or the expression level of each gene previously measured in a wild-type non-human model organism (in other words, a non-human model organism that has not developed cataracts).

[0128] For example, the expression level of each gene measured in the measurement step is designated as "D'," and the expression level of each gene previously measured in a wild-type non-human model organism is designated as "E'." When the value of "D' / E'" is, for example, 0.8 to 1.2, 0.9 to 1.1, or approximately 1.0, the candidate substance can be determined to be a therapeutic or preventive agent for cataracts.

[0129] For example, the expression level of each gene measured in the measurement step is designated as "D'", and the expression level of each gene previously measured in the non-human model organism (in other words, a non-human model organism that has developed cataract) is designated as "F'". When the value of "D' / F'" is, for example, 1.5 or more, 2.0 or more, 3.0 or more, 5.0 or more, or 10.0 or more, it can be determined that the candidate substance is a therapeutic or preventive agent for cataract. The upper limit of "D' / F'" is not limited, and may be, for example, 10.0 or 20.0.

[0130] [5. Other] <1> In a subject (e.g., a human or non-human animal), the CDC42EP1 gene, the ARTN gene, the XKR8 gene, the ACTG2 gene, the RPS6KA1 gene, the SLC16A11 gene, the PLEKHG6 gene, the TMBIM1 gene, the AIFM3 gene, the LDLRAD2 gene, the COL1A1 gene, the CARD10 gene, the ACHE gene, the NUDT8 gene, the LRRC30 gene, the ADAMTS13 gene, the KRTAP10-6 gene, the BZRAP1 gene, the IQCF5 gene, the GGT6 A method for diagnosing cataracts, comprising the step of measuring the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26) selected from the group consisting of the CHO gene, the PLEKHA4 gene, the OBP2B gene, the LILRA5 gene, the SH3TC1 gene, the HHAT gene, and the RP11-9L18.3 gene.

[0131] <2> In the above step, the expression level of at least one gene (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) selected from the group consisting of the XKR8 gene, the ACTG2 gene, the SLC16A11 gene, the PLEKHG6 gene, the LRRC30 gene, the IQCF5 gene, the GGT6 gene, the PLEKHA4 gene, the OBP2B gene, and the LILRA5 gene is measured. <1> A method for diagnosing cataracts according to claim 1.

[0132] <3> The cataract is a cortical cataract, a posterior subcapsular cataract, an anterior subcapsular cataract, a nuclear cataract, an age-related cataract, a juvenile cataract, a diabetic cataract, a congenital cataract, a traumatic cataract, or an atopic cataract. <1> or <2> A method for diagnosing cataracts according to claim 1.

[0133] [5. Software implementation example] The functions of the gene identification device 1 (hereinafter referred to as the "device") can be realized by a program for causing a computer to function as the device, and a program for causing a computer to function as each control block of the device (particularly each part included in the control unit 10).

[0134] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program to realize each function described in each of the above embodiments.

[0135] The program may be non-transitory and may be recorded in one or more computer-readable recording media. The recording media may or may not be included in the system and device. In the latter case, the program may be provided to the device via any wired or wireless transmission medium.

[0136] In addition, some or all of the functions of each of the control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of the present disclosure. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0137] Furthermore, each process described in each of the above embodiments may be executed by AI (Artificial Intelligence). In this case, the AI ​​may be executed by the control device or another device (for example, an edge computer or a cloud server). EXAMPLES

[0138] <1. Identification of genes involved in cataracts> Genes involved in cataract were identified using expression level data 21 of 62 patients suffering from cataract, for which expression level analysis using a microarray was performed. FIG. 20 is a diagram showing the breakdown of the severity levels of disease symptoms in the expression level data 21 used in the examples of the present invention. In this example, a total of 50 pieces of data corresponding to LOCS grades C1 to C3 were extracted as data of interest, and steps A, B, and C were performed. In each of steps A, B, and C, 100 candidate genes were selected.

[0139] Fig. 21 is a Venn diagram showing the overlap of the candidate genes selected in step A, the candidate genes selected in step B, and the candidate genes selected in step C. From the results shown in Fig. 21, the 26 candidate genes selected in all of steps A, B, and C were identified as genes involved in cataracts.

[0140] In addition, the 47 candidate genes selected in both steps A and B, the 31 candidate genes selected in both steps B and C, and the 51 candidate genes selected in both steps A and C may be identified as genes involved in cataracts.

[0141] <2. Analysis of gene expression changes in cataract model organisms> We confirmed whether the expression levels of 26 genes (CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene (in other words, Lcn4 gene), LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene) identified as genes whose expression levels change in human cataract (cortical cataract) patients were also changed with the progression of cataract in a cataract model organism (specifically, Ihara Cataract Rat (ICR)). In addition, the rats used as a model organism for cataracts do not have five genes that exist in humans (NUDT8 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, and RP11-9L18.3 gene). Therefore, in the following test, the other 21 genes were tested.

[0142] First, lenses were extracted from each of (i) wild-type rats (4 weeks old), (8 weeks old), and (10 weeks old), (ii) 4-week-old ICR rats, (iii) 8-week-old ICR rats, and (iv) 10-week-old ICR rats. Three rats were prepared for each of the above (i) to (iv).

[0143] Total RNA was purified from the extracted lenses using a commercially available kit, following the protocol attached to the kit.

[0144] Using a commercially available kit and primers specific to each of the 21 genes, RT-qPCR was performed to quantify the expression levels of each of the 21 genes in the rats (i) to (iv) above. The specific method of RT-qPCR followed the protocol attached to the kit.

[0145] For each of the 21 genes, the ratio of the expression level in rats (ii) (3 rats), (iii) (3 rats), and (iv) (3 rats) to the expression level in rats (i) (3 rats) for comparison was calculated.

[0146] The calculation results are shown in Figures 22 to 24. In the CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene, the expression levels increased with the progression of cataract (for example, in cortical cataract, which is the early stage of cataract). On the other hand, the expression level of the OBP2 gene (in other words, the Lcn4 gene) decreased with the progression of cataract (for example, in cortical cataract, which is the early stage of cataract). [Industrial Applicability]

[0147] The present invention can be used in the treatment, prevention and diagnosis of diseases. [Explanation of symbols]

[0148] 1 Gene identification device 12 Extraction part 13 Selection Department 14 Specific part S1, S11~S14 Extraction process S2 Selection Process Steps S201 - S203, Process A Steps S204 - S207, Process B Steps S208 - S211, Process C S3 Specific Process

Claims

1. an extraction step of extracting a plurality of pieces of data of interest that satisfy a predetermined condition from expression level data in which the expression level of each of a plurality of genes contained in biological samples collected from each of a plurality of subjects suffering from a disease is associated with the severity level of a symptom of the disease for each of the plurality of subjects; A selection process that executes at least two or more of the following processes: (1) a process A of selecting a predetermined number of candidate genes involved in the aggravation of the symptoms based on a result of a principal component analysis of the plurality of pieces of data of interest; (2) a process B of selecting a predetermined number of candidate genes involved in the aggravation from the plurality of pieces of data of interest using a predetermined machine learning model; and (3) a process C of selecting a predetermined number of candidate genes involved in the aggravation based on a minimum spanning tree generated based on a result of the principal component analysis of the plurality of pieces of data of interest; an identifying step of identifying the candidate gene selected in any of the two or more steps as a gene involved in the disease; A method for identifying a gene, comprising:

2. In the extraction step, determining coordinates of the expression level data of each of the subjects in a two-dimensional coordinate system in which a first principal component and a second principal component obtained by principal component analysis of the expression level data are set as X and Y coordinates, respectively; classifying the expression level data of each of the plurality of subjects into a plurality of sets corresponding to the severity levels, and calculating central coordinates for each of the plurality of sets; Analyzing a correlation between a reference value indicating a first principal component value obtained by principal component analysis of a distance matrix indicating the distance between the center coordinates of the plurality of sets and the severity level of the set; When the magnitude relationship between the reference value of the set having the lowest severity level and the reference value of the set having the second lowest severity level is different from the magnitude relationship between the reference value of the set having the nth lowest severity level and the reference value of the set having the n+1th lowest severity level, extracting the expression amount data included in the sets having the lowest severity level to the set having the nth lowest severity level as the plurality of pieces of interest data; n is an integer of 2 or more; The method for identifying a gene according to claim 1.

3. In the step A, classifying the plurality of pieces of data of interest into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of pieces of data of interest; determining a hypothetical permutation of each of the groups based on the magnitude of the determined first principal component value, and selecting a predetermined number of genes whose expression levels increase or decrease according to the hypothetical permutation as the candidate genes; The method for identifying a gene according to claim 1 or 2.

4. In the step B, Dividing the plurality of pieces of data of interest into a predetermined number of groups based on a first principal component value determined by a principal component analysis of the plurality of pieces of data of interest; determining a virtual permutation for each of the predetermined number of groups, the virtual permutation being based on the magnitude of the determined first principal component value; inputting the plurality of pieces of data of interest to which a virtual label indicating the virtual permutation has been added into the predetermined machine learning model, and selecting a predetermined number of genes whose expression levels linearly increase or decrease according to the virtual permutation as the candidate genes; The method for identifying a gene according to claim 1.

5. In the step C, generating a minimum spanning tree using a group of points obtained by plotting points corresponding to each of the plurality of pieces of data of interest in a two-dimensional coordinate system in which a first principal component value and a second principal component value determined by a principal component analysis of the plurality of pieces of data of interest are set as an X coordinate and a Y coordinate, respectively; selecting, as a starting point, a point among the end points included in the minimum spanning tree that has the lowest severity level; (1) performing a linear regression analysis on the correlation between a moving distance when moving along the minimum spanning tree from the starting point to another point included in the minimum spanning tree, and (2) an amount of change between the expression level of each of the plurality of genes corresponding to the starting point and the expression level of each of the plurality of genes corresponding to the other point; selecting a predetermined number of the candidate genes involved in the aggravation based on the magnitude of the absolute value of the slope of the regression line obtained by the linear regression analysis from among genes whose coefficient of determination in the linear regression analysis is equal to or greater than a predetermined threshold value; The method for identifying a gene according to claim 1.

6. In the step C, if the increase / decrease tendency of the first principal component value when moving from the center coordinate of the point group having the lowest severity level to the center coordinate of the point group having the second lowest severity level is different from the increase / decrease tendency of the first principal component value when moving from the center coordinate of the point group having the nth lowest severity level to the center coordinate of the point group having the n+1th lowest severity level, delete the point groups corresponding to the data from the point group having the n+1th lowest severity level to the point group having the highest severity level from the point groups obtained by plotting the expression amount data in the two-dimensional coordinate system to generate the minimum spanning tree, n is an integer of 2 or more; The gene identification method according to claim 5 .

7. an extracting unit that extracts a plurality of pieces of data of interest that satisfy a predetermined condition from expression level data in which the expression level of each of a plurality of genes contained in a biological sample collected from each of a plurality of subjects suffering from a disease is associated with a severity level of a symptom of the disease for each of the plurality of subjects; A selection unit that executes at least two or more of the following steps: (1) step A of selecting a predetermined number of candidate genes involved in the aggravation of the symptoms based on a result of a principal component analysis of the plurality of pieces of data of interest; (2) step B of selecting a predetermined number of candidate genes involved in the aggravation from the plurality of pieces of data of interest using a predetermined machine learning model; and (3) step C of selecting a predetermined number of candidate genes involved in the aggravation based on a minimum spanning tree generated based on a result of the principal component analysis of the plurality of pieces of data of interest; an identification unit that identifies the candidate gene selected in any of the two or more steps as a gene involved in the disease; Equipped with Gene identification device.

8. A control program for causing a computer to function as the gene identification apparatus according to claim 7, the control program causing a computer to function as the extraction unit, the selection unit, and the identification unit.

9. A method for obtaining data for diagnosing cataracts, comprising a step of measuring the expression level of at least one gene selected from the group consisting of the CDC42EP1 gene, the ARTN gene, the XKR8 gene, the ACTG2 gene, the RPS6KA1 gene, the SLC16A11 gene, the PLEKHG6 gene, the TMBIM1 gene, the AIFM3 gene, the LDLRAD2 gene, the COL1A1 gene, the CARD10 gene, the ACHE gene, the NUDT8 gene, the LRRC30 gene, the ADAMTS13 gene, the KRTAP10-6 gene, the BZRAP1 gene, the IQCF5 gene, the GGT6 gene, the PLEKHA4 gene, the OBP2B gene, the LILRA5 gene, the SH3TC1 gene, the HHAT gene, and the RP11-9L18.3 gene in a biological sample collected from a subject.

10. An administration step of administering a candidate substance for a therapeutic or preventive agent for cataract to a non-human model organism; a measuring step of measuring the expression level of at least one gene selected from the group consisting of CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene in the non-human model organism after the administration step; A screening method for a therapeutic or preventive agent for cataracts, comprising a determination step of determining whether the candidate substance is a therapeutic or preventive agent for cataracts by comparing the expression level of each gene measured in the measurement step with a control expression level.

11. A marker for diagnosing cataracts, comprising at least one gene selected from the group consisting of CDC42EP1 gene, ARTN gene, XKR8 gene, ACTG2 gene, RPS6KA1 gene, SLC16A11 gene, PLEKHG6 gene, TMBIM1 gene, AIFM3 gene, LDLRAD2 gene, COL1A1 gene, CARD10 gene, ACHE gene, NUDT8 gene, LRRC30 gene, ADAMTS13 gene, KRTAP10-6 gene, BZRAP1 gene, IQCF5 gene, GGT6 gene, PLEKHA4 gene, OBP2B gene, LILRA5 gene, SH3TC1 gene, HHAT gene, and RP11-9L18.3 gene.

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