Molecular marking method and system of RNA regulatory network of lncRNA UCA1 in cerebral ischemia injury

Abstract

The invention relates to the technical field of gene engineering, and discloses a molecular marking method and system for an RNA regulatory network of lncRNA UCA1 in cerebral ischemia injury, and the method comprises the following steps: establishing a cerebral ischemia injury scene of an experimental subject, and analyzing the interaction between the lncRNA UCA1 and other RNAs; the method comprises the following steps: performing scene grading on a cerebral ischemia injury scene, and measuring lncRNA UCA1, target RNA, a biological pathway of an experimental subject and lncRNA UCA1 expression related substances, target RNA expression related substances, biological pathway quantity and ischemia related cell data of ischemia related cells of the experimental subject in the graded scene; the cell numerical value of the ischemia related cell is calculated, and the lncRNA UCA1 expression level of the lncRNA UCA1 is analyzed by using the lncRNA UCA1 expression related substance; identifying cell-grading association and pathway-grading association between the cell numerical value and the grading scene and between the biological pathway quantity and the grading scene; the molecular marker in the cerebral ischemia injury scene is selected from the lncRNA UCA1 and the target RNA by using RNA-cell association and RNA-pathway association. According to the invention, the numeralization ability of the molecular marker in cerebral ischemia injury can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of genetic engineering, and in particular to a molecular labeling method and system for an RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury. Background Art

[0002] The process of molecular markers of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury refers to the process of identifying the molecular markers of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury and marking the molecular markers, wherein the molecular markers refer to indicators of the biological pathogenesis process, and the pathogenesis process can be indicated and judged according to the molecular markers, thereby diagnosing the disease category. Molecular marker technology is often used in clinical tumor diagnosis.

[0003] At present, existing literature has shown that lncRNA UCA1 can be used for the diagnosis of diseases such as tumors and cerebral ischemic injury. By observing the changes in the expression level of lncRNA UCA1, diseases such as tumors and cerebral ischemic injury can be diagnosed. However, observing the changes in the expression level of lncRNA UCA1 cannot intuitively reflect the severity of diseases such as tumors and cerebral ischemic injury. This is because the expression level of lncRNA UCA1 cannot be quantified. Usually, only the expression content of lncRNA UCA1 can be observed, such as the binding site during expression, but the expression content cannot be quantified. Secondly, cerebral ischemic injury is not only related to lncRNA UCA1, but may also be related to other RNAs, such as miRNA, 1ncRNA, pseudogene, circRNA and ceRNA. Finally, cerebral ischemic injury is related to cell, signal flux and other data, and the existing technology often mixes cell, signal flux and other data with RNA expression data, thereby judging whether cerebral ischemic injury exists through cell, signal flux and other data with RNA expression data, but the severity of cerebral ischemic injury is not distinguished. Therefore, the quantification ability of molecular markers in cerebral ischemic injury is insufficient. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a molecular marker method and system for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, which can improve the numerical ability of molecular markers in cerebral ischemic injury.

[0005] In a first aspect, the present invention provides a molecular marker method for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, comprising:

[0006] Establishing a brain ischemic injury scenario of an experimental subject, querying other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyzing the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario, obtaining an interaction analysis result, and determining a target RNA associated with the lncRNA UCA1 from the other RNAs using the interaction analysis result;

[0007] Performing scene classification on the cerebral ischemia injury scene to obtain a classification scene, and measuring the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substance, the target RNA expression-related substance, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject under the classification scene;

[0008] Based on the ischemia-related cell data, calculating the cell values ​​of the ischemia-related cells, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance, and analyzing the target RNA expression level of the target RNA using the target RNA expression-related substance;

[0009] Identify the cell-grading association and pathway-grading association between the cell value, the biological pathway amount and the graded scene, respectively; select strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association; identify the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively; and identify the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively;

[0010] The RNA-cell association and the RNA-pathway association are used to select molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA, and the molecular markers are molecularly labeled to obtain molecular labeling results in the cerebral ischemia injury scenario.

[0011] In a possible implementation of the first aspect, establishing a cerebral ischemic injury scenario of an experimental subject includes:

[0012] Anesthetizing the experimental subject to obtain an anesthetized subject;

[0013] determining the brain position and torso position of the anesthetized subject;

[0014] Performing shaving and disinfection treatment on the middle position between the brain and the trunk of the anesthetized subject to obtain a pre-treated subject;

[0015] After cutting the neck skin of the pre-treated subject, the unilateral carotid artery in the neck was freed to obtain the free unilateral carotid artery;

[0016] Ligate the external carotid artery, internal carotid artery and proximal end of aorta on the neck to obtain ligated external carotid artery, ligated internal carotid artery and ligated proximal end of aorta;

[0017] The free unilateral carotid artery, the ligated external carotid artery, the ligated internal carotid artery and the ligated proximal end of the aorta are used to establish a brain ischemic injury scenario of the experimental subject.

[0018] In a possible implementation of the first aspect, in the cerebral ischemic injury scenario, analyzing the interaction between the lncRNA UCA1 and the other RNAs to obtain the interaction analysis results includes:

[0019] Using a preset in vitro transcription method to prepare a biotin-labeled RNA probe of the lncRNA UCA1;

[0020] An RNA extract solution is extracted from the cytoplasm of cells in the cerebral ischemia injury scenario;

[0021] Incubating the biotin-labeled RNA probe and the RNA extract to obtain an RNA-RNA complex;

[0022] After separating the RNA-RNA complex from the RNA extract using preset streptavidin-labeled magnetic beads, eluting the RNA-RNA complex to obtain an eluted RNA-RNA complex;

[0023] The correlation between the lncRNA UCA1 and the other RNAs in the eluted RNA-RNA complex was calculated using the following formula:

[0024]

[0025] Wherein, S represents the correlation between the lncRNA UCA1 and the other RNAs, Ct 1下拉 represents the Ct of eluted RNA-RNA complex in the pull-down data of experimental group 1, Ct 2下拉 represents the Ct of the eluted RNA-RNA complex in the pull-down data of control group 2, Ct 2input represents the Ct of the eluted RNA-RNA complex in the input data of control group 2, Ct 1inputrepresents the Ct of eluted RNA-RNA complex in the input data of experimental group 1;

[0026] When the correlation between the lncRNA UCA1 and the other RNA is less than the preset correlation, the interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result;

[0027] When the correlation between the lncRNA UCA1 and the other RNA is not less than the preset correlation, the absence of interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result.

[0028] In a possible implementation manner of the first aspect, the grading of the cerebral ischemic injury scene to obtain a graded scene includes:

[0029] collecting injury indicators in the cerebral ischemic injury scenario;

[0030] The damage indicators include damage location, damage depth, damage length and damage area;

[0031] The damage index is normalized using the following formula to obtain a normalized index:

[0032]

[0033] Among them, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, x kj represents the jth value of the kth damage index;

[0034] The indicator information entropy corresponding to the standardized indicator is calculated using the following formula:

[0035]

[0036] Among them, e k represents the indicator information entropy corresponding to the kth standardized indicator, J represents the total number of samples of the standardized indicator value, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, and K represents the total number of categories of the standardized index;

[0037] Based on the indicator information entropy, the information entropy redundancy of the standardized indicator is calculated using the following formula:

[0038] d k =1-e k

[0039] Among them, d krepresents the information entropy redundancy of the kth standardized indicator, e k Represents the indicator information entropy corresponding to the kth standardized indicator;

[0040] According to the information entropy redundancy, the index weight of the filtering index is calculated using the following formula:

[0041]

[0042] Among them, w k represents the indicator weight of the kth standardized indicator, d k represents the information entropy redundancy of the kth standardized indicator, and K represents the total number of categories of the standardized indicator;

[0043] Based on the indicator weight, the injury degree of the cerebral ischemia injury scenario under the injury indicator is calculated using the following formula:

[0044]

[0045] Where H represents the degree of damage, w k represents the indicator weight of the kth standardized indicator, x′ kj represents the normalized index corresponding to the jth value of the kth damage index;

[0046] Based on the injury degree, the cerebral ischemia injury scene is graded to obtain a graded scene.

[0047] In a possible implementation of the first aspect, the measuring of the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substance, the target RNA expression-related substance, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject in the hierarchical scenario includes:

[0048] Detecting whether the lncRNA UCA1 is genetically expressed;

[0049] When the lncRNA UCA1 is genetically expressed, querying the binding site of the lncRNA UCA1;

[0050] The binding site of the lncRNA UCA1 is used as the lncRNA UCA1 expression-related substance;

[0051] Detecting whether the target RNA is genetically expressed;

[0052] When the target RNA is genetically expressed, querying the binding site of the target RNA;

[0053] Using the binding site of the target RNA as a target RNA expression-related substance;

[0054] Using a preset aggregation migration retention experiment to detect the amount of the biological pathway;

[0055] The ischemia-related cell data of ischemia-related cells were detected using a preset microphotometer and a blood cell counting disk.

[0056] In a possible implementation manner of the first aspect, calculating the cell value of the ischemia-related cells based on the ischemia-related cell data includes:

[0057] Based on the ischemia-related cell data, the cell proliferation rate of the ischemia-related cells was calculated using the following formula:

[0058] OD 0 =v 0 *ε*s

[0059]

[0060] Among them, cell breeding rate refers to the cell proliferation rate, OD 0 Represents the absorbance of ischemia-related cell solution in the graded scenario, OD 1 represents the absorbance of the ischemia-related cell solution of the experimental subject before constructing the cerebral ischemia injury scenario, v 0 represents the volume of the ischemia-related cell solution in the graded scenario in the ischemia-related cell data before dilution, ε represents the dilution multiple of the ischemia-related cell solution in the graded scenario, and s represents the absorbance per unit of the volume before dilution in the ischemia-related cell data;

[0061] Based on the ischemia-related cell data, the apoptosis rate of the ischemia-related cells was calculated using the following formula:

[0062]

[0063] Among them, apoptosisrate represents the apoptosis rate, m apoptosis Represents the number of apoptotic cells in ischemia-related cell data, m all represents the total number of cells in the experimental subject before constructing the cerebral ischemic injury scenario;

[0064] Based on the ischemia-related cell data, the cell clone formation rate of the ischemia-related cells was calculated using the following formula:

[0065]

[0066] Among them, colony formation rate represents the colony formation rate, cl represents the number of cloned cells in the ischemia-related cell data, and sl represents the number of seeded cells in the ischemia-related cell data;

[0067] Based on the ischemia-related cell data, the cell activity of the ischemia-related cells is calculated using the following formula:

[0068]

[0069] Among them, cell activity represents cell activity, OD 0 Represents the absorbance of ischemia-related cell solution in the graded scenario, OD 1 represents the absorbance of the ischemia-related cell solution of the experimental subject before constructing the cerebral ischemia injury scenario;

[0070] The cell proliferation rate, the cell apoptosis rate, the cell clone formation rate and the cell activity are taken as the cell values ​​of the ischemia-related cells.

[0071] In a possible implementation of the first aspect, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance includes:

[0072] Based on the lncRNA UCA1 expression-related substances, the lncRNA UCA1 expression level of the lncRNA UCA1 was calculated using the following formula:

[0073]

[0074] Among them, P represents the expression level of lncRNA UCA1, β 0 represents the preset constant term, β 1 , β 1 , β 2 , β N Represents Y 1 , Y 2 , Y N The coefficient of the independent variable, N represents Y 1 , Y 2 , Y N The number of independent variables, Y 1 , Y 2 , Y N Indicates lncRNA UCA1 expression-related substances.

[0075] In a possible implementation of the first aspect, the identifying the cell-grading association and the pathway-grading association between the cell value, the biological pathway amount and the grading scenario, respectively, includes:

[0076] Arrange the cell values ​​in ascending order to obtain a cell value sequence;

[0077] Arranging the graded scenes in ascending order of damage degrees of the graded scenes to obtain a graded scene sequence;

[0078] The cell-level association between the cell value sequence and the level scene sequence is calculated using the following formula:

[0079]

[0080]

[0081] Among them, ρ X,Y represents the cell-level association, X represents the cell value sequence, Y represents the level scene sequence, σ X represents the standard deviation of the cell numerical sequence, σ Y represents the standard deviation of the hierarchical scene sequence, cov represents the covariance between X and Y, E[(XE(X))(YE(Y))] represents the expectation of (XE(X))(YE(Y)), E(X 2 ) indicates X 2 Expected, E(Y 2 ) indicates Y 2 expectations, represents the mean of X, X i represents the i-th cell value in the cell value sequence, n represents the length of the cell value sequence, represents the mean of Y, E(XY) represents the expectation of XY, and E(Y) represents the expectation of Y;

[0082] Arranging the biological pathway quantities in ascending order to obtain a biological pathway quantity sequence;

[0083] Pathway-level associations are calculated between the biological pathway quantity sequence and the level scene sequence.

[0084] In a possible implementation of the first aspect, the selecting the molecular marker in the cerebral ischemic injury scenario from the lncRNA UCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association includes:

[0085] Determining whether the RNA-cell association and the RNA-pathway association are both greater than a preset association degree;

[0086] When both the RNA-cell association and the RNA-pathway association are greater than the preset association degree, molecular markers corresponding to the RNA-cell association and the RNA-pathway association are selected from the lncRNA UCA1 and the target RNA.

[0087] In a second aspect, the present invention provides a molecular marker system for the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, the system comprising:

[0088] An RNA determination module is used to establish a brain ischemic injury scenario of an experimental subject, query other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyze the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario, obtain an interaction analysis result, and use the interaction analysis result to determine a target RNA associated with the lncRNA UCA1 from the other RNAs;

[0089] A data measurement module is used to classify the cerebral ischemia injury scenario to obtain a classified scenario, and measure the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, the target RNA expression-related substances, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject under the classified scenario;

[0090] a level analysis module, for calculating the cell values ​​of the ischemia-related cells based on the ischemia-related cell data, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance, and analyzing the target RNA expression level of the target RNA using the target RNA expression-related substance;

[0091] an association identification module, for identifying the cell-grading association and the pathway-grading association between the cell value, the biological pathway amount and the graded scene, respectively, selecting strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association, identifying the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively, and identifying the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively;

[0092] The molecular marker module is used to select the molecular markers in the cerebral ischemia injury scenario from the lncRNAUCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association, and molecularly mark the molecular markers to obtain the molecular marker results in the cerebral ischemia injury scenario.

[0093] Compared with the prior art, the technical principle and beneficial effects of this solution are:

[0094] The embodiment of the present invention establishes a brain ischemia injury scenario of the experimental subject to construct a mouse head brain ischemia model that can observe lncRNA UCA1 data, cell data, signal pathway data and other data. Furthermore, the embodiment of the present invention analyzes the interaction between the lncRNA UCA1 and the other RNAs to identify other RNAs associated with lncRNA UCA1 on the premise that lncRNA UCA1 can be used as a molecular marker for brain ischemia in mice, and uses these RNAs as suspicious molecular markers. The embodiment of the present invention performs scene classification on the brain ischemia injury scenario to quantify the severity of the brain ischemia injury scenario. Furthermore, the embodiment of the present invention measures the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject and the lncRNA of the ischemia-related cells of the experimental subject in the classification scenario. UCA1 expression-related substances, target RNA expression-related substances, biological pathway amounts and ischemia-related cell data are used to digitize subsequent lncRNAUCA1 expression levels and target RNA expression levels using the lncRNAUCA1 expression-related substances and target RNA expression-related substances, and to digitize the relationship between the lncRNAUCA1 expression level and the target RNA expression level and the severity of cerebral ischemia injury using the biological pathway amounts and ischemia-related cell data as intermediaries. Further, the embodiment of the present invention analyzes the lncRNAUCA1 expression level of the lncRNAUCA1 by using the lncRNAUCA1 expression-related substances, so as to digitize the lncRNAUCA1 expression level of the lncRNAUCA1 by using the lncRNAUCA1 expression-related substances. The embodiment of the present invention identifies the cell-grading association and pathway-grading association between the cell values, the biological pathway amounts and the graded scenes, respectively, so as to identify the relationship between different cell values, biological pathway amounts and the degree of injury of the graded scenes, thereby digitizing the degree of injury of the graded scenes by different cell values ​​and biological pathway amounts. Further, the embodiment of the present invention identifies the lncRNA The RNA-cell association between the UCA1 expression level and the target RNA expression level and the strongly associated cells is used to identify the relationship between different lncRNA UCA1 expression levels, target RNA expression levels and strongly associated cells, so as to quantify the degree of damage in the graded scenario through different lncRNA UCA1 expression levels and target RNA expression levels. The embodiment of the present invention selects molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA by utilizing the RNA-cell association and the RNA-pathway association, so as to quantify the degree of cerebral ischemia injury through the lncRNA UCA1 and the target RNA that are highly associated with strongly associated cells and strongly associated pathways.Therefore, the molecular marker method and system for the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury proposed in the embodiment of the present invention can improve the numerical capability of molecular markers in cerebral ischemic injury. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0096] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0097] Figure 1 A schematic diagram of a molecular marker method for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury provided in one embodiment of the present invention;

[0098] Figure 2 A schematic diagram of a module of a molecular marker system for the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0099] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0100] The embodiment of the present invention provides a molecular marker method for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, and the execution subject of the molecular marker method of RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury includes but is not limited to at least one of the electronic devices that can be configured to execute the method provided by the embodiment of the present invention, such as a server, a terminal, etc. In other words, the molecular marker method of RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0101] See also Figure 1 FIG. 1 is a flow chart of a molecular marker method for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury provided by an embodiment of the present invention. Figure 1 The molecular marker methods of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury described in the paper include:

[0102] S1. Establish a brain ischemic injury scenario for an experimental subject, query other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyze the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario, obtain an interaction analysis result, and use the interaction analysis result to determine a target RNA associated with the lncRNA UCA1 from the other RNAs.

[0103] The embodiment of the present invention establishes a brain ischemia injury scenario of the experimental subject to construct a mouse head brain ischemia model that can observe lncRNA UCA1 data, cell data, signal pathway data and other data.

[0104] The experimental subjects are mice, and the brain ischemic injury scenario is a whole-body model of mice with brain damage.

[0105] In one embodiment of the present invention, the establishment of a brain ischemia injury scenario for an experimental subject comprises: anesthetizing the experimental subject to obtain an anesthetized subject; determining the brain position and torso position of the anesthetized subject; shaving and disinfecting the middle position between the brain position and torso position of the anesthetized subject to obtain a pre-treated subject; after cutting the neck skin of the pre-treated subject, freeing the unilateral carotid artery in the neck to obtain a free unilateral carotid artery; ligating the external carotid artery, the internal carotid artery and the proximal end of the aorta on the neck to obtain a ligated external carotid artery, a ligated internal carotid artery and a ligated proximal end of the aorta; and using the free unilateral carotid artery, the ligated external carotid artery, the ligated internal carotid artery and the ligated proximal end of the aorta to establish a brain ischemia injury scenario for the experimental subject.

[0106] The torso position refers to the position on the mouse's body below the neck and above the legs, and the middle position refers to the mouse's neck.

[0107] Optionally, the process of establishing a brain ischemia injury scenario for the experimental subject using the free unilateral carotid artery, the ligated external carotid artery, the ligated internal carotid artery and the ligated proximal end of the aorta refers to using the whole body of the mouse including the free unilateral carotid artery, the ligated external carotid artery, the ligated internal carotid artery and the ligated proximal end of the aorta as the brain ischemia injury scenario.

[0108] Furthermore, in the embodiment of the present invention, other RNAs in the experimental subject except lncRNA UCA1 include miRNA, 1ncRNA, pseudogene, circRNA, ceRNA and other RNAs.

[0109] Furthermore, the embodiment of the present invention analyzes the interaction between the lncRNA UCA1 and the other RNAs to identify other RNAs associated with lncRNA UCA1 on the premise that lncRNA UCA1 can be used as a molecular marker for cerebral ischemia in mice, and use these RNAs as suspected molecular markers.

[0110] In one embodiment of the present invention, in the brain ischemic injury scenario, analyzing the interaction between the lncRNA UCA1 and the other RNA to obtain the interaction analysis result includes: preparing a biotin-labeled RNA probe of the lncRNA UCA1 using a preset in vitro transcription method; extracting an RNA extract of the other RNA from the cell cytoplasm in the brain ischemic injury scenario; incubating the biotin-labeled RNA probe and the RNA extract to obtain an RNA-RNA complex; separating the RNA-RNA complex from the RNA extract using preset streptavidin-labeled magnetic beads, and eluting the RNA-RNA complex to obtain an eluted RNA-RNA complex; and calculating the correlation between the lncRNA UCA1 and the other RNA in the eluted RNA-RNA complex using the following formula:

[0111]

[0112] Wherein, S represents the correlation between the lncRNA UCA1 and the other RNAs, Ct 1下拉 represents the Ct of eluted RNA-RNA complex in the pull-down data of experimental group 1, Ct 2下拉 represents the Ct of the eluted RNA-RNA complex in the pull-down data of control group 2, Ct 2input represents the Ct of the eluted RNA-RNA complex in the input data of control group 2, Ct 1input represents the Ct of eluted RNA-RNA complex in the input data of experimental group 1;

[0113] When the correlation between the lncRNA UCA1 and the other RNA is less than the preset correlation, the existence of interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result; when the correlation between the lncRNA UCA1 and the other RNA is not less than the preset correlation, the absence of interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result.

[0114] Among them, the biotin-labeled RNA probe refers to the biotin-labeled RNA probe composed of the lncRNA UCA1, the RNA extract refers to the cell cytoplasm extract containing the other RNA extracted from the cell cytoplasm, the pull-down data refers to the sample data of the eluted RNA-RNA complex prepared by the pull-down experiment, the pull-down experiment is a form of affinity purification, which is very similar to immunoprecipitation, except that the bait protein replaces the antibody, and the input data refers to the sample data of the eluted RNA-RNA complex prepared by the positive control experiment.

[0115] It should be noted that the formula The Ct in the figure represents the amplification cycle number corresponding to when the fluorescence signal of the amplified product reaches the set fluorescence threshold during the qPCR amplification process of the eluted RNA-RNA complex, and the qPCR refers to real-time fluorescence quantitative polymerase chain reaction.

[0116] Optionally, the process of incubating the biotin-labeled RNA probe and the RNA extract to obtain an RNA-RNA complex refers to placing the biotin-labeled RNA probe into the RNA extract. If the biotin-labeled RNA probe can interact with other RNAs in the RNA extract, the biotin-labeled RNA probe will have binding points for other RNAs, and the biotin-labeled RNA probe can bind to other RNAs through the binding points. The fragment where the biotin-labeled RNA probe binds to the other RNA is a binding site, thereby using the biotin-labeled RNA probe containing the binding site as an RNA-RNA complex.

[0117] Optionally, the process of eluting the RNA-RNA complex to obtain the eluted RNA-RNA complex refers to the process of washing away other impurities on the RNA-RNA complex.

[0118] Optionally, the process of determining the target RNA associated with the lncRNA UCA1 from the other RNAs using the interaction analysis result refers to taking the RNA that interacts with the lncRNA UCA1 as the target RNA.

[0119] S2. Classify the cerebral ischemia injury scenario to obtain a classified scenario, and measure the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, target RNA expression-related substances, biological pathway amounts, and ischemia-related cell data of the ischemia-related cells of the experimental subject under the classified scenario.

[0120] The embodiment of the present invention performs scene grading on the cerebral ischemia injury scene to quantify the severity of the cerebral ischemia injury scene.

[0121] The graded scenarios refer to cerebral ischemic injury scenarios with different degrees of injury.

[0122] In one embodiment of the present invention, the cerebral ischemia injury scene is graded to obtain the graded scene, including: collecting injury indicators in the cerebral ischemia injury scene; wherein the injury indicators include injury location, injury depth, injury length and injury area; and using the following formula to standardize the injury indicators to obtain a standardized indicator:

[0123]

[0124] Among them, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, x kj represents the jth value of the kth damage index;

[0125] The indicator information entropy corresponding to the standardized indicator is calculated using the following formula:

[0126]

[0127] Among them, e k represents the indicator information entropy corresponding to the kth standardized indicator, J represents the total number of samples of the standardized indicator value, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, and K represents the total number of categories of the standardized index;

[0128] Based on the indicator information entropy, the information entropy redundancy of the standardized indicator is calculated using the following formula:

[0129] d k =1-e k

[0130] Among them, d k represents the information entropy redundancy of the kth standardized indicator, e k Represents the indicator information entropy corresponding to the kth standardized indicator;

[0131] According to the information entropy redundancy, the index weight of the filtering index is calculated using the following formula:

[0132]

[0133] Among them, w k represents the indicator weight of the kth standardized indicator, d k represents the information entropy redundancy of the kth standardized indicator, and K represents the total number of categories of the standardized indicator;

[0134] Based on the indicator weight, the injury degree of the cerebral ischemia injury scenario under the injury indicator is calculated using the following formula:

[0135]

[0136] Where H represents the degree of damage, w k represents the indicator weight of the kth standardized indicator, x′ kj represents the normalized index corresponding to the jth value of the kth damage index;

[0137] Based on the injury degree, the cerebral ischemia injury scene is graded to obtain a graded scene.

[0138] Optionally, based on the injury degree, the cerebral ischemia injury scene is graded to obtain a graded scene, which means dividing the injury degree into several level intervals from low to high according to the size of the value. The higher the level, the more serious the injury degree. The cerebral ischemia injury scenes with the same injury degree belonging to the same interval are scenes of the same level.

[0139] Furthermore, the embodiment of the present invention measures the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject and the lncRNA UCA1 expression-related substances, the target RNA expression-related substances, the biological pathway amounts and the ischemia-related cell data of the ischemia-related cells of the experimental subject in the grading scenario, so as to use the lncRNA UCA1 expression-related substances and the target RNA expression-related substances to quantify the subsequent lncRNA UCA1 expression level and the target RNA expression level, and use the biological pathway amounts and the ischemia-related cell data as intermediaries to quantify the correlation between the lncRNA UCA1 expression level, the target RNA expression level and the severity of cerebral ischemia damage.

[0140] The biological pathway quantity refers to the quantitative value of the signal pathway, and the ischemia-related cell data includes the number of cells and the absorbance of the cells.

[0141] In one embodiment of the present invention, the measuring of the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, target RNA expression-related substances, biological pathway amounts, and ischemia-related cell data of the ischemia-related cells of the experimental subject in the grading scenario includes: detecting whether the lncRNA UCA1 undergoes gene expression; when the lncRNA UCA1 undergoes gene expression, querying the binding site of the lncRNA UCA1; using the binding site of the lncRNA UCA1 as the lncRNA UCA1 expression-related substance; detecting whether the target RNA undergoes gene expression; when the target RNA undergoes gene expression, querying the binding site of the target RNA; using the binding site of the target RNA as the target RNA expression-related substance; detecting the biological pathway amount using a preset coagulation migration retention experiment; and detecting the ischemia-related cell data of ischemia-related cells using a preset microphotometer and blood cell counting disc.

[0142] Among them, the binding site refers to the part where two different RNAs bind, the aggregation migration retention experiment refers to a technology used to study the interaction between transcription factors and their related DNA binding sequences, the microphotometer is used to measure the absorbance of cells, and the blood cell counting plate is used to measure the number of cells.

[0143] Optionally, the process of detecting whether the lncRNA UCA1 is genetically expressed and detecting whether the target RNA is genetically expressed can be achieved by using a dual luciferase reporter gene.

[0144] S3. Based on the ischemia-related cell data, the cell values ​​of the ischemia-related cells are calculated, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, and the lncRNA UCA1 expression level of the lncRNA UCA1 is analyzed using the lncRNA UCA1 expression-related substance, and the target RNA expression level of the target RNA is analyzed using the target RNA expression-related substance.

[0145] In one embodiment of the present invention, the calculating the cell value of the ischemia-related cells based on the ischemia-related cell data includes: calculating the cell proliferation rate of the ischemia-related cells based on the ischemia-related cell data using the following formula:

[0146] OD 0 =v 0 *ε*s

[0147]

[0148] Among them, cell breeding rate refers to the cell proliferation rate, OD 0 Represents the absorbance of ischemia-related cell solution in the graded scenario, OD 1 represents the absorbance of the ischemia-related cell solution of the experimental subject before constructing the cerebral ischemia injury scenario, v 0 represents the volume of the ischemia-related cell solution in the graded scenario in the ischemia-related cell data before dilution, ε represents the dilution multiple of the ischemia-related cell solution in the graded scenario, and s represents the absorbance per unit of the volume before dilution in the ischemia-related cell data;

[0149] Based on the ischemia-related cell data, the apoptosis rate of the ischemia-related cells was calculated using the following formula:

[0150]

[0151] Among them, apoptosis rate represents the cell apoptosis rate, m apoptosis Represents the number of apoptotic cells in ischemia-related cell data, m all represents the total number of cells in the experimental subject before constructing the cerebral ischemic injury scenario;

[0152] Based on the ischemia-related cell data, the cell clone formation rate of the ischemia-related cells was calculated using the following formula:

[0153]

[0154] Among them, colony formation rate represents the colony formation rate, cl represents the number of cloned cells in the ischemia-related cell data, and sl represents the number of seeded cells in the ischemia-related cell data;

[0155] Based on the ischemia-related cell data, the cell activity of the ischemia-related cells is calculated using the following formula:

[0156]

[0157] Among them, cell activity represents cell activity, OD 0 Represents the absorbance of ischemia-related cell solution in the graded scenario, OD 1 represents the absorbance of the ischemia-related cell solution of the experimental subject before constructing the cerebral ischemia injury scenario;

[0158] The cell proliferation rate, the cell apoptosis rate, the cell clone formation rate and the cell activity are taken as the cell values ​​of the ischemia-related cells.

[0159] Furthermore, the embodiment of the present invention analyzes the lncRNA UCA1 expression level of the lncRNA UCA1 by using the lncRNA UCA1 expression-related substance, so as to quantify the lncRNA UCA1 expression level of the lncRNA UCA1 by using the lncRNA UCA1 expression-related substance.

[0160] The lncRNA UCA1 expression level refers to a value that measures the conversion of lncRNA UCA1 into biologically active protein molecules through transcription and translation.

[0161] In one embodiment of the present invention, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance comprises: calculating the lncRNA UCA1 expression level of the lncRNA UCA1 based on the lncRNA UCA1 expression-related substance using the following formula:

[0162]

[0163] Among them, P represents the expression level of lncRNA UCA1, β 0 represents the preset constant term, β 1 , β 1 , β 2 , β N Represents Y 1 , Y 2 , Y N The coefficient of the independent variable, N represents Y 1 , Y 2 , Y N The number of independent variables, Y 1 , Y 2 , Y N Indicates lncRNA UCA1 expression-related substances.

[0164] S4. Identify the cell-grading association and pathway-grading association between the cell numerical value, the biological pathway quantity and the graded scenario, respectively; select strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association; identify the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively; and identify the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively.

[0165] The embodiment of the present invention identifies the cell-grading association and pathway-grading association between the cell numerical value, the biological pathway amount and the grading scene respectively, so as to identify the relationship between different cell numerical values, biological pathway amounts and the degree of damage of the grading scene, thereby quantifying the degree of damage of the grading scene through different cell numerical values ​​and biological pathway amounts.

[0166] The cell-grading association refers to the correlation between the cell value and the grading scenario, and the pathway-grading association refers to the correlation between the biological pathway quantity and the grading scenario.

[0167] In one embodiment of the present invention, the identifying the cell-grade association and pathway-grade association between the cell values, the biological pathway amounts and the graded scenes respectively includes: arranging the cell values ​​in ascending order to obtain a cell value sequence; arranging the graded scenes in ascending order according to the degree of damage of the graded scenes to obtain a graded scene sequence; and calculating the cell-grade association between the cell value sequence and the graded scene sequence using the following formula:

[0168]

[0169] Among them, ρ X,Y represents the cell-level association, X represents the cell value sequence, Y represents the level scene sequence, σ X represents the standard deviation of the cell numerical sequence, σ Y represents the standard deviation of the hierarchical scene sequence, cov represents the covariance between X and Y, E[(XE(X))(YE(Y))] represents the expectation of (XE(X))(YE(Y)), E(X 2 ) indicates X 2 Expected, E(Y 2 ) indicates Y 2 expectations, represents the mean of X, X i represents the i-th cell value in the cell value sequence, n represents the length of the cell value sequence, represents the mean of Y, E(XY) represents the expectation of XY, and E(Y) represents the expectation of Y;

[0170] The biological pathway quantities are arranged in order from small to large to obtain a biological pathway quantity sequence; and the pathway-level association between the biological pathway quantity sequence and the level scene sequence is calculated.

[0171] Optionally, the principle of calculating the pathway-grade association between the biological pathway quantity sequence and the graded scene sequence is similar to the principle of calculating the cell-grade association between the cell value sequence and the graded scene sequence, and is not repeated here.

[0172] Furthermore, in an embodiment of the present invention, the strongly associated cell refers to a cell whose cell-hierarchical association is greater than a preset correlation, and the pathway-hierarchical association refers to a pathway whose pathway-hierarchical association is greater than the preset correlation.

[0173] Optionally, the process of selecting strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grade association and the pathway-grade association refers to selecting ischemia-related cells and biological pathways with high correlation using the values ​​of the cell-grade association and the pathway-grade association.

[0174] Furthermore, the embodiment of the present invention identifies the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells respectively, so as to identify the relationship between different lncRNA UCA1 expression levels and target RNA expression levels and the strongly associated cells respectively, thereby quantifying the degree of damage of the graded scenario through different lncRNA UCA1 expression levels and target RNA expression levels.

[0175] Wherein, the RNA-cell association includes lncRNA UCA1-cell association and target RNA-cell association.

[0176] Optionally, the identifying of the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells respectively, and identifying the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways respectively are similar to the principle of calculating the cell-hierarchical association between the cell numerical sequence and the hierarchical scene sequence mentioned above, and are not elaborated here.

[0177] Wherein, the RNA-pathway association includes lncRNA UCA1-pathway association and target RNA-pathway association.

[0178] S5. Selecting molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association, and molecularly labeling the molecular markers to obtain molecular labeling results in the cerebral ischemia injury scenario.

[0179] The embodiment of the present invention selects molecular markers in the cerebral ischemic injury scenario from the lncRNA UCA1 and the target RNA by utilizing the RNA-cell association and the RNA-pathway association, so as to quantify the degree of cerebral ischemic injury through the lncRNA UCA1 and the target RNA that are highly associated with strongly associated cells and strongly associated pathways.

[0180] Among them, the molecular marker refers to an indicator of the biological pathogenesis. The molecular marker can be used to indicate and judge the pathogenesis, thereby diagnosing the disease category. Molecular marker technology is often used in clinical tumor diagnosis.

[0181] In one embodiment of the present invention, the use of the RNA-cell association and the RNA-pathway association to select the molecular marker in the cerebral ischemic injury scenario from the lncRNA UCA1 and the target RNA includes: judging whether the RNA-cell association and the RNA-pathway association are both greater than a preset correlation degree; when the RNA-cell association and the RNA-pathway association are both greater than the preset correlation degree, selecting the molecular marker corresponding to the RNA-cell association and the RNA-pathway association from the lncRNA UCA1 and the target RNA.

[0182] It can be seen that the embodiment of the present invention establishes a brain ischemia injury scenario of the experimental subject to construct a mouse head brain ischemia model that can observe lncRNA UCA1 data, cell data, signal pathway data and other data. Furthermore, the embodiment of the present invention analyzes the interaction between the lncRNA UCA1 and the other RNAs to identify other RNAs associated with lncRNA UCA1 on the premise that lncRNA UCA1 can be used as a molecular marker for brain ischemia in mice, and uses these RNAs as suspicious molecular markers. The embodiment of the present invention performs scene classification on the brain ischemia injury scenario to quantify the severity of the brain ischemia injury scenario. Furthermore, the embodiment of the present invention measures the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, the target RNA expression-related substances, the biological pathway amount and the ischemia-related cell data of the ischemia-related cells of the experimental subject under the classification scenario to quantify the subsequent lncRNA using the lncRNA UCA1 expression-related substances and the target RNA expression-related substances. UCA1 expression level, target RNA expression level, and use biological pathway quantity and ischemia-related cell data as intermediaries to quantify the association between lncRNA UCA1 expression level, target RNA expression level and the severity of cerebral ischemia injury. Further, the embodiment of the present invention analyzes the lncRNA UCA1 expression level of the lncRNA UCA1 by using the lncRNA UCA1 expression-related substance, so as to quantify the lncRNA UCA1 expression level of the lncRNA UCA1 by using the lncRNA UCA1 expression-related substance. The embodiment of the present invention identifies the cell-graded association and the pathway-graded association between the cell value and the biological pathway quantity and the graded scene, respectively, to identify the relationship between different cell values, biological pathway quantities and the degree of damage of the graded scene, thereby quantifying the degree of damage of the graded scene through different cell values ​​and biological pathway quantities. Further, the embodiment of the present invention identifies the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively, to identify the relationship between different lncRNA UCA1 expression levels and the target RNA expression levels and the strongly associated cells, thereby quantifying the degree of damage of the graded scene through different lncRNA The UCA1 expression level and the target RNA expression level are used to numerically quantify the degree of injury in the graded scenario. In the embodiment of the present invention, the molecular markers in the cerebral ischemic injury scenario are selected from the lncRNA UCA1 and the target RNA by utilizing the RNA-cell association and the RNA-pathway association.It is used to quantify the degree of cerebral ischemic injury through lncRNA UCA1 and target RNA that are highly associated with strongly associated cells and strongly associated pathways. Therefore, the molecular marker method and system of RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury proposed in the embodiment of the present invention can improve the quantification ability of molecular markers in cerebral ischemic injury.

[0183] like Figure 2 Shown is a functional module diagram of a molecular marker system of RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury according to the present invention.

[0184] The molecular marker system 200 of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury described in the present invention can be installed in an electronic device. According to the functions implemented, the molecular marker system of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury can include an RNA determination module 201, a data measurement module 202, a level analysis module 203, an association identification module 204 and a molecular marker module 205. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0185] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0186] The RNA determination module 201 is used to establish a brain ischemic injury scenario of the experimental subject, query other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyze the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario, obtain an interaction analysis result, and determine the target RNA associated with the lncRNA UCA1 from the other RNAs using the interaction analysis result;

[0187] The data measurement module 202 is used to classify the cerebral ischemia injury scenario to obtain a classified scenario, and measure the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, target RNA expression-related substances, biological pathway amounts, and ischemia-related cell data of the ischemia-related cells of the experimental subject under the classified scenario;

[0188] The level analysis module 203 is used to calculate the cell values ​​of the ischemia-related cells based on the ischemia-related cell data, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, analyze the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance, and analyze the target RNA expression level of the target RNA using the target RNA expression-related substance;

[0189] The association identification module 204 is used to identify the cell-grading association and the pathway-grading association between the cell value, the biological pathway amount and the graded scene, respectively, select strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association, identify the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively, and identify the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively;

[0190] The molecular marker module 205 is used to select molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association, and molecularly mark the molecular markers to obtain molecular marker results in the cerebral ischemia injury scenario.

[0191] In detail, the modules in the molecular marker system 200 of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury in the embodiment of the present invention are used in the same manner as described above. Figure 1 The molecular marker method of the RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury described in the literature is the same technical means and can produce the same technical effects, so it will not be repeated here.

[0192] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0193] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0194] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0195] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A molecular marker method for RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, characterized in that: The method comprises: Establishing a brain ischemic injury scenario of an experimental subject, querying other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyzing the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario to obtain an interaction analysis result, and determining a target RNA associated with the lncRNA UCA1 from the other RNAs using the interaction analysis result; Performing scene classification on the cerebral ischemia injury scene to obtain a classification scene, and measuring the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substance, the target RNA expression-related substance, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject under the classification scene; Based on the ischemia-related cell data, calculating the cell values ​​of the ischemia-related cells, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance, and analyzing the target RNA expression level of the target RNA using the target RNA expression-related substance; Identify the cell-grading association and pathway-grading association between the cell value, the biological pathway amount and the graded scene, respectively; select strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association; identify the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively; and identify the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively; The RNA-cell association and the RNA-pathway association are used to select molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA, and the molecular markers are molecularly labeled to obtain molecular labeling results in the cerebral ischemia injury scenario.

2. The method according to claim 1, characterized in that The cerebral ischemic injury scenario of the experimental subject is established, including: Anesthetizing the experimental subject to obtain an anesthetized subject; determining the brain position and torso position of the anesthetized subject; Performing shaving and disinfection treatment on the middle position between the brain and the trunk of the anesthetized subject to obtain a pre-treated subject; After cutting the neck skin of the pre-treated subject, the unilateral carotid artery in the neck was freed to obtain the free unilateral carotid artery; Ligate the external carotid artery, internal carotid artery and proximal end of aorta on the neck to obtain ligated external carotid artery, ligated internal carotid artery and ligated proximal end of aorta; The free unilateral carotid artery, the ligated external carotid artery, the ligated internal carotid artery and the ligated proximal end of the aorta are used to establish a brain ischemic injury scenario of the experimental subject.

3. The method according to claim 1, characterized in that In the brain ischemic injury scenario, analyzing the interaction between the lncRNA UCA1 and the other RNAs to obtain the interaction analysis results includes: Using a preset in vitro transcription method to prepare a biotin-labeled RNA probe of the lncRNA UCA1; An RNA extract solution is extracted from the cytoplasm of cells in the cerebral ischemia injury scenario; Incubating the biotin-labeled RNA probe and the RNA extract to obtain an RNA-RNA complex; After separating the RNA-RNA complex from the RNA extract using preset streptavidin-labeled magnetic beads, eluting the RNA-RNA complex to obtain an eluted RNA-RNA complex; The correlation between the lncRNA UCA1 and the other RNAs in the eluted RNA-RNA complex was calculated using the following formula: Wherein, S represents the correlation between the lncRNA UCA1 and the other RNAs, Ct 1下拉 represents the Ct of eluted RNA-RNA complex in the pull-down data of experimental group 1, Ct 2下拉 represents the Ct of the eluted RNA-RNA complex in the pull-down data of control group 2, Ct 2input represents the Ct of the eluted RNA-RNA complex in the input data of control group 2, Ct 1input represents the Ct of eluted RNA-RNA complex in the input data of experimental group 1; When the correlation between the lncRNA UCA1 and the other RNA is less than the preset correlation, the interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result; When the correlation between the lncRNA UCA1 and the other RNA is not less than the preset correlation, the absence of interaction between the lncRNA UCA1 and the other RNA is taken as the interaction analysis result.

4. The method according to claim 1, characterized in that: The step of performing scene classification on the cerebral ischemia injury scene to obtain a classified scene includes: collecting injury indicators in the cerebral ischemic injury scenario; The damage indicators include damage location, damage depth, damage length and damage area; The damage index is normalized using the following formula to obtain a normalized index: Among them, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, x kj represents the jth value of the kth damage index; The indicator information entropy corresponding to the standardized indicator is calculated using the following formula: Among them, e k represents the indicator information entropy corresponding to the kth standardized indicator, J represents the total number of samples of the standardized indicator value, x′ kj represents the standardized index corresponding to the jth value of the kth damage index, and K represents the total number of categories of the standardized index; Based on the indicator information entropy, the information entropy redundancy of the standardized indicator is calculated using the following formula: d k =1-e k Among them, d k represents the information entropy redundancy of the kth standardized indicator, e k Represents the indicator information entropy corresponding to the kth standardized indicator; According to the information entropy redundancy, the index weight of the filtering index is calculated using the following formula: Among them, w k represents the indicator weight of the kth standardized indicator, d k represents the information entropy redundancy of the kth standardized indicator, and K represents the total number of categories of the standardized indicator; Based on the indicator weight, the injury degree of the cerebral ischemia injury scenario under the injury indicator is calculated using the following formula: Where H represents the degree of damage, w k represents the indicator weight of the kth standardized indicator, x′ kj represents the normalized index corresponding to the jth value of the kth damage index; Based on the injury degree, the cerebral ischemia injury scene is graded to obtain a graded scene.

5. The method according to claim 1, characterized in that The measuring of the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, the target RNA expression-related substances, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject in the classification scenario includes: Detecting whether the lncRNA UCA1 is genetically expressed; When the lncRNA UCA1 is genetically expressed, querying the binding site of the lncRNA UCA1; The binding site of the lncRNA UCA1 is used as the lncRNA UCA1 expression-related substance; Detecting whether the target RNA is genetically expressed; When the target RNA is genetically expressed, querying the binding site of the target RNA; Using the binding site of the target RNA as a target RNA expression-related substance; Using a preset aggregation migration retention experiment to detect the amount of the biological pathway; The ischemia-related cell data of ischemia-related cells were detected using a preset microphotometer and a blood cell counting disk.

6. The method according to claim 1, characterized in that The step of calculating the cell value of the ischemia-related cells based on the ischemia-related cell data comprises: Based on the ischemia-related cell data, the cell proliferation rate of the ischemia-related cells was calculated using the following formula: OD0=v0*ε*s Wherein, cell breeding rate represents the cell proliferation rate, OD0 represents the absorbance of the ischemia-related cell solution in the graded scenario, OD1 represents the absorbance of the ischemia-related cell solution of the experimental subject before the cerebral ischemia injury scenario is constructed, v0 represents the volume of the ischemia-related cell solution in the graded scenario before dilution in the ischemia-related cell data, ε represents the multiple of dilution of the ischemia-related cell solution in the graded scenario, and s represents the absorbance of each unit of the volume before dilution in the ischemia-related cell data; Based on the ischemia-related cell data, the apoptosis rate of the ischemia-related cells was calculated using the following formula: Among them, apoptosis rate represents the cell apoptosis rate, m apoptosis Represents the number of apoptotic cells in ischemia-related cell data, m all represents the total number of cells in the experimental subject before constructing the cerebral ischemic injury scenario; Based on the ischemia-related cell data, the cell clone formation rate of the ischemia-related cells was calculated using the following formula: Among them, colony formation rate represents the colony formation rate, cl represents the number of cloned cells in the ischemia-related cell data, and s1 represents the number of seeded cells in the ischemia-related cell data; Based on the ischemia-related cell data, the cell activity of the ischemia-related cells is calculated using the following formula: Among them, cell activity represents cell activity, OD0 represents the absorbance of the ischemia-related cell solution in the graded scenario, and OD1 represents the absorbance of the ischemia-related cell solution of the experimental subject before the construction of the cerebral ischemia injury scenario; The cell proliferation rate, the cell apoptosis rate, the cell clone formation rate and the cell activity are taken as the cell values ​​of the ischemia-related cells.

7. The method according to claim 1, characterized in that The step of analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance comprises: Based on the lncRNA UCA1 expression-related substances, the lncRNA UCA1 expression level of the lncRNA UCA1 was calculated using the following formula: Among them, P represents the expression level of lncRNA UCA1, β0 represents the preset constant term, β1, β2, β N Indicates Y1, Y2, Y N The coefficient of the independent variable, N represents Y1, Y2, Y N The number of independent variables, Y1, Y2, Y N Indicates lncRNA UCA1 expression-related substances.

8. The method according to claim 1, characterized in that The identifying the cell-grading association and pathway-grading association between the cell value, the biological pathway amount and the grading scene respectively includes: Arrange the cell values ​​in ascending order to obtain a cell value sequence; Arranging the graded scenes in ascending order of damage degrees of the graded scenes to obtain a graded scene sequence; The cell-level association between the cell value sequence and the level scene sequence is calculated using the following formula: Among them, ρ X,Y represents the cell-level association, X represents the cell value sequence, Y represents the level scene sequence, σ X represents the standard deviation of the cell numerical sequence, σ Y represents the standard deviation of the hierarchical scene sequence, cov represents the covariance between X and Y, E[(XE(X))(YE(Y))] represents the expectation of (XE(X))(YE(Y)), E(X 2 ) indicates X 2 Expected, E(Y 2 ) indicates Y 2 expectations, represents the mean of X, X i represents the i-th cell value in the cell value sequence, n represents the length of the cell value sequence, represents the mean of Y, E(XY) represents the expectation of XY, and E(Y) represents the expectation of Y; Arranging the biological pathway quantities in ascending order to obtain a biological pathway quantity sequence; Pathway-level associations are calculated between the biological pathway quantity sequence and the level scene sequence.

9. The method according to claim 1, characterized in that: The method of selecting the molecular markers in the cerebral ischemic injury scenario from the lncRNA UCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association includes: Determining whether the RNA-cell association and the RNA-pathway association are both greater than a preset association degree; When both the RNA-cell association and the RNA-pathway association are greater than the preset association degree, molecular markers corresponding to the RNA-cell association and the RNA-pathway association are selected from the lncRNA UCA1 and the target RNA.

10. A molecular marker system of RNA regulatory network of lncRNA UCA1 in cerebral ischemic injury, characterized in that: The system comprises: An RNA determination module is used to establish a brain ischemic injury scenario of an experimental subject, query other RNAs except lncRNA UCA1 in the experimental subject under the brain ischemic injury scenario, analyze the interaction between the lncRNA UCA1 and the other RNAs in the brain ischemic injury scenario, obtain an interaction analysis result, and use the interaction analysis result to determine a target RNA associated with the lncRNA UCA1 from the other RNAs; A data measurement module is used to classify the cerebral ischemia injury scenario to obtain a classified scenario, and measure the lncRNA UCA1, the target RNA, the biological pathway of the experimental subject, and the lncRNA UCA1 expression-related substances, the target RNA expression-related substances, the biological pathway amount, and the ischemia-related cell data of the ischemia-related cells of the experimental subject under the classified scenario; a level analysis module, for calculating the cell values ​​of the ischemia-related cells based on the ischemia-related cell data, wherein the cell values ​​include cell proliferation rate, cell apoptosis rate, cell clone formation rate and cell activity, analyzing the lncRNA UCA1 expression level of the lncRNA UCA1 using the lncRNA UCA1 expression-related substance, and analyzing the target RNA expression level of the target RNA using the target RNA expression-related substance; an association identification module, for identifying the cell-grading association and the pathway-grading association between the cell value, the biological pathway amount and the graded scene, respectively, selecting strongly associated cells and strongly associated pathways from the ischemia-related cells and the biological pathways using the cell-grading association and the pathway-grading association, identifying the RNA-cell association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated cells, respectively, and identifying the RNA-pathway association between the lncRNA UCA1 expression level and the target RNA expression level and the strongly associated pathways, respectively; The molecular marker module is used to select the molecular markers in the cerebral ischemia injury scenario from the lncRNA UCA1 and the target RNA by using the RNA-cell association and the RNA-pathway association, and molecularly mark the molecular markers to obtain the molecular marker results in the cerebral ischemia injury scenario.

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