Bladder cancer fine typing method and system based on aptamer difference analysis

Abstract

The invention relates to a bladder cancer fine typing method and system based on aptamer difference analysis, and the method comprises the steps: incubating a sample containing bladder cancer cells through a probe package, and carrying out high-throughput gene sequencing on the incubated sample to obtain sequencing data; performing abnormal sample removal on the sequencing data to obtain aptamer data; performing standardization processing on the aptamer data to obtain standard data including a healthy sample group and a case sample group; selecting a differential expression aptamer from the standard data; and typing a to-be-detected sample through the differential expression aptamer. According to the present invention, the expression difference of the specific nucleic acid aptamer in the bladder cancer cell can be identified so as to achieve the fine typing.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a method and system for fine typing of bladder cancer based on nucleic acid aptamer difference analysis. Background Art

[0002] Bladder cancer is a common malignant tumor in the urinary system. With the continuous advancement of molecular biology technology, the molecular typing of bladder cancer has gradually emerged. Molecular typing of bladder cancer helps predict the patient's response to treatment and evaluate the prognosis of the disease. However, the molecular typing of bladder cancer is still in its early stages. Compared with other cancers, there is still a lack of unified, mature and practical typing schemes. Therefore, establishing a more complete molecular typing system is crucial to guide the treatment of bladder cancer.

[0003] Aptamers are a type of single-stranded DNA or RNA molecule with a unique three-dimensional structure that can specifically bind to various targets, including small molecules, proteins, and cancer cells, and are widely used in the screening and identification of disease biomarkers. In the field of precise classification of bladder cancer, aptamers have shown great application potential. As a new type of molecular tool, they can significantly improve the ability to identify tumor cells, provide important support for the development of personalized treatment plans, and provide more accurate diagnosis and treatment options for bladder cancer patients.

[0004] The current Cell-SELEX is a screening technology that targets intact cells and is used to obtain DNA aptamers that specifically bind to cell surface proteins. This technology can recognize the natural conformation of membrane proteins and realize the screening of multi-target aptamers. In the study of bladder cancer, multiple nucleic acid aptamers have been reported, such as the EBA aptamer that specifically binds to EN2, the SPL3C aptamer that binds to CKAP4, and the TB-5 aptamer that binds to NCL. However, due to the high heterogeneity of bladder cancer, these nucleic acid aptamers targeting a single marker are not sufficient to achieve accurate typing of bladder cancer. Summary of the invention

[0005] The present invention provides a bladder cancer fine typing method and system based on nucleic acid aptamer difference analysis, which are used to solve the defects of the prior art.

[0006] The present invention provides a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis, comprising:

[0007] S1: Acquiring sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample;

[0008] S2: removing abnormal samples from the sequencing data to obtain nucleic acid aptamer data;

[0009] S3: standardizing the nucleic acid aptamer data to obtain standard data including a healthy sample group and a case sample group;

[0010] S4: selecting differentially expressed aptamers based on the standard data;

[0011] S5: Typing the sample to be tested by using the differentially expressed aptamers.

[0012] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S2 further comprises:

[0013] S21: receiving sequencing data based on molecular ID tags;

[0014] S22: screening the sequencing data to obtain filtered data;

[0015] S23: merging the filtered data corresponding to each sample to obtain group data;

[0016] S24: Screening the group data to obtain nucleic acid aptamer data.

[0017] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S22 specifically includes:

[0018] S221: filtering aptamers in the sequencing data whose aptamer length is less than or equal to a first preset threshold to obtain first filtered data;

[0019] S222: calculating the longest common subsequence between the aptamer and the primer in the sequencing data by a dynamic programming algorithm;

[0020] S223: removing primer dimers whose primer length and the longest common subsequence are greater than a second preset threshold in the first filtered data to obtain second filtered data, and outputting the second filtered data as filtered data.

[0021] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S222 further comprises:

[0022] S2221: Construct a two-dimensional array;

[0023] S2222: traversing the bases of the sequences of the aptamers and primers in the sequencing data, filling the two-dimensional array, and obtaining a two-dimensional matrix;

[0024] S2223: Output the tail array of the two-dimensional matrix as the longest common subsequence.

[0025] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S24 further comprises:

[0026] S241: Calculate the CPM value of each aptamer according to the abundance of the nucleic acid aptamer in the group data;

[0027] S242: Remove samples with CPM values ​​less than or equal to 1 in the group data to obtain the nucleic acid aptamer data.

[0028] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S3 further comprises:

[0029] S31: Dividing the samples in the nucleic acid aptamer data into a healthy sample group and a case sample group;

[0030] S32: Selecting the median for the healthy sample group and the case sample group respectively to obtain a reference sample;

[0031] S33: calculating the expression difference value and expression level value of each aptamer according to the reference sample;

[0032] S34: calculating a normalization factor according to the expression difference value;

[0033] S35: Standardizing the nucleic acid aptamer data according to the standardization factor to obtain standard data.

[0034] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, the expression of the expression difference value in step S33 is:

[0035] M = log2(CPM sample )-log2(CPM reference );

[0036] Where M is the expression difference value of a single aptamer between the reference sample and other samples, CPM sample is the CPM value of other samples, CPM reference is the CPM value of the reference sample;

[0037] The expression of the expression level value in step S33 is:

[0038] A=0.5×(log2(CPM sample )+log2(CPM reference ));

[0039] Among them, A is the expression level value of a single aptamer between the reference sample and other samples;

[0040] The expression of the normalization factor in step S34 is:

[0041] scaling factor = 2 mean(M) ;

[0042] Among them, scaling factor is the standardization factor, mean (M) is the mean of the expression difference values ​​of the screened samples;

[0043] The expression of the standard data in step S35 is:

[0044] CPM normalized =log2(CPM original ×scaling factor+1);

[0045] Among them, CPM normalized is the sample CPM value in the standard data, CPM original is the original CPM value of a single aptamer.

[0046] According to a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided by the present invention, step S4 further comprises:

[0047] S41: fitting the healthy sample group and the case sample group respectively through a linear model to obtain a fitted healthy sample group and a fitted case sample group;

[0048] S42: Calculating the difference in expression results between the fitted healthy sample group and the fitted case sample group;

[0049] S43: Select the aptamer group corresponding to the maximum difference in expression results as the differentially expressed aptamer.

[0050] According to a method for fine typing of bladder cancer based on nucleic acid aptamer difference analysis provided by the present invention, the expression of the difference in expression results in step S42 is:

[0051] C = β1-β2;

[0052] Among them, C is the difference in expression results of the current group, β1 is the model coefficient corresponding to the aptamer belonging to the fitted healthy sample group of the current group, and β2 is the model coefficient corresponding to the aptamer belonging to the case healthy sample group of the current group.

[0053] The present invention also provides a bladder cancer fine typing system based on nucleic acid aptamer differential analysis, comprising:

[0054] Receiving module: used for receiving sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample;

[0055] Screening module: used to remove abnormal samples from the sequencing data and obtain nucleic acid aptamer data;

[0056] Standardization module: used to standardize the nucleic acid aptamer data to obtain standard data including healthy sample group and case sample group;

[0057] Selection module: used for selecting differentially expressed aptamers based on the standard data;

[0058] Application module: used for typing the sample to be tested by using the differentially expressed aptamer.

[0059] The present invention provides a method and system for fine typing of bladder cancer based on nucleic acid aptamer differential analysis. Through the method based on nucleic acid aptamer differential analysis, the expression difference of specific nucleic acid aptamers in bladder cancer cells can be identified, thereby achieving fine typing. This typing method is more detailed and accurate, which helps doctors to formulate more accurate treatment plans for patients. In the data processing process, the present invention adopts strict screening and standardization steps to ensure the accuracy and reliability of sequencing data, which can reduce the possibility of misdiagnosis and missed diagnosis and improve the accuracy of diagnosis. In addition, the present invention can provide patients with personalized treatment plans by identifying differentially expressed aptamers in bladder cancer. Since different patients may have different differentially expressed aptamers, it may be more effective to formulate personalized treatment plans based on these differences. The typing method of the present invention is fast and efficient, and can quickly provide detailed typing information about bladder cancer patients, thereby helping relevant personnel make decisions more quickly and optimize the treatment path of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic flow chart of a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis provided in an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of the structure of a bladder cancer fine typing system based on nucleic acid aptamer differential analysis provided in an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of a distribution of differentially expressed aptamers provided in an embodiment of the present invention;

[0064] Figure 4A schematic diagram of the binding of the nucleic acid aptamer seq12a sequence provided in an embodiment of the present invention by flow cytometry;

[0065] Figure 5 Another schematic diagram of the distribution of differentially expressed aptamers provided in an embodiment of the present invention.

[0066] Reference numerals:

[0067] 100, receiving module; 200, screening module; 300, standardization module; 400, selection module; 500, application module. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0069] In order to better understand the present invention, the terms appearing in the embodiments of the present invention are first explained below.

[0070] Differential analysis is a statistical technique widely used in biological and medical research. It aims to compare samples under different experimental conditions to reveal significant differences at the gene, protein or other molecular levels. Through differential analysis, biomarkers whose expression changes between disease and health states, different treatment groups, or time points can be found. Common differential analysis applications include differential analysis of gene expression (such as RNA-Seq data), quantitative differential analysis in proteomics, and comparison of metabolite levels in metabolomics. Based on statistical models and hypothesis testing methods, differential analysis can quantify the differences between different groups and evaluate their significance, which is crucial for revealing potential biological mechanisms, screening disease markers, and identifying therapeutic targets. Modern differential analysis often combines multivariate analysis and data regularization techniques to improve robustness to noise and data imbalance, thereby enhancing the credibility of the results.

[0071] Aptamers are a type of single-stranded DNA or RNA molecule with a unique three-dimensional structure that can specifically bind to various targets, including small molecules, proteins, and cancer cells, and are widely used in the screening and identification of disease biomarkers. In the field of precision diagnosis and treatment of bladder cancer, aptamers have shown great application potential. As a new type of molecular tool, they can significantly improve the ability to identify tumor cells, provide important support for the development of personalized treatment plans, and provide more accurate diagnosis and treatment options for bladder cancer patients.

[0072] However, due to the high heterogeneity of bladder cancer, these aptamers targeting a single marker are not sufficient to achieve accurate diagnosis of bladder cancer. Therefore, for tumors with high heterogeneity such as bladder cancer, differential analysis of aptamers based on efficient data extraction can not only reveal the specificity of aptamers to different subtypes, but also provide potential mechanism support for molecular recognition, thereby achieving more accurate cancer diagnosis and the formulation of personalized treatment plans. This differential analysis based on aptamer data provides new tools and strategies for cancer molecular typing, and is expected to enhance the application value of aptamers in precision cancer medicine.

[0073] Combine the following Figures 1 to 5 Embodiments of the present invention are described.

[0074] like Figure 1 As shown, the present invention provides a method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis, comprising:

[0075] S1: Acquiring sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample.

[0076] In step S1, firstly, a universal enrichment library probe package for bladder cancer is used, which is a C+CU library probe package in this embodiment. After incubating bladder cancer cells and normal cells, the nucleic acid aptamers bound to the cells are separated and high-throughput sequencing is performed to obtain molecular ID tags. This technology gives each nucleic acid aptamer in the experiment a unique identity tag to prevent abnormal aptamer quantity caused by specific amplification during high-throughput sequencing.

[0077] S2: removing abnormal samples from the sequencing data to obtain nucleic acid aptamer data.

[0078] Wherein, step S2 further comprises:

[0079] S21: receiving sequencing data based on the molecular ID tag.

[0080] In the first specific embodiment of the present invention, after accurate experimental data is obtained based on the molecular ID tag, the abundance of the binding nucleic acid aptamer for each sample is calculated according to the UMI of the experimental library and the different samples connected to the back end of the primer, wherein the front and back end primers of the library are AAGGAGCAGCGTGGAGGATA (SEQ ID NO: 1) and CTCATGGACGTGCTGGTGAC (SEQ ID NO: 2), respectively, and the UMIs corresponding to the six cancer cell samples are CCTGTCTTGTCTGCCTACCT (SEQ ID NO: 3), CCTGTCTTGTCTGCCTAGAT (SEQ ID NO: 4), CCTGTCTTGTCTGCCTTACT (SEQ ID NO: 5), CCTGTCTTGTCTGCCTTGAT (SEQ ID NO: 6), CCTGTCTTGTCTGCCTTCGT (SEQ ID NO: 7), CCTGTCTTGTCTGCCTTCTGCAT (SEQ ID NO: 8), and the UMIs corresponding to the six normal cell samples are CCTGTCTTGTCTGCCTGAGT (SEQ ID NO: 9), CCTGTCTTGTCTGCCTCTAT (SEQ ID NO: 10), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 11), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 12), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 13), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 14), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 15), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 16), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 17), CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 18), and CCTGTCTTGTCTGCCTCTGAGT (SEQ ID NO: 19). NO:10), CCTGTCTTGTCTGCCTCGGT (SEQ ID NO:11), CCTGTCTTGTCTGCCTAAGT (SEQ ID NO:12), CCTGTCTTGTCTGCCTATCA (SEQ ID NO:13), CCTGTCTTGTCTGCCTATGC (SEQ ID NO:14).

[0081] In another specific embodiment of the present invention, after accurate experimental data is obtained based on the molecular ID tag, the abundance of the binding nucleic acid aptamer for each sample is calculated based on the experimental library and the UMI of different samples connected to the back end of the primer. The front and back primers of the two experiments in this embodiment are AAGGAGCAGCGTGGAGGATA (SEQ ID NO: 1) and CTCATGGACGTGCTGGTGAC (SEQ ID NO: 2). In the first experiment of urine samples of patients and healthy subjects, two repeated experiments were performed on five healthy subjects' urine samples, and the corresponding UMIs were CCTGTCTTGTCTGCCTACCT (SEQ ID NO: 3), CCTGTCTTGTCTGCCTAGAT (SEQ ID NO: 4), CCTGTCTTGTCTGCCTTACT (SEQ ID NO: 5), CCTGTCTTGTCTGCCTTGAT (SEQ ID NO: 6), CCTGTCTTGTCTGCCTTCGT (SEQ ID NO: 7), CCTGTCTTGTCTGCCTTCGT (SEQ ID NO: 8), CCTGTCTTGTCTGCCTGCAT (SEQ ID NO: 9), CCTGTCTTGTCTGCCTGAGT (SEQ ID NO: 10), CCTGTCTTGTCTGCCTCGGT (SEQ ID NO: 11), CCTGTCTTGTCTGCCTCGGT (SEQ ID NO: 12). NO:11), CCTGTCTTGTCTGCCTAAGT (SEQ ID NO:12), and the corresponding UMIs of the patient's urine samples were CCTGTCTTGTCTGCCTATCA (SEQ ID NO:13), CCTGTCTTGTCTGCCTATGC (SEQ ID NO:14), CCTGTCTTGTCTGCCTTTCA (SEQ ID NO:15), CCTGTCTTGTCTGCCTTTGC (SEQ ID NO:16), CCTGTCTTGTCTGCCTTTAG (SEQ ID NO:17), CCTGTCTTGTCTGCCTGCGC (SEQ ID NO:18), CCTGTCTTGTCTGCCTGACA (SEQ ID NO:19), CCTGTCTTGTCTGCCTGTCT (SEQ ID NO:20), CCTGTCTTGTGCCTCATA (SEQ ID NO:21), and CCTGTCTTGTGCCTCAGC (SEQ ID NO:22).In the second type of patient preoperative and postoperative urine sample experiment, the three preoperative urine samples were repeated twice and the corresponding UMIs were CCTGTCTTGTCTGCCTACCT (SEQ ID NO: 3), CCTGTCTTGTCTGCCTAGAT (SEQ ID NO: 4), CCTGTCTTGTCTGCCTTACT (SEQ ID NO: 5), CCTGTCTTGTCTGCCTTGAT (SEQ ID NO: 6), CCTGTCTTGTCTGCCTTCGT (SEQ ID NO: 7), CCTGTCTTGTCTGCCTGCAT (SEQ ID NO: 8), and the corresponding UMIs of the postoperative urine samples were CCTGTCTTGTCTGCCTGAGT (SEQ ID NO: 9), CCTGTCTTGTCTGCCTCTAT (SEQ ID NO: 10), CCTGTCTTGTCTGCCTCGGT (SEQ ID NO: 11), CCTGTCTTGTCTGCCTAAGT (SEQ ID NO: 12), CCTGTCTTGTCTGCCTATCA (SEQ ID NO: 13). NO:13), CCTGTCTTGTCTGCCTATGC (SEQ ID NO:14).

[0082] S22: Screening the sequencing data to obtain filtered data.

[0083] Wherein, step S22 specifically includes:

[0084] S221: Filter the aptamers whose aptamer length is less than or equal to a first preset threshold in the sequencing data to obtain first filtered data.

[0085] After obtaining accurate experimental data based on the molecular ID tag in step S1, in step S22, the abundance of the binding nucleic acid aptamer for each sample is first calculated based on the experimental library and the UMI of different samples connected to the back end of the primer. Then, based on the abundance of the nucleic acid aptamer, for each aptamer, the aptamer that is too short, that is, the aptamer with a length of less than or equal to 20, is initially excluded. Subsequently, the influence of primer dimers on the experimental results needs to be further removed.

[0086] Specifically, the high-throughput sequencing data results are sequences and sequencing copy numbers including aptamers, primers, sample tags (UMIs) and ID tags. Step S21 is specifically as follows: first, the copy number of each sequence is normalized to 1, so that accurate screening and enrichment library data can be obtained; the normalized data of each sample in the enrichment library is classified by library primers and different sample tags; by re-counting the same aptamers in each sample, statistical data based on molecular ID tags are obtained, and the final count is the accurate copy number of the aptamer of each sample in the screening and enrichment library.

[0087] S222: Calculate the longest common subsequence between the aptamer and the primer in the sequencing data by a dynamic programming algorithm.

[0088] Primer dimers may produce a small amount of base mutations during amplification, and all dimers cannot be accurately found directly by string matching. Therefore, a dynamic programming algorithm is used to calculate the longest common subsequence between the aptamer and the primer in steps S222 to S223.

[0089] Wherein, step S222 further includes:

[0090] S2221: Construct a two-dimensional array.

[0091] S2222: Traverse the bases of the sequences of the aptamers and primers in the sequencing data, fill the two-dimensional array, and obtain a two-dimensional matrix.

[0092] S2223: Output the tail array of the two-dimensional matrix as the longest common subsequence.

[0093] In steps S2221 to S2223, first, a two-dimensional array L is established, where L[i][j] represents the longest common subsequence length of the first i characters of the first sequence and the first j characters of the second sequence; subsequently, the first row and the first column are initialized to 0, because the longest common subsequence length of any sequence and the empty sequence is 0; secondly, the bases of the two sequences are traversed, if the characters are the same, that is, x[i-1]=y[j-1], then L[i][j]=L[i-1][j-1]+1, if the characters are different, take L[i][j]=max(L[i-1][j],L[i][j-1]); finally, the lower right corner of the matrix L[m][n] is the longest common subsequence length of the two sequences.

[0094] S223: removing primer dimers whose primer length and the longest common subsequence are greater than a second preset threshold in the first filtered data to obtain second filtered data, and outputting the second filtered data as filtered data.

[0095] In step S223, based on the longest common subsequence calculated in step S222, the ratio of the longest common subsequence to the primer length is calculated, and then a threshold is set to 0.9. If it is greater than 0.9, it is a primer dimer and is removed.

[0096] S23: Merge the filtered data corresponding to each sample to obtain group data.

[0097] The aptamer data of each sample after filtering in step S22 are merged. If the aptamer does not exist in a sample, it is counted as 0, and finally the original data similar to genomic difference analysis is obtained. The difference is that the genomic data is the number of genes corresponding to different samples, while the aptamer data is the abundance of aptamers corresponding to different samples.

[0098] S24: Screening the group data to obtain nucleic acid aptamer data.

[0099] Wherein, step S24 further comprises:

[0100] S241: Calculate the CPM value of each aptamer according to the abundance of the nucleic acid aptamer in the group data;

[0101] S242: Remove samples with CPM values ​​less than or equal to 1 in the group data to obtain the nucleic acid aptamer data.

[0102] The purpose of step S24 is to further filter the nucleic acid aptamer data to exclude the influence of non-specific aptamers with low abundance on the analysis. Specifically, the CPM of each aptamer is calculated, that is, the number of counts per million, which is the original reads counts divided by the total number of reads multiplied by 1*10 6 , and screen out aptamers with CPM greater than 1 in at least K / 2 samples, where K represents the number of samples, which helps to remove aptamers from abnormal samples and ensure the reliability of subsequent analysis.

[0103] S3: Standardizing the nucleic acid aptamer data to obtain standard data including a healthy sample group and a case sample group.

[0104] Wherein, step S3 further comprises:

[0105] S31: Divide the samples in the nucleic acid aptamer data into a healthy sample group and a case sample group.

[0106] S32: Select the median for the healthy sample group and the case sample group respectively to obtain a reference sample.

[0107] In steps S31 to S32, the samples are first divided into two groups, representing a healthy sample group and a cancer sample group, respectively. A median sample is selected from each group as a reference sample, that is, for each aptamer, the expression values ​​of all samples are sorted in ascending order, and the median is selected. If the number of samples is odd, the middle number is selected. If it is an even number, the average of the two middle numbers is selected.

[0108] S33: Calculate the expression difference value and expression level value of each aptamer according to the reference sample.

[0109] The expression of the expression difference value in step S33 is:

[0110] M = log2(CPM sample )-log2(CPM reference );

[0111] Where M is the expression difference value of a single aptamer between the reference sample and other samples, CPM sample is the CPM value of other samples, CPM reference is the CPM value of the reference sample.

[0112] The expression of the expression level value in step S33 is:

[0113] A=0.5×(log2(CPM sample )+log2(CPM reference ));

[0114] Among them, A is the expression level value of a single aptamer between the reference sample and other samples.

[0115] The above M value represents the expression difference of each aptamer between the reference sample and other samples, and the A value represents the average expression level of each aptamer in the two samples.

[0116] S34: Calculate a normalization factor according to the expression difference value.

[0117] The expression of the normalization factor in step S34 is:

[0118] scaling factor = 2 mean(M) ;

[0119] Among them, scaling factor is the standardization factor, and mean(M) is the mean of the expression difference values ​​of the screened samples.

[0120] In step S34, firstly, the first 30% and the last 30% of the M values ​​are removed, and the mean of the remaining M values ​​is calculated. Then, the mean is converted into a normalization factor by the above formula, i.e., in logarithmic form.

[0121] S35: Standardizing the nucleic acid aptamer data according to the standardization factor to obtain standard data.

[0122] The expression of the standard data in step S35 is:

[0123] CPM normalized =log2(CPM original ×scaling factor+1);

[0124] Among them, CPM normalized is the sample CPM value in the standard data, CPM original is the original CPM value of a single aptamer.

[0125] In step S35, by multiplying each sample CPM by the normalization factor in step S34, the expressiveness of different samples can be compared under the same benchmark.

[0126] S4: Select differentially expressed aptamers based on the standard data.

[0127] Wherein, step S4 further comprises:

[0128] S41: Fitting the healthy sample group and the case sample group respectively through a linear model to obtain a fitted healthy sample group and a fitted case sample group.

[0129] The purpose of step S41 is to represent the group information of the experiment by constructing a design matrix, specifically, to fit the aptamer data using a linear model and evaluate the expression levels of different aptamers in different groups. After fitting, the model will generate a set of parameters for each gene, including the expression level of each group and the corresponding standard error.

[0130] S42: Calculate the difference in expression results between the corresponding groups of the fitted healthy sample group and the fitted case sample group. The expression of the difference in expression results in step S42 is:

[0131] C = β1-β2;

[0132] Among them, C is the difference in expression results of the current group, β1 is the model coefficient corresponding to the aptamer belonging to the fitted healthy sample group of the current group, and β2 is the model coefficient corresponding to the aptamer belonging to the case healthy sample group of the current group.

[0133] The purpose of step S42 is to calculate the expression difference of each set of aptamers under the selected comparison, where β1 and β2 are the model coefficients of the two groups of samples respectively, and then the standard error is adjusted using the Bayesian method to improve the stability and reliability of the results.

[0134] In addition, step S42 also calculates the significance level of the corresponding groups of the fitted healthy sample group and the fitted case sample group, and the expression of the significance level through the t test is:

[0135]

[0136] in, To fit the sample mean of the healthy sample group, is the sample mean of the fitting case sample group, To fit the sample variance of the healthy sample group, The sample variance of the fitted case sample group, n1 is the total number of samples of the fitted healthy sample group, and n2 is the total number of samples of the fitted case sample group.

[0137] S43: Select the aptamer group corresponding to the maximum difference in expression results as the differentially expressed aptamer.

[0138] In step S43, significantly differentially expressed aptamers are extracted from the model results obtained in step S42, and information such as their expression changes and statistical significance is obtained. Visualization tools such as volcano maps and heat maps can also be used to display the distribution of differentially expressed aptamers.

[0139] S5: Typing the sample to be tested by using the differentially expressed aptamers.

[0140] In a specific embodiment, 88 differential aptamers were obtained based on the differential analysis of nucleic acid aptamers, and the differential value was the logarithmic ratio between the cancer cell sample and the normal cell sample. Figure 3 As shown in A, the color of each point in the volcano plot corresponds to the average value of the repeated cancer samples. On both sides are aptamers with large and significant differences between cancer cell samples and normal cell samples, which can be used to distinguish the two groups of samples. Figure 3 As shown in B, the differential aptamers obtained after standardization of the data of the two groups of samples can significantly distinguish cancer cell samples from normal cell samples. The results show that the binding modes of the C+CU probe package in YTS-1 and SV-HUC-1 are significantly different, and the differential aptamers extracted from the probe package aptamers can significantly distinguish bladder cancer cells YTS-1 and bladder normal epithelial tissue cells SV-HUC-1.

[0141] Specifically, Figure 3 Figure A in the middle is the difference analysis of sequencing data. The color of the circle represents the logarithmic mean abundance of cancer cell samples. The redder the color, the higher the abundance of the aptamer in the cancer sample, and the bluer the color, the lower the abundance of the aptamer in the cancer sample. Figure 3 Middle B is the standardized abundance of 88 differential nucleic acid aptamers in various bladder cancer cell YTS-1 and normal cell SV-HUC-1 samples. The redder the color, the higher the copy number, and the bluer the color, the lower the copy number.

[0142] In a specific embodiment, in order to verify whether the extracted aptamer can truly distinguish cancer cells from normal cells, the present invention further synthesized the seq12a sequence with the largest binding difference in YTS-1 and SV-HUC-1 cells for flow cytometry binding, and found that it binds to bladder cancer cells but not to normal cells. The equilibrium dissociation constant of seq12a was measured, and its Kd was 0.79±0.18nM, indicating that the differential analysis based on nucleic acid aptamers can distinguish cancer cells from normal cells, and is conducive to the selection of specific nucleic acid aptamers, such as Figure 4 As shown, Figure 4 A in the middle is the flow cytometry binding of nucleic acid aptamer seq12a on YTS-1 and SV-HUC-1 cells, where Random is a random control sequence. Figure 4 Where B is the equilibrium dissociation constant of nucleic acid aptamer seq12a.

[0143] In another specific embodiment, the present invention cooperates with the nucleic acid aptamer differential analysis of the probe package to initially explore the molecular typing of clinical urine samples. The specific results are as follows Figure 5 As shown, Figure 5 A in the middle is differential aptamer analysis. Figure 5 Figure B shows the sequence copy number analysis of differential aptamers in urine samples from five bladder cancer patients and five healthy subjects. The redder the color, the higher the copy number, and the bluer the color, the lower the copy number. Figure 5 Middle C is differential aptamer analysis, Figure 5 D in the middle is the sequence copy number analysis of differential nucleic acid aptamers in preoperative urine samples and postoperative urine samples of three bladder cancer patients. The redder the color, the higher the copy number, and the bluer the color, the lower the copy number.

[0144] like Figure 2 As shown, the present invention also provides a bladder cancer fine typing system based on nucleic acid aptamer differential analysis, comprising:

[0145] Receiving module 100: used for receiving sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample.

[0146] Screening module 200: used to remove abnormal samples from the sequencing data to obtain nucleic acid aptamer data.

[0147] The standardization module 300 is used to perform standardization processing on the nucleic acid aptamer data to obtain standard data including a healthy sample group and a case sample group.

[0148] Selection module 400 is used to select differentially expressed aptamers based on the standard data.

[0149] Application module 500: used to perform typing of the sample to be tested by using the differentially expressed aptamer.

[0150] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0151] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0152] The present invention discloses a method and system for fine typing of bladder cancer based on nucleic acid aptamer differential analysis. The main goal is to realize the possibility of preliminary molecular typing and clinical monitoring of bladder cancer based on nucleic acid aptamer differential analysis in combination with a bladder cancer nucleic acid aptamer probe package. The present application uses a bladder cancer nucleic acid aptamer probe package, which contains multiple nucleic acid aptamers that identify different levels of bladder cancer cell lines. Through nucleic acid aptamer differential analysis, differential aptamers can be selected from high-throughput sequencing results, which can distinguish bladder cancer cells from normal cells. Among them, the significantly upregulated aptamer seq12 binds to bladder cancer cells but not to normal cells, and has good specificity and affinity (Kd=0.79±0.18nM). Finally, the present invention also attempts to use nucleic acid aptamer differential analysis in combination with a probe package to realize clinical patient and healthy person typing and clinical course monitoring. Through the research and development of the nucleic acid aptamer-based typing technology of the present invention, it is expected to improve the early detection rate and treatment effect of bladder cancer, thereby reducing the risk of recurrence and mortality of patients.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis, characterized in that: include: S1: Acquiring sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample; S2: removing abnormal samples from the sequencing data to obtain nucleic acid aptamer data; S3: standardizing the nucleic acid aptamer data to obtain standard data including a healthy sample group and a case sample group; S4: selecting differentially expressed aptamers based on the standard data; S5: Typing the sample to be tested by using the differentially expressed aptamers.

2. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 1, characterized in that: Step S2 further comprises: S21: receiving sequencing data based on molecular ID tags; S22: screening the sequencing data to obtain filtered data; S23: merging the filtered data corresponding to each sample to obtain group data; S24: Screening the group data to obtain nucleic acid aptamer data.

3. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 2, characterized in that: Step S22 further includes: S221: filtering aptamers in the sequencing data whose aptamer length is less than or equal to a first preset threshold to obtain first filtered data; S222: calculating the longest common subsequence between the aptamer and the primer in the sequencing data by a dynamic programming algorithm; S223: removing primer dimers whose primer length and the longest common subsequence are greater than a second preset threshold in the first filtered data to obtain second filtered data, and outputting the second filtered data as filtered data.

4. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 3, characterized in that: Step S222 further includes: S2221: Construct a two-dimensional array; S2222: traversing the bases of the sequences of the aptamers and primers in the sequencing data, filling the two-dimensional array, and obtaining a two-dimensional matrix; S2223: Output the tail array of the two-dimensional matrix as the longest common subsequence.

5. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 2, characterized in that: Step S22 further includes: S241: Calculate the CPM value of each aptamer according to the abundance of the nucleic acid aptamer in the group data; S242: Remove samples with CPM values ​​less than or equal to 1 in the group data to obtain the nucleic acid aptamer data.

6. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 1, characterized in that: Step S3 further comprises: S31: Dividing the samples in the nucleic acid aptamer data into a healthy sample group and a case sample group; S32: Selecting the median for the healthy sample group and the case sample group respectively to obtain a reference sample; S33: calculating the expression difference value and expression level value of each aptamer according to the reference sample; S34: calculating a normalization factor according to the expression difference value; S35: Standardizing the nucleic acid aptamer data according to the standardization factor to obtain standard data.

7. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 6, characterized in that: The expression of the expression difference value in step S33 is: M=log2(CPM sample )-log2(CPM reference ); Where M is the expression difference value of a single aptamer between the reference sample and other samples, CPM sample is the CPM value of other samples, CPM reference is the CPM value of the reference sample; The expression of the expression level value in step S33 is: A=0.5×(log2(CPM sample )+log2(CPM reference )); Among them, A is the expression level value of a single aptamer between the reference sample and other samples; The expression of the normalization factor in step S34 is: scaling factor=2 mean(M) ; Among them, scaling factor is the standardization factor, mean (M) is the mean of the expression difference values ​​of the screened samples; The expression of the standard data in step S35 is: CPM normalized =log2(CPM original ×scaling factor+1); Among them, CPM normalized is the sample CPM value in the standard data, CPM original is the original CPM value of a single aptamer.

8. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 1, characterized in that: Step S4 further comprises: S41: fitting the healthy sample group and the case sample group respectively through a linear model to obtain a fitted healthy sample group and a fitted case sample group; S42: Calculating the difference in expression results between the fitted healthy sample group and the fitted case sample group; S43: Select the aptamer group corresponding to the maximum difference in expression results as the differentially expressed aptamer.

9. The method for fine typing of bladder cancer based on nucleic acid aptamer differential analysis according to claim 8, characterized in that: The expression of the expression result difference in step S42 is: C＝β1-β2； Among them, C is the difference in expression results of the current group, β1 is the model coefficient corresponding to the aptamer belonging to the fitted healthy sample group of the current group, and β2 is the model coefficient corresponding to the aptamer belonging to the case healthy sample group of the current group.

10. A bladder cancer fine typing system based on nucleic acid aptamer differential analysis, characterized in that: include: Receiving module: used for receiving sequencing data, wherein the sequencing data is obtained by incubating a sample containing bladder cancer cells with a probe package and performing high-throughput gene sequencing on the incubated sample; Screening module: used to remove abnormal samples from the sequencing data and obtain nucleic acid aptamer data; Standardization module: used to standardize the nucleic acid aptamer data to obtain standard data including healthy sample group and case sample group; Selection module: used for selecting differentially expressed aptamers based on the standard data; Application module: used for typing the sample to be tested by using the differentially expressed aptamer.

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