A system for the assisted diagnosis and / or prognosis evaluation of bladder cancer

Abstract

The application belongs to the field of biological medicine, and particularly relates to an auxiliary diagnosis and / or prognosis evaluation system for bladder cancer, comprising a data processing device; the system comprises a data input module and a data processing module; wherein the data input module acquires quantitative data of each detection target in a detection sample of a subject, and the quantitative data comprises data of relative modification level and relative expression level of core target points; the core target points comprise SPMD4 and SGK2; the data processing module calculates a comprehensive disease score of the subject according to the quantitative data of each detection target, and then outputs a corresponding prognosis state of the subject according to the comprehensive disease score. Compared with the prior art, the auxiliary diagnosis and / or prognosis evaluation system provided by the application comprehensively considers the influence of post-transcriptional methylation modification and relative expression level on the bladder cancer and the prognosis survival condition, breaks through the limitation of traditional single-omics analysis, and enhances the accuracy and reliability of judgment and evaluation.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and in particular to a system for the auxiliary diagnosis and / or prognostic assessment of bladder cancer. Background Technology

[0002] Bladder cancer, as one of the most common malignant tumors of the urinary system, has an extremely high recurrence rate and risk of progression. In the field of clinical diagnosis, the conventional clinical diagnostic pathway for bladder cancer mainly relies on invasive cystoscopy and pathological biopsy for confirmation, and the detection systems are usually designed around histological or cytological observation. To reduce the invasiveness of the examination, finding effective peripheral molecular markers and establishing screening and judgment methods to assist or replace traditional examination methods has become an important direction of research and development.

[0003] Existing methods or systems for detecting peripheral molecular markers to aid in diagnosis and prognostic assessment typically rely on conventional DNA methylation alterations, gene mutations, or the overall expression abundance of target genes as core detection targets. For example, conventional molecular diagnostic kits often use DNA methylation of microRNA genes or mutations in common oncogenes / tumor suppressor genes (such as PIK 3CA, UNC5C, etc.) as the main detection targets. However, recent studies have found that in addition to expression levels and DNA methylation, post-transcriptional mRNA methylation is also closely related to tumorigenesis, and post-transcriptional methylation plays a fine-grained regulatory role in disease development. Current technologies often overlook the fine-grained regulatory role of post-transcriptional methylation modifications in disease development and neglect the comprehensive consideration of expression abundance and post-transcriptional methylation, leading to unreliable judgment and assessment results. Summary of the Invention

[0004] Therefore, the purpose of this invention is to overcome the shortcomings of existing technologies that do not consider the impact of post-transcriptional family planning on disease development, and to apply reagents for detecting the relative expression levels of SPMD4 and SGK2, as well as the relative modification level of m6A, in the preparation of products for auxiliary diagnosis and / or prognostic assessment of bladder cancer. The relative expression levels of SPMD4 and SGK2, and the relative modification level of m6A, are significantly different in diseased tissues compared to healthy tissues, and these differences in relative expression levels have a significant impact on prognosis and survival. By detecting quantitative data of SPMD4 and SGK2, the presence of bladder cancer can be reflected and prognosis can be assessed. Compared to existing technologies, this invention considers both expression levels and post-transcriptional relative modification levels of m6A, allowing for more reliable auxiliary diagnosis and / or prognostic assessment.

[0005] This invention also provides a system for the auxiliary diagnosis and / or prognostic assessment of bladder cancer, including a data processing device; the data processing device includes: a data input module and a data processing module; wherein,

[0006] The data input module acquires quantitative data of each detection target in the subject's test sample. The quantitative data includes data on the relative modification level and relative expression level of m6A of the core target. The core target includes SPMD4 and SGK2. The data processing module calculates the subject's comprehensive disease score and poor prognosis score based on the quantitative data of each detection target, and then outputs the subject's corresponding prognostic status based on the comprehensive disease score and poor prognosis score.

[0007] In one embodiment, the data processing module performs the following data processing steps: Step S1: Using quantitative data from each detection target, calculate the disease comprehensive score RS and determine whether the patient has the disease; if the patient is diagnosed with the disease, proceed to step S2 for prognostic assessment; wherein, Disease comprehensive score RS=∑(α) i ·M i )+∑(β i ·E i ), In the formula, i represents the i-th detection target, used to distinguish different detection targets; Mi represents the m6A relative modification level data of the i-th detection target, Ei represents the relative expression level data of the i-th detection target; αi represents the weighting coefficient of the m6A relative modification level of the i-th detection target, and βi represents the weighting coefficient of the relative expression level of the i-th detection target. When a subject meets predetermined conditions, the subject is determined to be ill; the predetermined conditions include: the subject's comprehensive disease score RS is greater than a preset optimal cutoff value; Step S2: When a subject is diagnosed with bladder cancer, a poor prognostic score is calculated by comparing the relative expression level of the prognostic assessment target with the corresponding preset expression level. The system for auxiliary diagnosis and / or prognostic assessment provided by this invention comprehensively considers the impact of post-transcriptional methylation modification and relative expression levels on bladder cancer incidence and prognosis, overcoming the limitations of traditional single-mathematical analysis and enhancing the accuracy and reliability of judgment and assessment.

[0008] In one embodiment, the quantitative data acquired by the data input module further includes data on the relative modification level and / or relative expression level of m6A for auxiliary targets, as well as data on the relative modification level and relative expression level of m6A for prognostic assessment targets.

[0009] In one embodiment, the auxiliary targets include one or more of IFITM2DE, LncRNA00482, PCAT1, circRNAHIPK3, METTL14, and ALKBH5; the prognostic assessment targets include one or more of SPMD4, SGK2, LncRNA00482, circRNAHIPK3, METTL14, and ALKBH5. That is, in addition to the core targets, the auxiliary targets belonging to the prognostic assessment targets also simultaneously acquire data on the relative modification level and relative expression level of m6A. Moreover, the core targets, in the comparison between the diseased cohort and the healthy cohort, are detection targets that show differences in both the relative modification level and relative expression level of m6A, while the auxiliary targets are detection targets that show differences; the prognostic assessment targets, within the diseased cohort, can lead to different prognostic survival trends when their corresponding quantitative data differs from the median within the diseased cohort.

[0010] In one embodiment, the predetermined conditions for determining that a subject is ill further include: the relative expression level of SPMD4 is higher than the preset SMPD4 health reference value in the system, and the relative expression level of SGK2 is lower than the preset SGK2 health reference value in the system; wherein, the SMPD4 health reference value is the median of the relative expression level of SMPD4 in the transcriptome data of the healthy cohort in the TCGA database; and the SGK2 health reference value is the median of the relative expression level of SGK2 in the transcriptome data of the healthy cohort in the TCGA database.

[0011] In one embodiment, the mechanism for calculating the poor prognosis score is as follows: The initial value of the poor prognosis score is 0; the poor prognosis score is incremented by 1 for each condition AF that is met. A: The relative expression level of SPMD4 in the subjects' samples was lower than the preset expression level of SPMD4; B: The relative expression level of SGK2 in the subjects' samples was lower than the pre-specified expression level of SGK2; C: The relative expression level of LncRNA00482 in the subjects' samples was higher than the preset expression level of LncRNA00482; D: The relative expression level of circRNA HIPK3 in the subjects' samples was higher than the preset expression level of circRNA HIPK3; E: The relative expression level of METTL14 in the subjects' samples was lower than the preset expression level of METTL14; F: The relative expression level of ALKBH5 in the subjects' test samples was higher than the preset expression level of ALKBH5.

[0012] In one embodiment, the system further includes an output module that outputs a high risk of disease and a poor prognosis score when the subject judges to have the disease; outputs a low risk of disease when the subject judges not to have the disease; and outputs a poor prognosis / high lethal risk when the poor prognosis score is equal to the number of prognostic assessment targets.

[0013] In one embodiment, the comprehensive disease score RS=∑(α) i ·M i )+∑(β i ·E i In the formula: α corresponding to LncRNA00482, METTL14, and ALKBH5 in the auxiliary target sites. i ·M i The value assigned is 0, corresponding to β i Established through risk regression training; the β corresponding to IFITM2DE, PCAT1, and circRNA HIPK3 in the auxiliary targets. i ·E i The value assigned to 0 corresponds to α i Established through risk regression training; α of SPMD4 and SGK2. i With β i All were established through risk regression training; Establishing α through risk regression i and / or β i The method is as follows: Follow-up data on overall survival from the bladder cancer cohort in the TCGA database are used to determine the α-survival rate in all bladder cancer cohorts that require hazard regression. i The corresponding m6A relative modification level data of the detection target and all β values ​​that need to be established through risk regression. i The relative expression levels of the corresponding detection targets were used as covariates and input into the Cox proportional hazards regression model for training. α i The value is assigned as the partial regression coefficient β, which represents the relative modification level of the m6A at the i-th detection target obtained after Cox proportional hazards regression training. i The value is assigned as the partial regression coefficient of the relative expression level of the i-th detection target obtained after Cox proportional hazards regression training.

[0014] In one embodiment, the method further includes reagents and / or instruments for detecting the relative modification level and / or relative expression level of m6A at target sites in the test sample via quantitative real-time PCR; wherein the reagents include primers and / or probes for targeting the SMPD45'UTR region, the IFITM2 CDS region, the PCAT1 exon 2 region, and the circRNA HIPK3 backsplicing site region. These regions specifically exhibit m6A methylation modification characteristics different from those in healthy tissues; therefore, detecting quantitative data at specific locations can make the results of auxiliary diagnosis and / or prognostic assessment more reliable.

[0015] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0016] Figure 1 This is a cumulative distribution curve of gene expression. Figure 2 A four-quadrant diagram drawn when quadrant locking is enabled; Figure 3 Figure 1 shows the results of Me-RIP-qPCR and RT-qPCR detection in bladder cancer tissue and adjacent healthy tissue in clinical samples. Figure 4 Kaplan-Meier survival assessment curves plotted for clinical prognosis validation; Figure 5 This is a flowchart of the data processing device. Figure 6 To observe the IGV enrichment detection trajectory at the methylation enrichment sites. Detailed Implementation

[0017] To optimize and update the bladder cancer auxiliary diagnosis and / or prognostic assessment system, enabling it to comprehensively consider gene expression abundance and post-transcriptional methylation, and ultimately improve the reliability of the bladder cancer auxiliary diagnosis and / or prognostic assessment system, this invention aims to establish a system that comprehensively considers gene expression abundance and post-transcriptional methylation for disease auxiliary diagnosis and / or prognostic assessment, and the information processing flow of this system is conceived as follows: First, quantitative data on the detection targets are obtained. These targets include core targets, and the obtained quantitative data includes at least the post-transcriptional methylation modification level and relative expression level of the core targets. Preferably, the detection targets also include auxiliary targets and prognostic assessment targets, and at least the post-transcriptional methylation modification level or relative expression level of the auxiliary targets is obtained.

[0018] Secondly, the presence or absence of disease is determined based on the quantitative data obtained for each detection target.

[0019] Finally, when the disease is diagnosed, the survival prognosis is assessed by comparing the quantitative data of the prognostic assessment target with its corresponding preset value.

[0020] In the above information processing flow, the first step is to select the detection target. The specific methods for selecting the detection target and confirming the corresponding quantitative data to be obtained are as follows.

[0021] Posttranscriptional methylation modifications are most commonly associated with N6-methyladenosine (m6A) methylation of RNA. Studies have reported approximately one modification site per 2000 base pairs, with about 12,000 m6A sites existing in over 7000 human genes. Their dynamic regulation is closely related to tumorigenesis and development. Previous studies using MeRIP-seq have confirmed that bladder cancer tissue, compared to adjacent normal tissue, contains 2331 hypomethylated and 3819 hypermethylated mRNAs, as well as 105 hypermethylated lncRNAs and 238 hypermethylated circRNAs. However, the number of methylation sites is vast, and these numerous differentially modified features have not yet been effectively translated into clinical detection targets for auxiliary diagnosis and / or prognostic assessment.

[0022] In order to identify the most critical core targets and auxiliary targets that can be used to assist in the evaluation from a large number of potential methylation sites, and to select prognostic targets for prognosis assessment, the present invention performs screening according to the following steps.

[0023] (1) Obtaining high-throughput sequencing data and performing differential filtering: High-throughput MeRIP-seq and RNA-seq sequencing and combined analysis were used to obtain data on the difference in expression levels (log2FC) and m6A modification levels (log2FC) of the whole transcripts in bladder cancer tissue relative to adjacent normal tissue. Using conventional high-throughput sequencing library construction methods, the detected transcripts were mainly linear transcripts (mRNA and lncRNA) quantitative data. Therefore, the Ribo-Zero library construction method could also be used for sequencing to obtain quantitative data of circular transcripts (circRNA). The corresponding genes of the detected mRNAs, lncRNAs, and circRNAs were all candidate targets. To determine the threshold for further differential filtering, this invention used the measured quantitative data of linear transcripts as an example, combining the differences in expression levels and m6A modification levels in the transcripts, to divide the transcripts into m6A-modified transcripts (-m6A(+)) and non-m6A-modified transcripts (-m6A(-),) and plotted the cumulative gene expression distribution curve using conventional methods, such as... Figure 1 As shown. Figure 1In the diagram, the x-axis represents log2FC, which is the fold change in expression in the tumor relative to the adjacent normal tissue (0 represents no change, >0 represents upregulation, and <0 represents downregulation); the y-axis represents the cumulative proportion, i.e., "the proportion of transcripts corresponding to log2FC ≤ the corresponding y value". Figure 1 In the graph, the red curve representing the m6A(+) group shows a more significant cumulative trend on the right side of the horizontal axis (high-expression upregulation region) compared to the black curve representing the m6A(-) group, and exhibits some resistance on the left side (downregulation region). This phenomenon indicates that m6A modification generally tends to antagonize the negative regulation of genes and promote their upregulation. Given that the curves only show a clear inflection point separation in the region |log2FC|>1, in order to effectively filter out noisy genes fluctuating at the baseline and ensure that the selected targets have significant biological effects, we decided to use |log2FC|>1 and p<0.05 as the threshold for differential expression for subsequent differential filtering. Therefore, this invention uses the difference in expression level and the difference in m6A modification level |log2FC| both greater than 1, and both p less than 0.05 as screening conditions to narrow down the selection range of candidate targets.

[0024] The quantitative data of circular transcripts were processed using the same steps, with the same screening criteria: differences in expression levels and differences in m6A modification levels (|log2FC| both greater than 1, p less than 0.05). The same screening thresholds can be used to screen candidate targets for circRNAs.

[0025] (2) Quadrant locking to narrow down the selection range of core targets: Plot the expression level difference (log2FC) on the horizontal axis and the m6A modification level difference (log2FC) on the vertical axis as shown in the figure. Figure 2 The four-quadrant diagram shown prioritizes candidate targets in the first and second quadrants that exhibit high m6A methylation to further narrow down the selection of core targets.

[0026] (3) Trend verification using qPCR: To further verify the reproducibility and confidence of the candidate targets selected by high-throughput sequencing in steps (1) and (2) above, Me-RIP-qPCR and RT-qPCR were used to measure the relative modification level and relative expression level of m6A of candidate targets in bladder cancer tissue and adjacent healthy tissue in clinical samples. Some results are shown below. Figure 3 As shown, candidate targets with similar performance to those detected by high-throughput sequencing and qPCR were selected for further narrowing down the pool, improving reliability and reproducibility.

[0027] (4) Clinical prognostic verification: The above steps only reflect the relationship between the expression and modification levels of each candidate target and whether or not the disease is present. However, many differentially expressed targets are merely "byproducts" or general background factors after cancer development (i.e., pan-cancer non-specific genes, such as basic cell proliferation and metabolic changes). At least for core targets, this invention believes that pan-cancer specific targets should be proposed as much as possible. To this end, this invention analyzes the survival status of candidate targets after diagnosis: Expression level data for each candidate target in a bladder cancer cohort were obtained from the TCGA large-sample clinical cohort database. Within the cohort, samples were divided into high-expression and low-expression groups based on the median expression level of each candidate target—the high-expression group represented individuals whose expression level of the corresponding candidate target was higher than the median expression level of the candidate target in the cohort, and vice versa. Kaplan-Meier survival assessment curves were then plotted using follow-up survival data from the cohort. Survival assessment curves for some candidate targets are shown below. Figure 4 As shown. Figure 4 The vertical axis represents survival probability, and the horizontal axis represents follow-up time. Since the data in this graph comes from a disease cohort, it reflects the impact of a candidate target on prognostic survival in bladder cancer. The wider the groove (separation) between the two curves, the stronger the prognostic ability of the indicator. In the survival assessment curves for SMPD4 and SGK2, the red line representing the Low expression group shows a significantly steeper decline over time than the blue line, and the proportion of patients ultimately maintaining survival is significantly lower in the Low expression group. This intuitive curve separation clearly demonstrates that SMPD4 and SGK2 expression characteristics below the median in the disease cohort are directly associated with worse overall survival in bladder cancer patients. The same applies to METTL14. ALKBH5, LINC00482, and HIPK3 show the opposite trend, i.e., the High expression group (red line) has a worse prognosis.

[0028] While pan-cancer nonspecific genes are generally abnormally expressed in cancer, they often fail to produce significant survival prediction differences within specific bladder cancer patient populations. Only those genes closely related to specific invasion, metastasis, or drug resistance mechanisms of bladder cancer can show a clear "red-blue line separation" in survival curves.

[0029] Therefore, the above analysis, by observing whether these candidate targets can determine the life or death of bladder cancer patients, reverse-engineers those detection targets that truly participate in driving the malignant progression of bladder cancer. For auxiliary targets, targets that show "red-blue line separation" in survival curves are also preferentially selected to maximize reliability. Moreover, for assessment targets to be used in prognostic evaluation, the above survival curve analysis is also used to select specific assessment targets with clinical prognostic value and to provide a data basis for prognostic assessment steps.

[0030] In summary (1) to (4), this invention selects candidate targets that simultaneously exhibit differences in m6A modification levels and expression levels, and whose survival curves show "red-blue line separation" as core targets. The core targets ultimately selected by this invention include SPMD4 and SGK2. When obtaining quantitative data for the detection targets, it is necessary to simultaneously obtain data on the relative m6A modification level and relative expression level of the core targets. Specifically, in bladder cancer tissues, the SPMD4 transcript shows a higher relative m6A modification level and a higher relative expression level than healthy tissues. However, when its relative expression level is lower than the median relative expression level in the disease cohort, it indicates a worse prognosis. Similarly, the SGK2 transcript in bladder cancer tissues shows a higher relative m6A modification level and a lower relative expression level than healthy tissues. However, when its relative expression level is lower than the median relative expression level of SGK2 in the disease cohort, it indicates a worse prognosis.

[0031] The auxiliary targets selected in this invention include those that exhibit only differences in expression levels or only differences in m6A modification levels as described in the above steps. For such auxiliary targets, at least quantitative data showing differences in expression are required. Specifically, the auxiliary targets selected in this invention include IFITM2DE (indicated by lower relative m6A modification levels of mRNA in bladder cancer tissue compared to healthy tissue), LncRNA00482 (indicated by higher relative expression levels in bladder cancer tissue compared to healthy tissue, with a worse prognosis when the relative expression level is higher than the median in the disease cohort), PCAT1 (indicated by higher relative m6A modification levels of mRNA in bladder cancer tissue compared to healthy tissue), and circRNA HIPK3 (indicated by lower relative m6A modification levels in bladder cancer tissue compared to healthy tissue, with a worse prognosis when the relative expression level is higher than the median in the disease cohort). In addition, enzymes specifically associated with m6A modification are selected as auxiliary targets, as the relative expression levels of these enzymes directly affect post-transcriptional methylation modification levels and further reflect disease risk; therefore, data on the relative expression levels of these enzymes are necessary. Therefore, auxiliary targets also include: methyltransferase METTL14 (which is associated with methylation, and its relative expression level in the survival curve is lower than the median in the disease cohort, indicating a worse prognosis) and / or demethylase ALKBH5 (which is associated with methylation, and its relative expression level in the survival curve is higher than the median in the disease cohort, indicating a worse prognosis).

[0032] The prognostic targets selected in this invention are preferably the core targets and are detection targets that show red-blue curve separation in the survival curve in step (4), including one or both of SPMD4 and SGK2. Auxiliary targets that show red-blue curve separation in the survival curve can also be included as prognostic targets. However, in this case, the relative expression level data of the auxiliary targets that are prognostic targets still need to be obtained for prognostic evaluation, including one or more of LncRNA00482, circRNAHIPK3, METTL14, and ALKBH5.

[0033] After establishing the detection target, the present invention ultimately formed the following bladder cancer auxiliary diagnosis and / or prognostic assessment system.

[0034] Example 1 The bladder cancer auxiliary diagnosis and / or prognostic assessment system includes a data processing device that determines whether cancer is present and assesses prognosis based on quantitative data of the input detection targets. Specifically, the data processing device includes a data input module, a data processing module, and an output module. The data input module acquires quantitative data of the detection targets, the data processing module performs auxiliary diagnosis and prognostic assessment, and the output module outputs the results. The detection targets include core targets and auxiliary targets, and the quantitative data includes data on the relative modification level and / or relative expression level of m6A. Details are as follows.

[0035] The data input module acquires quantitative data for each target in the subject's test sample. This quantitative data includes data on the relative modification and expression levels of m6A for core targets, the relative modification and / or expression levels of m6A for auxiliary targets, and the relative modification and expression levels of m6A for prognostic assessment targets. The core targets include SPMD4 and SGK2; the auxiliary targets include one or more of IFITM2DE, LncRNA00482, PCAT1, circRNAHIPK3, METTL14, and ALKBH5; and the prognostic assessment targets include one or more of SPMD4, SGK2, LncRNA00482, circRNAHIPK3, METTL14, and ALKBH5.

[0036] The acquired quantitative data is transmitted to the data processing module for data processing.

[0037] The data processing module performs, for example Figure 5 And the data processing steps described below: Step S1: Using the acquired quantitative data of each detection target, calculate the disease comprehensive score RS and determine whether the subject is diseased. If the disease comprehensive score RS is greater than the optimal cutoff value, the relative expression level of SPMD4 is higher than the preset SMPD4 health reference value in the system, and the relative expression level of SGK2 is lower than the preset SGK2 health reference value in the system, then the subject is judged to be diseased, and step S2 is continued for prognostic assessment; otherwise, the subject is judged not to be diseased, and the transmission signal value output module outputs the result.

[0038] In step S1, the comprehensive disease score RS = ∑(α i ×M i )+∑(β i ×E i ), i represents the i-th detection target, used to distinguish different detection targets; M i E represents the relative modification level data of m6A for the i-th detection target. i α represents the relative expression level data of the i-th detection target. i β represents the weighting coefficient for the relative modification level of m6A at the i-th detection target. i The weighting coefficient α represents the relative expression level of the i-th detection target. i and β i It is pre-stored in the system and will be called by the data processing module when it performs calculations.

[0039] For the auxiliary targets LncRNA00482, METTL14, and ALKBH5, since the relative expression level difference of these targets in the comparison between diseased and healthy cohorts affects the risk of disease, their corresponding α i ·M i The value assigned is 0, corresponding to β i Established through risk regression training.

[0040] For auxiliary targets IFITM2DE, PCAT1, and circRNA HIPK3, since the relative m6A modification level difference between diseased and healthy cohorts influences the risk of disease, their corresponding β... i ·E i The value assigned to 0 corresponds to α i Established through risk regression training.

[0041] For the core targets SPMD4 and SGK2, where both relative differences in m6A modification levels and relative expression levels influence disease risk, their corresponding α... i With β i All were established through risk regression training.

[0042] Establishing α through risk regression i and / or β i The method is as follows: Follow-up data on overall survival from the bladder cancer cohort in the TCGA database are used to determine the α-survival rate in all bladder cancer cohorts that require hazard regression. i The corresponding m6A relative modification level data of the detection target and all β values ​​that need to be established through risk regression. i The relative expression levels of the corresponding detection targets were used as covariates and input into the Cox proportional hazards regression model for training. α i The value is assigned as the partial regression coefficient β, which represents the relative modification level of the m6A at the i-th detection target obtained after Cox proportional hazards regression training. i The value is assigned as the partial regression coefficient of the relative expression level of the i-th detection target obtained after Cox proportional hazards regression training.

[0043] The optimal cutoff value is pre-existing in the system and is called when needed. The method for determining it is as follows: extract the characteristic data of the clinical validation cohort composed of the confirmed bladder cancer patient cohort and the healthy cohort in the TCGA database, plot the receiver operating characteristic (ROC) curve in the conventional way, and take the RS value corresponding to the maximum point of the output Youden Index (i.e., sensitivity + specificity - 1) as the system's preset optimal cutoff value.

[0044] The SMPD4 health reference value is the median relative expression level of SMPD4 in the transcriptome data of healthy cohorts in the TCGA database; the SGK2 health reference value is the median relative expression level of SGK2 in the transcriptome data of healthy cohorts in the TCGA database. Step S2: When step S1 determines that the subject is ill, a poor prognosis score is calculated by comparing the relative expression level of the prognostic assessment target with the preset expression level of the corresponding target in the pre-existing system to reflect the risk of poor prognosis. The result is then output by the signal value output module. The prognostic assessment target includes one or more of SPMD4, SGK2, LncRNA00482, circRNA HIPK3, METTL14, and ALKBH5. The system has a poor prognosis score, with an initial value of 0; the score increases by 1 for each condition AF met. A: The relative expression level of SPMD4 in the subjects' samples was lower than the preset expression level of SPMD4. B: The relative expression level of SGK2 in the subjects' samples was lower than the pre-defined expression level of SGK2. C: The relative expression level of LncRNA00482 in the test samples of the subjects was higher than the preset expression level of LncRNA00482; D: The relative expression level of circRNA HIPK3 in the subjects' test samples was higher than the preset expression level of circRNA HIPK3. E: The relative expression level of METTL14 in the subject's sample was lower than the pre-stored preset expression level of METTL14. F: The relative expression level of ALKBH5 in the subjects' samples was higher than the preset expression level of ALKBH5 in the pre-stored samples; The preset expression level of each of the above prognostic assessment targets is the median relative expression level of that prognostic assessment target in the TCGA bladder cancer cohort.

[0045] The final poor prognosis score is output to the output module.

[0046] The output module outputs results based on the data processing module. Specifically, when the data processing module determines that the patient has the disease in step S1, it outputs "high risk of disease" and "poor prognosis score obtained in step S2". When the poor prognosis score equals the number of prognostic assessment targets, it outputs "poor prognosis / high lethal risk". When the data processing module determines that the patient does not have the disease in step S1, it outputs "low risk of disease".

[0047] Compared to existing technologies, the system for auxiliary diagnosis and / or prognostic assessment provided by this invention comprehensively considers the impact of post-transcriptional methylation modification and relative expression levels on bladder cancer incidence and prognosis, breaking through the limitations of traditional single-mathematical analysis. By analyzing the influence and causal relationship network of 'modification-expression-disease-death', it enhances the accuracy and reliability of judgment and assessment.

[0048] Example 2 Furthermore, the high-level bladder cancer auxiliary diagnosis and / or prognostic assessment system, in addition to having a data processing device, also includes reagents and / or instruments for detecting the relative modification level and / or relative expression level of m6A at the detection target in the test sample.

[0049] Specifically, the reagents include: TRIzol extraction reagent for extracting and fragmenting RNA, nucleic acid purification centrifuge column, and metal ion fragmentation buffer for specifically fragmenting RNA to approximately 100 nt; anti-m6A monoclonal antibody, Protein A / G-conjugated superparamagnetic sorting beads, and matching low-temperature incubation and washing buffer for immunosorting; reverse transcriptase and its standard reaction solution for reverse transcription and amplification, specific amplification primers targeting each detection target and matching TaqMan probes, internal control primers or primers and matching probes containing a target housekeeping gene (GAPDH), positive / negative modification controls with known modification states, and PCR premix of hot-start Taq DNA polymerase for qPCR amplification. The instrument includes a real-time PCR instrument.

[0050] The method for detecting the relative m6A modification level is as follows: Tumor tissue and adjacent normal tissue were collected from bladder cancer patients. Following standard procedures, total RNA was extracted using reagents for RNA extraction and fragmentation, and then fragmented into 100 nt fragments. A portion of the RNA was used as an input control, while the remaining RNA was incubated with anti-m6A antibody at 4°C for 2 hours. M6A-modified transcripts were enriched and purified using magnetic beads. Subsequently, the transcripts were reverse transcribed into cDNA using the aforementioned reagents for reverse transcription and amplification. Quantitative fluorescence amplification (qPCR) was performed on the target sites for which the relative m6A modification level data was required (standard procedure example: 95°C pre-denaturation for 3 min; followed by 40 cycles of 95°C for 15 s and 60°C for 30 s). After obtaining the qPCR data, the relative m6A modification level data for each target site was calculated. Specific experimental procedures and calculation methods are standard and will not be elaborated further.

[0051] The method for detecting relative transcription levels is as follows: After extracting total RNA, the reserved raw fragmented RNA (Input sample) that has not undergone immunoprecipitation is reverse transcribed into cDNA using the reagents described above for reverse transcription and amplification. Then, RT-qPCR is performed using specific amplification primers targeting the detection target and housekeeping gene (GAPDH) to detect the relative expression level of each target. This method for detecting relative transcription levels is also a standard method and will not be elaborated further.

[0052] qPCR is used as the actual detection method because it has a shorter experimental cycle and can obtain target quantitative data more quickly compared to high-throughput sequencing. However, since qPCR uses primers to target the detection site and detects fluorescence during amplification for quantification, and the amplification length of qPCR is usually preferably 100-200, its length cannot completely cover the entire detection target site, but can only target a part of the target site. Although the detection of m6A relative modification level is performed by qPCR after m6A enrichment, this invention considers that m6A modification may exhibit concentrated and specific modification in a certain region of the gene transcript. If the qPCR primers can be designed directly to amplify concentrated and specific modification sites, the accuracy and reliability of the system can be further increased.

[0053] Therefore, this invention uses high-throughput MeRIP-seq and RNA-seq sequencing data from clinical bladder cancer tissue and adjacent healthy tissue, as described above, to plot IGV enrichment detection trajectory maps and observe the m6A modification enrichment locations of each detection target. For example, tissues with different enrichment locations... Figure 6 As shown in the figure, the blue / red peaks represent the m6A modification signal intensity in that region. The figure reveals that SMPD4 exhibits a very narrow peak characteristic in the tumor group, highly enriched in the 5'UTR region, while the normal group shows an almost linear peak at this location, indicating that m6A modification is concentrated in the 5'UTR region. This further confirms the reliability of this core target. Additionally, the IFITM2 modification peak is precisely enriched in the CDS region, the PCAT1 modification peak is highly enriched in exon 2, and the circRNA HIPK3 modification peak is concentrated in the backsplicing site region.

[0054] Therefore, the primers or primers and probes in the reagents are specifically targeted at the SMPD4 5'UTR region, the IFITM2 CDS region, the PCAT1 exon 2 region, and the circRNA HIPK3 reverse splice site region to perform MeRIP-qPCR to detect the relative level of m6A modification.

[0055] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments and claims of this application are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that, unless otherwise stated, “a plurality” means two or more; the terms “first,” “second,” “third,” etc., are used only to distinguish and not to describe a particular order or sequence, nor should they be construed as indicating or implying relative importance. The term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items. When the above description relates to drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0056] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. Application of reagents for detecting the relative expression levels of SPMD4 and SGK2 and the relative modification level of m6A in the preparation of products for auxiliary diagnosis and / or prognostic assessment of bladder cancer.

2. A system for the auxiliary diagnosis and / or prognostic assessment of bladder cancer, characterized in that, Includes a data processing device; the data processing device includes: a data input module and a data processing module; wherein, The data input module acquires quantitative data of each detection target in the subject's test sample. The quantitative data includes data on the relative modification level and relative expression level of m6A of the core target. The detection target includes the core target, which includes SPMD4 and SGK2. The data processing module calculates the subject's overall disease score and poor prognosis score based on the quantitative data of each detection target, and then outputs the subject's corresponding prognostic status based on the overall disease score and poor prognosis score.

3. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 2, characterized in that, The data processing module performs the following data processing steps: Step S1: Using the quantitative data from each detection target, calculate the comprehensive disease score RS, where: Disease comprehensive score RS=∑(α) i ·M i )+∑(β i ·E i ); In the formula, i represents the i-th detection target point, used to distinguish different detection targets; M i E represents the relative modification level data of m6A for the i-th detection target. i α represents the relative expression level data of the i-th detection target. i β represents the weighting coefficient of m6A relative modification level for the i-th detection target. i The weighting coefficient representing the relative expression level of the i-th detection target; When a subject meets predetermined conditions, the subject is determined to be ill; the predetermined conditions include: the subject's comprehensive disease score RS is greater than a preset optimal cutoff value; Step S2: When a subject is ill, compare the relative expression level of the subject's prognostic assessment target with the corresponding preset expression level, and calculate the poor prognosis score.

4. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 3, characterized in that, The detection target also includes auxiliary targets and prognostic assessment targets; the quantitative data acquired by the data input module also includes data on the relative modification level and / or relative expression level of m6A of the auxiliary targets, and data on the relative modification level and relative expression level of m6A of the prognostic assessment targets; the auxiliary targets include one or more of IFITM2DE, LncRNA00482, PCAT1, circRNA HIPK3, METTL14, and ALKBH5; the prognostic assessment targets include two or more of SPMD4, SGK2, LncRNA00482, circRNA HIPK3, METTL14, and ALKBH5, of which at least SPMD4 and SGK2 are included.

5. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 4, characterized in that, The predetermined conditions also include: the relative expression level of SPMD4 is higher than the preset SMPD4 health reference value, and the relative expression level of SGK2 is lower than the preset SGK2 health reference value.

6. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 5, characterized in that, The mechanism for calculating the poor prognostic score is as follows: The initial value of the poor prognosis score is 0; the poor prognosis score is incremented by 1 for each condition AF that is met. A: The relative expression level of SPMD4 in the subjects' samples was lower than the preset expression level of SPMD4; B: The relative expression level of SGK2 in the subjects' samples was lower than the pre-specified expression level of SGK2; C: The relative expression level of LncRNA00482 in the subjects' samples was higher than the preset expression level of LncRNA00482; D: The relative expression level of circRNA HIPK3 in the subjects' samples was higher than the preset expression level of circRNA HIPK3; E: The relative expression level of METTL14 in the subjects' samples was lower than the preset expression level of METTL14; F: The relative expression level of ALKBH5 in the subjects' test samples was higher than the preset expression level of ALKBH5.

7. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 6, characterized in that, The system also includes an output module that outputs a high risk of disease and a poor prognosis score when the subject judges to have the disease; and outputs a low risk of disease when the subject judges not to have the disease. When the poor prognosis score equals the number of prognostic assessment targets, the output is poor prognosis / high lethal risk.

8. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 4, characterized in that, In the comprehensive disease score RS=∑(α) i ·M i )+∑(β i ·E i In the formula: α corresponding to the auxiliary targets LncRNA00482, METTL14, and ALKBH5 i ·M i The value assigned is 0, corresponding to β i Established through risk regression training; auxiliary targets IFITM2DE, PCAT1, and circRNA HIPK3 corresponding to β i ·E i The value assigned to 0 corresponds to α i Established through risk regression training; α of SPMD4 and SGK2. i With β i All were established through risk regression training; Establishing α through risk regression i and / or β i The method is as follows: Follow-up data on overall survival from the bladder cancer cohort in the TCGA database are used to determine the α-survival rate in all bladder cancer cohorts that require hazard regression. i The corresponding m6A relative modification level data of the detection target and all β values ​​that need to be established through risk regression. i The relative expression levels of the corresponding detection targets were used as covariates and input into the Cox proportional hazards regression model for training. α i The value is assigned as the partial regression coefficient β, which represents the relative modification level of the m6A at the i-th detection target obtained after Cox proportional hazards regression training. i The value is assigned as the partial regression coefficient of the relative expression level of the i-th detection target obtained after Cox proportional hazards regression training.

9. The bladder cancer auxiliary diagnosis and / or prognostic assessment system according to claim 4, characterized in that, The method for determining the optimal cutoff value is as follows: extract the characteristic data of the clinical validation cohort composed of the confirmed bladder cancer patient cohort and the healthy cohort in the TCGA database, plot the receiver operating characteristic curve, and take the RS value corresponding to the maximum point of the output Youden index as the optimal cutoff value preset by the system.

10. The bladder cancer auxiliary diagnostic and / or prognostic assessment system according to any one of claims 1-9, characterized in that, It also includes reagents and / or instruments for detecting the relative modification level and / or relative expression level of m6A target in the test sample by quantitative real-time PCR; The reagents include primers and / or probes for targeting the SMPD4 5'UTR region, the IFITM2 CDS region, the PCAT1 exon 2 region, and the circRNA HIPK3 reverse splice site region.

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