A biomarker for diagnosing meningitis and its application

Abstract

The present invention relates to a biomarker for diagnosing meningitis and its application, and relates to the field of gene detection technology. The biomarker includes at least one of 36 genes, and the biomarker can indicate whether the subject to be evaluated who provides a biological sample is at high risk of meningitis infection.

Description

Technical Field

[0001] The present invention relates to the technical field of gene detection, and in particular to a biomarker for diagnosing meningitis and application thereof. Background Art

[0002] Metagenomic clinical testing technology is a new clinical technology that uses second-generation high-throughput sequencing to identify and diagnose microbial infections from the perspective of genetic material. The microbial sequences included in high-throughput sequencing are relatively limited, and more than 90% of the sequence content is human sequences. However, only a small number of microbial sequences have been used for infectious identification in clinical applications to date, and most human sequences still lack application and analysis. Among them, macrotranscriptome technology uses transcriptome sequencing technology to detect RNA viral load, bacteria, fungi and other pathogenic microbial transcripts, which serves as an important basis for whether the patient has a microbial infection, but the gene expression of the human itself has not been analyzed and used.

[0003] Although metatranscriptomics has been well applied in neurological infections, common brain diseases and tuberculous brain require metatranscriptomics results as an important basis for clinical diagnosis. However, for diseases related to cerebrospinal fluid infection, the consistency between metagene detection results and clinical practice has always been poor and has not been well promoted and applied. Summary of the invention

[0004] In response to the above problems, the present invention provides a biomarker for the diagnosis of meningitis. Starting from the metagene human transcriptome, specific genes are obtained as biomarkers, which can indicate whether the subject to be evaluated who provides the biological sample is at high risk of meningitis infection.

[0005] In order to achieve the above objectives, the present invention provides a biomarker for diagnosing meningitis, comprising at least one of the following genes: TRBV12-5, DSTN, CA2, IGFBP6, PI3, C15orf48, TIMM13, ANKRD13C, PEA15, CXCL10, H2AC14, H4C5, ZMYND11, PIM1, GBP1, ISG15, IGF2, RNA5SP481, RNA5SP283, RNA5SP202, RNA5SP226, RNA5SP403, RNA5SP72, RNA5SP160, S100A9, FCGR3B, MTND1P21, RPS29P26, RPL21P31, SNRPCP9, PRAMEF29P, KRTAP21-4P, RPL36AP8, RPS3AP7, SLC7A5P2, or POLR2KP1.

[0006] During the research, the inventors found that the macrogene detection technology has not been well applied to diseases related to cerebrospinal fluid infection. The main reasons are as follows: 1. In the conventional detection of pathogenic microorganisms, the amount of microorganisms detected must reach a certain limit to be determined as pathogenic bacteria. However, in the actual cerebrospinal fluid detection process, a small amount of microorganisms are detected in a large number of samples. The reason for this situation is that the inventors believe that these small amounts of detected microorganisms may be microorganisms from the test or air source, or they may indeed be microorganisms that infect the patient, but it is impossible to clarify it by relying solely on macrogene technology, which may result in false positive cases. 2. At the same time, macrogene technology mainly uses nucleic acids for microbial identification, while diseased brain, tuberculous brain, Cryptococcus neoformans brain, etc., the former two invade the interior of the cell, and the microorganisms in the circulatory system are very limited. As a fungus, Cryptococcus neoformans has a low load, and due to the cell wall structure of the fungus, Cryptococcus neoformans is even more difficult to extract. Therefore, many common causes of cerebrospinal fluid infection cannot have better sensitivity in macrogene detection, which will result in false negative cases. 3. Not only that, when the virus invades the patient's brain parenchyma, the viral load in the cerebrospinal fluid will be very limited, but the patient will have obvious symptoms of infection, which cannot be detected at all by macro-gene testing.

[0007] After a large number of research experiments, the inventors found that no matter whether pathogenic microorganisms invade the brain parenchyma or the cerebral cortex, local inflammatory reactions will be caused, and the transcripts of the corresponding body will undergo huge changes, that is, the expression of the immune pathways and transcripts of the infected patient itself may directly reflect the infection status of the patient, but due to the low depth data of the macrotranscriptome and the technical difficulties in predicting meningitis, it has always been a problem to use the macrotranscriptome for infection auxiliary diagnosis. Therefore, the inventors obtain the gene expression of each transcript in the macrotranscriptome of the biological sample, use the rank sum test to perform differential gene analysis of clinical grouping, evaluate the differential gene expression in the infection group and the non-infection group, and use GO, KEGG and other databases to enrich the pathway, use lasso regression to perform regression analysis of the characteristic gene set, determine the genes and contribution rates (i.e., weighted values) corresponding to the above-mentioned 36 cerebrospinal fluid infection-related transcript signals after removing the redundant effect, and use the above-mentioned specific 36 genes and contribution indexes to obtain the evaluation model, and calculate and analyze the evaluation model to indicate that the object to be evaluated of the biological sample is at high risk of meningitis infection.

[0008] In one embodiment, the biomarkers include: CXCL10, IGF2, RNA5SP202, RNA5SP226, RNA5SP72, S100A9, and POLR2KP1.

[0009] In one embodiment, the biomarkers include the following genes: TRBV12-5, DSTN, CA2, IGFBP6, PI3, C15orf48, TIMM13, ANKRD13C, PEA15, CXCL10, H2AC14, H4C5, ZMYND11, PIM1, GBP1, ISG15, IGF2, RNA5SP481, RNA5SP283, RNA5SP202, RNA5SP226, RNA5SP403, RNA5SP72, RNA5SP160, S100A9, FCGR3B, MTND1P21, RPS29P26, RPL21P31, SNRPCP9, PRAMEF29P, KRTAP21-4P, RPL36AP8, RPS3AP7, SLC7A5P2, and POLR2KP1.

[0010] The present invention also provides the use of the biomarker in developing and / or preparing a product for diagnosing meningitis.

[0011] The above application can solve the problem of patients with occult infection despite negative metagenomic test results, locate infection signals from human transcripts, and solve the problem of unclear sources of infection due to metagenomic low-sequence testing and suspected neurological abnormalities but no clear cause.

[0012] In one embodiment, the application includes the following steps: collecting cerebrospinal fluid, extracting the macrotranscriptome in the cerebrospinal fluid, and detecting the expression level of the biomarker.

[0013] The present invention also provides a product for diagnosing meningitis, which comprises: a reagent for detecting the expression level of the biomarker in a biological sample.

[0014] In one embodiment, the biological sample is obtained from cerebrospinal fluid.

[0015] The present invention also provides a system for diagnosing meningitis, the system comprising:

[0016] Analytical device: used to detect the expression level of the biomarker in the biological sample of the subject to be evaluated, and input it into the evaluation model for analysis;

[0017] Output device: used to output the above analysis results.

[0018] In one embodiment, the evaluation model is as follows: Among them, Risk is the meningitis infection risk value of the subject to be evaluated, e represents the natural constant, k represents the kth mRNA with non-zero weight value, c represents the weight value, expression represents the TPM value of mRNA, and n represents the number of genes.

[0019] In one embodiment, the weighted value is set as follows:

[0020] When the gene is TRBV12_5, the weighted value is -0.487553275;

[0021] When the gene is DSTN, the weighted value is 0.183814987;

[0022] When the gene is CA2, the weighted value is 0.512654584;

[0023] When the gene is IGFBP6, the weighted value is 0.230692277;

[0024] When the gene is PI3, the weighted value is -0.048969818;

[0025] When the gene is C15orf48, the weighted value is -0.260142365;

[0026] When the gene is TIMM13, the weighted value is 0.143632698;

[0027] When the gene is ANKRD13C, the weighted value is -0.29938557;

[0028] When the gene is PEA15, the weighted value is 0.0054829;

[0029] When the gene is CXCL10, the weighted value is -0.317186117;

[0030] When the gene is H2AC14, the weighted value is -0.021527034;

[0031] When the gene is H4C5, the weighted value is 0.0544751;

[0032] When the gene is ZMYND11, the weighted value is 0.502305371;

[0033] When the gene is PIM1, the weighted value is -0.114266971;

[0034] When the gene is GBP1, the weighted value is -0.199360039;

[0035] When the gene is ISG15, the weighted value is -0.03091624;

[0036] When the gene is IGF2, the weighted value is 0.991757711;

[0037] When the gene is RNA5SP481, the weighted value is 0.093864768;

[0038] When the gene is RNA5SP283, the weighted value is -0.005563655;

[0039] When the gene is RNA5SP202, the weighted value is -0.587122701;

[0040] When the gene is RNA5SP226, the weighted value is 0.281441276;

[0041] When the gene is RNA5SP403, the weighted value is -0.14454107;

[0042] When the gene is RNA5SP72, the weighted value is -0.200900429;

[0043] When the gene is RNA5SP160, the weighted value is -0.000116441;

[0044] When the gene is S100A9, the weighted value is -0.01575733;

[0045] When the gene is FCGR3B, the weighted value is -0.114721492;

[0046] When the gene is MTND1P21, the weighted value is -0.09680476;

[0047] When the gene is RPS29P26, the weighted value is -0.094079988;

[0048] When the gene is RPL21P31, the weighted value is -0.093022725;

[0049] When the gene is SNRPCP9, the weighted value is 0.109758396;

[0050] When the gene is PRAMEF29P, the weighted value is -0.814286332;

[0051] When the gene is KRTAP21_4P, the weighted value is 0.015612173;

[0052] When the gene is RPL36AP8, the weighted value is 0.104934447;

[0053] When the gene is RPS3AP7, the weighted value is -0.278675375;

[0054] When the gene is SLC7A5P2, the weighted value is 0.119072739;

[0055] When the gene is POLR2KP1, the weighted value is -0.383658175.

[0056] Since the amount of macrotranscriptome data is small, far lower than that of ordinary transcriptomes, there is no feasible analysis idea in the prior art for transcriptome analysis of human genes in cerebrospinal fluid, and what form of gene expression index to use. In addition, the human genome has a large number of genes, and there is a lack of a lot of research and verification on which genes are clearly correlated with infection, and there is a lack of clear analysis models and algorithms for how to quantify the expression of different genes into specific infection judgment indicators. The expression of sample transcripts in different parts of different patients also varies. How to use the transcripts obtained from cerebrospinal fluid to predict the nature of infection is a blank area of ​​current technology. Therefore, the inventors constructed the above-mentioned evaluation model, using 36 genes as biomarkers, combined with the weighted values ​​corresponding to each gene, to calculate whether each object to be evaluated that provides a biological sample is at high risk of meningitis infection.

[0057] In one embodiment, the analysis includes: when the risk value of meningitis infection of the subject to be evaluated is greater than 3.626, it indicates that the subject to be evaluated is at high risk of meningitis infection.

[0058] The inventors used the ROC algorithm to calculate the optimal cutoff value, selected the optimal threshold point of the ROC curve based on the Youden index, and obtained the optimal model constraint value of the evaluation model to be 3.626, that is, when the meningitis infection risk value (or absolute value) of the subject to be evaluated is greater than 3.626, it indicates that the subject to be evaluated is at high risk of meningitis infection.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] A biomarker for diagnosing meningitis and its application of the present invention, the biomarker starts from the human transcriptome of the macrogene, obtains a specific gene as a biomarker, and through the evaluation model, prompts whether the subject to be evaluated who provides the biological sample is at high risk of meningitis infection. The system for diagnosing meningitis of the present invention can analyze the infection status of the patient from the human part of the macrotranscriptome results, can accurately and efficiently verify the infection of the patient, improve the accuracy of other infection identification technologies such as macrogenes, and can accurately test microbial infections that are difficult to detect, such as diseased brains, and is a key test and scientific research technology that is superior to current common infection detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1This is the expression heat map of some differentially expressed genes in the positive group and the negative group in Example 1. In the patient annotations at the top of the heat map, blue represents infected patients and orange represents non-infected patients. The heat map from red to blue represents the centralized gene expression levels.

[0062] Figure 2 This is the GO pathway to which DEGs were enriched in Example 1.

[0063] Figure 3 This is the KEGG pathway to which the DEGs in Example 1 were enriched.

[0064] Figure 4 Schematic diagram of the lasso regression model in Example 1, log(λ) score around X, weight index on the Y axis, and lines corresponding to the constraints of each gene.

[0065] Figure 5 Schematic diagram of the number of genes corresponding to the optimal log(λ) value parameter under lasso regression in Example 1, wherein the two dotted lines from right to left represent the auxiliary lines of the lowest log(λ) value and the lowest log(λ) variance value, respectively.

[0066] Figure 6 This is the CUTOFF curve diagram in Example 2.

[0067] Figure 7 This is the AUC curve diagram in Example 3.

[0068] Figure 8 This is the DCA decision curve diagram in Example 3.

[0069] Fig. 9 The risk score box plot is for the positive patient samples and negative samples in Example 3 after evaluation using the evaluation model in Example 2, where 1 is the positive patient sample and 2 is the negative sample.

[0070] Fig.10 The risk score box plot is for infection risk scores between different infection subtypes and negative, where 3 is Cryptococcal meningitis, 4 is common bacterial meningitis, 5 is tuberculosis meningitis, 6 is viral meningitis, and 7 is a negative sample.

[0071] Fig.11 This is the AUC curve diagram in Example 5.

[0072] Fig.12 This is a graph showing the comparison between the evaluation model and the true value in Example 5. DETAILED DESCRIPTION

[0073] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0075] source:

[0076] Unless otherwise specified, the reagents, materials, and equipment used in this example are all commercially available; the experimental methods are all conventional experimental methods in the art unless otherwise specified.

[0077] Example 1

[0078] Biomarkers were screened.

[0079] 1. Obtain 153 metatranscriptome detection results.

[0080] 53 samples from patients with positive meningitis and 100 samples from patients with negative meningitis were collected. The criteria for determining positive and negative meningitis were as follows: samples from patients with positive meningitis were tested for the same pathogenic microorganism by both the full set of dual processes of the macro-gene and were consistent with the clinical indicators; samples from patients with negative meningitis were tested for no pathogenic microorganism by both the full set of dual processes of the macro-gene. Specifically, the dual processes refer to the DNA detection process and the RNA detection process. The DNA detection process detects whether the DNA of the microorganism is detected, indicating the existence of the microorganism, and the RNA detection process detects whether the microorganism is transcribed, indicating the survival of the microorganism. A positive result from both processes generally confirms that the microorganism is the pathogenic source.

[0081] 2. Quality control.

[0082] The hg38 version of the genome transcript downloaded from the NCBI public database was used as a comparison reference to calculate the gene expression level of each transcript, and the standardized depth was used as the basis for expression. After quality control, 28,022 gene expression data were obtained.

[0083] 3. Differential gene analysis.

[0084] 1. Use the rank sum test to perform differential gene analysis of clinical grouping and evaluate the differential gene expression in the positive group and the negative group: Use the rank sum test to perform non-parametric test on the gene expression data after quality control, set the grouping as negative group: negative samples (100 cases), positive group: positive patient samples (53 cases), set the significant P value threshold to 0.05, and calculate the mean expression of all significant differential genes (DEGs) in the negative group and the positive group at the same time. Calculate the mean difference as the difference parameter FC, take the FC threshold as greater than 1 or less than -1, and get a total of 1779 DEGs. The expression heat map of some differential genes in the positive group and the negative group is shown in the figure below. Figure 1 shown.

[0085] 2. Pathway enrichment using GO, KEGG and other databases: The main enriched pathways are SRP-dependent co-translational protein targeting membrane, humoral immune response, viral transcription, immunoglobulin-mediated immune response, complement activation, defense response to bacteria, chemokine activity, etc., a total of 136 GO pathways (such as Figure 2 ), as well as 15 KEGG pathways such as coronavirus disease COVID-19, JAK-STAT signaling pathway, IL-17 signaling pathway, and interaction between viral proteins and cytokines and cytokine receptors (as shown in Figure 3 shown).

[0086] 4. Screen and obtain biomarkers and corresponding weighted values.

[0087] Lasso regression was used to perform regression analysis on the characteristic gene set to determine the predicted gene set (i.e., the 36 biomarkers in this embodiment) and the contribution rate (i.e., the weighted value) after removing the redundant effect: lasso regression was used to screen and weight genes, and genes with too high correlation were removed to obtain a simple and accurate prognostic model, such as Figure 4 As shown, the lines correspond to the constraints of each gene. The binomial algorithm is used for calculation, the total specificity is set to 1 (100%), and 10 branches are set for cross-validation to calculate the contribution rate of each gene. The results are as follows Figure 5 As shown, Figure 5The two dotted lines from right to left, the first one corresponds to the parameter value with the smallest error variance, and the second one corresponds to the parameter value with the smallest average error. Considering that there are few studies in the current field, the minimum λ value is used as the optimal parameter value, and the corresponding number of genes is 36, namely TRBV12-5, DSTN, CA2, IGFBP6, PI3, C15orf48, TIMM13, ANKRD13C, PEA15, CXCL10, H2AC14, H4C5, ZMYND11, PIM1, GBP 1. ISG15, IGF2, RNA5SP481, RNA5SP283, RNA5SP202, RNA5SP226, RNA5SP403, RNA5SP72, RNA5SP160, S100A9, FCGR 3B, MTND1P21, RPS29P26, RPL21P31, SNRPCP9, PRAMEF29P, KRTAP21-4P, RPL36AP8, RPS3AP7, SLC7A5P2, POLR2KP1.

[0088] Example 2

[0089] Build an evaluation model.

[0090] Using the genes and weighted values ​​obtained in Example 1, an evaluation model for meningitis diagnosis is obtained, and the meningitis infection risk value of the object to be evaluated for each sample is calculated. The evaluation model of this example is as follows.

[0091]

[0092] Among them, Risk is the meningitis infection risk value of the subject to be evaluated, e represents the natural constant, k represents the kth mRNA with non-zero weight value, c represents the weight value, expression represents the TPM value of mRNA, and n represents the number of genes.

[0093] The genes and weighted values ​​obtained in Example 1 were substituted into the evaluation model to obtain an evaluation model for meningitis diagnosis, as shown below.

[0094] Risk=(TRBV12_5×-0.487553275)+(DSTN×0.183814987)+(CA2×0.512654584)+(IGFBP6×0.230692277)+(PI3×-0.04896 9818)+(C15orf48×-0.260142365)+(TIMM13×0.143632698)+(ANKRD13C×-0.29938557)+(PEA15×0.0054829)+(CXCL10×- 0.317186117)+(H2AC14×-0.021527034)+(H4C5×0.0544751)+(ZMYND11×0.502305371)+(PIM1×-0.114266971)+(GBP1×-0.199360039)+(ISG15×-0.03091624)+(IGF2×0.991757711)+(RNA5SP481×0.093864768)+(RNA5SP283×-0.005563655) +(RNA5SP202×-0.587122701)+(RNA5SP226×0.281441276)+(RNA5SP403×-0.14454107)+(RNA5SP72×-0.200900429)+(RNA5SP160×-0.000116441)+(S100A9×-0.01575733)+(FCGR3B×-0.114721492)+(MTND1P21×-0.09680476)+(RPS29P26×-0 .094079988)+(RPL21P31×-0.093022725)+(SNRPCP9×0.109758396)+(PRAMEF29P×-0.814286332)+(KRTAP21_4P×0.015 612173)+(RPL36AP8×0.104934447)+(RPS3AP7×-0.278675375)+(SLC7A5P2×0.119072739)+(POLR2KP1×-0.383658175).

[0095] Then, the OLS algorithm was used to calculate the linear regression model, and the prediction points were set to (0.0001, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.9999). The nomogram was used to analyze the contribution of all genes and risk scores to the results, and it was found that the risk score had the best prediction range for the results.

[0096] The calibration curve was drawn using the calibrate algorithm, and the boost parameter was set to 1000. The curve had a good agreement with the true value in the range of 0-1, where a C-index greater than 0.5 indicated predictive relevance, 0.7-0.9 indicated good predictability, and above 0.9 indicated that the model was sufficiently robust.

[0097] The optimal cutoff value is calculated using the ROC algorithm, and the optimal threshold point of the ROC curve is selected based on the Youden index. The results are as follows Figure 6 As shown, the optimal value of the evaluation model constraint is 3.626.

[0098] That is, samples with a meningitis infection risk value (or absolute value) greater than 3.626 indicate that the subject to be evaluated has a high risk of meningitis infection.

[0099] Example 3

[0100] Test the evaluation model.

[0101] 1. Another 26 samples of meningitis-positive patients and 50 samples of meningitis-negative patients were taken to verify the evaluation model obtained in Example 2.

[0102] The DCA decision curve and ROC curve were calculated, and the consistency test was performed using the rcorrcens algorithm, and the C-index was 0.984, as shown in Figure 7 As shown in the figure, the larger the area under the curve, the stronger the model's predictive power for the results. The index of this model is 0.984, indicating a very high degree of conformity. The area under the ROC curve confirms that the prediction effect is good. The DCA decision curve shows that the exponential decay of the model error is limited. The slower the curve decays, the more stable the model effect is. Figure 8 The results show that the evaluation model has good results. The positive patient samples and negative samples of this embodiment are evaluated using the evaluation model of Example 2. The results are as follows Fig. 9 As shown, the layers are clear and the resolution is good.

[0103] Second, the positive group (53 cases) of Example 1 was simply classified, including different microbial infection types such as tuberculosis meningitis, cryptococcal meningitis, viral meningitis, common bacterial meningitis, etc. The evaluation model was used to calculate the meningitis infection risk value between different infection subtypes and negatives, and the deviation of the evaluation model for different classifications was analyzed. The results are as follows Fig.10 As shown, the results confirmed that the model scores were stable among different subtypes, there were no significant differences among different subtypes, and there was no obvious subtype preference in the evaluation of infection, that is, this evaluation model is suitable for the diagnosis of various types of meningitis.

[0104] 3. Calculate the specificity, sensitivity, and accuracy of the evaluation model.

[0105] The results of the evaluation model are compared with the results of the metagenomic test using a formula to obtain the TPR (specificity), TNR (sensitivity), and ACC (accuracy) of the evaluation model. The formula is shown below.

[0106] TPR<-TP / (TP+FN)

[0107] TNR<-TN / (TN+FP)

[0108] ACC<-(TP+TN) / (TP+TN+FP+FN)

[0109] Among them, TP is the accurate sample size of infection determined by the evaluation model, FP is the accurate sample size of non-infection determined by the evaluation model, TN is the difference sample size of infection determined by the evaluation model, and FN is the difference sample size of non-infection determined by the evaluation model.

[0110] The calculation results show that the TPR is 96.2963% specificity, TNR is 92% sensitivity, and ACC is 93.50649% accuracy. The results show that the evaluation model has a high model efficiency and compliance.

[0111] Example 4

[0112] Build a system for meningitis diagnosis.

[0113] The system for diagnosing meningitis comprises:

[0114] Analytical device: used to detect the expression level of the biomarker obtained in Example 1 in the biological sample of the subject to be evaluated, and input the expression level value of the biomarker into the evaluation model for evaluation and analysis;

[0115] Output device: used to output the above analysis results.

[0116] The ROC algorithm is used to calculate the optimal cutoff value, the optimal threshold point of the ROC curve is selected based on the Youden index, and the optimal value of the evaluation model constraint is 3.626.

[0117] That is, samples with a meningitis infection risk value (or absolute value) greater than 3.626 indicate that the subject to be evaluated has a high risk of meningitis infection.

[0118] Example 5

[0119] A biomarker for the diagnosis of meningitis infection.

[0120] Referring to the method of Example 1, the parameter value with the smallest error variance (i.e., the gene value with the most stable error) was selected, and the following 7 genes were screened as biomarkers: CXCL10, IGF2, RNA5SP202, RNA5SP226, RNA5SP72, S100A9, and POLR2KP1.

[0121] When the above 7 genes are selected as biomarkers, the weighted value (i.e., contribution rate) of each gene is calculated as shown below.

[0122] When the gene is CXCL10, the weighted value is -0.0366315995505745;

[0123] When the gene is IGF2, the weighted value is 0.155714769424072;

[0124] When the gene is RNA5SP202, the weighted value is -0.226055269674305;

[0125] When the gene is RNA5SP226, the weighted value is 0.120421069422702;

[0126] When the gene is RNA5SP72, the weighted value is -0.0263266707106252;

[0127] When the gene is S100A9, the weighted value is -0.0449593764401363;

[0128] When the gene is POLR2KP1, the weighted value is -0.0723773240912382.

[0129] The genes and weighted values ​​obtained in this example are substituted into the evaluation model of Example 2 to obtain an evaluation model for meningitis diagnosis, as shown below.

[0130] Risk=(CXCL10×-0.0366315995505745)+(IGF2×0.155714769424072)+(RNA5SP202×-0.226055269674305)+(RNA5SP226×0.12 0421069422702)+(RNA5SP72×-0.0263266707106252)+(S100A9×-0.0449593764401363)+(POLR2KP1×-0.0723773240912382).

[0131] The evaluation model obtained in Example 5 was verified using 26 meningitis-positive patient samples and 50 meningitis-negative samples in Example 3. The verification results are as follows: Fig.11 , Fig.12 shown.

[0132] According to the verification results, it can be seen that Fig.11 The AUC of the evaluation model of Example 5 is 0.873, which is an excellent model AUC value, but compared with the multi-gene evaluation model of Example 2, there is still a large difference in performance; Fig.12 In the example 5, the evaluation model has a certain deviation from the true value.

[0133] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A biomarker for the diagnosis of meningitis, characterized in that: include: CXCL10, IGF2, RNA5SP202, RNA5SP226, RNA5SP72, S100A9, and POLR2KP1.

2. The biomarker according to claim 1, characterized in that The biomarkers included the following genes: TRBV12-5, DSTN, CA2, IGFBP6, PI3, C15orf48, TIMM13, ANKRD13C, PEA15, CXCL10, H2AC14, H4C5, ZMYND11, PIM1, GBP1, ISG15, IGF2, RNA5SP481, RNA5SP283, RNA5SP202, RNA5SP226, RNA5SP403, RNA5SP72, RNA5SP160, S100A9, FCGR3B, MTND1P21, RPS29P26, RPL21P31, SNRPCP9, PRAMEF29P, KRTAP21-4P, RPL36AP8, RPS3AP7, SLC7A5P2, and POLR2KP1.

3. Use of the biomarker according to any one of claims 1 to 2 in the preparation of a product for diagnosing meningitis.

4. The use according to claim 3, characterized in that: The application comprises the following steps: collecting cerebrospinal fluid, extracting the macrotranscriptome in the cerebrospinal fluid, and detecting the expression level of the biomarker according to any one of claims 1-2.

5. A product for diagnosing meningitis, characterized in that: The product comprises: a reagent for detecting the expression level of the biomarker described in any one of claims 1-2 in a biological sample.

6. The product according to claim 5, characterized in that The biological sample is obtained from cerebrospinal fluid.

7. A system for diagnosing meningitis, characterized in that: The system includes: Analytical device: used to detect the expression level of the biomarker described in any one of claims 1-2 in the biological sample of the subject to be evaluated, and input it into the evaluation model for analysis; Output device: used to output the above analysis results.

8. The system according to claim 7, characterized in that The evaluation model is as follows: Among them, Risk is the meningitis infection risk value of the subject to be evaluated, e represents the natural constant, k represents the kth mRNA with non-zero weight value, c represents the weight value, expression represents the TPM value of mRNA, and n represents the number of genes.

9. The system according to claim 8, characterized in that The weighted values ​​are set as follows: When the gene is TRBV12_5, the weighted value is -0.487553275; When the gene is DSTN, the weighted value is 0.183814987; When the gene is CA2, the weighted value is 0.512654584; When the gene is IGFBP6, the weighted value is 0.230692277; When the gene is PI3, the weighted value is -0.048969818; When the gene is C15orf48, the weighted value is -0.260142365; When the gene is TIMM13, the weighted value is 0.143632698; When the gene is ANKRD13C, the weighted value is -0.29938557; When the gene is PEA15, the weighted value is 0.0054829; When the gene is CXCL10, the weighted value is -0.317186117; When the gene is H2AC14, the weighted value is -0.021527034; When the gene is H4C5, the weighted value is 0.0544751; When the gene is ZMYND11, the weighted value is 0.502305371; When the gene is PIM1, the weighted value is -0.114266971; When the gene is GBP1, the weighted value is -0.199360039; When the gene is ISG15, the weighted value is -0.03091624; When the gene is IGF2, the weighted value is 0.991757711; When the gene is RNA5SP481, the weighted value is 0.093864768; When the gene is RNA5SP283, the weighted value is -0.005563655; When the gene is RNA5SP202, the weighted value is -0.587122701; When the gene is RNA5SP226, the weighted value is 0.281441276; When the gene is RNA5SP403, the weighted value is -0.14454107; When the gene is RNA5SP72, the weighted value is -0.200900429; When the gene is RNA5SP160, the weighted value is -0.000116441; When the gene is S100A9, the weighted value is -0.01575733; When the gene is FCGR3B, the weighted value is -0.114721492; When the gene is MTND1P21, the weighted value is -0.09680476; When the gene is RPS29P26, the weighted value is -0.094079988; When the gene is RPL21P31, the weighted value is -0.093022725; When the gene is SNRPCP9, the weighted value is 0.109758396; When the gene is PRAMEF29P, the weighted value is -0.814286332; When the gene is KRTAP21_4P, the weighted value is 0.015612173; When the gene is RPL36AP8, the weighted value is 0.104934447; When the gene is RPS3AP7, the weighted value is -0.278675375; When the gene is SLC7A5P2, the weighted value is 0.119072739; When the gene is POLR2KP1, the weighted value is -0.383658175.

10. The system according to claim 7, characterized in that The analysis includes: when the meningitis infection risk value of the subject to be evaluated is greater than 3.626, it indicates that the subject to be evaluated has a high risk of meningitis infection.

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