A set of biomarker compositions for the diagnosis of tuberculosis

Abstract

The application provides a biomarker composition for diagnosing tuberculosis, the biomarker composition screened by the application can be used for diagnosing tuberculosis, and the area under the ROC curve (AUC) of the model for diagnosing tuberculosis is greater than 0.9, when the cut-off value (diagnosis threshold value) is 0.696, the sensitivity and specificity of diagnosis are both greater than 80%.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular relates to a group of biomarker compositions for diagnosing tuberculosis. Background Art

[0002] Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis (MTB). MTB is a common pathogen that usually parasitizes in host macrophages and can be effectively transmitted through the respiratory system of the human body. It can cause more serious lung diseases in infected hosts (PREDA M, BC, ZOB DL, et al. The bidirectional relationship between pulmonary tuberculosis and lung cancer [J]. Int J Environ Res Public Health, 2023, 20 (2): 1282.). In addition, MTB is a highly adaptable Gram-positive bacterium that is mainly transmitted between people (HUNTER R L. The pathogenesis of tuberculosis: the early infiltrate of post-primary (adult pulmonary) tuberculosis: a distinct disease entity [J]. Front Immunol, 2018, 9: 2108.). Tuberculosis has always been a key infectious disease that is controlled globally and in my country. Early diagnosis and treatment are important means to control the spread of tuberculosis. However, due to the lack of rapid and accurate diagnostic technology, the diagnosis of tuberculosis has always failed to meet clinical needs. Therefore, finding a suitable method to diagnose tuberculosis (TB) as early as possible is of great significance for formulating clinical treatment plans and improving prognosis.

[0003] Currently, there are several methods for diagnosing tuberculosis. The first is traditional bacteriological detection, including sputum smear microscopy and Mycobacterium tuberculosis culture, but these methods are not only time-consuming, but also have low sensitivity. The second is molecular biological detection, including Xpert MTB / RIF and linear probe, which mainly targets the DNA of Mycobacterium tuberculosis for detection. Although these methods are fast and accurate, the sensitivity of molecular biological methods is poor in low-bacterial samples, and the quality of sputum samples is required, and the corresponding equipment is required for detection, which is limited in primary medical institutions and comprehensive medical institutions. The third method is based on the immune response of the human body to Mycobacterium tuberculosis, that is, the tuberculosis infection T cell detection technology, but this method cannot distinguish between latent infected persons and active tuberculosis patients. Other conventional biochemical detection or imaging detection has poor specificity in distinguishing tuberculosis from non-tuberculosis diseases. WHO recommends the identification of tuberculosis biomarkers based on non-sputum samples and the development of corresponding detection reagents. Therefore, it is urgent to find new tuberculosis diagnostic biomarkers and develop an early and rapid diagnostic method.

[0004] Non-coding RNA (non-coding RNA) is produced during the process of transcription of the human genome into primary transcription products, which is more specific in tissue and time and space than messenger RNA (mRNA), and participates in the immune response and pathological injury process of the body in various ways. Among them, long non-coding RNA (lncRNA) accounts for 70%-80% of non-coding RNA. lncRNA is a kind of RNA molecule with a length of more than 200 nucleotides. Although lncRNA does not encode proteins, it can regulate epigenetic modification, transcription, translation and post-translational modification through interaction with DNA, RNA and protein, thereby playing an important biological function. Abnormal expression of lncRNA has been found in various diseases, including cancer, cardiovascular disease, autoimmune disease, neuroinflammation and various viral and bacterial infections.

[0005] More and more studies have shown that lncRNA plays an important regulatory role in the process of Mycobacterium tuberculosis infection. lncRNA can not only induce autophagy and apoptosis of macrophages, but also participate in the regulation of immune cell immune defense response to Mycobacterium tuberculosis. Given the key role of lncRNA in the immune response of tuberculosis, it has great potential as a new biomarker for the diagnosis of tuberculosis. lncRNA can be detected in almost all body fluids, including blood, urine, sputum, cerebrospinal fluid and pleural effusion. SUMMARY

[0006] The present application screens a set of biomarker compositions for diagnosing tuberculosis, which has strong diagnostic efficiency for diagnosing tuberculosis. Based on this, the present application is completed.

[0007] In a first aspect, the present application provides a biomarker composition for diagnosing tuberculosis, which comprises five lncRNAs, i.e. NR_104128, Inc-MXRA7, ENST00000565797, Inc-NT5C3A and ENST00000583184, wherein the sequence of NR_104128 is shown as SEQ ID NO. 1, the sequence of Inc-MXRA7 is shown as SEQ ID NO. 2, the sequence of ENST00000565797 is shown as SEQ ID NO. 3, the sequence of Inc-NT5C3A is shown as SEQ ID NO. 4, and the sequence of ENST00000583184 is shown as SEQ ID NO. 5.

[0008] Further, the biomarker is from whole blood, plasma or serum of a patient.

[0009] Further, the biomarker composition is used to calculate the prediction probability value of a sample according to the relative expression amount of the five lncRNAs, and when the probability value is greater than or equal to 0.696, the patient is diagnosed as having tuberculosis.

[0010] Further, the prediction probability value of the sample is calculated by an AdaBoost model.

[0011] Further, the tuberculosis is a disease caused by infection of Mycobacterium tuberculosis.

[0012] Further, the tuberculosis includes primary tuberculosis, secondary tuberculosis, blood disseminated tuberculosis, tracheal-bronchial tuberculosis, tuberculous pleurisy, and bacterin-negative tuberculosis.

[0013] Further, the infection of Mycobacterium tuberculosis includes primary infection, secondary infection and extrapulmonary infection.

[0014] In a second aspect, the present application provides use of a biomarker composition in preparation of a reagent for diagnosing tuberculosis, wherein the biomarker composition comprises the five lncRNAs of the first aspect of the present application, and the reagent comprises a reagent for detecting the relative expression amount of the five lncRNAs in a biological sample of a patient.

[0015] Further, the biomarker is from whole blood, plasma or serum of a patient.

[0016] Further, the biomarker composition is used to calculate the prediction probability value of a sample according to the relative expression amount of the five lncRNAs, and when the probability value is greater than or equal to 0.696, the patient is diagnosed as having tuberculosis.

[0017] Furthermore, the predicted probability value of the sample is calculated using the AdaBoost model.

[0018] Furthermore, the tuberculosis is a disease caused by infection with Mycobacterium tuberculosis.

[0019] Furthermore, the tuberculosis includes primary tuberculosis, secondary tuberculosis, blood type disseminated tuberculosis, tracheobronchial tuberculosis, tuberculous pleurisy, smear-negative tuberculosis, etc.

[0020] Furthermore, the Mycobacterium tuberculosis infection includes: primary infection, secondary infection, and extrapulmonary infection.

[0021] In a third aspect, the present invention provides a kit for diagnosing tuberculosis, wherein the kit comprises the reagent as described in the second invention of the present invention.

[0022] Furthermore, the kit may be one or more of a nucleic acid detection kit, an immunofluorescence kit, a gene chip detection kit, a molecular hybridization kit and / or an in situ hybridization staining kit.

[0023] Furthermore, the diagnostic method of the kit includes one or more of PCR method / qPCR method, linear probe method, high-resolution melting curve method and / or gene chip method.

[0024] Beneficial effects

[0025] The marker composition screened by the present invention can be used to diagnose tuberculosis. The area under the ROC curve (AUC) of the model for tuberculosis diagnosis is greater than 0.9. When the cut-off value (diagnostic threshold) is 0.696, the sensitivity and specificity of the diagnosis are both greater than 80%. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is the ROC of the AdaBoost model for diagnosing tuberculosis in the training set.

[0027] Figure 2 This is the ROC of the AdaBoost model for diagnosing tuberculosis in the validation set.

[0028] Figure 3 ROC of the AdaBoost model for diagnosing tuberculosis in the test set. DETAILED DESCRIPTION

[0029] The following is a further description of specific embodiments of the present invention. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the embodiments described below may be combined with each other as long as they do not conflict with each other.

[0030] The experimental methods in the following examples are conventional methods, and the test materials used in the following examples are commercially available unless otherwise specified.

[0031] Example 1 Screening of 5 lncRNAs

[0032] 1. Inclusion criteria

[0033] 1) Patients with confirmed tuberculosis: According to the industry standard of WS-288-2017 Tuberculosis Diagnosis, patients with clinical symptoms of tuberculosis, imaging suggesting active tuberculosis lesions, and one of the following conditions: sputum / body fluid smear test positive, Mycobacterium tuberculosis culture positive, Mycobacterium tuberculosis molecular biology detection positive, or histopathology positive.

[0034] 2) Patients with clinically diagnosed tuberculosis: According to the industry standard of WS-288-2017 Tuberculosis Diagnosis, patients with clinical symptoms of tuberculosis and imaging manifestations of tuberculosis, IGRA test positive, and effective anti-tuberculosis treatment.

[0035] 3) Non-tuberculosis other disease control: Patients with suspected tuberculosis in clinical symptoms and imaging, but ultimately diagnosed as other diseases, including tumors (diagnosed by pathology or cytology), pneumonia (found other pathogen diseases and effective treatment), etc. which need to be differentiated from tuberculosis.

[0036] 4) Latent tuberculosis infection: No clinical symptoms of tuberculosis, no abnormalities in imaging examination, positive tuberculosis infection T cell detection, and no history of tuberculosis.

[0037] 5) Healthy controls: No clinical symptoms of tuberculosis, no abnormalities in imaging examination, negative IGRA detection, and no history of tuberculosis or tuberculosis exposure.

[0038] 2. Chip detection

[0039] 1) Isolation of PBMCs from peripheral blood

[0040] Whole blood was collected, the blood sample was diluted with culture medium, mixed, and then added to sample density separation liquid to extract PBMCs.

[0041] 2) RNA extraction

[0042] Total RNA extraction was performed, and the total RNA obtained was qualified after electrophoresis quality control and was ready for use.

[0043] 3) cRNA labeling

[0044] The total RNA was amplified and labeled, and then the labeled cRNA was purified.

[0045] 4) Chip hybridization

[0046] Hybridization in rolling hybridization oven, 1.65 μg cRNA was loaded, and the chip was washed.

[0047] 5) Chip scanning

[0048] The hybridized chip was scanned. The data was read, and finally normalized by limma package in R software, and the algorithm used was Quantile.

[0049] 6) Chip detection data

[0050] 10 cases of tuberculosis patients, 10 cases of latent infection and 10 cases of healthy people were included, and chip detection was completed. Based on the original chip data, WGCNA analysis was applied, combined with intergroup difference analysis, 45 lncRNAs that were different between tuberculosis and the other two groups were screened. In subsequent analysis, 5 lncRNAs were identified by qPCR verification and LASSO regression analysis, and composed a diagnostic model. The expression of 5 lncRNAs in chip data is shown in Table 1.

[0051] Table 1. Analysis of lncRNA chip results

[0052]

[0053] Note: TB, tuberculosis patients; LTBI, latent infection; HC, healthy people. Fc, fold change (difference multiple).

[0054] Example 2 Diagnosis performance of lncRNA combination

[0055] 1. Determination of relative expression

[0056] The calculation method of relative expression is as follows: △Ct = Ct 标志物 -Ct 内参基因 ; Relative expression (Relative expression level) = 2 -△Ct

[0057] Reagents, instruments and raw data of qPCR detection:

[0058] ① Reagents

[0059] lncRNA chip: Agilent Human (4*180K) ceRNA chip

[0060] qPCR reference gene: GAPDH

[0061] The target primer sequence is shown in Table 2

[0062] Table 2. Target primer sequences

[0063]

[0064] Reagents for RNA extraction (QIAGEN), reverse transcription (TOYOBO), and qPCR detection (Thermo Fisher Scientific) were all commercial reagents.

[0065] 2. Instrument: ABI 7500

[0066] 3. Raw detection data

[0067] Table 3. qPCR detection raw expression (relative expression) in training set

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[0078] Note: TB tuberculosis patients, LTBI latent infected, HC healthy people, NTB non-tuberculosis lung disease patients.

[0079] Table 4. qPCR detection raw expression (relative expression) in verification set

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[0084] Note: TB tuberculosis patients, LTBI latent infected, HC healthy people, NTB non-tuberculosis lung disease patients.

[0085] Table 5. qPCR detection of raw expression (relative expression) in independent test set

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[0094] Note: TB1, tuberculosis patients; TB2, clinically diagnosed tuberculosis patients; LTBI, latent tuberculosis infection; HC, healthy people; NTB, non-tuberculosis pulmonary disease patients.

[0095] 2. ROC analysis of the diagnostic performance of the AdaBoost model of the combination of 5 lncRNAs

[0096] The training set included 134 tuberculosis patients, 38 latent infection cases, 46 healthy people and 54 non-tuberculosis pulmonary disease patients; the validation set included 58 tuberculosis patients, 17 latent infection cases, 20 healthy people and 24 non-tuberculosis pulmonary disease patients. The diagnostic performance of the AdaBoost model in distinguishing tuberculosis patients from other control groups was evaluated by the receiver operating characteristic curve (ROC).

[0097] AdaBoost mainly trains multiple decision trees through iteration, and adjusts the weight of each decision tree according to its error rate. Among them, each classifier gives higher weight to the misjudgment samples, so that the next round of training pays more attention to these samples, thereby gradually improving the accuracy of the overall model. In this study, the AdaBoost model was constructed based on the relative expression of 5 lncRNAs, and multiple decision trees based on different orders of 5 lncRNAs were constructed. By weighting the sample tuberculosis probability values output by each decision tree and summing them up, a comprehensive prediction probability value was finally output for each sample, i.e. the probability of the sample suffering from tuberculosis, the probability value being between 0 and 1. The receiver operating characteristic curve (ROC) analysis was performed on the tuberculosis probability values of these samples (such as Figure 1The results show that the probability value of 0.696 is used as the cut-off value (diagnostic threshold). Among them, controls in the figure are LTBI+HC+NTB.

[0098] The results show that the area under the ROC curve (AUC) of the model for tuberculosis diagnosis in the training set and the validation set is greater than 0.9. When the cut-off value (diagnostic threshold) is 0.696, the sensitivity and specificity in the training set are 89% (83%-93%) and 91% (87%-95%), respectively; the sensitivity and specificity in the validation set are 81% (69%-91%) and 84% (71%-93%), respectively.

[0099] In a separate test set, including 99 tuberculosis patients, 25 clinically diagnosed tuberculosis patients, 10 latent infected persons, 36 healthy people and 36 non-tuberculosis lung disease patients, the AUC of the model (still using the probability value of 0.696 as the diagnostic threshold) for diagnosing tuberculosis is 0.92 (0.88-0.95), and the sensitivity and specificity are 75% (67%-83%) and 94% (89%-98%).

[0100] Table 5. Performance of AdaBoost model for diagnosing tuberculosis

[0101]

Claims

1. A biomarker composition for diagnosing tuberculosis, the biomarker composition consisting of five lncRNAs, namely NR_104128, Inc-MXRA7, ENST00000565797, Inc-NT5C3A and ENST00000583184, wherein the sequence of NR_104128 is shown in SEQ ID NO.1, the sequence of Inc-MXRA7 is shown in SEQ ID NO.2, the sequence of ENST00000565797 is shown in SEQ ID NO.3, the sequence of Inc-NT5C3A is shown in SEQ ID NO.4, and the sequence of ENST00000583184 is shown in SEQ ID NO.

5.

2. Use of a set of reagents for detecting the relative expression levels of a biomarker composition in the preparation of a reagent for diagnosing tuberculosis, wherein the biomarker composition is the five lncRNAs according to claim 1.

3. The use according to claim 2, wherein the predicted probability value of the sample is calculated based on the relative expression levels of the five lncRNAs, and when the probability value is greater than or equal to 0.696, the patient is diagnosed with tuberculosis.

4. The application as claimed in claim 3, wherein the predicted probability value of the sample is calculated by an AdaBoost model.

5. A kit for diagnosing tuberculosis, comprising a reagent for detecting the relative expression level of a biomarker composition, wherein the biomarker composition is the five lncRNAs according to claim 1.

6. The kit according to claim 5, wherein the kit can be one or more of a nucleic acid detection kit, an immunofluorescence kit, a gene chip detection kit, a molecular hybridization kit and / or an in situ hybridization staining kit.

7. The kit according to claim 5, wherein the diagnostic method of the kit comprises one or more of PCR / qPCR, line probe, high-resolution melting curve and / or gene chip methods.

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