CN-122017235-A - Biomarker combination and detection method for tuberculosis infection stage diagnosis

Abstract

The invention discloses a biomarker combination and detection method for tuberculosis infection stage diagnosis, which specifically comprises the following steps of S1, sample acquisition and processing, S2, detection processing, S3, data processing, S4 and marker screening. The biomarker combination and the detection method for tuberculosis infection stage diagnosis distinguish ATB and HC, ATB and LTBI, and AUC of LTBI and HC are all more than or equal to 0.875 through the core marker combination, wherein the AUC of a plurality of markers reaches 1.0, the diagnosis efficiency is obviously superior to that of the existing TST and IGRAs methods, the diagnosis accuracy is higher, the 'transition state' metabolic characteristics of the LTBI are clear, the accurate division of different stages of tuberculosis infection is realized, the problem that the prior art can not distinguish core pain points of the LTBI and the ATB is solved, the method has good stage specificity and better immune relevance, and provides a basis for researching the disease progress mechanism and screening the therapeutic target by revealing the relativity of metabolites and CD4 + /CD8 + T cell subsets.

Inventors

• Rao Kemeng

Assignees

• 三二〇一医院

Dates

Publication Date

20260512

Application Date

20260225

Claims (8)

1. 1. The biomarker combination and detection method for tuberculosis infection stage diagnosis is characterized by comprising the following steps: S1, sample collection and processing; s2, detecting absolute counts and proportions of subgroups such as CD3 + T cells, CD3 + CD4 + T cells, CD3 + CD8 + T cells and the like by adopting a Navios EX flow cytometer through a specific monoclonal antibody combined marker, then adopting an ultra-high performance liquid chromatography-orbitrap mass spectrometry system, separating by adopting an HSS T3 chromatographic column, collecting data in a positive and negative ionization mode, carrying out peak detection, comparison and standardization by adopting Progenesis QI software, and identifying metabolites by combining with a database; S3, data processing, namely respectively carrying out data multisource statistical analysis and path enrichment analysis; S4, screening the markers, namely obtaining 10 core candidate biomarkers after removing drug related metabolites through cross screening among three groups, and screening the markers with AUC more than or equal to 0.875 by ROC curve analysis for constructing a diagnostic model.
2. 2. The method for detecting and combining biomarkers for the staging diagnosis of tuberculosis infection according to claim 1, wherein the step S1 of sample collection and processing comprises the steps of: T1, study subjects, namely a healthy control group HC, a latent tuberculosis infection group LTBI and an active tuberculosis group ATB subject which meet the nano-ranking standard, wherein each group is at least 12; T2, sample collection, namely, pre-treatment early morning fasting elbow venous blood is collected and is respectively used for lymphocyte subgroup detection and plasma metabonomics analysis; And T3, quality control, namely preparing quality control samples QC, and inserting 1 QC sample into every 5-10 samples to ensure the stability and repeatability of a detection system.
3. 3. The method for diagnosing a tuberculosis infection stage according to claim 2, wherein EDTA-K 2 anticoagulation vacuum blood collection tube is used for lymphocyte subpopulation detection in the step T2, and liquid nitrogen quick freezing after plasma separation is used for plasma metabonomics analysis, and preservation is carried out at-80 ℃.
4. 4. The method for diagnosing a tuberculosis infection stage according to claim 1, wherein the specific monoclonal antibody combination in the step S2 is labeled with CD3-FITC, CD4-APC and CD8-APC-Cy7.
5. 5. The method for diagnosing a tuberculosis infection stage according to claim 1, wherein the database in the step S2 is HMDB database or METLIN database.
6. 6. The method for detecting and combining biomarkers for the staging diagnosis of tuberculosis infection according to claim 1, wherein the multivariate statistical analysis in step S3 is to evaluate the effects of three sets of metabolic spectrum separation using a PCA/OPLS-DA model, screen for differential metabolites using VIP >1 and P <0.05 as criteria, and the pathway enrichment analysis is to classify HMDB the differential metabolites and KEGG pathway enrichment, defining a stage-specific metabolic pathway.
7. 7. The method for diagnosing a stage of tuberculosis infection according to claim 1, wherein the 10 core candidate biomarkers obtained in the step S4 include penultimate N-docosahexaenoic acid-gamma-aminobutyric acid, dodecanedioic acid, glycerophosphatidylethanolamine, 5-octenoyl carnitine, glycoxycholic acid 3-glucuronic acid, allochenodeoxycholic acid, porphobinogen, aspartic acid-glutamic acid dipeptide, 2- (3, 4-dihydroxyphenyl) acetamide, and 4-hydroxy-5- (phenyl) valerate-O-glucuronide.
8. 8. The method for diagnosing a tuberculosis infection stage according to claim 7, wherein the accurate differentiation of HC, LTBI, ATB is achieved by detecting the expression level of 10 core markers in blood plasma and combining the parameters of CD4 + /CD8 + T cell subsets in peripheral blood and utilizing the correlation characteristics of metabolites and immune cells.

Description

Biomarker combination and detection method for tuberculosis infection stage diagnosis Technical Field The invention relates to the technical field of clinical medical treatment, in particular to a biomarker combination and a detection method for tuberculosis infection stage diagnosis. Background Tuberculosis is a relatively high global mortality infectious disease, and a worldwide about quarter of the population is infected with mycobacterium tuberculosis, with latent tuberculosis infection (LTBI) accounting for about 25% of it being the primary potential source of Active Tuberculosis (ATB). Current diagnosis of LTBI and ATB relies mainly on Tuberculin Skin Test (TST) and interferon-gamma release test (IGRAs, such as QFT-Plus), but these methods suffer from significant drawbacks: 1. the LTBI cannot be reliably distinguished from ATB, nor is the risk of LTBI conversion to ATB predicted; 2. the detection cost is high, the flow is complex, the special laboratory support is relied on, and the popularization in high tuberculosis burden areas with limited resources is difficult; 3. only reflecting the "static" memory state of the host immune system for pathogen antigens, and failing to reveal the dynamic progression of infection; 4. TST specificity is affected by bcg vaccination and nontuberculous mycobacterial infection, and traditional IGRAs do not adequately detect cd8+ T cell responses. Therefore, there is a need to develop a novel diagnostic technique and biomarker combination that dynamically reflects the infection status, distinguishes disease stages, and has high clinical accessibility. Disclosure of Invention (One) solving the technical problems Aiming at the defects of the prior art, the invention provides a biomarker combination and a detection method for tuberculosis infection stage diagnosis, breaks through static detection limitation, reveals the 'transition state' of LTBI between HC and ATB on metabolic spectrum for the first time, dynamically reflects the continuous evolution process of tuberculosis infection, solves the defect that the traditional method cannot embody the dynamic change of infection, has high diagnosis efficiency, 10 core marker combinations have excellent diagnosis performance, wherein the AUC of distinguishing ATB from HC by 5-octenoyl carnitine and the like reaches 1.0, the AUC of distinguishing ATB from LTBI by aspartic acid-glutamic acid dipeptide and the like reaches 1.0, the AUC of distinguishing LTBI from HC by dodecanedioic acid is 1.0, is obviously superior to TST and IGRAs, has definite mechanism, establishes the relativity of metabolites and immune cell subsets, clarifies the role of paths such as lipid metabolism, bile acid metabolism and the like in tuberculosis infection progress, does not need diagnostic value and immune pathology indication function, has high clinical accessibility, is based on the minimally invasive detection of plasma samples, is relatively simple, is convenient, facilitates popularization, and is convenient to realize the diagnosis of the problem of the traditional laboratory, has high cost, and can realize the diagnosis of the current health state, and the current state is realized by the current experiment, and has the health state is more than the health state. (II) technical scheme In order to achieve the above purpose, the invention is realized by the following technical scheme: the biomarker combination and detection method for tuberculosis infection stage diagnosis specifically comprises the following steps: S1, sample collection and processing; s2, detecting absolute counts and proportions of subgroups such as CD3 + T cells, CD3 +CD4+ T cells, CD3 +CD8+ T cells and the like by adopting a Navios EX flow cytometer through a specific monoclonal antibody combined marker, then adopting an ultra-high performance liquid chromatography-orbitrap mass spectrometry system, separating by adopting an HSS T3 chromatographic column, collecting data in a positive and negative ionization mode, carrying out peak detection, comparison and standardization by adopting Progenesis QI software, and identifying metabolites by combining with a database; S3, data processing, namely respectively carrying out data multisource statistical analysis and path enrichment analysis; S4, screening the markers, namely obtaining 10 core candidate biomarkers after removing drug related metabolites through cross screening among three groups, and screening the markers with AUC more than or equal to 0.875 by ROC curve analysis for constructing a diagnostic model. Preferably, the step S1 sample collection and processing specifically includes the following steps: T1, study subjects, namely a healthy control group HC, a latent tuberculosis infection group LTBI and an active tuberculosis group ATB subject which meet the nano-ranking standard, wherein each group is at least 12; T2, sample collection, namely, pre-treatment early morning fasting elbow venous blood is collected and is