Marker for microbiological positive diagnosis of tuberculous pleurisy pleural effusion as well as screening method and application of marker

Abstract

The invention discloses a marker for microbiological positive diagnosis of tuberculous pleurisy pleural effusion as well as a screening method and application of the marker, and relates to the technical field of biological detection. The marker comprises at least one of lactic dehydrogenase, an immunoglobulin lambda variant 2-18, a peptide hormone retinol binding protein 4, an immunoglobulin lambda variant 3-21 and a microtubulin alpha-1C chain. The lactic dehydrogenase combined immunoglobulin lambda variants 2-18 in the marker can significantly improve the microbiological positive diagnostic rate of tuberculous pleurisy pleural effusion.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, in particular to a microbiologically positive diagnostic marker for tuberculous pleurisy pleural effusion, a screening method and application thereof. Background Art

[0002] Tuberculous pleurisy is a type of tuberculosis caused by infection with Mycobacterium tuberculosis (MTB), primarily occurring in inflammatory lesions of the pleura. Accurate diagnosis of tuberculous pleurisy is crucial for effective treatment and disease control. However, its clinical symptoms are similar to those of other types of pleurisy, such as pleurisy caused by non-tuberculous bacterial infections, tumor pleurisy, and autoimmune pleurisy, making its diagnosis complex.

[0003] Current methods for diagnosing tuberculous pleurisy mainly include microbiological positive diagnosis such as pleural effusion smear staining and bacterial culture. This diagnostic method confirms Mycobacterium tuberculosis by directly detecting the pathogen or its components.

[0004] In clinical practice, distinguishing between pathogen-positive and -negative pleural effusions in patients with tuberculous pleurisy is of great diagnostic and therapeutic significance. A positive pathogen means that the presence of Mycobacterium tuberculosis can be directly confirmed through bacterial culture or molecular biological testing in the pleural effusion. This facilitates rapid diagnosis of tuberculous pleurisy and selection of the most appropriate anti-tuberculosis treatment regimen based on the pathogen type and drug sensitivity test results. In contrast, a negative pathogen may require a more comprehensive diagnosis based on clinical manifestations, pleural effusion biochemical testing, and pleural biopsy, and treatment selection may rely more on experience and clinical inference. Therefore, rapid diagnosis of a positive pathogen not only improves the targeted and effective treatment but also reduces unnecessary antibiotic use, lowers medical costs, and optimizes resource allocation. However, due to the generally low abundance of Mycobacterium tuberculosis in pleural effusions and their slow growth, traditional pleural effusion testing methods have low sensitivity and specificity, making them unable to meet the clinical needs for rapid and accurate diagnosis. Summary of the Invention

[0005] The main purpose of the present invention is to propose a marker for the microbiological diagnosis of tuberculous pleural effusion and its screening method and application, aiming to solve the problems of traditional pleural effusion detection methods in the prior art that are time-consuming and have low detection specificity.

[0006] To achieve the above objectives, the present invention proposes a marker for the microbiologically positive diagnosis of tuberculous pleural effusion, wherein the marker comprises at least one of lactate dehydrogenase, immunoglobulin lambda variant 2-18, peptide hormone retinol binding protein 4, immunoglobulin lambda variant 3-21, and tubulin α-1C chain.

[0007] The present invention also provides a method for screening markers for microbiologically positive diagnosis of tuberculous pleural effusion, comprising the following steps:

[0008] S10. Collect pleural effusion samples from tuberculous pleurisy patients and healthy people, wherein the tuberculous pleurisy patients include positive patients and negative patients, the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the positive patients are all positive, and the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the negative patients are all negative;

[0009] S20, performing proteomic analysis on each pleural effusion sample in step S10 to obtain information on differentially expressed proteins, wherein the differentially expressed proteins include a first differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of negative patients, a second differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of positive patients, and a third differentially expressed protein obtained by comparing the proteome of pleural effusion samples of negative patients with that of positive patients;

[0010] S30, performing metabolomics analysis on the pleural effusion samples of the positive patients and the negative patients in step S10, and comparing the metabolomes of the pleural effusion samples of the negative patients and the positive patients to obtain information on differentially expressed metabolites;

[0011] S40, performing association analysis on the information of the expressed metabolites and the information of the third differentially expressed protein, so as to correspond the proteins related to the metabolic pathway mapped by the differentially expressed metabolites to the third differentially expressed protein, thereby obtaining a first potential marker protein;

[0012] S50, comparing the first potential marker protein with the first differentially expressed protein and the second differentially expressed protein, and screening out a protein from the third differentially expressed proteins that is different in type from the first differentially expressed protein and the second differentially expressed protein, which is the second potential marker protein;

[0013] S60, screening a protein with a non-zero regression coefficient from the second potential marker protein by LASSO and capable of distinguishing negative patients from positive patients, to obtain a third potential marker protein;

[0014] S70. Construct a random forest model using the third potential marker protein and the pleural effusion components of healthy people, positive patients, and negative patients in step S10, and verify the fourth potential marker protein to obtain a fourth potential marker protein that can distinguish between negative patients and positive patients, that is, a marker.

[0015] In one embodiment, in step S20, the proteomic analysis includes relative and absolute quantitative proteomic analysis based on iTRAQ technology:

[0016] The proteomic abundance of each pleural effusion sample was normalized, and proteins with a fold difference greater than 0.585 and P < 0.05 were screened out.

[0017] In one embodiment, in step S30, the metabolomics analysis includes non-targeted metabolomics analysis:

[0018] The metabolomic information of pleural effusion samples from negative and positive patients was normalized, and metabolites with a difference fold > 1.5 and P < 0.05, and a difference fold < 0.67 and P < 0.05 were screened out.

[0019] In one embodiment, in step S20, the information of the differentially expressed proteins includes the abundance of the differentially expressed proteins; and / or,

[0020] In step S30, the information of the differentially expressed metabolites includes the abundance of the differentially expressed metabolites.

[0021] In one embodiment, step S50 includes:

[0022] Comparing the first potential marker protein with the first differentially expressed protein and the second differentially expressed protein, screening out proteins of different types from the first differentially expressed protein and the second differentially expressed protein from the third differentially expressed proteins, and obtaining information about the target protein;

[0023] A validation cohort is provided, comprising pleural effusion samples from healthy individuals, positive patients, and negative patients. Proteomic analysis is performed on each pleural effusion sample in the validation cohort to obtain differentially expressed proteins between healthy individuals and positive patients, healthy individuals and negative patients, and positive patients and negative patients in the validation cohort. The target protein is validated by parallel reaction monitoring analysis to obtain a second potential marker protein that can distinguish between negative and positive patients in the validation cohort.

[0024] In one embodiment, step S70 further includes a verification step:

[0025] The content of the marker in the pleural effusion of the positive patient and the negative patient in step S10 is detected by enzyme-linked immunosorbent assay, and the effectiveness of the marker is confirmed by evaluation using a Pearson correlation coefficient model or a ROC curve model.

[0026] The present invention also provides a use of the aforementioned marker in preparing a kit for detecting tuberculous pleurisy.

[0027] In the technical solution of the present invention, the five markers of lactate dehydrogenase, immunoglobulin λ variant 2-18, peptide hormone retinol binding protein 4, immunoglobulin λ variant 3-21, and tubulin α-1C chain can be used to quickly distinguish between negative and positive patients of tuberculous pleurisy. Since the fewer the types of markers, the lower the detection cost in clinical tests, the best two markers are lactate dehydrogenase and immunoglobulin λ variant 2-18. These two markers can be used to quickly distinguish between negative and positive patients of tuberculous pleurisy, thereby improving the positive diagnosis rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0029] Figure 1 (A) is a flowchart of the comprehensive analysis of relative and absolute quantitative proteomics and non-targeted metabolomics based on iTRAQ technology in Example 1 provided by the present invention; Figure 1 Middle (B) is a flow chart of parallel reaction monitoring (PRM) analysis and machine learning in Example 1 provided by the present invention; Figure 1 Middle (C) is a flow chart of marker validation by enzyme-linked immunosorbent assay and ROC curve provided by the present invention;

[0030] Figure 2 (A) is the statistical result of LV218 analyzed by PRM in Example 1 provided by the present invention; Figure 2 Middle (B) is the statistical result of PRM analysis of LG3BP in Example 1 provided by the present invention; Figure 2 Middle (C) is the statistical result of RET4 analyzed by PRM in Example 1 provided by the present invention; Figure 2 Middle (D) is the statistical result of PRM analysis LV321 in Example 1 provided by the present invention; Figure 2 Middle (E) is the statistical result of PRM analysis TAB1C in Example 1 provided by the present invention; Figure 2 Middle (F) is the statistical result of PRM analysis of LV746 in Example 1 provided by the present invention; Figure 2 (G) is the ALDOA statistical result of PRM analysis in Example 1 provided by the present invention; Figure 2 (H) is the CFAH statistical result of the PRM analysis in Example 1 provided by the present invention; Figure 2 (I) is the statistical result of TPIS analysis by PRM in Example 1 provided by the present invention; Figure 2Middle (J) is the statistical result of HBB analyzed by PRM in Example 1 provided by the present invention; Figure 2 (K) is the statistical result of the machine learning positive rate in Example 1 provided by the present invention; Figure 2 (L) is the statistical result of the machine learning prediction accuracy in Example 1 provided by the present invention;

[0031] Figure 3 Middle (A) is the result of ELISA verification of LV218 level provided by the present invention; Figure 3 Middle (B) is the result of ELISA verification of RET4 level provided by the present invention; Figure 3 Middle (C) is the result of ELISA verification of TBAIC level provided by the present invention; Figure 3 Middle (D) is the result of ELISA verification of LV321 level provided by the present invention; Figure 3 Middle (E) is the correlation analysis result of LDH and LV218 provided by the present invention; Figure 3 Middle (F) is the correlation analysis result of LDH and RET4 provided by the present invention; Figure 3 Middle (G) is the correlation analysis result of LDH and TAB1C provided by the present invention; Figure 3 Middle (H) is the correlation analysis result of LDH and LV321 provided by the present invention; Figure 3 (I) is the ROC curve verification of LDH provided by the present invention; Figure 3 Middle (J) is the ROC curve verification of LV218 provided by the present invention; Figure 3 Middle (K) is the ROC curve verification of TAB1C provided by the present invention; Figure 3 Middle (L) is the ROC curve verification of the combination of LDH and LV218 provided by the present invention.

[0032] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, they are carried out according to conventional conditions or the conditions recommended by the manufacturer. Where the reagents or instruments used are not specified by the manufacturer, they are all conventional products that can be purchased commercially. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or schemes that A and B meet at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that the combination of such technical solutions does not exist and is not within the scope of protection required by the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] In clinical practice, distinguishing between pathogen-positive and -negative pleural effusions in patients with tuberculous pleurisy is of great diagnostic and therapeutic significance. A positive pathogen means that the presence of Mycobacterium tuberculosis can be directly confirmed through bacterial culture or molecular biological testing in the pleural effusion. This facilitates rapid diagnosis of tuberculous pleurisy and selection of the most appropriate anti-tuberculosis treatment regimen based on the pathogen type and drug sensitivity test results. In contrast, a negative pathogen may require a more comprehensive diagnosis based on clinical manifestations, pleural effusion biochemical testing, and pleural biopsy, and treatment selection may rely more on experience and clinical inference. Therefore, rapid diagnosis of a positive pathogen not only improves the targeted and effective treatment but also reduces unnecessary antibiotic use, lowers medical costs, and optimizes resource allocation. However, due to the generally low abundance of Mycobacterium tuberculosis in pleural effusions and their slow growth, traditional pleural effusion testing methods have low sensitivity and specificity, making them unable to meet the clinical needs for rapid and accurate diagnosis.

[0035] In view of this, the present invention provides a marker for the microbiologically positive diagnosis of tuberculous pleural effusion, wherein the marker includes at least one of lactate dehydrogenase (LDH), immunoglobulin lambda variable 2-18 (LV218), peptide hormone retinol binding protein 4 (RET4), immunoglobulin lambda variable 3-21 (LV321), and tubulin alpha-1C chain (TUBA1C).

[0036] In the technical solution of the present invention, the five markers of lactate dehydrogenase, immunoglobulin λ variant 2-18, peptide hormone retinol binding protein 4, immunoglobulin λ variant 3-21, and tubulin α-1C chain can be used to quickly distinguish between negative and positive patients of tuberculous pleurisy. Since the fewer the types of markers, the lower the detection cost in clinical tests, the best two markers are lactate dehydrogenase and immunoglobulin λ variant 2-18. These two markers can be used to quickly distinguish between negative and positive patients of tuberculous pleurisy, thereby improving the positive diagnosis rate.

[0037] The present invention also provides a method for screening markers for microbiologically positive diagnosis of tuberculous pleural effusion, comprising the following steps:

[0038] S10. Collect pleural effusion samples from tuberculous pleurisy patients and healthy people, wherein the tuberculous pleurisy patients include positive patients and negative patients, the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the positive patients are all positive, and the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the negative patients are all negative;

[0039] S20, performing proteomic analysis on each pleural effusion sample in step S10 to obtain information on differentially expressed proteins, wherein the differentially expressed proteins include a first differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of negative patients, a second differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of positive patients, and a third differentially expressed protein obtained by comparing the proteome of pleural effusion samples of negative patients with that of positive patients;

[0040] S30, performing metabolomics analysis on the pleural effusion samples of the positive patients and the negative patients in step S10, and comparing the metabolomes of the pleural effusion samples of the negative patients and the positive patients to obtain information on differentially expressed metabolites;

[0041] S40, performing association analysis on the information of the expressed metabolites and the information of the third differentially expressed protein, so as to correspond the proteins related to the metabolic pathway mapped by the differentially expressed metabolites to the third differentially expressed protein, thereby obtaining a first potential marker protein;

[0042] S50, comparing the first potential marker protein with the first differentially expressed protein and the second differentially expressed protein, and screening out a protein from the third differentially expressed proteins that is different in type from the first differentially expressed protein and the second differentially expressed protein, which is the second potential marker protein;

[0043] S60, screening a protein with a non-zero regression coefficient from the second potential marker protein by LASSO and capable of distinguishing negative patients from positive patients, to obtain a third potential marker protein;

[0044] S70. Construct a random forest model using the third potential marker protein and the pleural effusion components of healthy people, positive patients, and negative patients in step S10, and verify the fourth potential marker protein to obtain a fourth potential marker protein that can distinguish between negative patients and positive patients, that is, a marker.

[0045] In the technical solution of the present invention, healthy people, negative patients (i.e., people with diseases whose microbiological test results are negative), and positive patients (i.e., people with diseases whose microbiological test results are positive) are first obtained, and then proteomics of the three groups of people are tested. By comparing the two groups, the first differentially expressed proteins (87), the second differentially expressed proteins (111), and the third differentially expressed proteins (59) are obtained. Metabolomics analysis is performed on the negative and positive patients to obtain differential metabolites between the two groups. The information of the differential metabolites is further jointly analyzed with the information of the third differentially expressed proteins to obtain proteins in the third differentially expressed proteins that are related to the metabolic process of the differentially expressed metabolites, which are the first potential marker proteins. This is the first round of screening steps; the second round of screening steps is: comparing the types of the first potential marker protein, the first differentially expressed protein, and the second differentially expressed protein, eliminating the types of the first potential marker protein that are the same as the first differentially expressed protein and the second differentially expressed protein, and the remaining first potential marker protein is the second potential marker protein. This step excludes the differentially expressed proteins in the first potential marker protein that distinguish between healthy people and negative patients, and between healthy people and positive patients, and only retains the differentially expressed proteins that distinguish between negative patients and positive patients, further improving the subsequent positive detection rate; the third round of screening steps is: LASSO screening; the fourth round of screening is: random forest model; after completing four rounds of screening, the final protein obtained is the marker. The marker obtained by the screening method of the present invention can be used to quickly detect and distinguish between negative and positive patients of tuberculous pleurisy, thereby improving the positive detection rate.

[0046] In some embodiments, in step S20, the proteomic analysis includes relative and absolute quantitative proteomic analysis based on iTRAQ technology: the proteomic abundance of each pleural effusion sample is normalized, and proteins with a fold difference greater than 0.585 and a P value less than 0.05 are screened. With this conditional setting, the differentially expressed proteins screened can better distinguish different groups of people.

[0047] In some embodiments, in step S30, the metabolomics analysis includes non-targeted metabolomics analysis: normalizing the metabolomics information of pleural effusion samples from negative and positive patients to screen for metabolites with a fold difference greater than 1.5 and P < 0.05, and a fold difference less than 0.67 and P < 0.05. By setting these conditions, the differentially expressed metabolites screened can better distinguish different groups of people.

[0048] In some embodiments, in step S20, the information on the differentially expressed proteins includes the abundance of the differentially expressed proteins; and / or, in step S30, the information on the differentially expressed metabolites includes the abundance of the differentially expressed metabolites.

[0049] In some embodiments, step S50 includes: comparing the first potential marker protein with the types of the first differentially expressed protein and the second differentially expressed protein, screening out proteins from the third differentially expressed proteins that are different from the types of the first differentially expressed protein and the second differentially expressed protein, and obtaining information about the target protein; providing a validation cohort, the validation cohort including pleural effusion samples from healthy people, positive patients, and negative patients, performing proteomic analysis on each pleural effusion sample in the validation cohort, obtaining differentially expressed proteins between healthy people and positive patients, healthy people and negative patients, and positive patients and negative patients in the validation cohort, validating the target protein by parallel reaction monitoring analysis, and obtaining a second potential marker protein that can distinguish between negative patients and positive patients in the validation cohort. It should be noted that the experimental method and experimental parameters of the proteomic analysis in the validation cohort are consistent with those of step S20.

[0050] In some embodiments, step S70 is followed by a verification step: detecting the level of the marker in the pleural effusion of the positive and negative patients in step S10 by enzyme-linked immunosorbent assay, and evaluating the marker using a Pearson correlation coefficient model or a receiver operating characteristic (ROC) curve model to confirm whether the marker is effective. A higher absolute value of the Pearson correlation coefficient (close to 1 or -1) or a larger area under the ROC curve (AUC value close to 1) indicates that the marker has good discriminatory ability, thereby quickly confirming the effectiveness of the marker.

[0051] The present invention also provides a use of the aforementioned marker in preparing a kit for detecting tuberculous pleurisy. Since the kit contains the aforementioned marker, it has all the beneficial effects of the aforementioned marker, which will not be described in detail here.

[0052] The technical solutions of the present invention are further described in detail below in conjunction with specific embodiments and drawings. It should be understood that the following embodiments are only used to explain the present invention and are not used to limit the present invention.

[0053] Example 1 Screening of markers

[0054] 1. Participants and Pleural Fluid Collection

[0055] This study was approved by the Ethics Committee of Shenzhen Third People's Hospital (Ethics Approval No. 2022-200-02), and written informed consent was obtained from all participants. All procedures related to pleural effusion collection adhered to the principles of the Declaration of Helsinki.

[0056] Initially, 27 participants were recruited for this study and designated the screening cohort. These participants were divided into healthy individuals without tuberculous pleurisy (CON group) and patients with tuberculous pleurisy. The CON group consisted of 10 participants without tuberculous pleurisy, and the patients with tuberculous pleurisy were divided into the PEMN-MT group (10 patients with tuberculous pleurisy whose pleural effusions were negative for Mycobacterium tuberculosis by microbiological testing (pleural effusion culture, Xpert assay, and Mycobacterium tuberculosis DNA testing) and the PEMP-MT group (7 patients with tuberculous pleurisy whose pleural effusions were positive by culture, Xpert assay, and Mycobacterium tuberculosis DNA testing) (see Table 1). The chi-square test was performed using GraphPad Prism 7.0 to analyze the data on gender, effusion culture, Xpert, TB-DNA test positivity, cough, fever, chest pain, dyspnea, effusion location, and effusion volume in the CON and PEMN-MT groups, and the PEMN-MT and PEMP-MT groups. The results are shown in Table 1 。

[0057] Table 1 Information of all participants in proteomic and metabolomic analyses

[0058]

[0059] a Pva escompare the CON vs.EEMN-MI.Bold indcatesP<0.05.

[0060] b P values compare the PEMN-MT vs. PEMP-MT.Bold indicates P<0.05.

[0061] c paired t-tests for pairs of data sets.

[0062] d Chi-square tests.

[0063] "-" means not detect in this study.

[0064] In addition, the first validation cohort was established, consisting of 19 participants in the CON group, 39 participants in the PEMN-MT group, and 16 participants in the PEMP-MT group (see Table 2). All participants were adults who underwent thoracentesis at the Southern University of Science and Technology Shenzhen Third People's Hospital between April and September 2024.

[0065] Table 2 Information of all participants in the parallel reaction monitoring (PRM) analysis

[0066]

[0067] a P values compare the CON vs.PEMN-MT.Bold indicates P<0.05.

[0068] b P values compare the PEMN-MT vs.PEMP-MT.Bold indicates P<0.05.

[0069] c paired t-tests for pairs of data sets.

[0070] d Chi-square tests.

[0071] "-" means not detect in this study.

[0072] Detection methods: Mycobacterium tuberculosis culture was performed using the MGIT 960 system; acid-fast bacilli (AFB) were detected using Ziehl-Neelsen staining; Routine tests such as the MTB / RIF test and the T-SPOT test are performed by clinical laboratories according to manufacturer's guidelines and standard methods for the detection of tuberculosis in pleural effusion and sputum. The MGIT 960 system was purchased from Becton Dickinson and the Ziehl-Neelsen system was purchased from BASO Diagnostics. MTB / RIF reagent was purchased from Cepheid, and T-SPOT reagent was purchased from Oxford Immunotec.

[0073] It is understood that all participants were divided into groups according to the following criteria: (1) CON group: healthy people diagnosed with non-tuberculous pleurisy; (2) PEMN-MT group: patients diagnosed with tuberculous pleurisy and with negative microbiological test results; (3) PEMP-MT group: patients diagnosed with tuberculous pleurisy and with positive results of pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test.

[0074] 2. Experimental design and statistical basis:

[0075] This experimental design is divided into four parts (see Figure 1 ): Figure 1 (A) shows the experimental design of the first part, which is relative and absolute quantitative proteomics analysis based on iTRAQ technology, and also shows the experimental design of the second part, which is non-targeted metabolomics analysis, as well as the correlation analysis of the two components; Figure 1 Middle (B) shows that the experimental design of the third part is parallel reaction monitoring (PRM) analysis, and also shows the experimental design of the fourth part using machine learning to distinguish between the PEMN-MT group and the PEMP-MT group.

[0076] (1) Proteomic analysis

[0077] In the first part, 10 pleural effusion samples from the CON group, 10 pleural effusion samples from the PEMN-MT group, and 7 pleural effusion samples from the PEMP-MT group were randomly selected from the screening cohort. The 27 samples were centrifuged at 1000 r / min for 10 min, and 1 mL of each sample was added to 2×SDT (2% SDS, 100 mM DTT, 100 mM Tris-HCL, lysis buffer pH 7.6, purchased from Beijing Biolab Technology Co., Ltd., catalog number: KD0365) to obtain a mixed solution. The mixed solution was incubated at 95°C for 5 min and then centrifuged again at 40,000×g for 10 min to obtain the supernatant. TMProtein concentrations in all supernatants were measured using a BCA protein quantification kit. Protein digestion was performed using the filter-aided sample preparation (FASP) method. The following steps were performed: the supernatants were concentrated using a 10 kDa molecular weight cutoff ultrafiltration centrifuge tube to obtain the treated supernatant; the treated supernatant was resuspended in a urea solution containing 8 M urea and 150 mM Tris-HCl (pH 8.0). Next, all samples were added with 50 mM iodoacetamide, reacted in the dark for 0.5 h, and then treated twice with the urea solution. The samples were then digested with trypsin at 37°C for 12 h. After enzymatic digestion, peptides were obtained by washing, centrifugation, and vacuum drying. The peptides were then labeled with 8-plex iTRAQ reagents and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS / MS). All LC-MS / MS data were processed using MASCOT 2.2 (Matrix Science) software. The specific steps are as follows: Import the mass spectrometry data (.raw format) obtained from LC-MS / MS analysis into MASCOT software for processing. Select the UniProt protein database in MASCOT and set relevant search parameters, such as enzymatic digestion rules (trypsin), maximum mismatch allowed (1 mismatch), and tolerance for mass spectrometry fragmentation (±0.5-1Da) and parent ion mass error (±10-20ppm). MASCOT will compare the theoretical peptides of the proteins in the database with the mass spectrometry data and calculate the matching score of each peptide and protein. Perform quantitative analysis of iTRAQ tags and use MASCOT to process and quantify tag data. The protein differences between the CON group and PEMN-MT group, the CON group and PEMP-MT group, and the PEMN-MT group and PEMP-MT group were analyzed by unpaired two-sided Welch's t-test. The screening criteria for differentially expressed proteins (DEPs) were |log2FC|>0.585 and p<0.05. The first differentially expressed proteins, the second differentially expressed proteins, and the third differentially expressed proteins were obtained, with a total of 87 first differentially expressed proteins, 111 second differentially expressed proteins, and 59 third differentially expressed proteins.

[0078] (2) Non-targeted metabolomics

[0079] In the second part, the following operations were performed on the supernatant obtained from each of the 17 pleural effusion samples (10 PEMN-MT group pleural effusion samples and 7 PEMP-MT group pleural effusion samples selected in step (1)) after SDT treatment, incubation, and centrifugation: 250 μL of sample supernatant was thoroughly mixed with 250 μL of ice-cold acetone-ethanol-methanol (1:1:1, v / v) mixture and incubated at 95°C for 5 minutes, and then centrifuged at 40,000 × g for 10 minutes. The supernatant after centrifugation was collected and subjected to non-targeted LC-MS / MS analysis. LC-MS data were acquired on a Waters VION IMS Q-TOF mass spectrometer equipped with an electrospray ionization (ESI) source in positive and negative ion modes; mass-to-charge ratio (m / z), retention time (RT), and peak intensity were measured using the Human Metabolome Database (http: / / www.hmdb.ca), Metlin (https: / / metlin.scripps.edu), and LipidMaps (http: / / www.lipidmaps.org).

[0080] The specific steps of bioinformatics analysis are as follows: The raw mass spectrometry data were preprocessed by noise removal and baseline correction to improve data quality. The raw data were Z-score standardized and normalized using the internal standard method to ensure data consistency and comparability. Through OPLS-DA analysis, the goal of the model is to separate the samples according to their classification information (PEMN-MT group and PEMP-MT group), maximize the differences between groups and reduce the differences within groups. OPLS-DA provides VIP values to measure the importance of each metabolite in the classification model. The metabolite differences between the PEMN-MT group and the PEMP-MT group were analyzed. Metabolites with VIP values generally greater than 1 were considered differential metabolites and were associated with the separation of categories; the significance of the differential metabolites was further verified by combining the t-test; the differentially expressed metabolites were confirmed by combining the P value (less than 0.05) and the fold change (FC, >1.5 or <0.67).

[0081] (3) Bioinformatics and statistical analysis

[0082] The following bioinformatics analyses were performed on the protein difference data between the CON group and the PEMN-MT group, the CON group and the PEMP-MT group, and the PEMN-MT group and the PEMP-MT group:

[0083] In Gene Ontology (GO, http: / / www.geneontology.org / ), biological processes, cellular components, and molecular functions were analyzed using the OmicsBean tool (http: / / www.omicsbean.cn / ) (PMID: 31624233). Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG, http: / / www.genome.jp / kegg / ) and Reactome (https: / / reactome.org / ). Protein interaction network (PPI) analysis was performed using the STRING tool.

[0084] All differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs) in the comparisons between the CON group and the PEMN-MT group, the CON group and the PEMP-MT group, and the PEMN-MT group and the PEMP-MT group were analyzed using the HMDB, Metlin, and LipidMaps databases. Subsequently, IPA software (QIAGEN) was used to comprehensively analyze the DEPs and DEMs in the three groups. IPA software was used to measure p-values, calculated as -log10(p-value), to indicate the likelihood of underlying networks containing DEPs and DEMs. Combined proteomic and metabolomic analysis further narrowed down the differentially expressed proteins associated with the PEMN-MT and PEMP-MT groups. Simultaneous analysis of proteomic and metabolomic data can identify proteins and metabolites that are simultaneously associated with the disease. These combined biomarkers can improve diagnostic sensitivity and specificity. The goal is to combine the analysis of DEPs and DEMs to provide more comprehensive and accurate biological information, revealing the interaction mechanisms within cells or organisms, and providing strong support for the discovery of diagnostic biomarkers for tuberculous pleurisy.

[0085] After joint analysis of proteomics and metabolomics, proteins that were different from the first differentially expressed protein and the second differentially expressed protein were screened out, and 50 potential marker proteins were obtained.

[0086] (4) Parallel reaction monitoring analysis

[0087] The third part aims to extract proteins and prepare peptides from 74 pleural effusion samples in the first validation cohort using the same iTRAQ technology as in step (1). The peptide fragmentation resolution of Orbitrap was set to 35,000, and the raw data of peptide fragments were processed using Proteome Discoverer and Skyline software (v19.1). Some DEPs were verified by PRM analysis. The specific method is as follows: proteins from cells or tissues were extracted and lysed using RIPA buffer (Tris-HCl (pH 7.4): 50mM; NaCl: 150mM; NP-40: 1%; Sodium deoxycholate: 0.5%; SDS: 0.1%; 1mM EDTA; 1mM EGTA; 1× protease inhibitor cocktail). Then, protein quantification and enzymatic digestion (trypsin) were performed to obtain peptides. The peptides were separated by liquid chromatography to ensure the best separation effect. Parallel reaction monitoring analysis was performed using a high-resolution mass spectrometer (Orbitrap), and the mass-to-charge ratio (m / z) and corresponding fragment ions of the target peptide were set. In mass spectrometry, specific precursor ions of target peptides are selected and their respective fragment ions are monitored for quantitative analysis. Data are analyzed using MaxQuant to quantify target protein expression levels and compare them with protein levels in pleural effusion from healthy individuals.

[0088] (5) Machine Learning

[0089] The fourth part aims to use machine learning to analyze 50 potential protein biomarkers and 7 pleural effusion component detection indicators to find markers for the microbiological diagnosis of tuberculous pleurisy pleural effusion.

[0090] First, the Least Absolute Shrinkage and Selection Operator (LASSO) was used to select features for PEMP-MT from the training dataset based on importance, sparsity, relevance, and generalization ability. Subsequently, each of these potential marker protein features was subjected to ten five-fold cross-validations using random forests (RF) and random survival forests (RSF), each with a different random seed to ensure the stability of the feature selection results. LASSO and RF analyses were implemented using the scikit-learn package, while RSF was performed using the scikit-survival package in Python.

[0091] Fifty potential protein biomarkers obtained by PRM analysis were selected (Immunoglobulin lambda variable 2-18, LV218), peptide hormone retinol binding protein 4 (RET4), immunoglobulin lambda variable 3-21 (LV321), tubulin alpha-1C chain (TBA1C), alpha-1-antitrypsin (SERPINA1), complement C3 (C3), complement factor H (CFAH), complement C7 (C7), hemopexin (HPX), albumin (ALB), human complement C1q subcomponent subunit C (C1q subunit C), and 50 potential protein biomarkers obtained by PRM analysis. C, C1QC), Alpha-1-acid glycoprotein 2 (ORM2), Immunoglobulin lambda variable 7-46 (LV746), Galectin-3-binding protein (LG3BP), Basement membrane-specific heparan sulfate proteoglycan core (HSPG2), Immunoglobulin lambda variable 5-39 (LV539), Fibrinogen beta chain (FGB), Immunoglobulin lambda constant 7 (IGLC7), Immunoglobulin lambda variable 3-10 (Immunoglobulin lambda constant 7, IGLC7), 3-10,LV310), fibronectin (FN1), histidine-rich glycoprotein (HRG), coagulation factor XIII A chain (F13A1), Immunoglobulin kappa variable 2-30 (IGKV230), Fibrinogen alpha chain (FGA), Immunoglobulin lambda constant 6 (IGLC6), Alpha-2-HS-glycoprotein (AHSG), Fructose-bisphosphate aldolase A (ALDOA), Annexin A7 (ANXA7), Apolipoprotein C-1 (APOC1), Complement factor B (CFB), Ceruloplasmin (CP), Filamin-A (FLNA), Gelsolin (GSN), Glutathione S-transferase ω-1 S-transferase omega-1 (GSTO1), histone H1-5 (Histone H1.5, H1-5), hemoglobin subunit beta (HBB), immunoglobulin gamma 1 (Immunoglobulin heavy constant gamma 1, IGHG1), immunoglobulin gamma 2 (Immunoglobulin heavy constant gamma 2, IGHG2), immunoglobulin mu (Immunoglobulin heavy constant mu, IGHM), immunoglobulin heavy variable 3-7 (IGHV37), immunoglobulin kappa constant (IGKC), immunoglobulin lambda-like polypeptide 5 (IGLL5), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), immunoglobulin J chain (Immunoglobulin J chain, JCHAIN), serum paraoxonase / arylesterase 1 (PON1), S100 calcium-binding protein A8 (Protein S100-A8,Methods: A total of 12 pleural effusion components (white blood cell count, monocyte percentage, polymorphonuclear leukocyte percentage, adenosine deaminase level, lactate dehydrogenase content, total protein content, and glucose content) were used to establish an effective diagnostic model to distinguish patients with PEMP-MT from those with PEMN-MT. The specific modeling steps included: first, using the Least Absolute Shrinkage and Selection Operator (LASSO) to screen potential marker protein features for PEMP-MT from the training dataset based on importance, sparsity, relevance, and generalization ability. Each of the potential marker protein features was then subjected to ten five-fold cross-validations using random forests (RF) and random survival forests (RSF), each using a different random seed to ensure the stability of the feature selection results. The model was trained on 66.6% of the dataset, and internal validation was performed by splitting the training set into 20% parts to iteratively monitor model progress. This cross-validation process was repeated five times to ensure model stability and generalization ability.

[0092] Finally, five markers were screened out: lactate dehydrogenase, immunoglobulin λ variant 2-18, peptide hormone retinol binding protein 4, immunoglobulin λ variant 3-21, and tubulin α-1C chain.

[0093] Example 2

[0094] This embodiment provides a marker for the microbiological diagnosis of tuberculous pleural effusion positive, wherein the markers are LDH and LV218.

[0095] Example 3

[0096] This embodiment provides a marker for the microbiological diagnosis of tuberculous pleural effusion positive, wherein the markers are LDH and RET4.

[0097] Example 4

[0098] This embodiment provides a marker for the microbiological diagnosis of tuberculous pleural effusion positive, wherein the markers are LDH and TAB1C.

[0099] Example 5

[0100] This embodiment provides a marker for the microbiological diagnosis of tuberculous pleural effusion positive, wherein the markers are LDH and LV321.

[0101] Example 6

[0102] This embodiment provides a marker for the microbiological diagnosis of positive tuberculous pleural effusion, wherein the marker is LDH.

[0103] Example 7

[0104] This embodiment provides a marker for the microbiologically positive diagnosis of tuberculous pleural effusion, wherein the marker is LV218.

[0105] Example 8

[0106] This embodiment provides a marker for the microbiological diagnosis of positive tuberculous pleural effusion, wherein the marker is TAB1C.

[0107] Performance Testing

[0108] A second validation cohort was established and the pleural effusion levels of the markers were validated using an enzyme-linked immunosorbent assay (ELISA). This validation cohort included 64 participants in the PEMN-MT group and 28 participants in the PEMP-MT group (see Table 3). All participants were adults who underwent thoracentesis at the Southern University of Science and Technology Shenzhen Third People's Hospital between April 2024 and January 2025. ELISA validation tests were performed on the markers described in Examples 2 to 9.

[0109] Table 3 Information of all participants analyzed by ELISA

[0110]

[0111] Abbreviations: CON, control; PEMN-MT, pleural effusion culture negative for Mycobacterinm tubercnlosis; PEMP-MT, pleural effusion culture positive for Mycobacterium tuberculosis; BMI, body mass index; WBC, white blood cells; CRP, C-readtive protein; IL-6, interleukin-6; PCT,

[0112] Procaleitonin; CEA, carcinoembryonic antigen; ESR, erythrocytesedimentation rate; MN, monocyte; PMN, polymorphonuclear leukocyte; ADA

[0113] adenosine deaminase; LDH, lactate dehydrogenase; AFB, acid-fast bacillus;

[0114] P values compare the PEMN-MT vs.PEMP-MT.Bold indicates P<0.05.

[0115] a paired t-tests for pairs of data sets

[0116] bChi-square tests.

[0117] "-" means not detect in this study.

[0118] The purchase information of the enzyme-linked immunosorbent assay kits used is as follows: LV218 (Yibo (Wuhan) Science and Technology Co., Ltd., catalog number: E16286h), RET4 (Wuhan Sanying Biotechnology Co., Ltd., catalog number: KE00056), TAB1C (Yibo (Wuhan) Science and Technology Co., Ltd., catalog number: E6603h) and LV321 (Yibo (Wuhan) Science and Technology Co., Ltd., catalog number: E16403h). All enzyme-linked immunosorbent assay kits contain microplates, corresponding antibodies (LV218, RET4, TAB1C, LV321) immobilized on the microplates, substrate solution, standards (LV218, RET4, TAB1C, LV321), washing solution, blocking solution, washing buffer and reaction termination solution. The targets for detection were LV218, RET4, TAB1C, and LV321. The detection method was as follows: Enzyme-linked immunosorbent assay (ELISA) was performed by coating LV218, RET4, TAB1C, and LV321 antibodies on a microplate. These antibodies were then bound to the target molecules in the pleural effusion samples. An enzyme-labeled secondary antibody was then added for reaction. Finally, the substrate reaction developed color, and the absorbance (OD value) was measured to quantify the target molecule concentration. The target molecule content in the pleural effusion samples was calculated using a standard curve of a standard. ELISA kits were used to detect the levels of LV218, RET4, TAB1C, and LV321 in the pleural effusion samples of the subjects, respectively. The test results are shown in Table 3.

[0119] Table 3 Analysis of marker contents in the PEMN-MT and PEMP-MT groups in the second validation cohort

[0120]

[0121] As shown in Table 3, the levels of LV218, RET4, and TAB1C were significantly different between the PEMP-MT group and the PEMN-MT group (P < 0.05). There was no significant difference in the level of LV321 between the PEMP-MT group and the PEMN-MT group, making further Pearson correlation coefficient analysis unsuitable.

[0122] Pearson correlation coefficient analysis was performed on the results of the marker content in Examples 2 to 5 in the PEMN-MT group and the PEMP-MT group. The results are shown in Table 4.

[0123] Table 4 Statistical results of Pearson correlation coefficient analysis of markers in Examples 2-5

[0124] markers Sample size <![CDATA[R 2 Value]]> p-value LV218+LDH 91 0.073 0.02 RET4+LDH 91 0.013 0.33 TAB1C+LDH 91 0.081 0.01 LV321+LDH 91 0.002 0.69

[0125] As shown in Table 4, the Pearson correlation coefficient analysis of the LV218 and LDH combination, and the TAB1C and LDH combination in the PEMP-MT group were significantly different from those in the PEMN-MT group (P < 0.05), indicating that the LV218 and LDH combination, and the TAB1C and LDH combination have the potential to distinguish the PEMP-MT group from the PEMN-MT group.

[0126] ROC curve analysis was performed on the marker content results of Examples 2 and 6-8 in the PEMN-MT and PEMP-MT groups, and the results were verified by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The results are shown in Table 5.

[0127] Table 5. Results of ROC curve model analysis of markers in Example 2 and Examples 6-8

[0128] markers AUC 95% confidence interval p-value LDH 0.7566 0.6401-0.8710 0.0002 LV218 0.7465 0.6299-0.8631 0.0002 TAB1C 0.5469 0.3988-0.6949 0.4979 LV218+LDH 0.7194 0.6437-0.7951 ＜0.0001

[0129] As shown in Table 5, the AUC of the LV218+LDH combination for distinguishing the PEMP-MT group from the PEMN-MT group was 0.7194, the 95% confidence intervals were 0.6437-0.7951, and the p-values were <0.0001. Compared with the markers in other examples, the total content of LV218 and LDH was the optimal marker indicator for distinguishing the PEMP-MT group from the PEMN-MT group.

[0130] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of the present invention.

Claims

1. A marker for the microbiological diagnosis of tuberculous pleural effusion, characterized in that: The markers include at least one of lactate dehydrogenase, immunoglobulin lambda variant 2-18, peptide hormone retinol binding protein 4, immunoglobulin lambda variant 3-21, and tubulin alpha-1C chain.

2. A method for screening markers for microbiologically positive diagnosis of tuberculous pleural effusion, characterized in that: The following steps are involved: S10. Collect pleural effusion samples from tuberculous pleurisy patients and healthy people, wherein the tuberculous pleurisy patients include positive patients and negative patients, the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the positive patients are all positive, and the pleural effusion culture, Xpert test, and Mycobacterium tuberculosis DNA test results of the negative patients are all negative; S20, performing proteomic analysis on each pleural effusion sample in step S10 to obtain information on differentially expressed proteins, wherein the differentially expressed proteins include a first differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of negative patients, a second differentially expressed protein obtained by comparing the proteome of pleural effusion samples of healthy subjects with that of positive patients, and a third differentially expressed protein obtained by comparing the proteome of pleural effusion samples of negative patients with that of positive patients; S30, performing metabolomics analysis on the pleural effusion samples of the positive patients and the negative patients in step S10, and comparing the metabolomes of the pleural effusion samples of the negative patients and the positive patients to obtain information on differentially expressed metabolites; S40, performing association analysis on the information of the expressed metabolites and the information of the third differentially expressed protein, so as to correspond the proteins related to the metabolic pathway mapped by the differentially expressed metabolites to the third differentially expressed protein, thereby obtaining a first potential marker protein; S50, comparing the first potential marker protein with the first differentially expressed protein and the second differentially expressed protein, and screening out a protein from the third differentially expressed proteins that is different in type from the first differentially expressed protein and the second differentially expressed protein, which is the second potential marker protein; S60, screening a protein with a non-zero regression coefficient from the second potential marker protein by LASSO and capable of distinguishing negative patients from positive patients, to obtain a third potential marker protein; S70. Construct a random forest model using the third potential marker protein and the pleural effusion components of healthy people, positive patients, and negative patients in step S10, and verify the fourth potential marker protein to obtain a fourth potential marker protein that can distinguish between negative patients and positive patients, that is, a marker.

3. The screening method for a marker for the microbiologically positive diagnosis of tuberculous pleural effusion as claimed in claim 2, wherein: In step S20, the proteomic analysis includes relative and absolute quantitative proteomic analysis based on iTRAQ technology: The proteomic information of each pleural effusion sample was normalized, and proteins with a fold difference greater than 0.585 and P < 0.05 were screened out.

4. The screening method for a marker for the microbiologically positive diagnosis of tuberculous pleural effusion according to claim 2, wherein In step S30, the metabolomics analysis includes non-targeted metabolomics analysis: The metabolomic information of pleural effusion samples from negative and positive patients was normalized, and metabolites with a difference fold > 1.5 and P < 0.05, and a difference fold < 0.67 and P < 0.05 were screened out.

5. The screening method for markers for microbiologically positive diagnosis of tuberculous pleural effusion according to claim 2, wherein In step S20, the information of the differentially expressed proteins includes the abundance of the differentially expressed proteins; and / or, In step S30, the information of the differentially expressed metabolites includes the abundance of the differentially expressed metabolites.

6. The method for screening markers for the microbiologically positive diagnosis of tuberculous pleural effusion according to claim 2, wherein: Step S50 includes: Comparing the first potential marker protein with the first differentially expressed protein and the second differentially expressed protein, screening out proteins of different types from the first differentially expressed protein and the second differentially expressed protein from the third differentially expressed proteins, and obtaining information about the target protein; A validation cohort is provided, comprising pleural effusion samples from healthy individuals, positive patients, and negative patients. Proteomic analysis is performed on each pleural effusion sample in the validation cohort to obtain differentially expressed proteins between healthy individuals and positive patients, healthy individuals and negative patients, and positive patients and negative patients in the validation cohort. The target protein is validated by parallel reaction monitoring analysis to obtain a second potential marker protein that can distinguish between negative and positive patients in the validation cohort.

7. The method for screening markers for the microbiologically positive diagnosis of tuberculous pleural effusion according to claim 2, wherein: Step S70 is followed by a verification step: The content of the marker in the pleural effusion of the positive patient and the negative patient in step S10 is detected by enzyme-linked immunosorbent assay, and the effectiveness of the marker is confirmed by evaluation using a Pearson correlation coefficient model or a ROC curve model.

8. Use of the marker according to claim 1 in preparing a kit for detecting tuberculous pleurisy.

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