Tuberculosis molecular marker combination and application thereof

Abstract

The invention belongs to the field of biological medicine, and relates to a tuberculosis molecular marker combination and application thereof. Specifically, the invention relates to a molecular marker combination, which comprises (A) nucleic acid for coding GLRX protein, and one or more of nucleic acid for coding FCGR1A protein, nucleic acid for coding IFITM1 protein and nucleic acid for coding UBE2L6 protein; or (B) a GLRX protein, and one or more selected from the group consisting of an FCGR1A protein, an IFITM1 protein, and a UBE2L6 protein. The molecular marker combination can effectively diagnose the active tuberculosis or effectively distinguish the active tuberculosis from latent tuberculosis infection and normal control, and has high sensitivity and specificity.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and relates to a combination of tuberculosis molecular markers and their uses. Background Art

[0002] Tuberculosis is an infectious disease that seriously threatens human health. According to the statistics of the World Health Organization, there are nearly ten million new cases and over one million death cases globally every year. Mycobacterium tuberculosis is the main pathogen of tuberculosis, and humans are its only host. Infection can cause lesions in the lungs and multiple tissues and organs throughout the body. Since pulmonary tuberculosis is a chronic respiratory infectious disease, its pathogenesis process experiences latent tuberculosis infection (LTBI) and active tuberculosis (ATB). Latently infected individuals have no clinical symptoms, and the risk of developing active tuberculosis during their lifetime is 5%-15%. This risk may be even higher for immunocompromised patients. Currently, the gold standard for the diagnosis of active tuberculosis is still the etiological test result, but a considerable proportion of smear-negative patients among clinical patients cannot be detected by existing etiological methods. Although the tuberculin skin test (TST) and interferon gamma release assays (IGRA), which have been widely used in recent years, can quickly detect tuberculosis infections, they cannot determine whether it is latent infection or active tuberculosis. At the same time, imaging methods are also often used in the auxiliary diagnosis of clinical tuberculosis, but they are easily confused with other pulmonary infectious diseases, which is not conducive to formulating effective treatment plans. Therefore, there is an urgent need to establish a new diagnostic method for active tuberculosis, and the primary task is to find new differential diagnostic markers.

[0003] In recent years, by using high-throughput sequencing technology to explore the changes in the transcriptional profiles of the host under infection and disease states, it is expected to discover new diagnostic markers from the perspective of the host response mechanism. Neutrophils are important immune cells in the human body, the main phagocytes in the blood, and the front line of the body's defense against pathogen invasion. They usually reach the inflammatory site first after the body is infected, and there is a close correlation between their composition and quantity and the occurrence of tuberculosis.

[0004] The FCGR1A protein (High affinity immunoglobulin gamma Fc receptor I), a high-affinity Fc-gamma receptor, mediates the IgG effector function on monocytes and triggers antibody-dependent cell-mediated cytotoxicity (ADCC) of virus-infected cells.

[0005] The IFITM1 protein (Interferon-induced transmembrane protein 1, an interferon-induced antiviral protein) can inhibit the entry of viruses into the cytoplasm of host cells. Although viruses can enter cells through endocytosis, this protein can prevent subsequent viral fusion and the release of viral contents into the cytoplasm, and is active against a variety of viruses, including influenza A virus, SARS coronaviruses (SARS-CoV and SARS-CoV-2), Marburg virus (MARV), Ebola virus (EBOV), dengue virus (DNV), West Nile virus (WNV), human immunodeficiency virus type 1 (HIV-1), and hepatitis C virus (HCV). This protein can inhibit virus entry mediated by the influenza virus hemagglutinin protein, virus entry mediated by MARV and EBOV GP1,2, and virus entry mediated by the SARS-CoV and SARS-CoV-2 S proteins. By inhibiting ERK activation or arresting cell growth in the G1 phase in a p53-dependent manner, the IFITM1 protein plays a key role in the anti-proliferative effect of IFN-γ.

[0006] The GLRX protein (Glutaredoxin-1) is a member of the glutaredoxin family, and its DNA sequence is located on the negative strand of chromosome 5. The GLRX protein is a cytoplasmic enzyme that, in the presence of NADPH and glutathione reductase, has glutathione disulfide oxidoreductase activity, catalyzes the reversible reduction of glutathione-protein mixed disulfides, and can also reduce low-molecular-weight disulfide bonds and the activity of proteins. GLRX makes a great contribution to the antioxidant defense system. By controlling the S-glutathionylation state of signal transduction mediators, GLRX is crucial for a variety of signal transduction pathways. GLRX is associated with β-amyloid toxicity and Alzheimer's disease.

[0007] UBE2L6 (Ubiquitin / ISG15-conjugating enzyme E2 L6) is a member of the E2 ubiquitin-conjugating enzyme family, and its primary structure is highly similar to the enzyme encoded by the UBE2L3 gene. UBE2L6 catalyzes the covalent attachment of ubiquitin or ISG15 to other proteins and plays a role in E6 / E6-AP-induced p53 / TP53 ubiquitination. UBE2L6 promotes the ubiquitination and subsequent proteasomal degradation of FLT3.

[0008] Currently, there is a need to develop new technical means for the rapid diagnosis of active pulmonary tuberculosis. Summary of the Invention

[0009] Through in-depth research and creative work, the present inventor utilized neutrophil RNA-seq transcriptome library analysis to discover differentially expressed genes with up-regulated expression levels that are closely related to the active tuberculosis state, and screened out genes FCGR1A, IFITM1, GLRX, and UBE2L6 that can distinguish ATB from LTBI. Further, the present inventor detected the expression levels of candidate genes in target cells in the validation group (ATB, LTBI, and HC: neutrophil RNA-seq analysis) and the evaluation group (ATB, LTBI, PN, and HC: real-time quantitative analysis of neutrophil-expressed genes), and confirmed that the expression levels of the aforementioned 4 genes were significantly and specifically increased in ATB. At the same time, it can effectively distinguish ATB from non-diseased tuberculosis infections and non-tuberculosis-infected healthy controls. The present inventor also evaluated through statistical analysis that the combination of these four genes (abbreviated as the FIGU combination) can effectively distinguish active tuberculosis (ATB) from latent tuberculosis infection (ATBI) and normal controls (HC), and has the potential to be used as a molecular identifier or molecular marker. Thus, the following invention is provided:

[0010] One aspect of the present invention relates to a molecular marker combination, comprising:

[0011] (A) nucleic acid encoding the GLRX protein, and one or more selected from nucleic acid encoding the FCGR1A protein, nucleic acid encoding the IFITM1 protein, and nucleic acid encoding the UBE2L6 protein; or

[0012] (B) the GLRX protein, and one or more selected from the FCGR1A protein, the IFITM1 protein, and the UBE2L6 protein.

[0013] In some embodiments of the present invention, the molecular marker combination comprises:

[0014] (1) nucleic acid encoding the GLRX protein, nucleic acid encoding the FCGR1A protein, nucleic acid encoding the IFITM1 protein, and nucleic acid encoding the UBE2L6 protein;

[0015] (2) nucleic acid encoding the GLRX protein, nucleic acid encoding the FCGR1A protein, and nucleic acid encoding the IFITM1 protein;

[0016] (3) nucleic acid encoding the GLRX protein, nucleic acid encoding the FCGR1A protein, and nucleic acid encoding the UBE2L6 protein;

[0017] (4) nucleic acid encoding the GLRX protein, nucleic acid encoding the IFITM1 protein, and nucleic acid encoding the UBE2L6 protein;

[0018] (5) nucleic acid encoding the GLRX protein and nucleic acid encoding the FCGR1A protein;

[0019] (6) Nucleic acids encoding GLRX protein and nucleic acids encoding IFITM1 protein;

[0020] (7) Nucleic acids encoding GLRX protein and nucleic acids encoding UBE2L6 protein;

[0021] (8) GLRX protein, FCGR1A protein, IFITM1 protein and UBE2L6 protein;

[0022] (9) GLRX protein, FCGR1A protein and IFITM1 protein;

[0023] (10) GLRX protein, FCGR1A protein and UBE2L6 protein;

[0024] (11) GLRX protein, IFITM1 protein and UBE2L6 protein;

[0025] (12) GLRX protein and FCGR1A protein;

[0026] (13) GLRX protein and IFITM1 protein; or

[0027] (14) GLRX protein and UBE2L6 protein.

[0028] In some embodiments of the present invention, the molecular marker combination, wherein,

[0029] The amino acid sequence of GLRX protein is as shown in SEQ ID NO: 1,

[0030] The amino acid sequence of FCGR1A protein is as shown in SEQ ID NO: 2,

[0031] The amino acid sequence of IFITM1 protein is as shown in SEQ ID NO: 3, and / or

[0032] The amino acid sequence of UBE2L6 protein is as shown in SEQ ID NO: 4.

[0033] In some embodiments of the present invention, the molecular marker combination, wherein,

[0034] The nucleic acids encoding GLRX protein, the nucleic acids encoding FCGR1A protein, the nucleic acids encoding IFITM1 protein and the nucleic acids encoding UBE2L6 protein are independently DNA or independently RNA;

[0035] Preferably, the nucleic acids encoding GLRX protein, the nucleic acids encoding FCGR1A protein, the nucleic acids encoding IFITM1 protein and the nucleic acids encoding UBE2L6 protein are all DNA or all RNA;

[0036] Preferably,

[0037] The sequence of the nucleic acid encoding the GLRX protein is as shown in SEQ ID NO:5,

[0038] The sequence of the nucleic acid encoding the FCGR1A protein is as shown in SEQ ID NO:6,

[0039] The sequence of the nucleic acid encoding the IFITM1 protein is as shown in SEQ ID NO:7, and / or

[0040] The sequence of the nucleic acid encoding the UBE2L6 protein is as shown in SEQ ID NO:8.

[0041] In some embodiments of the present invention, the molecular marker combination is a molecular marker combination for tuberculosis;

[0042] Preferably, it is used for diagnosing active pulmonary tuberculosis (ATB), differentiating active pulmonary tuberculosis from latent tuberculosis infection (LTBI), differentiating active pulmonary tuberculosis from bacterial pneumonia (PN), differentiating active pulmonary tuberculosis from healthy individuals (HC), or differentiating active pulmonary tuberculosis from "latent tuberculosis infection, healthy individuals or bacterial pneumonia".

[0043] Another aspect of the present invention relates to the use of the molecular marker combination described in any one of the present invention or a reagent for detecting the molecular marker combination in the preparation of a drug, and the drug is used for diagnosing active pulmonary tuberculosis, differentiating active pulmonary tuberculosis from latent tuberculosis infection, differentiating active pulmonary tuberculosis from bacterial pneumonia, differentiating active pulmonary tuberculosis from healthy individuals, or differentiating active pulmonary tuberculosis from "latent tuberculosis infection, healthy individuals or bacterial pneumonia".

[0044] In some embodiments of the present invention, for the above use, the reagent comprises specific primers for each nucleic acid in the molecular marker combination, or comprises antibodies that specifically bind to each protein in the molecular marker combination; preferably, the antibody is conjugated with a detectable label, such as a radioisotope, a fluorescent substance, a colored substance or an enzyme. The specific primers can be designed and synthesized according to methods well known to those skilled in the art.

[0045] In some embodiments of the present invention, for the above use, the reagent comprises specific primers for each nucleic acid in (1) to (7) above, or comprises antibodies that specifically bind to each protein in (8) to (14) above; preferably, the antibody is conjugated with a detectable label, such as a radioisotope, a fluorescent substance, a colored substance or an enzyme.

[0046] In some embodiments of the present invention, for the use described above, the relative expression levels of each nucleic acid in the molecular marker combination are detected by real-time fluorescence quantitative PCR (qPCR);

[0047] Preferably, the relative expression levels of each nucleic acid in any one of (1) to (7) are detected by qPCR.

[0048] In some embodiments of the present invention, for the use described above, the MYO1F gene, ACTB gene or GAPDH gene is used as an internal reference gene;

[0049] Preferably, the MYO1F gene is used as an internal reference gene.

[0050] In some embodiments of the present invention, for the use described above, the sample to be detected is a peripheral blood sample, PBMC sample or neutrophil sample; preferably, the neutrophil sample is a peripheral blood neutrophil sample; more preferably, the sample is a cDNA sample obtained by reverse transcription of total RNA extracted from peripheral blood neutrophils.

[0051] In some embodiments of the present invention, for the use described above,

[0052] The relative expression levels of the nucleic acid encoding the GLRX protein, the nucleic acid encoding the FCGR1A protein, the nucleic acid encoding the IFITM1 protein and the nucleic acid encoding the UBE2L6 protein are calculated to determine whether the sample is ATB, LTBI, HC or PN.

[0053] In some embodiments of the present invention, for the use described above,

[0054] P is calculated by the following formula TB :

[0055]

[0056] Where:

[0057] fcgr1a, ifitm1, glrx and ube2l6 respectively represent the relative expression levels of the nucleic acid encoding FCGR1A (such as the FCGR1A gene), the nucleic acid encoding IFITM1 (such as the IFITM1 gene), the nucleic acid encoding GLRX (such as the GLRX gene) and the nucleic acid encoding UBE2L6 (such as the UBE2L6 gene);

[0058] The cut-off value is 0.274;

[0059] When P TB is greater than 0.274, the sample is determined to be ATB; when the predicted probability value is less than or equal to 0.274, the sample is diagnosed as LTBI, HC or PN.

[0060] In some embodiments of the present invention, the use, wherein,

[0061] Calculate P by the following formula TB :

[0062]

[0063] Wherein:

[0064] fcgr1a, ifitm1, glrx and ube2l6 respectively represent the relative expression levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene;

[0065] The cut-off value is 0.274;

[0066] When P TB is greater than 0.274, the sample is judged as ATB; when the predicted probability value is less than or equal to 0.274, the sample is diagnosed as LTBI, HC or PN.

[0067] In some embodiments of the present invention, the use, wherein, the level of the combination of molecular markers described in any one of the present invention in a subject is compared with the cut-off value of the Receiver Operating Characteristic curve (ROC curve), and the receiver operating characteristic curve is the receiver operating characteristic curve of the level of the combination of molecular markers described in any one of the present invention for ATB and HC, ATB and LTBI, or ATB and PN, wherein:

[0068] If the level of the combination of molecular markers of the subject is greater than the cut-off value, it is judged as ATB;

[0069] If the level of the combination of molecular markers of the subject is less than or equal to the cut-off value, it is judged as HC, LTBI or PN;

[0070] Preferably, the number of ATB samples, and HC samples or LTBI samples or PN samples for drawing the working curve is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, greater than or equal to 50, greater than or equal to 80, greater than or equal to 100, greater than or equal to 200 or greater than or equal to 500, 10 - 1000, 20 - 500, 30 - 300, 30 - 200, 20 - 40 or 50 - 100.

[0071] In some embodiments of the present invention, for the described use, the levels of the FCGR1A gene, the IFITM1 gene, the GLRX gene, and the UBE2L6 gene are compared with the cut-off value of the receiver operating characteristic curve, and the receiver operating characteristic curve is the receiver operating characteristic curve of the levels of the FCGR1A gene, the IFITM1 gene, the GLRX gene, and the UBE2L6 gene for the comparison between ATB and HC, ATB and LTBI, or ATB and PN, where:

[0072] If the levels of the FCGR1A gene, the IFITM1 gene, the GLRX gene, and the UBE2L6 gene of a subject are greater than the cut-off value, it is determined as ATB;

[0073] If the levels of the FCGR1A gene, the IFITM1 gene, the GLRX gene, and the UBE2L6 gene of a subject are less than or equal to the cut-off value, it is determined as HC, LTBI, or PN;

[0074] Preferably, the number of ATB samples, and HC samples or LTBI samples or PN samples for drawing the working curve is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, greater than or equal to 50, greater than or equal to 80, greater than or equal to 100, greater than or equal to 200, or greater than or equal to 500, 10 - 1000, 20 - 500, 30 - 300, 30 - 200, 20 - 40, or 50 - 100.

[0075] Another aspect of the present invention relates to a method selected from the following:

[0076] A method for diagnosing active pulmonary tuberculosis, a method for differentiating active pulmonary tuberculosis from latent tuberculosis infection, a method for differentiating active pulmonary tuberculosis from healthy people, or a method for differentiating active pulmonary tuberculosis from "latent tuberculosis infection and healthy people",

[0077] comprising the step of detecting the level of the combination of molecular markers described in any one of the present invention in a sample to be tested.

[0078] In some embodiments of the present invention, for the described method, the reagent for detecting the combination of molecular markers contains specific primers for each nucleic acid in the combination of molecular markers, or contains antibodies that specifically bind to each protein in the combination of molecular markers; preferably, the antibody is linked with a detectable label, such as a radioactive isotope, a fluorescent substance, a colored substance, or an enzyme.

[0079] In some embodiments of the present invention, for the described method, the relative expression levels of each nucleic acid in the combination of molecular markers are detected by real-time fluorescence quantitative PCR (qPCR);

[0080] Preferably, the relative expression levels of the respective nucleic acids in any one of (1) to (7) are detected by qPCR.

[0081] In some embodiments of the present invention, in the method, the MYO1F gene, ACTB gene or GAPDH gene is used as an internal reference gene;

[0082] Preferably, the MYO1F gene is used as an internal reference gene.

[0083] In some embodiments of the present invention, in the method, the sample to be detected is a peripheral blood sample, PBMC sample or neutrophil sample; preferably, the neutrophil sample is a peripheral blood neutrophil sample; more preferably, the sample is a cDNA sample obtained by reverse transcription of total RNA extracted from peripheral blood neutrophils.

[0084] In some embodiments of the present invention, in the method,

[0085] The relative expression levels of the nucleic acid encoding the GLRX protein, the nucleic acid encoding the FCGR1A protein, the nucleic acid encoding the IFITM1 protein, and the nucleic acid encoding the UBE2L6 protein are calculated to determine whether the sample is ATB, LTBI, HC or PN.

[0086] In some embodiments of the present invention, in the method,

[0087] Calculate P by the following formula TB :

[0088]

[0089] Where:

[0090] fcgr1a, ifitm1, glrx and ube2l6 respectively represent the relative expression levels of the nucleic acid encoding FCGR1A (such as the FCGR1A gene), the nucleic acid encoding IFITM1 (such as the IFITM1 gene), the nucleic acid encoding GLRX (such as the GLRX gene), and the nucleic acid encoding UBE2L6 (such as the UBE2L6 gene);

[0091] The cut-off value is 0.274;

[0092] When P TB is greater than 0.274, the sample is determined to be ATB; when the predicted probability value is less than or equal to 0.274, the sample is diagnosed as LTBI, HC or PN.

[0093] In some embodiments of the present invention, in the method,

[0094] Calculate P by the following formulaTB :

[0095]

[0096] Wherein:

[0097] The relative expression levels of fcgr1a, ifitm1, glrx, and ube2l6 represent the relative expression levels of the FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene, respectively;

[0098] The cut-off value is 0.274;

[0099] When P TB is greater than 0.274, the sample is judged as ATB; when the predicted probability value is less than or equal to 0.274, the sample is diagnosed as LTBI, HC, or PN.

[0100] In some embodiments of the present invention, for the method described above, the level of the combination of molecular markers of any one of the present invention in a subject is compared with the cut-off value of the Receiver Operating Characteristic curve (ROC curve), and the receiver operating characteristic curve is the receiver operating characteristic curve of the level of the combination of molecular markers of any one of the present invention for ATB and HC, ATB and LTBI, or ATB and PN, wherein:

[0101] If the level of the combination of molecular markers of the subject is greater than the cut-off value, it is judged as ATB;

[0102] If the level of the combination of molecular markers of the subject is less than or equal to the cut-off value, it is judged as HC, LTBI, or PN;

[0103] Preferably, the number of ATB samples, and HC samples or LTBI samples or PN samples for drawing the working curve is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, greater than or equal to 50, greater than or equal to 80, greater than or equal to 100, greater than or equal to 200, or greater than or equal to 500, 10 - 1000, 20 - 500, 30 - 300, 30 - 200, 20 - 40, or 50 - 100.

[0104] In some embodiments of the present invention, the method, wherein the levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene are compared with the cut-off value of the receiver operating characteristic curve, and the receiver operating characteristic curve is the receiver operating characteristic curve of the levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene for the comparison between ATB and HC, ATB and LTBI or ATB and PN, wherein:

[0105] If the levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene of the subject are greater than the cut-off value, it is judged as ATB;

[0106] If the levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene of the subject are less than or equal to the cut-off value, it is judged as HC, LTBI or PN;

[0107] Preferably, the number of ATB samples, as well as HC samples or LTBI samples or PN samples for drawing the working curve, is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, greater than or equal to 50, greater than or equal to 80, greater than or equal to 100, greater than or equal to 200 or greater than or equal to 500, 10 - 1000, 20 - 500, 30 - 300, 30 - 200, 20 - 40 or 50 - 100.

[0108] The present invention focuses on the transcriptome analysis of human important immune cells - peripheral blood neutrophils by RNA-seq technology. Through the comparison of clinically active pulmonary tuberculosis patients with bacterial pneumonia patients, latent tuberculosis patients and normal control populations, a set of differentially expressed gene combinations closely related to the state of active pulmonary tuberculosis and available for differentiating active pulmonary tuberculosis from latent tuberculosis infection are screened out. On this basis, a real-time quantitative analysis system for differentiating ATB and LTBI by using the gene expression changes of peripheral blood neutrophils is established. And through clinical samples, the differential diagnosis ability of this gene combination system is further evaluated, and it is confirmed that the FIGU combination in this system can be used as a differential diagnosis marker for active pulmonary tuberculosis.

[0109] In the present invention, if not otherwise specified, the term "normal control group" or "healthy control group" refers to healthy people who are not active pulmonary tuberculosis, not latent infection, and not bacterial pneumonia, and are also called healthy people (HC).

[0110] In the present invention, the term "False Discovery Rate" (FDR) is a necessary parameter used in the analysis of expression profiles, which is the expected value of the proportion of the number of false rejections (rejecting the true (original) hypothesis) to the number of all rejected original hypotheses. FDR has the following advantages: (1) Its value can be flexibly adjusted. As a control index for the error rate of hypothesis testing, its control value can be flexibly selected according to needs, while the value of traditional hypothesis testing (FWER) is relatively fixed and is usually set at 0.05; (2) The meaning of FDR is clear and can be used as an evaluation index for the selected differential variables, while FWER is mainly used to control type I errors.

[0111] The term "log2Fold change" is the log2 value of the difference multiple of the gene expression levels between two groups of samples. In order to better display the difference multiple; if the difference multiple is not converted, the very large and very small ones together are not easy to display in a graph.

[0112] The term "FPKM" (Fragments Per Kilobase of exon model per Million mapped fragments) is the number of fragments per million mapped reads for each kilobase of transcription. The FPKM value is used to measure and count the expression levels of each gene in all samples of different groups. Its calculation formula is as follows:

[0113]

[0114] Where N represents the total number of Fragments uniquely mapped to the entire reference genome, C represents the number of Fragments uniquely mapped to the exons of this gene, and L represents the length (number of bases) of all exons of this gene. The FPKM algorithm corrects both the total data volume and the gene length and can be used for subsequent analysis of differential expression in transcriptomes.

[0115] The term "binary logistic regression analysis of SPSS" refers to a regression analysis used for a binary classification variable as the dependent variable, where the independent variable can have continuous variables to predict the significant relationship between the independent variable and the dependent variable. The logistic regression equation is: logit(p) = a + b1x1 +... bnxn (a = constant term, representing the natural logarithm of the odds ratio (the ratio of the probability of Y = 1 to the probability of Y = 0) when the independent variable x takes the value of 0).

[0116] The term "cut off value" or "critical value" or "threshold value" refers to a certain critical value set by the classifier to distinguish between normal and abnormal prediction results.

[0117] The GLRX gene, FCGR1A gene, IFITM1 gene, and UBE2L6 gene are also abbreviated as gene G, gene F, gene I, and gene U respectively in this article.

[0118] The GLRX protein, FCGR1A protein, IFITM1 protein, and UBE2L6 protein are also abbreviated as protein G, protein F, protein I, and protein U respectively in this article.

[0119] In the present invention, unless otherwise specified, the term "relative expression level" refers to the expression level relative to a reference gene (such as the MYO1F gene, ACTB gene, or GAPDH gene).

[0120] The level of the molecular marker combination can be the concentration, content, or expression level of each molecular marker, such as the relative expression level.

[0121] Advantages of the Invention

[0122] The molecular marker combination of the present invention can effectively diagnose ATB, effectively distinguish ATB from LTBI and HC, and has high sensitivity and specificity.

[0123] The present invention can be used in, but not limited to, the following situations: 1. Screening for ATB in the population; 2. Whether a close contact with positive LTBI is ATB; or 3. Cases of clinically suspected ATB that cannot be distinguished from PN by existing detection methods. Currently, there is no applicable differential diagnosis method for the above three situations. The present invention provides a new tuberculosis diagnosis marker, with its sensitivity and specificity greater than 90% and 70% respectively, meeting the application standards of the World Health Organization for host diagnosis markers, and having good clinical application value. Brief Description of the Drawings

[0124] Figures 1A to 1B : Neutrophil RNA-seq analysis to obtain differentially expressed genes between ATB and HC ( Figure 1A ), and between ATB and LTBI ( Figure 1B ).

[0125] Figures 2A to 2H : Statistical analysis of the differences in the expression levels of 8 candidate target genes in the evaluation group.

[0126] Figures 3A to 3C : ROC analysis results of the ATB differential diagnosis marker FIGU. The three AUC curves respectively show the differential diagnosis effects between ATB and LTBI ( Figure 3A ), between ATB and PN ( Figure 3B ), and between ATB and LTBI and HC ( Figure 3C ).

[0127] Figure 4:ROC analysis results for identifying the diagnostic efficacy of ATB using FIGU test, showing the diagnostic efficacy of differentiating ATB from LTBI in the test group.

[0128] Partial sequences involved in the present invention are as follows:

[0129] 1. GLRX protein

[0130] MAQEFVNCKIQPGKVVVFIKPTCPYCRRAQEILSQLPIKQG LLEFVDITATNHTNEIQDYLQQLTGARTVPRVFIGKDCIGGCSD LVSLQQSGELLTRLKQIGALQ (SEQ ID NO:1)

[0131] 2. FCGR1A protein

[0132] MWFLTTLLLWVPVDGQVDTTKAVITLQPPWVSVFQEETVTLHCEVLHLPGSSSTQWFLNGTATQTSTPSYRITSASVNDSGEYRCQRGLSGRSDPIQLEIHRGWLLLQVSSRVFTEGEPLALRCHAWKDKLVYNVLYYRNGKAFKFFHWNSNLTILKTNISHNGTYHCSGMGKHRYTSAGISVTVKELFPAPVLNASVTSPLLEGNLVTLSCETKLLLQRPGLQLYFSFYMGSKTLRGRNTSSEYQILTARREDSGLYWCEAATEDGNVLKRSPELELQVLGLQLPTPVWFHVLFYLAVGIMFLVNTVLWVTIRKELKRKKKWDLEISLDSGHEKKVISSLQEDRHLEEELKCQEQKEEQLQEGVHRKEPQGAT (SEQ ID NO:2)

[0133] 3. IFITM1 protein

[0134] MHKEEHEVAVLGPPPSTILPRSTVINIHSETSVPDHVVWSLF NTLFLNWCCLGFIAFAYSVKSRDRKMVGDVTGAQAYASTAKCL NIWALILGILMTIGFILLLVFGSVTVYHIMLQIIQEKRGY (SEQ ID NO:3)

[0135] 4. UBE2L6 protein

[0136] MMASMRVVKELEDLQKKPPPYLRNLSSDDANVLVWHALLLPDQPPYHLKAFNLRISFPPEYPFKPPMIKFTTKIYHPNVDENGQICLPIISSENWKPCTKTCQVLEALNVLVNRPNIREPLRMDLADLLTQNPELFRKNAEEFTLRFGVDRPS(SEQ ID NO:4)

[0137] 5. Nucleic acid sequence encoding the GLRX protein

[0138] ATGGCTCAAGAGTTTGTGAACTGCAAAATCCAGCCTGGGAAGGTGGTTGTGTTCATCAAGCCCACCTGCCCGTACTGCAGGAGGGCCCAAGAGATCCTCAGTCAATTGCCCATCAAACAAGGGCTTCTGGAATTTGTCGATATCACAGCCACCAACCACACTAACGAGATTCAAGATTATTTGCAACAGCTCACGGGAGCAAGAACGGTGCCTCGAGTCTTTATTGGTAAAGATTGTATAGGCGGATGCAGTGATCTAGTCTCTTTGCAACAGAGTGGGGAACTGCTGACGCGGCTAAAGCAGATTGGAGCTCTGCAGTAA(SEQ ID NO:5)

[0139] 6. Nucleic acid sequence encoding the FCGR1A protein

[0140]

[0141] 7. Nucleic acid sequence encoding the IFITM1 protein

[0142] ATGCACAAGGAGGAACATGAGGTGGCTGTGCTGGGGCCACCCCCCAGCACCATCCTTCCAAGGTCCACCGTGATCAACATCCACAGCGAGACCTCCGTGCCCGACCATGTCGTCTGGTCCCTGTTCAACACCCTCTTCTTGAACTGGTGCTGTCTGGGCTTCATAGCATTCGCCTACTCCGTGAAGTCTAGGGACAGGAAGATGGTTGGCGACGTGACCGGGGCCCAGGCCTATGCCTCCACCGCCAAGTGCCTGAACATCTGGGCCCTGATTCTGGGCATCCTCATGACCATTGGATTCATCCTGTTACTGGTATTCGGCTCTGTGACAGTCTACCATATTATGTTACAGATAATACAGGAAAAACGGGGTTACTAG(SEQ ID NO:7)

[0143] 8. Nucleic acid sequence encoding the UBE2L6 protein

[0144] ATGATGGCGAGCATGCGAGTGGTGAAGGAGCTGGAGGATCTTCAGAAGAAGCCTCCCCCATACCTGCGGAACCTGTCCAGCGATGATGCCAATGTCCTGGTGTGGCACGCTCTCCTCCTACCCGACCAACCTCCCTACCACCTGAAAGCCTTCAACCTGCGCATCAGCTTCCCGCCGGAGTATCCGTTCAAGCCTCCCATGATCAAATTCACAACCAAGATCTACCACCCCAACGTGGACGAGAACGGACAGATTTGCCTGCCCATCATCAGCAGTGAGAACTGGAAGCCTTGCACCAAGACTTGCCAAGTCCTGGAGGCCCTCAATGTGCTGGTGAATAGACCGAATATCAGGGAGCCCCTGCGGATGGACCTCGCTGACCTGCTGACACAGAATCCGGAGCTGTTCAGAAAGAATGCCGAAGAGTTCACCCTCCGATTCGGAGTGGACCGGCCCTCCTAA(SEQ ID NO:8)

[0145] 9. MYO1F protein

[0146]

[0147] 10. Nucleic acid sequence encoding MYO1F protein

[0148] Detailed implementation manners

[0149] The embodiments of the present invention will be described in detail below in conjunction with the embodiments. However, those skilled in the art will understand that the following embodiments are only used to illustrate the present invention and should not be construed as limiting the scope of the present invention. For those conditions not specified in the embodiments, they are carried out according to the conventional conditions or the conditions recommended by the manufacturer. For the reagents or instruments whose manufacturers are not indicated, they are all conventional products that can be obtained through commercial purchases.

[0150] Example 1: Research on Molecular Markers of Active Pulmonary Tuberculosis in Hosts

[0151] (I) Inclusion and exclusion criteria for research subjects

[0152] A total of 115 clinical samples (hereinafter referred to as the discovery group) were included in this study, including 53 cases of ATB, 21 cases of LTBI, and 41 cases of HC. The information of the included samples is shown in Table 1.

[0153] The diagnostic criteria for the selected active pulmonary tuberculosis patients (ATB) were based on the "Diagnosis of Pulmonary Tuberculosis, Health Industry Standard of the People's Republic of China (WS288—2017)", and the pathogen detection of sputum or bronchoalveolar lavage fluid specimens was positive (at least one of smear / culture / nucleic acid detection was positive). There was no previous history of tuberculosis (no old tuberculosis lesions were found by interrogation and X-ray chest radiography), and it was the first anti-tuberculosis treatment with less than 7 days of medication.

[0154] LTBI refers to those without a previous history of tuberculosis, without tuberculosis-related clinical symptoms, with normal X-ray chest radiographs but positive interferon-γ release assay.

[0155] HC is a non-active pulmonary tuberculosis / non-latent infection healthy control, who has no previous history of tuberculosis, no tuberculosis-related clinical symptoms, negative interferon-γ release assay, and normal X-ray chest radiographs.

[0156] The age of the research subjects was 18 to 65 years old. Pregnant or lactating women, patients with malignant tumors, those with immune system diseases or receiving immunotherapy, and those infected with human immunodeficiency virus were all excluded. This study was approved by the Ethics Committee of the Institute of Pathogen Biology, Chinese Academy of Medical Sciences. After obtaining the informed consent of the enrolled subjects, the inventor collected their peripheral blood for subsequent tests.

[0157] Table 1: Demographic data of the samples in the discovery group

[0158] Project Normal Control Group Tuberculosis Infection Group Active Pulmonary Tuberculosis Group Total 41 21 53 Age (Age + Range) 55-65 53-65 20-58 Gender (Male / Female) 18 / 23 6 / 15 27 / 26

[0159] (II) Transcriptome analysis of peripheral blood neutrophils:

[0160] 1. Sorting and enrichment of peripheral blood neutrophils

[0161] Collect 2 ml of peripheral venous blood from the enrolled subjects using a heparin anticoagulant tube, and isolate neutrophils from the venous blood. The specific operation steps are as follows:

[0162] 1) Take 1 ml of anticoagulated blood, add 20 ml of ACK red blood cell lysis buffer, incubate at 2°C - 8°C for 3 - 5 min. Wait until the liquid is clear, centrifuge at 2000 rpm for 5 minutes, and discard the supernatant;

[0163] 2) Resuspend the white blood cell pellet with 10 ml of PBS, centrifuge at 2000 rpm for 10 minutes, discard the supernatant, and resuspend the white blood cells with 100 μL of PBS;

[0164] 3) Add 3 μL of anti - CD45APC, 3 μL of anti - CD3PE, and 2 μL of anti - CD64FICT (BD biosciences), incubate at 2 - 8°C in the dark for 30 min;

[0165] 4) After incubation, add 3 ml of PBS, mix well and transfer to a 5 - ml flow cytometry tube, centrifuge at 2000 rpm for 5 min, and carefully discard the supernatant;

[0166] 5) Add 400 μL of PBS to resuspend the cells, and place them at 2 - 8°C in the dark for later use;

[0167] 6) Dilute the RNase inhibitor (40 U / μL) 20 - fold with RNase - free water. Add 8 μL of the diluted RNase inhibitor to a 1.5 - ml protein - low - adsorption centrifuge tube for collecting sorted cells;

[0168] 7) Adjust the flow cytometer, set the sorting mode to "Purity", collect 150 neutrophils, and sort the cells obtained in step 5) using the set flow cytometer;

[0169] 8) Immediately centrifuge the sorted cells to the bottom of the tube containing the RNase inhibitor for the following transcriptome library construction, or store at - 80°C.

[0170] 2. Transcriptome Library Construction and Sequencing

[0171] The cDNA library was prepared using the SMART-Seq v4 Ultra Low Input RNA Kit for Sequencing (Takara). The RNA-Seq sequencing library was constructed using the Nextera XT DNA Library Preparation Kit (Illumina). The first-strand cDNA synthesis was performed according to the specific steps in the kit protocol, adding Illumina Adapters and Indexes, and purifying the RNA-Seq library with AMPure magnetic beads; then, through steps such as final RNA-Seq library amplification and AMPure magnetic bead purification of the final RNA-Seq library, the RNA-seq library was obtained. After passing the quality inspection by Agilent 2100, it was sequenced on the machine.

[0172] The qualified library after quality inspection was sequenced using the Illumina Novaseq 6000 platform. The sequencing strategy was paired-end sequencing, with a length of approximately 150 bp.

[0173] 3. Transcriptome data analysis

[0174] Data quality control and alignment analysis: The Trimmomatic software was used to perform quality filtering on the original off-machine data, including removing adapter sequences (introduced during library construction), low-quality sequences (caused by the errors of the sequencer itself), and short sequences (<30 bp).

[0175] Gene expression quantification and normalization analysis: In this study, the Cufflinks software was used, and FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) was used to calculate the gene expression levels. Genes with FPKM values greater than 100 in at least 5 samples were retained for subsequent analysis.

[0176] Differentially expressed gene analysis and screening of candidate molecular markers: The DESeq2 algorithm was used to calculate the fold change (FC) of gene expression differences in pairwise comparisons, and the P-value and false discovery rate (FDR) were calculated for significance analysis. In this study, the screening criteria for significantly differentially expressed genes (DEGs) screened for the CD64 marker were: FPKM > 100, p < 0.05, |log2FC| > 0.5.

[0177] Differential expression genes were screened by calculating the fold change (FC) through pairwise comparison of three groups of samples, and the p-value and false discovery rate (FDR) were calculated for significance analysis. Among them, as Figure 1A and Figure 1B shown, the inventor screened 444 differentially expressed genes between ATB and HC, including 239 up-regulated genes and 205 down-regulated genes ( Figure 1A ); 257 differential genes between ATB and LTBI, including 146 up-regulated genes and 111 down-regulated genes ( Figure 1B ). According to the following screening criteria: 1. The change in gene expression has significant inter-group differences between ATB and HC, and between ATB and LTBI, p < 0.05; 2. The inter-group fold change log2FC > 1.0, 25 DEGs that meet the screening criteria were further screened out from the differentially expressed genes (as shown in the first column of Table 3).

[0178] (3) Verification of the results of differential expression gene analysis

[0179] To verify the reliability of the analysis results of the aforementioned 25 differentially expressed genes by flow cytometry sorting neutrophil transcriptome analysis, the inventor separately collected clinical samples as the verification group (15 ATB, 22 LTBI, 21 HC), and the information of the enrolled samples is shown in Table 2. The inclusion criteria are the same as those of the previous discovery group (115 cases). Using the magnetic bead sorting method, peripheral blood neutrophils of the samples were collected for neutrophil RNA-seq transcriptome analysis. 1283 differentially expressed genes between ATB and HC, and 1357 differentially expressed genes between ATB and LTBI were screened out. Among them, the 25 differentially expressed genes screened in the discovery group were also the differentially expressed genes screened by the verification group according to the inter-group differences between ATB and HC, and between ATB and LTBI, p < 0.05. The information of the differentially expressed genes is shown in Table 3, which proves the reliability of the screening results of the discovery group.

[0180] Table 2: Information of enrolled samples in the verification group

[0181] Project Normal Control Group Tuberculosis Infection Group Active Pulmonary Tuberculosis Group Total 21 22 15 Age (Age + Range) 55-60 53-60 20-58 Gender (Male / Female) 10 / 11 12 / 10 9 / 6

[0182] Table 3: Inter-group differences and fold changes of candidate genes for identifying active pulmonary tuberculosis in the verification group (magnetic bead sorted cell RNA-seq analysis)

[0183]

[0184]

[0185] (4) Screening of candidate molecular markers

[0186] The reliability of the discovery cohort analysis results was confirmed using the validation cohort, and on this basis, candidate markers were further screened for subsequent qPCR gene expression analysis and validation in separately collected clinical samples (evaluation group 1). The following parameters were comprehensively considered to screen candidate target genes: 1. Trend of increased gene expression: comparison between the ATB and HC groups, and between the ATB and LTBI groups, satisfying ATB mean / HC mean ≥ 1 and ATB mean / LTBI mean ≥ 1; 2. Measurement of the balance and stability of data among samples: standard deviation within the group (< mean within the group); 3. Degree of change in expression level: fold change between groups (> 1.5); 4. Accuracy of the detection data, avoiding excessively high or low gene expression levels. Based on the above parameters, 8 candidate genes were screened for real-time quantitative PCR determination of expression levels, including CARD16, GBP5, GLRX, FCGR1A, IFITM1, NDUFB1, STAT1, and UBE2L6. The mean and standard deviation within the group of the candidate genes are shown in Table 4.

[0187] Table 4: Mean and standard deviation within the group of candidate genes

[0188]

[0189]

[0190] Note:

[0191] 1. Gene expression level is FPKM;

[0192] 2. Mean: The mean within the group is calculated as the average of the gene expression levels of the samples;

[0193] 3. Stv: Standard deviation within the group.

[0194] Example 2: Establishment of qPCR Detection System for Clinical Samples and Verification of ATB Molecular Markers

[0195] Through transcriptomic analysis, the inventors screened 8 differentially expressed genes that were significantly associated with the active tuberculosis state. Based on this, gene combination markers were further screened, and a qPCR real-time quantitative analysis system for clinical detection was established.

[0196] 1. Research subjects and grouping

[0197] In this study, 108 test subjects were newly recruited (including 17 cases in the ATB group, 31 cases in the LTBI group, 30 cases in the HC group, and 30 cases in the PN group). Among them, ATB was diagnosed according to the diagnostic criteria for patients with active tuberculosis (ATB) in "Diagnosis of Tuberculosis, Health Industry Standard of the People's Republic of China (WS 288—2017)", and the pathogen detection of sputum or bronchoalveolar lavage fluid specimens was positive (at least one of smear / culture / nucleic acid detection was positive). There was no previous history of tuberculosis (no old tuberculosis lesions were found by medical history inquiry and X-ray chest radiography), and it was the first anti-tuberculosis treatment with less than 7 days of medication. LTBI refers to no previous history of tuberculosis, no tuberculosis-related clinical symptoms, normal X-ray chest radiography but positive interferon-γ release assay; PN are cases clinically diagnosed as bacterial pneumonia with clear blood cell test indicators and chest radiograph results. HC is non-active tuberculosis / non-latent infection healthy controls, who have no previous history of tuberculosis, no tuberculosis-related clinical symptoms, negative interferon-γ release assay, and normal X-ray chest radiography. The demographic information of the subjects in Evaluation Group 1 is shown in Table 5.

[0198] Table 5: Demographic data of the samples in Evaluation Group 1

[0199]

[0200] 2. Experimental methods

[0201] 1) Whole blood sample processing, cell magnetic sorting and enrichment: Operate according to the experimental instructions of commercial reagents. Pipette 0.8 ml of well-mixed whole blood into a 5-ml flow tube, and add 1.6 mL of 4°C pre-cooled separation solution to the flow tube at a ratio of 1:2, then blow and suck to mix evenly; add CD15+ magnetic beads (Invitrogen, US) and quickly add one portion of magnetic beads to the diluted blood, tighten the lid, and incubate and collect cells.

[0202] 2) Total cellular RNA extraction: Use the RNeasy Plus Mini Kit (Qiagen, Germany) to extract the total RNA of neutrophils sorted by magnetic beads. The specific operation is shown in the experimental manual.

[0203] 3) cDNA synthesis: Take the total cellular RNA and use SuperScript TM IV VILO TM Master Mix (Invitrogen, US) for reverse transcription to obtain the cDNA of cell samples.

[0204] 4) Detection of gene expression level: Using real-time fluorescence quantitative PCR reaction (TaqMan system), the true expression of the target gene in the host body was detected. Using the cDNA prepared in step 3) as a template, specific primer pairs or internal reference primer pairs were added for real-time quantitative PCR to obtain the amplification constants of each gene and the internal reference gene in the templates from each sample, and the relative expression levels of each target gene were calculated.

[0205] a) Prepare reaction system 1 and reaction system 2

[0206] Reaction system 1 (target gene): It was 20 μL and consisted of qPCR reaction polymerase Fast Advanced Master Mix, target gene Assay primer, and sample cDNA and nuclease-free water.

[0207] Reaction system 2 (internal reference gene): It was 20 μL and consisted of Fast Advanced Master Mix, Assay primer, and sample cDNA and nuclease-free water.

[0208] b) Real-time quantitative PCR detection:

[0209] Each reaction system prepared in step a) was subjected to real-time quantitative PCR detection on a QuantStudio TM 6 and 7 Flex real-time fluorescence quantitative PCR instrument (Applied Biosystems, US). The 2 -ΔΔCt method was used to calculate the relative expression levels of the target genes in each template. Reaction conditions: 50 °C for 2 min; pre-denaturation at 95 °C for 3 min; 95 °C for 1 s, 60 °C for 20 sec, 40 cycles, and fluorescence signals were collected during the extension stage.

[0210] The present inventors found that in neutrophils, MYO1F had a more stable performance than the common internal reference gene, so it was used as the internal reference gene in this detection system.

[0211] Using MYO1F as the internal reference gene, qPCR analysis was performed on 8 candidate target genes (CARD16, GBP5, GLRX, FCGR1A, IFITM1, NDUFB1, STAT1, and UBE2L6) in the neutrophils of the test population samples, and the 2 -ΔΔCt algorithm was used to calculate the expression levels of the target genes. GraphPad Prism 10.1.2 was used for comparative analysis of gene expression between two groups, Kruskal-Wallis one-way analysis of variance was used for statistics among four groups, and Dunn's test was used for pairwise comparison among multiple groups.

[0212] 3. Experimental results

[0213] The results are as Figures 2A to 2H shown. The results showed that all genes were significantly different when comparing ATB with HC, and ATB with LTBI (p < 0.0001); when comparing ATB with the PN group, GBP5, FCGR1A, IFITM1, NDUFB1, STAT1, and UBE2L6 showed varying degrees of increased expression trends (p < 0.05 to p < 0.0001). The expression levels of 8 candidate target genes were increased in ATB, but not in LTBI and PN, reflecting their close association with the disease state of active tuberculosis. The qPCR results were basically consistent with the analysis results of the validation group, that is, compared with other groups, the expression levels of target genes in the ATB group showed an obvious upward trend. This indicates that the analysis results of RNA-seq are accurate and reliable, and the detection system is working properly.

[0214] Example 3: Determination of Differential Diagnosis of Active Pulmonary Tuberculosis

[0215] In the present invention, using binary logistic regression analysis of SPSS, the joint expression levels of the 8 candidate genes verified in the early stage were calculated and statistically analyzed. According to the principal component analysis values, 4 genes with higher scores among the 8 genes (counting the FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene), that is, the genes that contribute the most to the grouping effect, were screened, and further, in the evaluation group, their discrimination efficiency for ATB versus LTBI, HC, and PN was confirmed through binary regression analysis and ROC values. Finally, a detection system for predicting the probability of ATB occurrence using FIGU was screened out, which can well distinguish ATB from LTBI samples, ATB from HC samples, and ATB from PN samples.

[0216] The inventor of the present invention detected the gene expression levels of neutrophils in the samples using regression analysis, calculated the respective expression levels of the FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene, and obtained the prediction probability P TB (Predicting probability of Tuberculosis) of the sample being ATB according to the corresponding threshold. TB Based on the statistically calculated P

[0217]

[0218] Among them, P TBTo determine the predicted value of the sample for ATB, the value range is 0 - 1; the calculation of the power value of the natural logarithm is obtained by multiplying the regression coefficient of the corresponding gene by the gene expression level. fcgr1a, ifitm1, glrx, and ube2l6 represent the expression levels of the FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene respectively. After substituting into the formula, the P value is calculated. TB The cut-off value for predicting the sample as ATB is 0.274, that is, when the combined gene predicted value P TB > 0.274, the sample is ATB, and when P TB < 0.274, it is LTBI, HC, or PN.

[0219] Example 4: Evaluation of Diagnostic Effect of FIGU

[0220] Using the qPCR detection system established above, the combined expression levels of the 4 genes in each sample of evaluation group 1 (108 cases) in Example 2 were quantitatively analyzed, and the ATB result was determined through the cut-off value of the combined expression level predicted value P TB . Meanwhile, this marker can also effectively distinguish ATB from HC, and ATB from LTBI for targeted differential diagnosis.

[0221] (I) Evaluation of the effect of the combined use of the respective expression levels of FIGU and the single gene expression level as differential diagnosis indicators for ATB and LTBI

[0222] As shown in Table 6, for unknown samples (such as those that may be ATB or LTBI), the expression levels of 4 target genes were obtained using the qPCR detection system, and the P value was calculated using the formula in Example 3 TB . Samples with a value greater than the cut-off (0.274) were judged as ATB, and those with a value less than this were judged as LTBI. The ROC value for this marker to distinguish between ATB and LTBI samples was 0.992 (0.977 - 1.008, p = 0), with a significant difference. The Youden index was 0.935. When the sensitivity was 1.000, the specificity was 0.935.

[0223] From the parameters in Table 6, the ROC values for single genes to distinguish ATB and LTBI were all greater than 0.65, with a significant difference (p < 0.05). However, the efficiency of using the predicted value P TB of the combined expression levels of the 4 genes to distinguish ATB and LTBI was significantly improved. The ROC value, Youden index, specificity, and sensitivity were all superior to those of single genes.

[0224] Table 6: ROC values, sensitivity, and specificity for the combined use of the respective expression levels of FIGU and single genes to distinguish between ATB and LTBI samples

[0225]

[0226] Remark:

[0227] a. According to non - parametric assumptions

[0228] b. Null hypothesis: True area = 0.5.

[0229] (2) Evaluation of the effect of combining the expression levels of two genes and three genes respectively as differential diagnostic indicators for ATB and LTBI

[0230] The present inventor also evaluated the prediction and discrimination effects of combining the expression levels of any two or three genes out of four genes. Among them, F, I, G, and U represent FCGR1A, IFITM1, GLRX, and UBE2L6 respectively. The gene combinations are represented by abbreviated letters respectively. For example, FIU is the combination identifier of the three genes FCGR1A, IFITM1, and UBE2L6, and so on. As shown in Table 7, considering all characteristic parameters, the area under the ROC curve, Youden index, specificity, and sensitivity of the four - gene combination are generally improved, indicating that the discrimination effect of the four - gene combination is indeed better than that of any combination of two or three genes.

[0231] Table 7: ROC values, specificity, and sensitivity analysis of any gene combination for differentiating ATB and LTBI

[0232]

[0233]

[0234] Remark:

[0235] a. According to non - parametric assumptions

[0236] b. Null hypothesis: True area = 0.5.

[0237] Example 5: Verification of Differential Diagnosis Effect of FIGU (1)

[0238] The present inventor mixed 17 ATB and 31 LTBI samples in evaluation group 1 and re - numbered them, and then used the qPCR detection system established previously to detect and calculate the respective expression levels of FIGU and P TB value of each sample, and judged whether the sample was an ATB sample according to the cut - off value. Among them, 19 cases were greater than 0.274, including 17 ATB (positive rate 100%, accuracy rate 100%) and 2 LTBI (false - positive rate 6.5%). 29 cases were less than 0.274, all of which were LTBI (accuracy rate 93.5%).

[0239] Meanwhile, the present inventor also evaluated the differential diagnostic effects of combining the respective expression levels of FIGU on the mixed group of ATB, LTBI, and HC, and the group of ATB and PN. The results are as Figures 3A to 3CAs shown. The results showed that using this biomarker, the area under the curve (AUC) for differentiating true ATB from a mixed sample of ATB, LTBI, and HC was 0.986 (p < 0.015, 95% confidence interval: 0.966 - 1.006), with a sensitivity and specificity of 0.941 and 0.935, respectively; the AUC for differentiating true ATB from a mixed population of ATB and PN samples was 0.861 (p < 0.047, 95% confidence interval: 0.757 - 0.965), with a sensitivity of 0.941 and a specificity of 0.733.

[0240] Example 6: Verification of Differential Diagnosis Effect of FIGU (2)

[0241] A differential diagnosis experiment was conducted in parallel with Evaluation Group 1. The samples were independent samples from different sampling locations with the same inclusion criteria. As shown in Table 8 below.

[0242] Table 8: Demographic data of samples in Evaluation Group 2

[0243]

[0244] After randomly mixing the samples of the three groups of ATB, HC, and LTBI, the expression levels of genes F, I, G, and U in the samples were obtained using the same qPCR detection method as in Evaluation Group 1. According to the corresponding PTB formula and the critical threshold (0.274), it was determined whether each sample was ATB. Among the positive samples, there were 21 cases of ATB (95.5%), 3 cases of LTBI (false positive rate 14.28%), and 3 cases of HC (false positive rate 15.0%).

[0245] Meanwhile, the inventors of the present invention also evaluated the differential diagnosis effect of the combination of the respective expression levels of FIGU on the mixed group of ATB, LTBI, and HC. The results are as Figure 4 shown. The results showed that using this biomarker, the AUC value (area under the ROC curve) for differentiating ATB from HC and LTBI in the mixed sample was 0.945 (95% confidence interval: 0.879 - 1), with a sensitivity of 0.955 and a specificity of 0.854.

[0246] Although the specific embodiments of the present invention have been described in detail, those skilled in the art will understand that, based on all the teachings disclosed, various modifications and substitutions can be made to those details, and such changes are within the protection scope of the present invention. The full scope of the present invention is given by the appended claims and any equivalents thereof.

Claims

1. Molecular marker combination, including: (A) a nucleic acid encoding a GLRX protein, and one or more selected from a nucleic acid encoding a FCGR1A protein, a nucleic acid encoding an IFITM1 protein, and a nucleic acid encoding a UBE2L6 protein; or (B) GLRX protein, and one or more selected from FCGR1A protein, IFITM1 protein and UBE2L6 protein.

2. The molecular marker combination according to claim 1, comprising: (1) nucleic acid encoding GLRX protein, nucleic acid encoding FCGR1A protein, nucleic acid encoding IFITM1 protein and nucleic acid encoding UBE2L6 protein; (2) nucleic acid encoding GLRX protein, nucleic acid encoding FCGR1A protein and nucleic acid encoding IFITM1 protein; (3) nucleic acid encoding GLRX protein, nucleic acid encoding FCGR1A protein, and nucleic acid encoding UBE2L6 protein; (4) nucleic acid encoding GLRX protein, nucleic acid encoding IFITM1 protein, and nucleic acid encoding UBE2L6 protein; (5) nucleic acid encoding GLRX protein and nucleic acid encoding FCGR1A protein; (6) nucleic acid encoding GLRX protein and nucleic acid encoding IFITM1 protein; (7) nucleic acid encoding GLRX protein and nucleic acid encoding UBE2L6 protein; (8) GLRX protein, FCGR1A protein, IFITM1 protein and UBE2L6 protein; (9) GLRX protein, FCGR1A protein and IFITM1 protein; (10) GLRX protein, FCGR1A protein and UBE2L6 protein; (11) GLRX protein, IFITM1 protein and UBE2L6 protein; (12) GLRX protein and FCGR1A protein; (13) GLRX protein and IFITM1 protein; or (14) GLRX protein and UBE2L6 protein.

3. The molecular marker combination according to any one of claims 1 to 2, wherein: The amino acid sequence of GLRX protein is shown in SEQ ID NO:

1. The amino acid sequence of FCGR1A protein is shown in SEQ ID NO:

2. The amino acid sequence of IFITM1 protein is shown in SEQ ID NO: 3, and / or The amino acid sequence of UBE2L6 protein is shown in SEQ ID NO:

4.

4. The molecular marker combination according to any one of claims 1 to 3, wherein: The nucleic acid encoding the GLRX protein, the nucleic acid encoding the FCGR1A protein, the nucleic acid encoding the IFITM1 protein, and the nucleic acid encoding the UBE2L6 protein are independently DNA or independently RNA; Preferably, the nucleic acid encoding the GLRX protein, the nucleic acid encoding the FCGR1A protein, the nucleic acid encoding the IFITM1 protein, and the nucleic acid encoding the UBE2L6 protein are all DNA or RNA; Preferably, The sequence of the nucleic acid encoding the GLRX protein is shown in SEQ ID NO:5, The sequence of the nucleic acid encoding the FCGR1A protein is shown in SEQ ID NO: 6, The sequence of the nucleic acid encoding the IFITM1 protein is shown in SEQ ID NO: 7, and / or The sequence of the nucleic acid encoding the UBE2L6 protein is shown in SEQ ID NO:

8.

5. The molecular marker combination according to any one of claims 1 to 4, which is a molecular marker combination for tuberculosis; Preferably, it is used to diagnose active pulmonary tuberculosis (ATB), to differentiate active pulmonary tuberculosis from latent tuberculosis infection (LTBI), to differentiate active pulmonary tuberculosis from healthy persons (HC), to differentiate active pulmonary tuberculosis from bacterial pneumonia (PN), or to differentiate active pulmonary tuberculosis from "latent tuberculosis infection, healthy persons and bacterial pneumonia".

6. Use of the molecular marker combination according to any one of claims 1 to 5 or an agent for detecting the molecular marker combination in the preparation of a drug for diagnosing active pulmonary tuberculosis, distinguishing active pulmonary tuberculosis from latent tuberculosis infection, distinguishing active pulmonary tuberculosis from healthy persons, distinguishing active pulmonary tuberculosis from bacterial pneumonia, or distinguishing active pulmonary tuberculosis from "latent tuberculosis infection, healthy persons and bacterial pneumonia".

7. The use according to claim 6, wherein The reagents include specific primers for each nucleic acid in the molecular marker combination, or antibodies that specifically bind to each protein in the molecular marker combination; preferably, the antibodies are linked to detectable markers, such as radioisotopes, fluorescent substances, colored substances or enzymes.

8. The use according to any one of claims 6 to 7, wherein The relative expression level of each nucleic acid in the molecular marker combination is detected by real-time fluorescence quantitative PCR (qPCR); Preferably, the relative expression level of each nucleic acid in any one of (1) to (7) is detected by qPCR; Preferably, the MYO1F gene, ACTB gene or GAPDH gene is used as an internal reference gene.

9. The use according to any one of claims 6 to 8, wherein The sample to be tested is a peripheral blood sample, a PBMC sample or a neutrophil sample; preferably, the neutrophil sample is a peripheral blood neutrophil sample; more preferably, the sample is a cDNA sample obtained by reverse transcription after total RNA is extracted from peripheral blood neutrophils.

10. The use according to any one of claims 6 to 9, wherein The relative expression levels of nucleic acids encoding GLRX protein, nucleic acids encoding FCGR1A protein, nucleic acids encoding IFITM1 protein, and nucleic acids encoding UBE2L6 protein are calculated to determine whether the sample is ATB, LTBI, HC, or PN; Preferably, P is calculated by the following formula TB : in: fcgr1a, ifitm1, glrx and ube2l6 represent the relative expression levels of a nucleic acid encoding FCGR1A (e.g., FCGR1A gene), a nucleic acid encoding IFITM1 (e.g., IFITM1 gene), a nucleic acid encoding GLRX (e.g., GLRX gene), and a nucleic acid encoding UBE2L6 (e.g., UBE2L6 gene), respectively; The cut off value is 0.274; When P TB When the predicted probability value is greater than 0.274, the sample is judged to be ATB; when the predicted probability value is less than or equal to 0.274, the sample is diagnosed as LTBI, HC, or PN.

11. The use according to any one of claims 6 to 10, wherein The level of the molecular marker combination of any one of claims 1 to 5 of the subject is compared with the cutoff value of the receiver operating characteristic curve, wherein the receiver operating characteristic curve is a receiver operating characteristic curve of the level of the molecular marker combination of any one of claims 1 to 5 for ATB and HC, ATB and LTBI or ATB and PN, wherein: If the level of the subject's molecular marker combination is greater than the cutoff value, it is judged as ATB; If the level of the subject's molecular marker combination was less than or equal to the cutoff value, it was judged as HC, LTBI, or PN; Preferably, the number of the ATB samples, and the HC samples, or the LTBI samples, or the PN samples used to draw the working curve is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, 20-40, or 50-100.

12. The use according to any one of claims 6 to 10, wherein The levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene were compared with the cutoff values ​​of the receiver operating characteristic curve, which was the receiver operating characteristic curve between the levels of FCGR1A gene, IFITM1 gene, GLRX gene and UBE2L6 gene and ATB and HC, ATB and LTBI or ATB and PN, wherein: If the levels of the subject's FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene were greater than the cutoff value, it was judged as ATB; If the levels of the FCGR1A gene, IFITM1 gene, GLRX gene, and UBE2L6 gene of the subject were less than or equal to the cutoff value, they were judged as HC, LTBI, or PN; Preferably, the number of the ATB samples, and the HC samples, or the LTBI samples, or the PN samples used to draw the working curve is independently greater than or equal to 10, greater than or equal to 20, greater than or equal to 30, 20-40, or 50-100.

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