Risk marker for active tuberculosis screening and application thereof

Abstract

The invention provides a risk marker for active tuberculosis screening and application thereof. Specifically, the invention provides application of a gene, mRNA, cDNA and protein of a marker for the occurrence risk of active tuberculosis or a detection reagent thereof, and the gene, the mRNA, the cDNA and the protein are used for preparing the detection reagent or the kit for judging the occurrence risk. Researches show that the active tuberculosis risk marker can be used as a marker for judging the occurrence of active tuberculosis, and has high sensitivity and specificity.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular, relates to risk markers for active tuberculosis screening and applications thereof. Background Art

[0002] At present, the screening methods for active tuberculosis mainly include symptom screening and chest X-ray screening. However, these methods have some defects: symptom screening has low sensitivity and will miss 50-60% of patients; chest X-ray screening has high sensitivity, but low throughput, can only detect one patient at a time, and poor accessibility. In field screening, the tested population needs to be gathered first and then tested uniformly. Rapid molecular detection methods based on tuberculosis DNA can also be used for tuberculosis screening, but the cost is relatively high. Moreover, it is based on sputum specimens, and normal people do not have sputum specimens, so it is difficult to detect. The samples of blood-based transcription markers are easy to obtain, and have the advantages of being fast, simple, and sensitive, which can make up for the defects of existing screening methods.

[0003] As the only commercialized blood transcription marker detection kit, Xpert-MTB-HR-Prototype is not stable in detecting tuberculosis. In some prospective study cohorts, it can meet the screening standards set by WHO (specificity 70%, sensitivity 90%), but in some cohorts, it cannot meet the standards. Among them, the performance of the KLF2 gene in the marker in the actual clinical evaluation is inconsistent with expectations: the transcriptional expression level of this gene in tuberculosis patients is believed to decrease, but in some evaluation studies, the expression level of this gene in tuberculosis patients increases, that is, the expression level of this gene in actual clinical detection is opposite to expectations. This is an important reason for the poor detection effect of this marker.

[0004] Therefore, the effectiveness of existing markers (Xpert-MTB-HR-Prototype) in identifying active tuberculosis is very unstable and cannot effectively screen for tuberculosis.

[0005] Therefore, there is an urgent need in this field to develop methods and applications for screening active tuberculosis that are accurate, efficient, and have stable detection effects. Summary of the invention

[0006] The present invention provides a method for screening active tuberculosis which is accurate, efficient and has stable detection effect and application thereof.

[0007] In a first aspect of the present invention, there is provided a use of a gene, mRNA, cDNA, protein, or a detection reagent thereof of an active tuberculosis risk marker for preparing a detection reagent or a kit, wherein the detection reagent or the kit is used to determine the risk of occurrence of active tuberculosis;

[0008] Among them, the active tuberculosis risk markers include: (A1) NXPH4.

[0009] In another preferred embodiment, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0010] In another preferred embodiment, the active tuberculosis risk marker also includes: (C1) KLF2.

[0011] In another preferred embodiment, the A1 and B1-B12 and C1 markers are selected from Table A:

[0012] Table A

[0013]

[0014] In another preferred embodiment, the gene, mRNA, cDNA, or protein of any marker shown in Table A of Active Tuberculosis Risk Markers is derived from humans.

[0015] In another preferred embodiment, the active tuberculosis risk marker is:

[0016] (A1) NXPH4; and

[0017] One or more markers selected from Group B: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0018] In another preferred embodiment, the active tuberculosis risk marker further comprises (A1) NXPH4 and at least 2 selected from group B markers.

[0019] In another preferred embodiment, the active tuberculosis risk marker is selected from the following group:

[0020] (1) (A1) NXPH4; (B5) C1QC; and (B10) LHFPL2;

[0021] (2) (B1) BATF2; (B4) FCGR1A; and (C1) KLF2.

[0022] In another preferred aspect of the present invention, there is provided a use of a gene, mRNA, cDNA, protein, or a detection reagent thereof of an active tuberculosis risk marker for preparing a detection reagent or a kit, wherein the detection reagent or the kit is used to determine the risk of occurrence of active tuberculosis;

[0023] Wherein, the active tuberculosis risk markers include: (B1) BATF2; (B4) FCGR1A; and (C1) KLF2.

[0024] In another preferred embodiment, the gene, mRNA, cDNA, or protein of the active tuberculosis risk marker is derived from humans.

[0025] In another preferred embodiment, the detection reagent or kit is used to detect the expression level of the risk marker in the sample to be tested.

[0026] In a second aspect of the present invention, a kit is provided, the kit comprising a detection reagent for detecting genes, mRNA, cDNA, or proteins, or a combination thereof, of active tuberculosis risk markers.

[0027] Among them, the active tuberculosis risk markers include: (A1) NXPH4.

[0028] In another preferred embodiment, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0029] In another preferred embodiment, the active tuberculosis risk marker also includes: (C1) KLF2.

[0030] In another preferred embodiment, the active tuberculosis risk marker is:

[0031] (A1) NXPH4; and

[0032] One or more markers selected from Group B: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0033] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B10) LHFPL2.

[0034] In another preferred embodiment of the present invention, a kit is provided, wherein the kit contains a detection reagent for detecting genes, mRNA, cDNA, or proteins, or a combination thereof, of active tuberculosis risk markers.

[0035] Among them, the active tuberculosis risk markers include: (B1) BATF2, (B4) FCGR1A and (C1) KLF2.

[0036] In another preferred embodiment, the detection reagent comprises:

[0037] (a) specific antibodies or specific binding molecules against the active tuberculosis risk marker; and / or

[0038] (b) Primers or primer pairs, probes or chips (such as nucleic acid chips or protein chips) for specifically amplifying the mRNA or cDNA of the active tuberculosis risk marker.

[0039] In another preferred embodiment, the subject to be detected is a human.

[0040] In another preferred embodiment, the subjects to be tested are inactive tuberculosis patients or active tuberculosis patients.

[0041] In another preferred embodiment, the detection is a detection of an in vitro sample.

[0042] In another preferred embodiment, the in vitro sample includes: a tissue sample, a cell sample, a blood sample, and a serum sample.

[0043] In another preferred embodiment, the detection reagent is coupled with or carries a detectable label.

[0044] In another preferred embodiment, the detectable label is selected from the following group: a chromophore, a chemiluminescent group, a fluorophore, an isotope or an enzyme.

[0045] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0046] In another preferred embodiment, the diagnostic (or detection) reagents include antibodies, primers, probes, sequencing libraries, nucleic acid chips (such as DNA chips) or protein chips.

[0047] In another preferred embodiment, the nucleic acid chip comprises a substrate and specific oligonucleotide probes spotted on the substrate, and the specific oligonucleotide probes comprise probes that specifically bind to any polynucleotide (mRNA or cDNA) of the active tuberculosis risk marker.

[0048] In another preferred embodiment, the protein chip comprises a substrate and specific antibodies spotted on the substrate, and the specific antibodies comprise specific antibodies against the active tuberculosis risk markers.

[0049] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0050] In another preferred embodiment, the kit contains genes, mRNA, cDNA and / or proteins of active tuberculosis risk markers as reference substances or quality control substances.

[0051] In another preferred embodiment, the kit further comprises a label or instructions, wherein the label or instructions indicate that the kit is used for (a) determining the risk of occurrence of active tuberculosis, and / or (b) evaluating the therapeutic effect of active tuberculosis.

[0052] In another preferred embodiment, the reagent includes primers, probes, gRNA or a combination thereof, more preferably a primer pair or probe for PCR (eg, qPCR, RT-PCR).

[0053] In another preferred embodiment, the detection of the active tuberculosis risk marker can be carried out by the following methods: sequencing, PCR, or a combination thereof.

[0054] In another preferred embodiment, the detection of the active tuberculosis risk marker can be quantitatively detected.

[0055] In a third aspect of the present invention, a detection method is provided, comprising the steps of:

[0056] (a) providing a test sample, wherein the test sample is selected from a blood sample;

[0057] (b) detecting the expression level of active tuberculosis risk marker genes in the test sample, recorded as C1; and

[0058] (c) comparing the active tuberculosis risk marker concentration C1 with a control reference value C0;

[0059] Among them, the active tuberculosis risk markers include:

[0060] (A1) NXPH4; or

[0061] (B1) BATF2, (B4) FCGR1A, and (C1) KLF2.

[0062] When the active tuberculosis risk marker includes (A1) NXPH4, if the test result of the active tuberculosis risk of the test subject meets the following conditions, it indicates that the risk of active tuberculosis of the test subject is high:

[0063] (1) When a certain marker is an upregulated marker in Table A in the test subject, and the expression level of the marker is higher than the reference value or standard value C0, the test subject has a high risk of developing active tuberculosis;

[0064] (2) When a certain marker is a down-regulated marker in Table A in the test subject, and the expression level of the marker is lower than the reference value or standard value C0, the test subject has a high risk of developing active tuberculosis.

[0065] In another preferred embodiment, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0066] In another preferred embodiment, the active tuberculosis risk marker is:

[0067] (A1) NXPH4; and

[0068] One or more markers selected from Group B: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0069] In another preferred embodiment, the method is an in vitro method.

[0070] In another preferred embodiment, the method is non-diagnostic and non-therapeutic.

[0071] In a fourth aspect of the present invention, there is provided an active tuberculosis diagnostic device, the device comprising:

[0072] (a) an input module, the input module being configured to input active tuberculosis risk marker data of a subject's blood sample;

[0073] Wherein, the risk markers include: (A1) NXPH4;

[0074] (b) a processing module, the processing module is configured to process active tuberculosis risk marker data and provide an assessment result of the risk of occurrence; wherein the processing includes: comparing the expression level C1 of the input marker with the control reference value C0;

[0075] Among them, if the input marker is an up-regulated marker in Table A, when C1 is significantly higher than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly higher than C0, it indicates that the subject has a low risk of developing active tuberculosis; if the input marker is a down-regulated marker in Table A, when C1 is significantly lower than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly lower than C0, it indicates that the subject has a low risk of developing active tuberculosis; and

[0076] (c) An output module, wherein the output module is configured to output the evaluation result.

[0077] In another preferred embodiment, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0078] In another preferred embodiment, the active tuberculosis risk marker also includes (C1) KLF2.

[0079] In another preferred embodiment, the active tuberculosis risk marker is:

[0080] (A1) NXPH4; and

[0081] One or more markers selected from Group B: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0082] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B10) LHFPL2.

[0083] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B4) FCGR1A; and (B8) GBP5.

[0084] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B4) FCGR1A.

[0085] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B4) FCGR1A.

[0086] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B4) FCGR1A; and (B12) C1QB.

[0087] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B4) FCGR1A.

[0088] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B12) C1QB.

[0089] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B10) LHFPL2.

[0090] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B8) GBP5.

[0091] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B11) ETV7.

[0092] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B8) GBP5.

[0093] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B10) LHFPL2.

[0094] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B12) C1QB.

[0095] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B4) FCGR1A.

[0096] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B4) FCGR1A; and (B5) C1QC.

[0097] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B8) GBP5.

[0098] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B2) ANKRD22.

[0099] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B5) C1QC.

[0100] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; and (B2) ANKRD22.

[0101] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B3) SERPING1.

[0102] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B2) ANKRD22; and (B11) ETV7.

[0103] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B8) GBP5; and (B10) LHFPL2.

[0104] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B8) GBP5.

[0105] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B10) LHFPL2; and (B12) C1QB.

[0106] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B12) C1QB.

[0107] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B1) BATF.

[0108] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B10) LHFPL2.

[0109] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF and (B5) C1QC.

[0110] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B8) GBP5; and (B11) ETV7.

[0111] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B12) C1QB.

[0112] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF and (B3) SERPING1.

[0113] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B11) ETV7; and (B12) C1QB.

[0114] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B10) LHFPL2; and (B11) ETV7.

[0115] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B10) LHFPL2.

[0116] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B10) LHFPL2.

[0117] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B8) GBP5.

[0118] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B5) C1QC.

[0119] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B3) SERPING1.

[0120] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B12) C1QB.

[0121] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B3) SERPING1; and (B11) ETV7.

[0122] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B5) C1QC; and (B11) ETV7.

[0123] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B11) ETV7.

[0124] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; and (B5) C1QC.

[0125] In another preferred embodiment, the active tuberculosis risk markers are: (A1) NXPH4; (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; and (B12) C1QB.

[0126] In another preferred embodiment, the active pulmonary tuberculosis risk markers are: (B1) BATF2, (B4) FCGR1A and (C1) KLF2.

[0127] In another preferred embodiment, the active pulmonary tuberculosis risk markers are: (B4) FCGR1A, (B8) GBP5 and (C1) KLF2.

[0128] In another preferred embodiment, the active pulmonary tuberculosis risk markers are: (B1) BATF2, (B8) GBP5 and (C1) KLF2.

[0129] In another preferred embodiment of the present invention, there is provided an active tuberculosis assessment device, the device comprising:

[0130] (a) an input module, the input module being configured to input active tuberculosis risk marker data of a subject's blood sample;

[0131] Wherein, the risk markers include markers (combinations) selected from the following groups:

[0132] (B1) BATF2, (B4) FCGR1A, and (C1) KLF2;

[0133] (A1) NXPH4; (B5) C1QC and (B10) LHFPL2;

[0134] (b) a processing module, wherein the processing module is configured to process active tuberculosis risk marker data and provide an assessment result of the risk of occurrence;

[0135] The processing includes: calculating the input marker gene according to a predetermined scoring formula to obtain a risk score; and comparing the score with a cutoff value to obtain a discrimination result; wherein, when the risk score is higher than the cutoff value, it indicates that the subject has a high risk of active tuberculosis; when the risk score is lower than the cutoff value, it indicates that the subject has a low risk of active tuberculosis; and

[0136] (c) an output module, wherein the output module is configured to output the evaluation result;

[0137] The risk score formula is: TB score = average Ct of up-regulated genes - average Ct of down-regulated genes.

[0138] In another preferred embodiment, the risk score is: TB score = (BATF2 + FCGR1A) / 2 - KLF2; wherein BATF2, FCGR1A and KLF2 in the formula refer to the Ct values ​​of BATF2, FCGR1A and KLF2, respectively.

[0139] In another preferred example, the risk score is: TB score = (C1QC + LHFPL2) / 2 - NXPH4; wherein C1QC, LHFPL2 and NXPH4 in the formula refer to the Ct values ​​of C1QC, LHFPL2 and NXPH4, respectively.

[0140] In another preferred example, the device further comprises a detection module, and the detection module is configured to detect the transcription or expression level of the risk marker.

[0141] In another preferred example, the device further comprises a storage module, and the storage module is configured to store preset marker thresholds and comparison result values.

[0142] In another preferred example, the device further includes a control module, and the control module is configured to control the operation of each module.

[0143] In another preferred embodiment, the detection module is selected from the following group: ELISA analyzer, PCR sequencer, sequencer, or a combination thereof.

[0144] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] Figure 1 The technical roadmap of the present invention is shown.

[0146] Figure 2 The figure shows the bootstrap method for screening candidate marker genes. The horizontal axis represents the number of times the gene was identified as a significantly differentially expressed gene in 10,000 differential expression analyses, and the vertical axis represents the AUC value of the gene for distinguishing active tuberculosis patients from healthy people. Blue dots represent down-regulated genes, and red dots represent up-regulated genes.

[0147] Figure 3 The figure shows the diagnostic effect of exhaustive method to identify gene combinations containing different numbers of genes (each combination must contain NXPH4). The horizontal axis represents the number of genes in the gene combination, and the vertical axis represents the AUC value of the gene combination to distinguish active tuberculosis patients from healthy people.

[0148] Figure 4 The AUC values ​​of the gene combination with the best diagnostic effect (A) (C1QC, LHFPL2, NXPH4) and (B) Xpert-MTB-HR-Prototype (GBP5, DUSP3, KLF2) for distinguishing active tuberculosis patients from healthy people in RNA-seq datasets are shown.

[0149] Figure 5The AUC values ​​of different gene combinations (including Xpert-MTB-HR-Prototype and the seven-gene markers previously screened by the present invention (FAM20A, LHFPL2, FCGR1A, C1QC, BATF2, C1QB, NXPH4)) for distinguishing active tuberculosis patients from healthy people in different microarray data sets are shown.

[0150] Figure 6 The screening sensitivity of different gene combinations in different microarray data sets for distinguishing active tuberculosis patients from healthy people is shown. The horizontal axis is the diagnostic sensitivity of the gene combination when the specificity is set to 70%.

[0151] Figure 7 The results showed that NXPH4 significantly improved the diagnostic effect of the marker: (A) When the number of genes in the gene combination was 2, the AUC value of the gene combination containing NXPH4 was significantly higher than that of the gene combination not containing NXPH4; (B) When the number of genes in the gene combination was 3, the AUC value of the gene combination containing NXPH4 was significantly higher than that of the gene combination not containing NXPH4.

[0152] Figure 8 AE shows the ROC curves of each marker combination in Example 3 of the present invention: (A) combination one (GBP5, DUSP3 and KLF2); (B) combination two (C1QC, C1QB and NXPH4); (C) combination three (C1QC, FCGR1CP and NXPH4); (D) combination four (FCGR1CP, C1QB and NXPH4); (E) HR combination (GBP5, DUSP3 and KLF2).

[0153] Fig. 9 The ROC curves of the individual markers in Example 4 of the present invention are shown.

[0154] Fig.10 The clinical sample collection process is shown.

[0155] Fig.11 A flow chart of the inclusion and exclusion of participants in the validation group is shown.

[0156] Fig.12The performance of each marker combination in Example 5 of the present invention in distinguishing confirmed tuberculosis patients from healthy people is shown: (A) combination 1 (GBP5, FCGR1A and NXPH4); (B) combination 2 (BATF2, FCGR1A and NXPH4); (C) combination 3 (GBP5, BATF2 and NXPH4); (D) combination 4 (GBP5, FCGR1A and KLF2); (E) combination 5 (BATF2, FCGR1A and KLF2); (F) combination 6 (GBP5, BATF2 and KLF2); (G) combination 7 (GBP5, DUSP3 and KLF2); (H) combination 8 (GBP5, FCGR1A, BATF2 and NXPH4); (I) combination 9 (GBP5, FCGR1A, BATF2 and KLF2).

[0157] Fig.13 The performance of each marker combination in Example 5 of the present invention in distinguishing tuberculosis from healthy controls is shown: (A) combination 1 (GBP5, FCGR1A and NXPH4); (B) combination 2 (BATF2, FCGR1A and NXPH4); (C) combination 3 (GBP5, BATF2 and NXPH4); (D) combination 4 (GBP5, FCGR1A and KLF2); (E) combination 5 (BATF2, FCGR1A and KLF2); (F) combination 6 (GBP5, BATF2 and KLF2); (G) combination 7 (GBP5, DUSP3 and KLF2); (H) combination 8 (GBP5, FCGR1A, BATF2 and NXPH4); (I) combination 9 (GBP5, FCGR1A, BATF2 and KLF2).

[0158] Fig.14 A summary of the detection performance of candidate marker combinations in Example 5 of the present invention is shown; red columns: HR control combination (GBP5, DUSP3 and KLF2); blue columns: combination 5 (BATF2, FCGR1A and KLF2). DETAILED DESCRIPTION

[0159] The inventors have conducted extensive and in-depth research and, after a large number of screenings, unexpectedly discovered markers for screening for active tuberculosis (or tuberculosis). Specifically, through database screening, 13 candidate genes (NXPH4, BATF2, ANKRD22, SERPING1, FCGR1A, C1QC, FCGR1BP, FCGR1CP, GBP5, SEPTIN4, HFPL2, ETV7, C1QB) were finally determined, and the correlation between NXPH4 and the risk of active tuberculosis was unexpectedly discovered for the first time. And the experiments of the present invention show that the screening sensitivity of the gene combination containing NXPH4 for active tuberculosis is significantly better than the screening sensitivity of the gene combination not containing NXPH4 for active tuberculosis. The present invention was completed on this basis.

[0160] The present invention also unexpectedly discovered that the marker combination consisting of two markers of Group B of the present invention ((B1) BATF2 and (B4) FCGR1A) and the existing active tuberculosis risk marker ((C1) KLF2) has extremely excellent active tuberculosis screening performance. Its screening performance is significantly better than the existing three-gene active tuberculosis screening combination (GBP5, DUSP3 and KLF2). The experiments of the present invention show that in identifying tuberculosis patients in the population, the marker combination 5 ((B1) BATF2, (B4) FCGR1A and (C1) KLF2) has a higher AUC and higher specificity than the existing marker combination (GBP5, DUSP3 and KLF2).

[0161] the term

[0162] As used herein, the terms "transcriptional marker" or "transcription marker" are used interchangeably and refer to one of the biomarkers that marks a specific disease state by detecting the level of gene transcription expression in tissues or cells. Biomarkers refer to biological indicators that can objectively measure physiological or pathological states, including changes at the physiological, biochemical, immune, cellular and molecular levels. Blood transcriptional markers detect the transcriptional expression level of specific genes in blood samples, thereby marking specific disease states.

[0163] Transcription markers, for example, transcription markers based on blood samples detect the transcription expression level of a single gene in the blood, or detect the transcription expression level of multiple genes and use a specific algorithm to calculate the marker score, which is often significantly different in patients from healthy people, and can therefore be used to identify disease states. Currently, transcription markers have been widely used in the diagnosis and screening of a variety of diseases, such as various types of tumors, infectious diseases, etc. As used herein, the terms "RNA sequencing" or "RNA-seq" are used interchangeably, both referring to a technology that can perform high-throughput sequencing of all RNA in cells or tissues, by randomly interrupting the cDNA obtained by reverse transcription of mRNA, constructing a sequencing library, and then sequencing on the machine, and performing high-throughput sequencing on the cDNA fragments, so that the relative content of mRNA transcribed from all genes can be detected simultaneously.

[0164] As used herein, the terms "microarray" or "Microarray" are used interchangeably and refer to a technology that uses high-density DNA fragments to attach to a solid surface such as a membrane or glass slide in a certain order or arrangement through high-speed robots or in situ synthesis, and uses isotope- or fluorescent-labeled DNA probes to conduct a large number of studies on gene expression and monitoring based on the principle of base complementary hybridization.

[0165] As used herein, the terms "transcriptome" or "transcriptome" are used interchangeably and refer to the sum of all gene transcription products in cells of a specific organism under a certain state or physiological condition.

[0166] As used herein, the term "active tuberculosis" refers to the presence of not only bacteriological and imaging evidence but also typical clinical symptoms of tuberculosis.

[0167] As used in this article, the terms "bootstrapping" or "bootstrapping" are used interchangeably and refer to a class of nonparametric Monte Carlo methods that perform statistical inferences on the distribution characteristics of the population by resampling the sample. This method makes full use of the given observation information, does not require other assumptions about the model or the addition of new observations, and is robust and efficient.

[0168] As used herein, the term "differential expression analysis" refers to performing a statistical test on the expression level of each gene in the two groups of samples by comparing the transcriptome data of the two groups of samples, and calculating the fold change of the gene expression level in the two groups.

[0169] As used herein, the term "receiver operating characteristic curve" or "ROC" is used interchangeably and refers to the ordering of samples according to the prediction results of the marker, and the prediction of the samples one by one as positive examples in this order. Each time, the values ​​of two important quantities (here, sensitivity and specificity are selected) are calculated and plotted with them as the horizontal and vertical coordinates, respectively, to obtain the ROC curve. The area under the ROC curve (AUC) reflects the ranking quality of the sample prediction, taking into account both sensitivity and specificity, and can comprehensively reflect the ability of the marker to distinguish active tuberculosis patients from healthy people.

[0170] As used herein, the term "sample" or "specimen" refers to material specifically associated with a subject from which specific information about the subject can be determined, calculated, or inferred. A sample may consist in whole or in part of biological material from a subject.

[0171] As used herein, the term "expression" includes the production of mRNA from a gene or gene portion, and includes the production of proteins encoded by the RNA or gene portion, and also includes the appearance of detection substances related to expression. For example, cDNA, the binding of binding ligands (such as antibodies) to genes or other oligonucleotides, proteins or protein fragments, and the chromogenic portion of the binding ligands are all included within the scope of the term "expression". Therefore, the increase in half-point density on an immunoblot such as a Western blot is also within the scope of the term "expression" based on biological molecules.

[0172] As used herein, the term "reference value" or "control reference value" refers to a value that is statistically related to a particular result when compared to an analytical result. In a preferred embodiment, the reference value is determined based on the mRNA expression and / or protein expression of a comparative active tuberculosis risk marker, and statistical analysis is performed. Some such studies are shown in the Examples section of this article. However, studies from the literature and user experience of the methods disclosed herein can also be used to produce or adjust reference values. Reference values ​​can also be determined by considering situations and results that are particularly relevant to the patient's ethnic group, medical history, genetics, age, and other factors.

[0173] Active TB and TB

[0174] Tuberculosis (TB) is an infectious disease caused by bacteria (mainly Mycobacterium tuberculosis, referred to as tuberculosis), and has long been the leading cause of death from a single infectious disease in the world. Tuberculosis can be divided into multiple stages, including active tuberculosis, subclinical tuberculosis, latent infection, etc. Among them, active tuberculosis patients are the most contagious and urgently need anti-tuberculosis treatment. Therefore, screening for active tuberculosis patients is an important part of tuberculosis prevention and control.

[0175] Since clinically (or clinically detected), the active tuberculosis has the same meaning as tuberculosis, and both refer to patients with tuberculosis (tuberculosis etiology-positive patients or clinically diagnosed patients) detected clinically. Therefore, the active tuberculosis described in the present invention has the same meaning as tuberculosis, and both refer to patients with tuberculosis (tuberculosis etiology-positive patients or clinically diagnosed patients) detected clinically.

[0176] Active TB risk markers

[0177] As used herein, the terms "active tuberculosis of the present invention" and "tuberculosis of the present invention" have the same meaning, and both refer to tuberculosis patients detected clinically (tuberculosis etiology-positive patients or clinically diagnosed patients).

[0178] As used herein, the term "active tuberculosis risk markers of the present invention" refers to one or more markers shown in Table A, and also refers to "transcriptional markers" of the present invention.

[0179] In the present invention, the terms "active tuberculosis risk marker protein of the present invention", "protein of the present invention", "polypeptide of the present invention", or "markers shown in Table A" are used interchangeably and refer to any one or more of the active tuberculosis risk markers of the present invention.

[0180] In the present invention, the terms "active tuberculosis risk marker gene" and "active tuberculosis risk marker polynucleotide" are used interchangeably and refer to the nucleotide sequence of any active tuberculosis risk marker shown in Table A.

[0181] It should be understood that when encoding the same amino acid, the substitution of nucleotides in the codon is acceptable. It should also be understood that when conservative amino acid substitutions are produced by nucleotide substitutions, the change of nucleotides is also acceptable.

[0182] In the case of obtaining the information of active tuberculosis risk markers, the nucleic acid sequence encoding it can be constructed based on it, and specific probes can be designed based on the nucleotide sequence. The full-length nucleotide sequence or its fragment can usually be obtained by PCR amplification, recombination or artificial synthesis. For PCR amplification, primers can be designed based on the nucleotide sequence of the active tuberculosis risk marker disclosed in the present invention, especially the open reading frame sequence, and a commercially available cDNA library or a cDNA library prepared by conventional methods known to those skilled in the art is used as a template to amplify and obtain the relevant sequence. When the sequence is long, it is often necessary to perform two or more PCR amplifications, and then the fragments amplified each time are spliced ​​together in the correct order.

[0183] Once the relevant sequence is obtained, it can be obtained in large quantities by recombinant methods. This is usually done by cloning it into a vector, then transferring it into cells, and then isolating the relevant sequence from the propagated host cells by conventional methods.

[0184] In addition, artificial synthesis methods can also be used to synthesize related sequences, especially when the fragment length is shorter. Usually, a long fragment of sequence can be obtained by synthesizing multiple small fragments first and then connecting them.

[0185] At present, the DNA sequence encoding the protein of the present invention (or its fragment, derivative) can be obtained completely by chemical synthesis. Then the DNA sequence can be introduced into various existing DNA molecules (such as vectors) and cells known in the art.

[0186] The polynucleotide sequence of the present invention can be used to express or produce recombinant active tuberculosis risk markers using conventional recombinant DNA technology.

[0187] NXPH4

[0188] Neurotropin 4 (NXPH4) is a synaptic secretory protein. NXPH family genes are highly expressed in the nervous system and can bind tightly to neuronal membrane proteins. Current studies suggest that high expression of NXPH4 is associated with a variety of tumors, including bladder cancer, non-small cell lung cancer, glioma, breast cancer, liver cancer, and pancreatic cancer. The association of this gene with tuberculosis has not yet been elucidated.

[0189] BATF2, FCGR1A, KLF2, LHFPL2, and C1QC

[0190] BATF2 (Basic Leucine Zipper ATF-Like Transcription Factor 2) is a member of the BATF subfamily of basic leucine zipper proteins. Members of the BATF family are expressed in a variety of immune cells and are involved in regulating the expression of a variety of genes. Studies have shown that BATF2 has an inhibitory effect in certain types of tumors, including liver cancer, lung cancer, and breast cancer. In addition, BATF2 is closely related to various functions of the immune system, such as regulating the development and function of T cells and B cells. During tuberculosis infection, the expression level of BATF2 may change, and this change can serve as a potential marker for the diagnosis of tuberculosis.

[0191] The FCGR1A (Fc Fragment of IgG Receptor Ia) gene encodes a high-affinity IgG Fc receptor (FcγRI), which is mainly present on the surface of immune cells. It plays a key role in mediating antibody-dependent cytotoxicity, phagocytosis, and immune complex clearance, and is an important receptor that regulates multiple functions of the immune system. The expression level and functional changes of FCGR1A are associated with certain autoimmune diseases (such as systemic lupus erythematosus and rheumatoid arthritis). In addition, FCGR1A has an inhibitory effect in certain types of tumors, including liver cancer, lung cancer, and breast cancer. In the diagnosis of tuberculosis, FCGR1A has important potential. Studies have shown that the expression of FCGR1A may be upregulated in tuberculosis infection, especially in patients with active tuberculosis. This high expression can serve as a potential marker for the diagnosis of tuberculosis.

[0192] The KLF2 (Krüppel-like factor 2) gene encodes a zinc finger transcription factor that is highly expressed in vascular endothelial cells and immune cells and is involved in regulating cell proliferation, differentiation, apoptosis, vascular homeostasis, immune response and metabolic regulation. KLF2 is closely related to cardiovascular disease, metabolic disease and immune-related diseases. Studies have shown that Mycobacterium tuberculosis infection can lead to a decrease in the expression of KLF2 in immune cells.

[0193] LHFPL2 (Lipoma HMGIC fusion partner-like 2) encodes a tetraspanin. LHFPL2 is mainly found on the cell membrane. The LHFPL2 gene plays an important role in various cancer-related pathways and immune processes, especially in renal clear cell carcinoma. Studies have shown that LHFPL2 shows differential expression in different cancers, with significant differences between tumors and normal tissues. The association of this gene with tuberculosis has not yet been elucidated.

[0194] The C1QC gene encodes the C chain of the C1q complement component in the complement system. C1q is the initiator of the classical complement pathway. It activates the complement cascade by recognizing and binding to antigen-antibody complexes, thereby playing a role in clearing pathogens and apoptotic cells. In addition, C1q is also involved in clearing apoptotic cells and cell fragments to prevent the occurrence of autoimmune reactions. Studies have shown that tuberculosis infection can lead to an upregulation of C1QB gene expression levels, enhance the activity of the classical complement pathway, and thus improve the immune system's response and clearance of Mycobacterium tuberculosis.

[0195] Specific antibodies

[0196] In the present invention, the terms "antibody of the present invention" and "antibody specific for active tuberculosis risk markers" are used interchangeably, and refer to antibodies that can be used to specifically bind to and detect active tuberculosis risk markers of the present invention.

[0197] The antibodies against active tuberculosis risk markers (Table A) of the present invention include polyclonal antibodies and monoclonal antibodies with specificity, especially monoclonal antibodies.

[0198] The present invention includes not only complete monoclonal or polyclonal antibodies, but also immunologically active antibody fragments, such as Fab' or (Fab)2 fragments; antibody heavy chains; antibody light chains; genetically engineered single-chain Fv molecules (Ladner et al., U.S. Patent No. 4,946,778); or chimeric antibodies, such as antibodies that have the binding specificity of mouse antibodies but still retain antibody portions from humans.

[0199] The antibodies of the present invention can be prepared by various techniques known to those skilled in the art. For example, the gene product of a purified human active tuberculosis risk marker or an antigenic fragment thereof can be administered to an animal to induce the production of polyclonal antibodies. Similarly, cells expressing human active tuberculosis risk marker proteins or antigenic fragments thereof can be used to immunize animals to produce antibodies. The antibodies of the present invention can also be monoclonal antibodies. Such monoclonal antibodies can be prepared using hybridoma technology.

[0200] Antibodies against human active tuberculosis risk marker proteins can be used in immunohistochemistry techniques to detect human active tuberculosis risk marker proteins in specimens (especially tissue samples or blood samples). Since active tuberculosis risk marker proteins exist in blood samples or tissue samples, their expression levels can be detected.

[0201] Risk detection device of the present invention

[0202] The present invention provides an active pulmonary tuberculosis diagnosis device, the device comprising:

[0203] (a) an input module, the input module being configured to input active tuberculosis risk marker data of a subject's blood sample;

[0204] The risk markers include:

[0205] (A1) NXPH4; or

[0206] (B1) BATF2, (B4) FCGR1A, and (C1) KLF2;

[0207] (b) a processing module, the processing module is configured to process active tuberculosis risk marker data and provide an assessment result of the risk of occurrence; wherein, when the risk marker includes (A1) NXPH4, the processing includes: comparing the input marker expression level C1 with a control reference value C0;

[0208] Among them, if the input marker is an up-regulated marker in Table A, when C1 is significantly higher than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly higher than C0, it indicates that the subject has a low risk of developing active tuberculosis; if the input marker is a down-regulated marker in Table A, when C1 is significantly lower than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly lower than C0, it indicates that the subject has a low risk of developing active tuberculosis; and

[0209] (c) An output module, wherein the output module is configured to output the evaluation result.

[0210] In another preferred embodiment, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

[0211] In another preferred embodiment, when the risk marker is a marker combination, such as: (B1) BATF2, (B4) FCGR1A and (C1) KLF2, the processing includes: calculating the input marker gene according to a predetermined scoring formula to obtain a risk score; and comparing the score with a cutoff value to obtain a discrimination result; wherein, when the risk score is higher than the cutoff value, it indicates that the subject has a high risk of active tuberculosis; when the risk score is lower than the cutoff value, it indicates that the subject has a low risk of active tuberculosis;

[0212] The risk score formula is: TB score = average Ct of up-regulated genes - average Ct of down-regulated genes.

[0213] In another preferred example, when the markers are the risk scores of (B1) BATF2, (B4) FCGR1A and (C1) KLF2, TB score = (BATF2+FCGR1A) / 2-KLF2; wherein BATF2, FCGR1A and KLF2 in the formula refer to the Ct values ​​of BATF2, FCGR1A and KLF2, respectively.

[0214] Detection Methods

[0215] Based on the differential expression of active tuberculosis risk markers in tissue samples or blood samples, the present invention also provides a corresponding method for determining the risk of active tuberculosis.

[0216] The present invention relates to a diagnostic test method for quantitatively and positionally detecting the protein level or mRNA level of active tuberculosis risk markers. These tests are well known in the art. The protein level or mRNA level of the human active tuberculosis risk marker detected in the test can be used to determine (including auxiliary determination) whether there is an active tuberculosis risk.

[0217] A preferred method is to perform quantitative detection of mRNA or cDNA by PCR / qPCR / RT-PCR.

[0218] A preferred method is to perform quantitative detection by sequencing mRNA or cDNA.

[0219] The polynucleotides of active tuberculosis risk markers can be used for the diagnosis of active tuberculosis risk. Part or all of the polynucleotides of the present invention can be fixed on a microarray or DNA chip as probes for differential expression analysis of genes in the analysis and gene diagnosis.

[0220] In addition, the present invention can also detect at the protein level. For example, antibodies against active tuberculosis risk markers can be fixed on a protein chip to detect active tuberculosis risk proteins in a sample.

[0221] Detection Kits

[0222] Based on the correlation between active tuberculosis risk markers and active tuberculosis risk, active tuberculosis risk markers can be used as markers for judging the risk of active tuberculosis.

[0223] The present invention also provides a kit for determining the risk of active tuberculosis, the kit containing a detection reagent, the detection reagent is used to detect genes, mRNA, cDNA, proteins, or a combination thereof of active tuberculosis risk markers. Preferably, the kit contains antibodies or immunoconjugates against active tuberculosis risk markers of the present invention, or active fragments thereof; or contains primers or primer pairs, probes or chips for specifically amplifying mRNA or cDNA of active tuberculosis risk markers.

[0224] In another preferred embodiment, the kit further comprises a label or instructions.

[0225] The main advantages of the present invention are:

[0226] (a) The present invention uses blood samples, which are more suitable for early screening and diagnosis, and are faster, more convenient and low-cost.

[0227] (b) Compared with the existing active tuberculosis screening method, the marker of the present invention has higher specificity and the detection result is more accurate and stable.

[0228] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods for which specific conditions are not specified in the following examples are usually performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the conditions recommended by the manufacturer. Unless otherwise stated, percentages and parts are weight percentages and weight parts.

[0229] Materials and methods

[0230] The identification of blood transcriptional markers involves four major steps:

[0231] (1) Blood transcriptome data of active tuberculosis patients and healthy people were collected from public databases and divided into a discovery set for marker screening and a validation set for marker validation;

[0232] (2) Screening candidate marker genes with the ability to distinguish active tuberculosis patients from healthy people and with stable effects through the bootstrap method;

[0233] (3) Limit the number of marker genes and use an exhaustive method to find the gene combination with the best screening performance as the transcriptional marker;

[0234] (4) Validate the transcriptional markers in the validation set and evaluate their screening efficacy for active tuberculosis in an independent dataset. Figure 1 ). The detailed technical scheme is shown in the embodiments of the present invention.

[0235] Example 1

[0236] 1.1 Obtaining blood transcriptome data

[0237] (1) Search for “tuberculosis” in the Gene Expression Omnibus (GEO) RNA-seq Experiments Interactive Navigator (GREIN) website, filter RNA-seq datasets containing blood samples from patients with active tuberculosis, and download the corresponding patient information and gene transcription expression profiles.

[0238] Finally, five RNA-seq datasets (GSE107991, GSE107994, GSE84076, GSE89403, and GSE94438) were included in the analysis;

[0239] (2) Search for “tuberculosis” in the GEO Data Sets search bar of the National Center for Biotechnology Information (NCBI), screen for microarray datasets containing blood samples of patients with active tuberculosis, and download the corresponding patient information and gene transcription expression profiles.

[0240] Finally, 7 microarray datasets (GSE19435, GSE19439, GSE19444, GSE25534, GSE28623, GSE34608, and GSE43834) were included in the analysis.

[0241] (3) Five RNA-seq datasets (1613 samples) were used as discovery sets, and seven microarray datasets (363 samples) were used as validation sets.

[0242] 1.2 Bootstrap method to screen candidate marker genes

[0243] (1) A total of 1613 samples from the five RNA-seq datasets were randomly sampled, with 30 active tuberculosis samples and 30 healthy samples each time. The edgeR package was then used for differential expression analysis, and genes with a fold change greater than 2 and an adjusted p value less than 0.05 were selected as differentially expressed genes. The above steps were repeated 10,000 times.

[0244] (2) The number of times each gene was identified as a differentially expressed gene in 10,000 repeated differential expression analyses (T) and the corresponding differential expression fold value were obtained, and the area under the receiver operating characteristic curve (AUC) of each gene for distinguishing active tuberculosis from healthy subjects in 1,613 samples was calculated. The results are shown in Table 2. Figure 2 shown.

[0245] Genes with T greater than 9700 and AUC greater than 0.85 were screened, and a total of 13 candidate genes were obtained, including 12 up-regulated genes (BATF2, ANKRD22, SERPING1, FCGR1A, C1QC, FCGR1BP, FCGR1CP, GBP5, SEPTIN4, LHFPL2, ETV7, C1QB) and 1 down-regulated gene (NXPH4), as shown in Table 1.

[0246] Table 1. 13 marker genes used to distinguish active tuberculosis from healthy people

[0247] Gene AUC Threshold of log2Exp N / 10,000 Mean log2FC Adjust up / down BATF2 0.884 4.821 9998 2.851 Increase ANKRD22 0.876 2.651 9996 2.961 Increase SERPING1 0.873 6.543 9997 2.687 Increase FCGR1A 0.873 7.052 9998 2.302 Increase Q1C 0.873 1.887 9997 2.785 Increase FCGR1BP 0.867 6.204 9994 2.069 Increase FCGR1CP 0.864 3.829 9993 2.554 Increase GBP5 0.863 8.819 9997 2.070 Increase SEPTIN4 0.863 3.149 9994 2.490 Increase LHFPL2 0.856 4.355 9764 1.450 Increase ETV7 0.855 4.294 9987 2.394 Increase Q1Q 0.853 3.184 9994 2.336 Increase NXPH4 0.852 0.754 9770 -1.711 Downward adjustment

[0248] Note: This table shows 13 marker genes, including (1) gene name, (2) AUC of each gene for distinguishing active tuberculosis from healthy subjects, (3) corresponding expression threshold (Threshold of log2 Exp), (4) the number of times each gene was identified as a significant differential gene in 10,000 differential expression analyses (Nout of 10,000), (5) average differential expression fold of each gene (Average log2 FC), and (6) the direction of differential expression of each gene in patients with active tuberculosis.

[0249] 1.3 Exhaustive screening of gene combinations

[0250] (1) The number of controlled genes does not exceed 3: The 12 up-regulated genes screened were arranged and combined (no more than 2), the mean expression was calculated, and then the expression of NXPH4 was subtracted to obtain the value as the marker score. This score was used to distinguish active tuberculosis from healthy people, and the AUC was calculated. According to the standards set by the WHO, the minimum screening standard for active tuberculosis is: when the specificity is 70%, the sensitivity is not less than 90%. Therefore, the sensitivity of the marker score was calculated when the specificity was set to 70%.

[0251] (2) All gene combinations (including 66 three-gene combinations and 12 two-gene combinations) were enumerated, and the AUC of each gene combination in the RNA-seq dataset was calculated. The results are shown in Figure 3 shown.

[0252] Among them, Figure 4 As shown in A, the gene combination consisting of C1QC, LHFPL2, and NXPH4 has the highest AUC value (AUC=0.9076). Other gene combinations also have good performance, with the lowest AUC value being 0.8778.

[0253] In contrast, Figure 4 As shown in B, the AUC value of the existing marker (Xpert-MTB-HR-Prototype) in the RNA-seq dataset is only 0.8713.

[0254] Example 2

[0255] Validation of markers in microarray datasets

[0256] A cohort containing blood microarray data of active tuberculosis patients and healthy people was collected from the NCBI database, including 7 microarray data sets GSE19435, GSE19439, GSE19444, GSE25534, GSE28623, GSE34608, and GSE43834, with a total of 363 patients (153 active tuberculosis patients and 210 healthy people). The AUC and sensitivity (specificity of 70%) of each gene combination in each microarray data set were calculated.

[0257] The experimental results are as follows Figure 5-7 All markers consisting of three genes (66 in total) had a median AUC value of more than 0.85 in the seven microarrays ( Figure 5 ), the median sensitivity of the 64 markers (when the specificity is 70%) exceeded 90% ( Figure 6 ), meeting the WHO screening criteria, indicating that the markers identified by the RNA-seq dataset also performed well in an independent microarray dataset.

[0258] In addition, among the 13 marker genes identified, the down-regulated gene NXPH4 is the marker gene unexpectedly discovered for the first time in the present invention. The inventors found that in the RNA-seq data set, the expression level of this gene in patients with active tuberculosis was significantly lower than that in healthy people, suggesting that this gene can be used as an internal reference gene in the marker.

[0259] Therefore, the present inventors evaluated the diagnostic performance of gene combinations comprising and excluding NXPH4.

[0260] The results are as follows Figure 7 As shown in Figure 2, when the number of genes in the gene combination is 2, the AUC value of the gene combination containing NXPH4 is significantly higher than that of the gene combination not containing NXPH4 ( Figure 7 A); When the number of genes in the gene combination is 3, the AUC value of the gene combination containing NXPH4 is significantly higher than that of the gene combination not containing NXPH4 ( Figure 7 B).

[0261] from Figure 7 The results show that when the number of genes is the same, the AUC and screening sensitivity of the gene combination containing NXPH4 are significantly higher than those of the gene combination not containing NXPH4, indicating that NXPH4 improves the diagnostic effect of the marker.

[0262] Example 3

[0263] In this example, existing tuberculosis risk markers were included, including the down-regulated expression gene KLF2, and the predictive effect of each marker combination on the risk of active tuberculosis was verified. Among them, the control combination was the three-gene marker GBP5, DUSP3, and KLF2 discovered by Sweeney et al. (now integrated into the GeneXpert platform of Cepheid, USA, Xpert-MTB-HR-Prototype kit, referred to as HR).

[0264] The marker combinations selected in this example are shown in Table 2 below.

[0265] Table 2

[0266] combination Upregulated gene 1 Upregulated gene 2 Down-regulated genes Control combination (HR) GBP5 DUSP3 KLF2 Combination 1 GBP5 Q1Q NXPH4 Combination 2 Q1C Q1Q NXPH4 Combination Three Q1C FCGR1CP NXPH4 Combination Four FCGR1CP Q1Q NXPH4

[0267] 2.1 Experimental methods

[0268] (1) Multiplex PCR: Multiplex PCR is used to identify the markers of the present invention, and a group of markers are detected in one PCR reaction. The detection distribution of multiplex PCR is shown in Table 3.

[0269] Table 3

[0270] Multiplex PCR FAM HEX ROX Control combination (HR) KLF2 DUSP3 GBP5 Combination 1 GBP5 NXPH4 Q1Q Combination 2 Q1C NXPH4 Q1Q Combination Three Q1C NXPH4 FCGR1CP Combination Four FCGR1CP NXPH4 Q1Q

[0271] The qPCR primer and probe sequences are shown in Table 4.

[0272] Table 4

[0273]

[0274] Note: F: upstream primer; R: downstream primer; P: probe.

[0275] (2) Composition of qPCR reaction system: Use Novagen Taq Pro Multiplex DNA Polymerase (High sensitivity) commercial qPCR premix, the concentration of the gene-specific primer to be detected is 80 nM, the concentration of the universal primer is 800 nM, the concentration of the probe is 100 nM, the reverse transcribed cDNA template is 4 μL, and the reaction system is supplemented with water to 20 μL.

[0276] (3) qPCR detection instrument: ABI7500 real-time fluorescence quantitative PCR instrument.

[0277] (4) The qPCR reaction procedure is shown in Table 5.

[0278] Table 5

[0279] temperature time Number of cycles 95℃ 5min 1cycle 95℃ 10s 10cycles 67℃(lighting)-1℃ / s 40s 10cycles 95℃ 10s 45cycles 60℃(lighting) 40s 45cycles

[0280] (5) Subject classification: The case group consisted of patients with etiologically positive tuberculosis (n=7), and the control group consisted of healthy subjects (n=20).

[0281] (6) Result analysis: Obtain the TB score for each sample and compare whether the case group and the control group can be distinguished based on the TB score. TB score = (upregulated gene 1 + upregulated gene 2) / 2 - downregulated gene, as shown in Table 6.

[0282] Table 6

[0283] combination TB score calculation Control combination (HR) (GBP5+DUSP3) / 2-KLF2 Combination 1 (GBP5+C1QB) / 2-NXPH4 Combination 2 (C1QC+C1QB) / 2-NXPH4 Combination Three (C1QC+FCGR1CP) / 2-NXPH4 Combination Four (FCGR1CP+C1QB) / 2-NXPH4

[0284] 2.2 Experimental Results

[0285] The experimental results are shown in Table 7 and Figure 8 As shown in AE.

[0286] Table 7

[0287] AUC(95CI) Sensitivity Max (95CI) Specificity Max (95CI) Sensitivity (specificity = 70%) qPCRHR 0.8857(0.7415-1.000) 0.8571(0.5714-1.000) 0.850(0.700-1.000) 0.8571(0.5714-1.000) Combination 1 0.8143(0.6407-0.9878) 1.000(1.000-1.000) 0.600(0.400-0.800) 0.7143(0.1429-1.000) Combination 2 0.7429(0.5339-0.9518) 1.000(1.000-1.000) 0.500(0.300-0.700) 0.5714(0.1429-1.000) Combination Three 0.7929(0.6014-0.9843) 0.800(0.600-0.950) 0.7143(0.4286-1.000) 0.7143(0.2857-1.000) Combination Four 0.6429(0.4159-0.8698) 0.8571(0.5714-1.000) 0.500(0.300-0.700) 0.4286(0-0.8607)

[0288] From the above results, we can see that the AUC values ​​of the four candidate combinations are lower than the control combination (HR). All four combinations contain C1QB or C1QC genes, both of which are related to the complement activation pathway. Therefore, it is speculated that their specificity is poor. Therefore, the combination with these two genes was excluded in the subsequent verification experiment.

[0289] Example 4

[0290] According to the results of Example 3, the combination containing C1QB / C1QC genes was excluded, and bioinformatics analysis was performed again to obtain 4 groups of candidate markers with better performance. Among them, 2 groups were two-gene marker combinations and 2 groups were three-gene marker combinations. The control combination was the three-gene marker GBP5, DUSP3, KLF2 discovered by Sweeney et al. (now integrated into the GeneXpert platform of Cepec, USA, Xpert-MTB-HR-Prototype kit, referred to as HR).

[0291] The preferred marker combinations selected in this example are shown in Table 8 below.

[0292] Table 8

[0293] combination Upregulated gene 1 Upregulated gene 2 Down-regulated genes Control combination (HR) GBP5 DUSP3 KLF2 Combination 1 BATF2 — NXPH4 Combination 2 GBP5 — NXPH4 Combination Three GBP5 FCGR1A NXPH4 Combination Four GBP5 BATF2 NXPH4

[0294] 4.2 Experimental methods

[0295] (1) Single-plex qPCR

[0296] The above five combinations (including the control combination) contain a total of four up-regulated genes (BATF2, GBP5, FCGR1A, DUSP3); two down-regulated genes (KLF2, NXPH4). The expression level of each gene was measured by single-plex PCR to analyze the performance of the single gene; then the two-gene, three-gene, and four-gene markers that can be formed by the above six genes were listed, and the performance of the listed multi-gene markers was analyzed.

[0297] The single-plex qPCR detection distribution is shown in Table 9.

[0298] Table 9

[0299] Singleplex PCR FAM HEX ROX BATF2 — — √ GBP5 — — √ FCGR1A — — √ DUSP3 — √ — NXPH4 — √ — KLF2 √ — —

[0300] The qPCR primer and probe sequences are shown in Table 10.

[0301] Table 10

[0302]

[0303]

[0304] Note: F: upstream primer; R: downstream primer; P: probe.

[0305] (2) Composition of qPCR reaction system: Use the commercial qPCR premix of Novozyme Taq Pro Multiplex DNA Polymerase (High sensitivity), the concentration of the specific primer of the gene to be detected is 250 nM, the concentration of the probe is 200 nM, the reverse transcribed cDNA template is 4 μL, and the reaction system is supplemented with water to 20 μL.

[0306] (3) qPCR detection instrument: ABI7500 real-time fluorescence quantitative PCR instrument.

[0307] (4) The qPCR reaction procedure is shown in Table 11.

[0308] Table 11

[0309] temperature time Number of cycles 95℃ 5min 1cycle 95℃ 10s 10cycles 67℃(lighting)-1℃ / s 40s 10cycles 95℃ 10s 45cycles 60℃(lighting) 40s 45cycles

[0310] (5) Subject classification: The case group consisted of patients with etiology-positive tuberculosis (n=21), and the control group consisted of healthy subjects (n=21).

[0311] (6) Result analysis: ① The performance of single gene analysis in distinguishing case group from control group was obtained. ② Based on the single gene detection results, the performance of multi-gene markers was analyzed, TB score = average Ct value of up-regulated genes - Ct value of down-regulated genes.

[0312] (a) The two-gene marker combinations that can be formed by the above six genes are shown in Table 12.

[0313] Table 12

[0314]

[0315]

[0316] (b) The three-gene marker combinations that can be formed by the above six genes are shown in Table 13.

[0317] Table 13

[0318] 1 GBP5 BATF2 NXPH4 2 GBP5 FCGR1A NXPH4 3 GBP5 DUSP3 NXPH4 4 BATF2 FCGR1A NXPH4 5 BATF2 DUSP3 NXPH4 6 FCGR1A DUSP3 NXPH4 7 GBP5 BATF2 KLF2 8 GBP5 FCGR1A KLF2 9 GBP5 DUSP3 KLF2 10 BATF2 FCGR1A KLF2 11 BATF2 DUSP3 KLF2 12 FCGR1A DUSP3 KLF2

[0319] (c) The four-gene marker combination that can be formed by the above six genes is shown in Table 14.

[0320] Table 14

[0321] 1 GBP5 BATF2 FCGR1A NXPH4 2 GBP5 BATF2 DUSP3 NXPH4 3 GBP5 FCGR1A DUSP3 NXPH4 4 BATF2 FCGR1A DUSP3 NXPH4 5 GBP5 BATF2 FCGR1A KLF2 6 GBP5 BATF2 DUSP3 KLF2 7 GBP5 FCGR1A DUSP3 KLF2 8 BATF2 FCGR1A DUSP3 KLF2

[0322] 4.2 Experimental Results

[0323] 4.2.1 Single gene measurement results

[0324] The ROC curve of the single gene test results is as follows Fig. 9 As shown in Tables 15-16.

[0325] Table 15

[0326]

[0327] Table 16

[0328]

[0329] Table 15 was used to analyze whether the performance of each gene was different from that of GBP5. The results showed that BATF2 was slightly better than GBP5 but there was no statistical difference; FCGR1A was significantly better than GBP5; and the AUC value of DUSP3 was the lowest among the up-regulated genes.

[0330] It can be seen from Table 16 that among the two down-regulated genes, KLF2 performed better than HXPH4.

[0331] 4.2.2 Multigene measurement results

[0332] The results of the 28 groups of multi-gene marker combinations measured in this example are shown in Table 17.

[0333] Table 17

[0334]

[0335]

[0336] The 28 gene combinations in Table 17 are sorted from high to low according to the AUC value. Since DUSP3 is the upregulated gene with the lowest AUC value in the single gene performance analysis, the combination containing DUSP3 was considered to be excluded. In addition, the reference Xpert-MTB-HR-Prototype kit uses three gene markers, so the two-gene combination was excluded. A total of 19 groups of markers were excluded.

[0337] Finally, 8 candidate marker combinations were determined, and the HR combination was retained as a control, as shown in Table 18.

[0338] Table 18

[0339] No. TB score calculation formula Number of genes AUC(95CI) Difference (p-value) 1 (GBP5+DUSP3) / 2-KLF2 control combination 3 0.9841(0.9571-1) —— 2 (GBP5+FCGR1A) / 2-KLF2 3 0.9864(0.9638-1) 0.7773 3 (BATF2+FCGR1A) / 2-KLF2 3 0.9841(0.9586-1) 1 4 (GBP5+BATF2+FCGR1A) / 3-KLF2 4 0.9841(0.9586-1) 1 5 (GBP5+BATF2) / 2-KLF2 3 0.9773(0.9443-1) 0.4193 6 (GBP5+BATF2+FCGR1A) / 3-NXPH4 4 0.9478(0.8872-1) 0.2306 7 (BATF2+FCGR1A) / 2-NXPH4 3 0.9433(0.8805-1) 0.1819 8 (GBP5+FCGR1A) / 2-NXPH4 3 0.941(0.8691-1) 0.2406 9 (GBP5+BATF2) / 2-NXPH4 3 0.9342(0.8611-1) 0.1744

[0340] Example 5 Validation of 8 groups of candidate markers in clinical samples

[0341] The 8 groups of candidate markers finally determined in Example 4 include: 6 groups of three-gene combination candidate markers, and 2 groups of four-gene combination candidate markers. The control combination is the three-gene marker GBP5, DUSP3, KLF2 discovered by Sweeney et al. (now integrated into the GeneXpert platform of Cepheid, USA, Xpert-MTB-HR-Prototype kit, referred to as HR). As shown in Table 19.

[0342] Table 19

[0343] combination Upregulated gene 1 Upregulated gene 2 Upregulated gene 3 Down-regulated genes Combination 1 GBP5 FCGR1A — NXPH4 Combination 2 BATF2 FCGR1A — NXPH4 Combination 3 BATF2 GBP5 — NXPH4 Combination 4 GBP5 FCGR1A — KLF2 Combination 5 BATF2 FCGR1A — KLF2 Combination 6 BATF2 GBP5 — KLF2 Combination 7 (HR combination) DUSP3 GBP5 — KLF2 Combination 8 GBP5 FCGR1A DUSP3 NXPH4 Combination 9 GBP5 FCGR1A DUSP3 KLF2

[0344] 5.1 Experimental Methods

[0345] Multiplex PCR was used to complete the detection of a group of markers in one PCR reaction. (1) The distribution of multiplex qPCR detection is shown in Table 20.

[0346] Table 20

[0347] combination FAM HEX ROX CY5 Combination 1 GBP5 NXPH4 FCGR1A — Combination 2 BATF2 NXPH4 FCGR1A — Combination 3 BATF2 NXPH4 GBP5 — Combination 4 GBP5 KLF2 FCGR1A — Combination 5 BATF2 KLF2 FCGR1A — Combination 6 BATF2 KLF2 GBP5 — Combination 7 (HR combination) DUSP3 KLF2 GBP5 — Combination 8 GBP5 NXPH4 FCGR1A DUSP3 Combination 9 GBP5 KLF2 FCGR1A DUSP3

[0348] (2) The qPCR primer and probe sequences are shown in Table 21.

[0349] Table 21

[0350]

[0351]

[0352] Note: F: upstream primer; R: downstream primer; P: probe.

[0353] (3) Composition of qPCR reaction system: Use the commercial qPCR premix of Novozyme Taq Pro Multiplex DNA Polymerase (High sensitivity), the concentration of the specific primer of the gene to be detected is 80 nM, the concentration of the universal primer is 800 nM, the concentration of the probe is 100 nM, the reverse transcribed cDNA template is 4 μL, and the reaction system is supplemented with water to 20 μL.

[0354] (4) qPCR detection instrument: ABI7500 real-time fluorescence quantitative PCR instrument.

[0355] (5) The qPCR reaction procedure is shown in Table 22.

[0356] Table 22

[0357] temperature time Number of cycles 95℃ 5min 1cycle 95℃ 10s 10cycles 67℃(lighting)-1℃ / s 40s 10cycles 95℃ 10s 45cycles 60℃(lighting) 40s 45cycles

[0358] (6) Calculation and composition of sample size

[0359] To estimate the sample size, the present invention uses the following elements: (1) an estimate of sensitivity (or specificity), (2) a confidence level, 1-α for statistical inference, where α is the probability of a type I error, and (3) the precision of the estimate of sensitivity (or specificity).

[0360] The present invention calculates the sample size as follows:

[0361]

[0362] is a predetermined sensitivity (or specificity) value. The estimated value of sensitivity (or specificity) is set according to the minimum screening requirements mentioned by WHO in TargetProductProfile, so a sensitivity of 90% and a specificity of 70% are assumed. For α = 0.05, substitute 1.96 into Z α / 2 For d, the maximum margin error is assumed to be 5%. Therefore, the present invention estimates the required number of samples as follows:

[0363] 1) Number of target positive samples: n se =1.96 2 ×0.9×(1-0.9) / 0.05 2 =139 (people)

[0364] 2) Number of target negative samples: n sp =1.96 2 ×0.7×(1-0.7) / 0.05 2 =323 (people)

[0365] (7) Experimental process

[0366] The clinical sample collection process is as follows: Fig.10 As shown. The subjects were suspected tuberculosis patients and close contacts of confirmed tuberculosis patients among the patients who visited the hospital. The remaining 1 ml of blood from the subjects' blood routine test was collected as the sample for this experiment. RNA was extracted and quality controlled within 6 hours after sample collection, and RNA was reverse transcribed into cDNA.

[0367] (8) Results Analysis

[0368] The TB score of each marker combination (Table 23) was obtained for each sample, and the case group and the control group were compared to see whether they could be distinguished based on the TB score.

[0369] Table 23

[0370] combination TB score calculation Combination 1 (GBP5+FCGR1A) / 2-NXPH4 Combination 2 (BATF2+FCGR1A) / 2-NXPH4 Combination 3 (GBP5+BATF2) / 2-NXPH4 Combination 4 (GBP5+FCGR1A) / 2-KLF2 Combination 5 (BATF2+FCGR1A) / 2-KLF2 Combination 6 (GBP5+BATF2) / 2-KLF2 Combination 7 (HR combination) (GBP5+DUSP3) / 2-KLF2 Combination 8 (GBP5+FCGR1A+BATF2) / 3-NXPH4 Combination 9 (GBP5+FCGR1A+BATF2) / 3-KLF2

[0371] Note: TB score = average Ct of up-regulated genes - Ct of down-regulated genes

[0372] 5.2 Experimental Results

[0373] 5.2.1 Number of subjects included in clinical sample validation

[0374] The flow chart of the inclusion and exclusion of participants in this validation is as follows: Fig.11 shown.

[0375] The subjects were distributed as follows: patients with etiology-positive tuberculosis (n=124), patients with clinically diagnosed tuberculosis (n=32), healthy people (n=302), and patients with other diseases (n=99).

[0376] 5.2.2 Identifying TB patients in the population

[0377] (1) Verification 1:

[0378] The performance of each marker in distinguishing confirmed TB patients (n=124) from healthy subjects (n=302) was analyzed. Among etiologically diagnosed TB patients and healthy subjects, the top 5 combinations in terms of AUC were combination 5, combination 0, combination 1, combination 4, and combination 6.

[0379] like Fig.12AI and Table 24. Combination 5 obtained the highest AUC of 0.911 (95% CI, 0.879-0.944), sensitivity of 83.1% (95% CI, 75.8-89.5), and specificity of 87.4% (95% CI, 83.4-91.1). The three-gene marker identified by Sweeney in this study was combination 7, with an AUC of 0.893 (95% CI, 0.852-0.933), a sensitivity of 81.5% (95% CI, 74.2-87.9), and a specificity of 91.1% (95% CI, 87.7-94.0). The AUCs of combination 1, combination 4, and combination 6 were 0.882 (95% CI, 0.846-0.918), 0.881 (95% CI, 0.845-0.916), and 0.873 (0.834-0.911), respectively.

[0380] According to the minimum triage requirements proposed by WHO, when the specificity was 70%, the sensitivity of combination 5 was 91.9% (95% CI, 86.3-96.8), the sensitivity of combination 1 was 91.1% (95% CI, 81.0-97.5), and the sensitivity of combination 3 was 90.3% (95% CI, 83.9-95.2), which met the minimum screening requirements of TPP.

[0381] Table 24 Performance of candidate markers in distinguishing confirmed TB patients from healthy controls

[0382]

[0383] (2) Verification 2

[0384] In actual population screening, it is hoped that suspected tuberculosis patients can be identified for confirmatory examination. Therefore, clinically diagnosed TB and etiologically confirmed TB were combined as the case group (n=156) to analyze the performance of the marker in distinguishing tuberculosis from healthy controls (n=302). The top 5 combinations in AUC were combination 5, combination 4, combination 7, combination 6, and combination 9.

[0385] like Fig.13As shown in AI and Table 25, the AUC of combination 5 was 0.859 (95% CI, 0.822-0.897), the sensitivity was 68.6% (95% CI, 61.5-75.6), and the specificity was 90.1% (95% CI, 86.1-93.0) (Table 2). The AUC of combination 4 was 0.841 (95% CI, 0.802-0.879), the sensitivity was 76.9% (95% CI, 69.9-83.3), and the specificity was 73.8% (95% CI, 68.9-78.8). The AUC of combination 7 was 0.838 (95% CI, 0.796-0.88), the sensitivity was 69.2% (95% CI, 61.5-76.3), and the specificity was 90.1% (95% CI, 86.8-93.4). The AUCs of combination 6 and combination 9 were 0.834 (95% CI, 0.793-0.874) and 0.831 (0.79-0.871), respectively. When the specificity was 70%, the sensitivities of combination 5, combination 4, combination 7, combination 6, and combination 9 were 82.7%, 78.8%, 76.9%, 81.4%, and 81.4%, respectively.

[0386] Table 26 Performance of candidate markers in distinguishing confirmed TB and suspected TB patients from healthy controls

[0387]

[0388] Therefore, in identifying tuberculosis patients in the population, marker combination 5 has a higher AUC and higher specificity than combination 7. The performance summary of candidate marker detection is shown in the figure below. Fig.14 shown.

[0389] discuss

[0390] In the present invention, the screening strategy of marker genes is improved: first, the bootstrapping method is used to screen the differentially expressed candidate genes that appear stably in different clinical data sets, and then the number of genes is controlled and the best performing gene combination is selected by exhaustive method. This strategy has two advantages over the existing Xpert-MTB-HR-Prototype:

[0391] (1) Continuous sampling during the bootstrap process can make the performance of the candidate genes finally screened more stable in different data sets;

[0392] (2) The method of Xpert-MTB-HR-Prototype for screening transcriptional markers is a forward search, which cannot traverse all gene combinations, so the markers obtained in the end may not be the best performing. The inventors traversed all combinations through an exhaustive method and identified the best performing gene combination when the number of genes was controlled to be 3.

[0393] Finally, the inventors screened out transcriptional markers consisting of 13 genes, including 12 up-regulated genes (BATF2, ANKRD22, SERPING1, FCGR1A, C1QC, FCGR1BP, FCGR1CP, GBP5, SEPTIN4, LHFPL2, ETV7, C1QB) and 1 down-regulated gene (NXPH4) (Table 1). The existing active tuberculosis marker KLF2 was incorporated to screen the preferred marker combination for tuberculosis detection. The gene combination of any composition of the marker genes of the present invention (including NXPH4) has good performance in distinguishing active tuberculosis patients from healthy people in different types of data sets (including RNA sequencing (RNA-seq) and microarray (Microarray)). In addition, the preferred marker combination of the present invention (BATF2, FCGR1A and KLF2) has better active tuberculosis screening performance than the existing three-gene marker combination (GBP5, DUSP3 and KLF2).

[0394] The key innovations of the present invention are:

[0395] (1) Among the 13 marker genes identified, the down-regulated gene NXPH4 is a new marker gene that has not been reported before. The present invention found that in the RNA-seq data set, the expression level of this gene in patients with active tuberculosis was significantly lower than that in healthy people, suggesting that this gene can be used as an internal reference gene in the marker; by evaluating the diagnostic performance of gene combinations containing NXPH4 and excluding NXPH4, the present invention found that when the number of genes is the same, the AUC and screening sensitivity of the gene combination containing NXPH4 are significantly higher than those of the gene combination not containing NXPH4 ( Figure 7 ), indicating that NXPH4 improved the diagnostic effect of the marker.

[0396] (2) The gene combination of the existing marker (Xpert-MTB-HR-Prototype) was optimized. The marker of the present invention performed better than Xpert-MTB-HR-Prototype in RNA-seq data sets and performed comparable to Xpert-MTB-HR-Prototype in microarrays.

[0397] (3) A variety of gene combination methods are provided: any two of the given 12 up-regulated genes can be combined, and then combined with the down-regulated gene NXPH4, and the resulting markers can all have good performance.

[0398] (4) The present invention emphasizes the screening use of the marker: In the validation work, the present invention evaluated the sensitivity of the marker according to the WHO screening standard, and found that the marker identified by the present invention has high sensitivity in different data sets and meets the WHO screening standard.

[0399] (5) We screened and obtained a new marker gene combination (GBP5, DUSP3, and KLF2) with better screening performance than the existing marker gene combination.

[0400] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. Use of a gene, mRNA, cDNA, protein, or a detection reagent thereof for an active tuberculosis risk marker, It is characterized in that Used to prepare a detection reagent or a kit, wherein the detection reagent or the kit is used to determine the risk of occurrence of active tuberculosis; Among them, the active tuberculosis risk markers include: (A1) NXPH4.

2. The use according to claim 1, It is characterized in that The active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

3. The use according to claim 2, It is characterized in that The active tuberculosis risk markers also include (A1) NXPH4 and at least 2 selected from group B markers.

4. The use according to claim 3, It is characterized in that The active tuberculosis risk marker is selected from the group consisting of: (1) (A1) NXPH4; (B5) C1QC; and (B10) LHFPL2; (2) (B1) BATF2; (B4) FCGR1A; and (C1) KLF2.

5. The use according to claim 1, It is characterized in that The gene, mRNA, cDNA, or protein of the active tuberculosis risk marker is derived from humans.

6. The use according to claim 1, It is characterized in that The detection reagent or kit is used to detect the expression level of the risk marker in the sample to be tested.

7. A kit, It is characterized in that The kit contains a detection reagent, which is used to detect genes, mRNA, cDNA, or proteins, or a combination thereof, of active tuberculosis risk markers. Among them, the active tuberculosis risk markers include: (A1) NXPH4; or (B1) BATF2; (B4) FCGR1A; and (C1) KLF2.

8. The kit according to claim 7, It is characterized in that When the active tuberculosis risk marker includes (A1) NXPH4, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

9. An active tuberculosis diagnostic device, It is characterized in that The device comprises: (a) an input module, the input module being configured to input active tuberculosis risk marker data of a subject's blood sample; Among them, the active tuberculosis risk markers include: (A1) NXPH4; or (B1) BATF2; (B4) FCGR1A; and (C1) KLF2; (b) a processing module, the processing module is configured to process active tuberculosis risk marker data and provide an assessment result of the risk of occurrence; wherein, when the active tuberculosis risk marker includes (A1) NXPH4, the processing includes: comparing the input marker expression level C1 with a control reference value C0; Among them, if the input marker is an up-regulated marker in Table A, when C1 is significantly higher than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly higher than C0, it indicates that the subject has a low risk of developing active tuberculosis; if the input marker is a down-regulated marker in Table A, when C1 is significantly lower than C0, it indicates that the subject has a high risk of developing active tuberculosis; when C1 is not significantly lower than C0, it indicates that the subject has a low risk of developing active tuberculosis; and (c) An output module, wherein the output module is configured to output the evaluation result.

10. The device according to claim 9, It is characterized in that When the active tuberculosis risk marker includes (A1) NXPH4, the active tuberculosis risk marker also includes any marker selected from the following group B, or a combination thereof: (B1) BATF2; (B2) ANKRD22; (B3) SERPING1; (B4) FCGR1A; (B5) C1QC; (B6) FCGR1BP; (B7) FCGR1CP; (B8) GBP5; (B9) SEPTIN4; (B10) LHFPL2; (B11) ETV7; (B12) C1QB.

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