CN118222703A - Application of biomarker in preparation of active tuberculosis diagnosis product and active tuberculosis diagnosis kit - Google Patents
Application of biomarker in preparation of active tuberculosis diagnosis product and active tuberculosis diagnosis kit Download PDFInfo
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Abstract
The application discloses application of a biomarker in preparing a product for diagnosing active tuberculosis patients and a kit for diagnosing active tuberculosis patients, wherein the biomarker is at least four of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene. According to the present application, it is possible to distinguish individuals having active tuberculosis or not (latent tuberculosis mycobacteria infection and healthy persons), and having good sensitivity and specificity.
Description
Technical Field
The application relates to the field of medicine and diagnosis, in particular to application of a biomarker in preparation of a product for diagnosing active tuberculosis and a kit for diagnosing active tuberculosis.
Background
Tuberculosis (TB) is an infectious disease caused by mycobacterium Tuberculosis (Mycobacterium Tuberculosis), which affects mainly the lungs, but may also affect other parts of the body, such as lymph nodes, bones, meninges, kidneys, etc. Tuberculosis is one of the global great public health problems, and is an important means for effectively controlling tuberculosis epidemic situation and reducing tuberculosis burden around the early detection, standard treatment and management of infectious source control measures developed by tuberculosis patients and identifying and finding tuberculosis patients.
There are typically three consequences of a mycobacterium tuberculosis infection: about 5% of the human bodies can completely remove germs, about 5% -10% of the human bodies can develop Active Tuberculosis (ATB), and about 90% of the infected people are in a mycobacterium tuberculosis latent infection (LTBI) state. Wherein, ATB is the type which has obvious tuberculosis clinical symptoms and signs and has the highest transmissibility and hazard and is most preferably detected and diagnosed; LTBI refers to a persistent infection state in which an organism is infected with mycobacterium tuberculosis that produces a persistent immune response to its antigenic stimulus without the occurrence of clinical manifestations and imaging changes of tuberculosis.
Tuberculosis diagnosis is mainly performed by etiology (including bacteriology and molecular biology) examination, and is performed by comprehensive analysis in combination with epidemic history, clinical manifestation, chest imaging examination, related auxiliary examination, differential diagnosis and the like. The mycobacterium isolated culture detection is a gold standard for tuberculosis laboratory diagnosis, has higher sensitivity, but has overlong detection period, generally 2-6 weeks, and has high requirements on the laboratory, thereby being not beneficial to early diagnosis of tuberculosis; sputum smear is low in price, fast in result report and simple and convenient to operate, but can be detected only by 5000-10000 bacteria/ml, has low sensitivity and poor specificity, and can not distinguish tubercle bacillus from other tubercle bacillus; the effectiveness of the molecular biological diagnosis technology is superior to that of sputum smear microscopy and mycobacterium separation culture method, the diagnosis capability of tuberculosis is greatly improved, and the method has good effect in the patients with bacterial positive tuberculosis, but has lower sensitivity in the patients with bacterial negative tuberculosis. The imaging examination has an important effect on the active focus in the lung with obvious active tuberculosis, but has lower diagnosis degree on patients with low lesion degree; tuberculin skin test and gamma-interferon release test, etc. can be used as diagnosis of mycobacterium tuberculosis infection, but ATB and LTBI cannot be distinguished, and sensitivity is also easily affected in case of immune limitation such as combined human immunodeficiency virus infection, etc.
In summary, the existing tuberculosis diagnosis methods have certain limitations, and a simple, effective and detection means capable of differential diagnosis of ATB and LTBI needs to be developed.
Disclosure of Invention
The application aims to provide application of a biomarker in preparation of a product for diagnosing active tuberculosis and a kit for diagnosing active tuberculosis.
The application adopts the following technical scheme:
In a first aspect, the application discloses the use of a biomarker comprising at least four of the FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene for the preparation of a product for diagnosing patients with active tuberculosis. In the present application, a biomarker for diagnosing active tuberculosis is provided, which can diagnose active tuberculosis, can distinguish individuals with active tuberculosis or without active tuberculosis (mycobacterium tuberculosis latent infection and healthy people), and has good sensitivity and specificity.
In one implementation of the application, the biomarkers are FAS gene, TNFSF10 gene, TRIM5 gene, and C5 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene and CASP1 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene.
In one implementation of the present application, the sample type detected by the product is peripheral blood. It should be noted that sputum is the main sample for tuberculosis diagnosis at present, the specimen quality has a great influence on the detection result, but qualified sputum samples are difficult to obtain, because nearly 1/3 of tuberculosis patients have no or little sputum, especially children patients and HIV infected patients. The tuberculosis detection technology based on the non-sputum specimen has important significance for finding more tuberculosis patients which are not easy to find in the prior art. The sample type detected in the application is peripheral blood, the peripheral blood sample is one of the most convenient sample types obtained from non-sputum samples, the tuberculosis diagnosis capability can be well improved, the blood sample is easy to integrate with a rapid molecular detection platform, the clinical transformation difficulty is low, and the method has good application prospect in the aspects of tuberculosis diagnosis and treatment monitoring.
In one implementation of the application, the product comprises a reagent or kit for detecting the expression level of the biomarker. It is noted that the expression level of the biomarker may be the expression level of the transcriptome level of the biomarker.
In one implementation of the application, patients with active tuberculosis are diagnosed by comparing the expression levels of the biomarkers.
In one implementation of the application, a score value for the biomarker is derived from the expression level of the biomarker, and the score value is compared to a cutoff value to diagnose an active tuberculosis patient.
In one implementation of the application, the expression level of the biomarker is detected by real-time fluorescent quantitative reverse transcription polymerase chain reaction (RT-qPCR). It should be noted that the expression level of the biomarker may also be detected by other techniques, such as RNA sequencing, western blotting, immunohistochemistry, flow cytometry, in situ hybridization, and the like. In the application, the RT-qPCR technology can quantify the mRNA level of the gene, and has short detection time, low detection cost and higher sensitivity and specificity.
In one implementation of the application, the scoring value is the average of the individual gene expression levels in the biomarker minus the average of housekeeping gene expression levels. The application can provide a calculation method for calculating the expression condition of the biomarkers of different samples by defining the scoring value as the average value of the expression quantity of each gene in the biomarkers minus the average value of the expression quantity of housekeeping genes.
In one implementation of the application, the expression level of each gene in the biomarker and the expression level of the housekeeping gene are Ct values of the corresponding genes obtained by real-time fluorescent quantitative reverse transcription polymerase chain reaction detection. The Ct value (Cycle Threshold) in qPCR detection refers to the number of cycles when the fluorescent signal reaches a predetermined Threshold in the PCR amplification process, and the Ct value is related to the content of the target nucleic acid in the sample, and the higher the content of the target nucleic acid in the sample, the fewer the number of cycles required to reach the Threshold, and the lower the Ct value. Therefore, in the present application, the expression level of a certain gene can be expressed by Ct value. For example, the expression level of the FAS gene can be expressed by Ct value detected by RT-qPCR of the FAS gene.
In one implementation of the application, the cutoff value is-4.965 to 0.4636. In the application, the diagnosis of active tuberculosis is carried out by detecting the expression quantity of the biomarker and selecting the cut-off value to be-4.965 to 0.4636, so that the kit has good sensitivity and specificity.
In one embodiment of the present application, if the score is equal to or less than the cutoff value, active tuberculosis is diagnosed, and if the score is greater than the cutoff value, no active tuberculosis is diagnosed.
In one implementation mode of the application, the product comprises a primer and a probe for detecting the biomarker, wherein the primer sequence and the probe sequence are selected from sequences shown in SEQ ID NO. 1-SEQ ID NO. 18.
In a second aspect the application discloses a kit for diagnosing a patient with active tuberculosis, the kit comprising reagents for detecting the expression level of a biomarker selected from at least four of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene.
In one implementation of the application, the biomarkers are FAS gene, TNFSF10 gene, TRIM5 gene, and C5 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene and CASP1 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene.
In one implementation mode of the application, the kit comprises a primer and a probe for detecting the biomarker, wherein the primer sequence and the probe sequence are selected from sequences shown in SEQ ID NO. 1-SEQ ID NO. 18.
In one implementation of the application, the kit further comprises reagents for detecting the expression level of the housekeeping gene. Incidentally, housekeeping genes (Housekeeping genes) refer to a class of genes that are ubiquitously expressed throughout all cell types and are responsible for encoding proteins necessary for maintaining the basic vital activities of the cells. The expression level of housekeeping genes is relatively stable and does not change greatly depending on the cell type, developmental stage or physiological state. The regulation of expression of such genes is generally less affected by external environmental factors and is the basis for cell survival and operation. According to the application, the expression level of the housekeeping gene is detected at the same time, so that the difference between samples can be corrected, and the accuracy of an experimental result is further improved.
In one implementation of the application, the housekeeping gene is selected from at least one of GAPDH, β -actin, ABL, GUSB, TUBB, UBC, B2M, SDHA, and PRLI3A genes. The housekeeping gene in the present application may be selected from other housekeeping genes other than those listed above, such as TBP, PDH, and α -Tubulin. Housekeeping genes can be used to normalize gene expression levels in biomarkers and to determine if sufficient sample is used in the assay or if the sample contains sufficient cells or nucleic acids.
In one implementation of the application, the housekeeping genes are GAPDH and β -actin.
In one implementation mode of the application, the kit comprises a primer and a probe for detecting the housekeeping gene, wherein the primer sequence and the probe sequence comprise sequences shown as SEQ ID NO. 19-SEQ ID NO.21 and/or SEQ ID NO. 22-SEQ ID NO. 24. In this case, a probe and a primer for detecting housekeeping gene GAPDH and/or β -actin can be provided.
The application has the beneficial effects that:
The application of the biomarker in the preparation of the active tuberculosis diagnosis product and the active tuberculosis diagnosis kit can diagnose active tuberculosis, can distinguish whether an individual has active tuberculosis or does not have active tuberculosis (mycobacterium tuberculosis latent infection and healthy people), and has good sensitivity and specificity.
Drawings
FIG. 1 is a schematic diagram showing differential expression genes and pathway analysis thereof of ATB, LTBI and HC according to example 1 of the present application.
FIG. 2 is a schematic diagram showing the results of analysis of the importance of differentially expressed genes according to example 1 of the present application.
FIG. 3 is a schematic representation of the results of expression of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes in the ATB, LTBI and HC populations according to example 1 of the present application.
FIG. 4 is a ROC curve of the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes of the application in example 1 in the screening cohort for ATB, LTBI and HC populations.
FIG. 5 is a ROC curve of the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes of example 1 of the present application distinguishing ATB and HC populations in the validation queue.
FIG. 6 is a ROC curve of the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes of the application in example 1 to distinguish ATB and LTBI populations in the validation queue.
FIG. 7 is a graph showing the relative expression levels of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes in ATB, LTBI and HC populations according to example 2 of the present application.
Fig. 8 is a ROC curve for the biomarker scores involved in example 3 of the present application to distinguish ATB populations from those without.
Fig. 9 is a ROC curve for the biomarker scores related to example 4 of the present application to distinguish ATB populations from those without.
Fig. 10 shows the biomarker score calculation result and cut-off value according to example 4 of the present application.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted in various situations, or replaced by other materials, methods. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning.
Aiming at the problems that the detection of tuberculosis has low sensitivity and specificity, long detection period and high sample requirement and can not distinguish active tuberculosis from latent tuberculosis infection in the prior art, the application creatively provides application of a biomarker in preparing a kit for diagnosing active tuberculosis patients.
In a specific embodiment, the biomarker may include at least a plurality of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene, and STAT2 gene. It is to be noted that, from the aspect of genes and molecules, the application analyzes the peripheral blood sample data set containing ATB, LTBI and healthy people to obtain differential genes, adopts random forest algorithm to screen key genes, and obtains a group of biomarkers for diagnosing active tuberculosis through ROC curve analysis, including FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes. These genes are significantly up-regulated in active tuberculosis patients, and have good sensitivity and specificity for diagnosis of ATB according to their expression differences.
In a specific embodiment, the biomarker may include at least four genes of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene, and STAT2 gene.
In a specific embodiment, the biomarkers are FAS gene, TNFSF10 gene, TRIM5 gene, and C5 gene; or the biomarkers are FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene and CASP1 gene; or biomarkers are FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene, and STAT2 gene.
In a specific embodiment, the sample type detected may be peripheral blood.
In a specific embodiment, the expression level of the biomarker can be detected.
In a specific embodiment, exemplary mRNA sequences for each gene in a biomarker can be queried in the NCBI database using the following accession numbers ：FAS,NM_000043.6;TNFSF10,NM_003810.4;TRIM5,NM_033034.3;C5,NM_001317163.2;CASP1,NM_033292.4;STAT2,NM_005419.4.
In a specific embodiment, patients with active tuberculosis may be diagnosed by comparing the expression levels of the biomarkers.
In a specific embodiment, a score value for the biomarker may be obtained from the expression level of the biomarker and compared to a cutoff value to diagnose an active tuberculosis patient.
In a specific embodiment, the expression level of the biomarker can be detected by real-time fluorescent quantitative reverse transcription polymerase chain reaction (RT-qPCR).
In one embodiment, the expression level of a housekeeping gene may also be detected. Thus, the difference between samples can be corrected by the expression level of the housekeeping gene, and the accuracy of the experimental result can be further improved.
In a specific embodiment, the housekeeping gene may be selected from at least one or more of GAPDH, β -actin, ABL, GUSB, TUBB, UBC, B2M, SDHA, and PRLI 3A.
In one embodiment, the scoring value of the biomarker may be calculated from the Ct value of RT-qPCR.
In a specific embodiment, the score is the average of the expression levels of each gene in the biomarker minus the average of the expression levels of the housekeeping gene.
In a specific embodiment, the average value of the expression level of each gene in the biomarker may refer to an arithmetic average value or a geometric average value of the expression level of each gene in the biomarker, and the average value of the expression level of the housekeeping gene may refer to an arithmetic average value or a geometric average value of the expression level of each housekeeping gene.
In a specific embodiment, the score is the geometric mean of the expression levels of the individual genes in the biomarker minus the arithmetic mean of the expression levels of the housekeeping genes.
In a specific embodiment, the expression level of each gene and the expression level of housekeeping gene in the biomarker are Ct values of the corresponding genes detected by RT-qPCR.
In one implementation of the application, the cutoff value is from-4.965 to 0.4636.
In one implementation of the present application, if the score value is equal to or less than the cutoff value, active tuberculosis is diagnosed, and if the score value is greater than the cutoff value, no active tuberculosis is diagnosed.
In one implementation of the application, the primer and probe sequences for detecting the expression level of the biomarker may be selected from the sequences shown in SEQ ID NO. 1-SEQ ID NO. 18.
In one implementation of the application, the primer and probe sequences for detecting the expression level of housekeeping genes may comprise sequences shown as SEQ ID NO. 19-SEQ ID NO.21 and/or SEQ ID NO. 22-SEQ ID NO. 24.
In one implementation of the application, active tuberculosis may be diagnosed based on biomarkers. In other words, active tuberculosis can be diagnosed based on the detection result of the biomarker of the present application.
In one implementation of the application, active tuberculosis may be diagnosed based on biomarkers in combination with other detection techniques or clinical parameters. In other words, the diagnosis of active tuberculosis may be performed in combination with the detection results of the biomarkers of the application, as well as other detection techniques (e.g. mycobacterial isolation culture detection, sputum smear detection, etc.) or clinical parameters (e.g. clinical symptoms, thoracic imaging examination results, etc.).
In one implementation of the application, the biomarkers can effectively distinguish active tuberculosis from patients with latent tuberculosis infection or healthy people, which can be used as part of tuberculosis diagnosis tests, and the individuals are classified to carry out a confirmatory tuberculosis diagnosis test on patients positive to the diagnosis test.
The application of the biomarker and the kit related by the application have the advantages that the number of genes to be detected is small, the detection cost can be reduced, the PCR detection can be carried out by adopting a blood sample, the sampling is convenient, the operation is simple and convenient, the detection time consumption is short, and the kit is suitable for popularization and use.
The application is further illustrated by the following examples. The following examples are merely illustrative of the present application and should not be construed as limiting the application. The reagents, kits, instruments and the like used in the examples are all commercially available, and the procedure is carried out according to instructions or general procedures in the art.
Example 1: screening of biomarkers
This example is directed to two peripheral blood gene chip data GSE83456 and GSE152532 published in the database (GEO, https:// www.ncbi.nlm.nih.gov/GEO /) normalized and combined to form a screening queue, comprising 109 clinically confirmed active tuberculosis patients (ATB), 69 clinically confirmed tuberculosis latent infected persons (LTBI) and 72 healthy persons (HC, healthy control). Since LTBI has some similar host responses to ATB, detection of both is also one of the difficulties in current tuberculosis detection, and thus, great attention is paid to the expression difference between ATB and LTBI, and a biomarker that can distinguish ATB from LTBI is sought.
Generation of differential genes: the peripheral blood gene expression of ATB, LTBI and HC is compared by T test through R language, and the differential gene expression between each group is obtained through P <0.0001,Fold change > 2 standard screening. FIG. 1 is a schematic diagram showing the differential expression genes and their pathway analysis of ATB, LTBI and HC according to example 1 of the present application, wherein 141 differential expression genes are selected between ATB and HC, 30 differential expression genes are selected between ATB and LTBI, and 29 differential expression genes co-exist therebetween finally (see part A and part B in FIG. 1). GO and KEGG enrichment analysis (see part C in fig. 1) was performed on these differential genes: GO analysis shows that the biological processes in which the differential genes are mainly enriched are significant in defensive responses, immune system functions, immune responses and responses to external stimuli; KEGG pathway enrichment analysis showed that the differential gene was involved in necrotic apoptosis and autophagy pathways were significantly correlated; indicating that the differential gene plays a key role in immune-related functions.
Screening of key genes: the importance of distinguishing ATB, LTBI and HC was evaluated for 29 differentially expressed gene pairs using a random forest algorithm. FIG. 2 is a schematic diagram showing the results of analysis of the importance of differentially expressed genes according to example 1 of the present application. The MEAN DECREASE IN accuracy and MEAN DECREASE IN GINI score indices calculated from the algorithm model were ranked (the larger the values of both represent the greater importance of the gene), and the two indices were ranked as in FIG. 2, where the first six different genes were FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2. Analysis of the expression of these 6 genes in the screening cohort in the ATB, LTBI and HC populations, FIG. 3 is a schematic representation of the expression of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes in the ATB, LTBI and HC populations according to example 1 of the present application, showing that the expression levels of these 6 genes were significantly up-regulated in ATB patients compared to both LTBI and HC (FIG. 3).
In this example, when the biomarker is selected, a combination of several genes with the forefront importance among the differentially expressed genes is selected as the biomarker in order to facilitate the subsequent detection. In this example, the first 4, 5 and 6 genes of importance were analyzed, and the combinations of these genes were found to be better in distinguishing ATB from LTBI and HC. Of course, other genes of the preceding importance in FIG. 2 may be selected as one of the genes of the biomarker, such as APOL1, DRAM, ATG3, GRAB2, PRKCD, NFE2L2, CCR2, SERPINA1, etc.
Screening of ATB biomarker combinations: the performance of different gene combinations to differentiate ATB patients was analyzed by R language using a linear support vector machine (linear support vector machine, SVM) algorithm model in combination with cross-validation, and the results were presented in a receiver operating characteristics analysis (ROC) curve analysis. Sensitivity and specificity values under different conditions are collected by changing a classification cut-off value, and an ROC curve is plotted by taking sensitivity (representing true positive rate) as an ordinate and 1-specificity (representing false positive rate) as an abscissa, wherein an area value (AUC) under the ROC curve is an important test accuracy index: in the case of AUC > 0.5, the closer the AUC is to 1, the better the diagnostic effect; the AUC is lower in accuracy when the AUC is 0.5-0.7, has certain accuracy when the AUC is 0.7-0.9, and has higher accuracy when the AUC is more than 0.9; at an AUC of 0.5, this indicates that the diagnostic method is completely ineffective and of no diagnostic value. Sensitivity (sensitivity): the greater the sensitivity, the better the sensitivity, and the ideal sensitivity is 100%. Specificity (specificity): the greater the specificity, the better the ideal specificity is 100%. The genes were added to the combination in order from high to low based on the key gene ordering derived from the random forest algorithm and the AUC values of the combination were calculated. FIG. 4 is a ROC curve of the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 gene discrimination screening cohorts of ATB, LTBI and HC populations according to example 1 of the present application, wherein in FIG. 4, the average ROC curve is shown in bold solid lines and the AUC results are shown in average AUC+ -SD values. The iterative calculation shows that the gene combinations of the first 4, the first 5 and the first 6 genes in FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes reach consistent AUC values in a screening queue, the AUC value between ATB and HC is 0.97, and the AUC value between ATB and LTBI is 0.86, which indicates that the first 4, the first 5 and the first 6 genes in FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 can well distinguish ATB from LTBI or HC and have relatively consistent performance (table 1 and FIG. 4).
Biomarker combination performance validation: the four related individual peripheral blood gene expression data of GSE107994, GSE19439, GSE19444 and GSE28623 are downloaded from the GEO public database and are respectively used as verification queues for verifying the screening performance of the key genes, and the population in the verification queues is completely independent from the population in the screening queues. The GSE107994 data contains 53 ATBs, 72 LTBIs and 50 HC; the GSE19439 data contained 13 ATBs, 17 LTBI and 12 HC; the GSE19444 data contained 21 ATBs, 21 LTBI and 12 HC; 46 ATBs, 25 LTBIs and 37 HC's are included in GSE28623 data. FIG. 5 is the ROC curves of the ATB and HC populations in the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 gene discrimination verification queues according to example 1 of the present application, and FIG. 6 is the ROC curves of the ATB and LTBI populations in the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 gene discrimination verification queues according to example 1 of the present application. In fig. 5 and 6, the average ROC curve is shown in bold solid lines and AUC results are shown as average auc±sd values. The key genes are subjected to iterative calculation, and the combination of the first 4 genes (namely FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes) in the FAS, TNFSF10, TRIM5 and C5 genes reaches good AUC values in ATB vs LTBI and ATB vs HC of all verification queues, wherein the AUC is higher than 0.8, and most of the AUC can be higher than 0.95, so that the accuracy is higher; the combination of the first 5 genes in the FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes (namely the FAS, TNFSF10, TRIM5, C5 and CASP1 genes) achieves better AUC values in ATB vs LTBI and ATBvs HC of all verification queues, the AUC is higher than 0.78, most of the AUC can be higher than 0.87, and the accuracy is higher; the combination of the first 6 genes of the FAS, TNFSF10, TRIM5, C5, CASP1, STAT2 genes (i.e., FAS, TNFSF10, TRIM5, C5, CASP1, and STAT2 genes) achieved better AUC values for both the ATB vs LTBI and the ATB vs HC in all validation queues, with AUCs above 0.85, mostly above 0.9, with higher accuracy (table 1 and fig. 5, 6).
Table 1 gene combinations AUC and standard deviation values of ROC analysis in screening and validation queue data
Example 2: verification of expression of biomarkers in clinical samples
The subjects of this example were given 36 of 12 clinically diagnosed active tuberculosis patients (ATB), 8 clinically diagnosed tuberculosis latent infectors (LTBI) and 16 healthy persons (HC). The testees all participate voluntarily.
RNA in the peripheral blood samples of each subject provided by the conventional blood sampling was extracted using the RNA extraction reagent TRIzoL of Invitrogen, and the procedure was strictly according to the instructions.
And detecting mRNA expression levels of FAS, TNFSF10, TRIM5, C5, CASP1, STAT2, beta-actin and GAPDH genes in each sample by using RNA as a template and adopting a probe method reverse transcriptase real-time fluorescence quantitative PCR (RT-qPCR), so as to obtain Ct values of the genes, and expressing the expression quantity of the corresponding genes by the Ct values of the genes. The present example provides a set of primer pairs and Taqman probes for detecting the expression level of each gene by using a quantitative PCR method, the nucleotide sequences of the primer pairs and the probe sequences of the target genes are shown in Table 2, SEQ ID No.1-18, and the primer pairs and the probe sequences of housekeeping genes are shown in SEQ ID No.19-24. The PCR reaction system is as follows: 1. Mu.L of DNA Taq polymerase+reverse transcriptase, 4. Mu.L of 5 Xreaction buffer, 0.6. Mu.L of forward primer, 0.6. Mu.L of reverse primer, 0.3. Mu.L of probe. The reaction procedure was set as follows: 15 minutes at 55 ℃;95 ℃ for 30 seconds; 95℃for 10 seconds and 58℃for 30 seconds, for a total of 40 cycles.
TABLE 2 Gene primer probe sequences
The relative expression of active tuberculosis biomarkers FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes was analyzed. The relative expression quantity between groups is calculated by adopting 2 -△△Ct, and the calculation formula is as follows: 2 -△△Ct＝2-（ΔCt Test set -ΔCt Control group ）＝2-[（Ct Target genes of test group -Ct Housekeeping genes of test set ）-（Ct Target gene of control group -Ct control housekeeping genes ）], FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes, beta-actin genes as internal reference genes, ATB or LTBI samples as test group, and HC samples as control group. FIG. 7 is a graph showing the relative expression levels of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes in ATB, LTBI and HC populations according to example 2 of the present application. Table 3 shows the expression difference data of each gene between ATB and LTBI, ATB and HC, and the difference hypothesis test uses T test. As can be seen from fig. 7 and table 3, the expression levels of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes were all significantly increased (P < 0.05) in the ATB group compared to the HC group and the ATB group compared to the LTBI group, and all showed 4.4-fold or more expression differences, which are consistent with the chip analysis results in example 1. These all indicate that these six genes are closely related to active tuberculosis, and that ATB, LTBI and HC can be distinguished obviously by using the expression levels of FAS, TNFSF10, TRIM5, C5, CASP1 and STAT2 genes.
Table 3 relative expression level differences of genes in different populations
Example 3: method for scoring biomarkers
The embodiment establishes a scoring method for detecting active tuberculosis, which comprises the following steps: a) Measuring the expression levels of the biomarkers FAS, TNFSF10, TRIM5, C5, CASP1, STAT2 genes, and housekeeping genes in a biological sample from the patient; b) A score was calculated based on each gene expression level, the score being defined as the arithmetic mean of biomarker expression levels minus the geometric mean of housekeeping gene expression levels. The scoring method is established based on Ct values of quantitative PCR tests. Illustratively, housekeeping genes are GAPDH and β -actin, and the scoring formulas for the gene combinations of FAS, TNFSF10, TRIM5, C5, CASP1, STAT2 of the samples are:
(formula I)
In formula I, FAS refers to the expression level of the FAS gene measured by the method of example 2, TNFSF10 refers to the expression level of the TNFSF10 gene measured by the method of example 2 (i.e., the Ct value of the TNFSF10 gene), TRIM5 refers to the expression level of the TRIM5 gene measured by the method of example 2 (i.e., the Ct value of the TRIM5 gene), C5 refers to the expression level of the C5 gene measured by the method of example 2 (i.e., the Ct value of the C5 gene), CASP1 refers to the expression level of the CASP1 gene measured by the method of example 2 (i.e., the Ct value of the CASP1 gene), STAT2 refers to the expression level of the STAT2 gene measured by the method of example 2 (i.e., the Ct value of the STAT2 gene), GAPDH refers to the expression level of the GAPDH gene measured by the method of example 2 (i.e., the Ct value of the GAPDH gene), and β -actin refers to the expression level of the β -actin gene measured by the method of example 2.
The score of each clinical sample of example 2 was calculated according to the above formula, with 12 cases of ATB and 24 cases of no ATB (LTBI 8 cases, HC 16 cases) in the clinical samples of example 2. The performance of this score to distinguish ATB from LTBI and HC was analyzed using the ROC method and fig. 8 is a ROC curve for biomarker scores related to example 3 of the present application to distinguish ATB populations from populations without ATB. As shown in fig. 8, scoring of the FAS, TNFSF10, TRIM5, C5, CASP1, STAT2 gene combinations was 1 at AUC values distinguishing ATB from LTBI and HC, indicating that the scoring method was very effective for the detection of active tuberculosis in these samples.
Example 4: scoring method performance verification
The present example has a total of 162 subjects, of which 62 clinically definite active tuberculosis patients (ATB), 38 clinically definite tuberculosis latent infectors (LTBI) and 62 healthy persons (HC). The testees all participate voluntarily. Venous blood was collected from each subject using conventional methods. The scores of each sample were obtained using the methods of example 2 and example 3.
Using ROC analysis of this score to identify performance of ATB, fig. 9 is a ROC curve for biomarker scores related to example 4 of the present application to distinguish ATB populations from those without ATB. As shown in fig. 9, AUC values reached 0.95, indicating that the scoring method accurately distinguished ATB patients from LTBI or HC patients.
The scoring method includes comparing the score to a predetermined cutoff value to identify the patient as having active tuberculosis or not having active tuberculosis. Identifying the patient as having active tuberculosis when the score is less than or equal to a predetermined cutoff value; and when the score is greater than a predetermined cutoff value, identifying the patient as not having active tuberculosis (tuberculosis latent infection or healthy person). How to set a reasonable cut-off value is very challenging, and a number of factors need to be considered. In one aspect, the expression level of the biomarker in a population may vary from environment to environment, such as a population in an area with high prevalence of tuberculosis, a population suffering from HIV or certain immunosuppressive health conditions, a population in close contact with individuals suffering from tuberculosis, and the like. On the other hand, the specificity or sensitivity index to be achieved to achieve different differentiation objectives is different. Only in the case of tuberculosis diagnosis test and confirmatory test. Tuberculosis triage tests require higher sensitivity to be achieved, and individuals are classified for confirmatory tuberculosis diagnostic tests or for further investigation of possible non-tuberculosis etiologies (patients negative for triage tests). Tuberculosis triage tests defined according to the Target Product Profile (TPP) of the tuberculosis priority diagnostic method set by the world health organization in 2014 optimally have 95% sensitivity and 80% specificity, or at least 90% sensitivity and 70% specificity, for any form of active tuberculosis; the confirmatory test should have a specificity of 98% and an overall sensitivity of 65%.
The performance of the different cut-off values was investigated for these samples of this example. Fig. 10 shows the biomarker score calculation result and cut-off value according to example 4 of the present application. As shown in fig. 10, for diagnosis of active tuberculosis: when the cut-off value of the score is set to be-1.987, the scoring method has 90% sensitivity and 90% specificity, and the performance index when the cut-off value is set to be-1.987 to 0.4636 can meet the minimum index requirement of the triage test set by the world health organization; the scoring method has 98% specificity and 62% sensitivity when the cutoff value is set to-4.965, and the performance index is close to the requirements of the confirmatory test index set by the world health organization.
The foregoing is a further detailed description of the application in connection with specific embodiments, and it is not intended that the application be limited to such description. It will be apparent to those skilled in the art that several simple deductions or substitutions can be made without departing from the spirit of the application.
Claims (10)
1. Use of a biomarker for the preparation of a product for diagnosing patients with active tuberculosis, characterized in that the biomarker comprises at least four genes of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene.
2. The use of claim 1, wherein the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, and C5 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene and CASP1 gene; or the biomarker is FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene and STAT2 gene.
3. The use of claim 1, wherein the sample type detected by the product is peripheral blood.
4. The use of claim 1, wherein the product comprises a reagent or kit for detecting the expression level of the biomarker.
5. The use of claim 4, wherein the expression level of the biomarker is detected by real-time fluorescent quantitative reverse transcription polymerase chain reaction.
6. The use of claim 5, wherein a score value for said biomarker is obtained from the expression level of said biomarker, said score value being the average of the expression levels of the individual genes in said biomarker minus the average of the expression levels of housekeeping genes, said individual gene expression levels in said biomarker and said housekeeping gene expression levels being Ct values of the corresponding genes detected by said real-time fluorescent quantitative reverse transcription polymerase chain reaction, and said score value being compared to a cutoff value to diagnose an active tuberculosis patient.
7. The use according to claim 6, wherein the cutoff value is-4.965 to 0.4636; if the scoring value is less than or equal to the cut-off value, diagnosing active tuberculosis; if the score value is greater than the cutoff value, no active tuberculosis is diagnosed.
8. The use according to claim 5, wherein the product comprises primers and probes for detecting the biomarker, the primer sequence and probe sequence being selected from the sequences shown in SEQ ID No.1 to SEQ ID No. 18.
9. A kit for diagnosing an active tuberculosis patient, the kit comprising reagents for detecting expression levels of biomarkers comprising at least four genes of FAS gene, TNFSF10 gene, TRIM5 gene, C5 gene, CASP1 gene, and STAT2 gene.
10. The kit of claim 9, wherein the kit comprises primers and probes for detecting the biomarker, and the primer sequence and probe sequence for detecting the biomarker are selected from the sequences shown in SEQ ID No.1 to SEQ ID No. 18.
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