Combined marker, tuberculosis diagnosis model based on LASSO regression analysis and construction method and application of tuberculosis diagnosis model

Abstract

The invention discloses a combined marker, a tuberculosis diagnosis model based on LASSO regression analysis and a construction method and application of the tuberculosis diagnosis model. According to the invention, six genes THBS1, NEDD4, SEMA7A, RNASEL, TAPBP and PLTP related to tuberculosis are screened based on bioinformatics, and are closely related to immune cell states; the six target genes are combined together to form a panel which is used as a combined marker for diagnosing tuberculosis, and development of a diagnostic kit based on the six combined markers is helpful for rapid screening of tuberculosis. The invention further constructs an LASSO regression model based on the combined marker, and the LASSO regression model as a diagnostic model can distinguish pulmonary tuberculosis patients from healthy people and has good diagnostic performance. A new direction is provided for pulmonary tuberculosis diagnosis based on mRNA characteristics, a valuable diagnosis model is provided, and accurate diagnosis of pulmonary tuberculosis patients is facilitated.

Description

Technical Field

[0001] The invention relates to a combined marker, a tuberculosis diagnosis model based on LASSO regression analysis, a construction method and application thereof, and belongs to the technical field of biomedicine. Background Art

[0002] Mycobacterium tuberculosis (Mtb) is the pathogen of tuberculosis (TB), which is mainly transmitted through the air. Almost all tissues and organs of the body may be infected. Pulmonary tuberculosis is the most common, and the main clinical manifestations are low fever, cough, hemoptysis, and chest pain. According to the World Health Organization, there will be 10.8 million new cases of tuberculosis and 1.25 million deaths worldwide in 2023, and due to the impact of COVID, the mortality rate of tuberculosis has remained high in recent years. It is estimated that at least 2 billion people (25%-30% of the world's population) are latent tuberculosis infected (LTBI). The immune response of pulmonary tuberculosis is different from LTBI, and active tuberculosis is contagious. Therefore, early and accurate diagnosis of active tuberculosis is crucial to strengthen patient care, improve prognosis, and delay the transmission cycle of Mycobacterium tuberculosis. The current standard method for detecting tuberculosis is to culture the microorganism, which takes 3-12 weeks. Smear microscopy for acid-fast bacilli is also the main method for diagnosing active tuberculosis (ATB), but the sensitivity of sputum smears is low. Therefore, there is an urgent need to find effective and rapid biomarkers for the diagnosis of tuberculosis.

[0003] miRNAs are single-stranded (approximately 22 nucleotides) noncoding RNAs responsible for post-transcriptional regulation of gene expression and play a regulatory role in many important physiological processes, such as cell proliferation and differentiation, body metabolism, and host immunity. Many studies have reported that miRNA and its target genes are potential diagnostic biomarkers or therapeutic targets for tuberculosis. Has-miR-423-5p inhibits autophagosome-lysosome fusion by post-transcriptionally regulating its direct target VPS33A, which is speculated to be an important cause of active tuberculosis. MiR-224-5p, miR-324-5p, and miR-488-5p regulate the expression of the common target gene CTLA4 to promote the polarization of tuberculosis-related macrophages. However, the inflammatory response caused by excessive macrophage activation is the initial cause of TB infection. Meng et al. found that after A549 cells were transfected with miRNA-4687-5p to induce Mtb infection, the expression level of its target NRAMP1 was reduced, cell apoptosis was increased, and cell proliferation was reduced, indicating that miRNA-4687-5p and NRAMP1 may be potential targets for tuberculosis treatment. The above studies show that miRNA and its target genes may be used as a biomarker for the diagnosis and precise treatment of tuberculosis. Summary of the invention

[0004] The purpose of the present invention is to provide a combined marker, a tuberculosis diagnosis model based on LASSO regression analysis, and a construction method and application thereof.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a combination of markers for diagnosing pulmonary tuberculosis, comprising NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1.

[0007] In a second aspect, the present invention provides use of the combined marker or a detection reagent thereof as described in the first aspect in the preparation of a product for diagnosing pulmonary tuberculosis.

[0008] Preferably, the product comprises a diagnostic kit, a diagnostic system or a device.

[0009] Preferably, the product is a kit, which comprises at least a reagent for detecting the expression amount of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids.

[0010] In a third aspect, the present invention provides a method for constructing a tuberculosis diagnosis model based on LASSO regression analysis, the construction method comprising the following steps:

[0011] Step 1: Sequencing RNA samples from lung tissue removed during surgery from clinical tuberculosis patients;

[0012] Step 2: Combine the sequencing results of step 1 with the tuberculosis blood sample miRNA dataset in the GEO database to screen for differentially expressed miRNAs;

[0013] Step 3: The target mRNA of the differentially expressed miRNA was predicted using the miRTarbase and Tarbase databases, and the intersection with the differentially expressed genes, differentially expressed mRNAs and immune-related genes in blood samples was taken to construct a miRNA-mRNA interaction network, and finally 6 target mRNAs NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 were screened out;

[0014] Step 4: Construct a tuberculosis diagnostic model by LASSO regression analysis, the diagnostic model is: RS = NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439; NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 in the diagnostic model represent their mRNA expression levels respectively. In the present invention, the 2-△△Ct method is used to quantify the mRNA expression level.

[0015] In a fourth aspect, the present invention provides a tuberculosis diagnosis kit, which comprises at least a reagent for detecting the expression levels of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids.

[0016] Preferably, the kit comprises at least a detection reagent for specifically detecting the mRNA expression of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1, and the detection reagent comprises qRT-PCR detection primers as shown in SEQ ID NOs: 3 to 14.

[0017] Preferably, the kit further comprises qRT-PCR detection primers for detecting the expression amount of the internal reference gene HPRT1 mRNA, and the sequences thereof are shown in SEQ ID NOs: 1-2.

[0018] Preferably, the kit is tested according to the following method: the risk score RS of the sample to be tested is calculated by the Lasso regression diagnostic model RS=NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439, and whether the sample suffers from tuberculosis is distinguished according to the risk score RS. The sample to be tested can be a blood sample of the subject.

[0019] In a fifth aspect, the present invention provides a tuberculosis diagnosis system, which includes a sample collection device, a sample detection device and a diagnosis device; wherein:

[0020] The sample collection device is configured as a device for collecting a blood sample of a subject; the sample detection device is a device capable of detecting the expression levels of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids in the blood sample; the diagnostic device includes a data acquisition module and a diagnostic module, the data acquisition module is configured to acquire the data detected by the sample detection device, and the diagnostic module is configured to input the data into a diagnostic model: RS=NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439, and judge the diagnostic result according to the diagnostic model output value RS.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention screened out a total of 6 immune-related genes (THBS1, NEDD4, SEMA7A, RNASEL, TAPBP, PLTP), which are closely related to the state of immune cells; these 6 target genes are combined into a panel and used as a combination marker for diagnosing pulmonary tuberculosis. The development of a diagnostic kit based on these six combination markers will help to achieve rapid screening of tuberculosis; the present invention further constructed a LASSO regression model based on the combination markers, which can distinguish between pulmonary tuberculosis patients and healthy people as a diagnostic model and has good diagnostic performance; the present invention provides a new direction for the diagnosis of pulmonary tuberculosis based on mRNA characteristics, and provides a valuable diagnostic model, which will be beneficial to the accurate diagnosis of pulmonary tuberculosis patients in combination with the kit and the diagnostic model. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Volcano plots of differentially expressed mRNA (left) and differentially expressed miRNA (right) in lung tissue; red indicates upregulation, blue indicates downregulation, and gray indicates no significant difference.

[0024] Figure 2 The Venn diagram shows the overlapping genes between the differentially expressed miRNAs in lung tissue RNA-seq and blood datasets GSE131174, GSE14964, GSE229020, and GSE25435 (left) and the expression of the five differentially expressed miRNAs in lung tissue PET-high / low (right).

[0025] Figure 3The miRNA-mRNA co-expression network diagram constructed for differentially expressed miRNAs and predicted target genes (A) and the sub-network diagram of the miRNA-mRNA network (B) (|R|>0.4, P<0.05) and the expression levels of 6 mRNAs in PET-high / low lung tissue samples (C).

[0026] Figure 4 The ROC curve validation results of the Lasso regression model in different data sets.

[0027] Figure 5 Validation of six mRNAs NEDD4 (A), PLTP (B), RNASEL (C), SEMA7A (D), TAPBP (E), and THBS1 (F) by quantitative real-time polymerase chain reaction (RT-PCR), among which, *P < 0.05, **P < 0.01, ***P < 0.001, ns: no significant difference.

[0028] Figure 6 ROC curve analysis shows the diagnostic performance of the six mRNAs alone (left) and the diagnostic performance of the prediction model constructed by combining the six mRNAs (right). DETAILED DESCRIPTION

[0029] In order to make the present invention more clearly understood, preferred embodiments are described in detail below with reference to the accompanying drawings.

[0030] The present invention aims to explore the potential application of microRNA target genes in the diagnosis of pulmonary tuberculosis. 18Whole transcriptome sequencing was performed on samples with high and low metabolic activity of F-FDG-PET / CT. Combined with the miRNA datasets of 4 TB blood samples from the GEO database, 5 differentially expressed microRNAs (has-miR-409-3p, has-miR-486-3p, has-miR-127-3p, has-miR-654-3p, has-miR-142-3p) were screened out. The miRTarbase and Tarbase databases were used to predict their target mRNAs, and the intersection with the differential genes, differentially expressed mRNAs and immune-related genes in blood samples was taken to construct a miRNA-mRNA interaction network, and finally 6 target mRNAs (NEDD4, PLTP, RNASEL, SEMA7A, TAPBP, THBS1) were screened out. Immune infiltration analysis showed that these mRNAs were closely related to the states of various immune cells. Finally, a multi-factor Lasso regression analysis prediction model was established, and further verified by real-time fluorescence quantitative PCR in the blood of 30 patients. The results showed that PLTP, RNASEL, SEMA7A, and TAPBP were upregulated, NEDD4 and THBS1 were downregulated, and the ROC curve analysis of the six mRNAs combination showed an area under the curve (AUC) of 0.79. In short, the present invention screened out six target genes that can be used as potential biomarkers for diagnosing pulmonary tuberculosis.

[0031] Example

[0032] 1. Differentially expressed mRNA and miRNA in lung tissue

[0033] The whole transcriptome sequencing maps of high and low metabolic activity areas of 18F-FDG PET / CT lung tissues of 9 patients with pulmonary tuberculosis were subjected to bioinformatics analysis, and differential expression analysis was performed using the DEGSeq Bioconductor software package (v1.42.0). |log2FC|>1&pvalue<0.05&tpm_FH>1&tpm_FL>1 was set as the threshold for significant differential expression of mRNA and miRNA, and differentially expressed mRNA (DEGs) and miRNA (DEmR) were obtained, respectively. There were 2014 mRNA differentially expressed (DEGs) in the lesions of TB patients, of which 1235 mRNAs were upregulated and 779 mRNAs were downregulated; 117 miRNAs were differentially expressed (DEmRs), of which 77 miRNAs were upregulated and 40 miRNAs were downregulated. The results are shown in Figure 1 shown.

[0034] 2. Screening of miRNA:

[0035] The DEmR data of the differentially expressed genes in the miRNA datasets of the four blood samples (GSE25435, GSE131174, GSE149645, GSE229020) were intersected to obtain the following miRNAs: has-miR-409-3p, has-miR-486-3p, has-miR-127-3p, has-miR-654-3p, has-miR-142-3p, as shown in Figure 2 shown.

[0036] 3. Targeted mRNA screening:

[0037] These miRNAs were uploaded to the miRTarbase and Tarbase databases, and all predicted target mRNAs were summarized. The predicted target mRNAs were intersected with the differentially expressed genes and DEGs data of the mRNA datasets of four blood samples (GSE19491, GSE28623, GSE73408, and GSE101705), and then intersected with the immune-related gene list downloaded from the ImmPort Portal database to obtain the final 74 immune-related target genes (38 downregulated and 36 upregulated). The Pearson correlation coefficient of the miRNA-mRNA interaction was calculated based on the gene expression matrix, and miRNA-mRNA pairs with |R|>0.4 and p-value<0.05 were screened, and the network diagram was visualized using Cytoscape (V3.9.1) software. Finally, 6 target mRNAs (NEDD4, PLTP, RNASEL, SEMA7A, TAPBP, and THBS1) were screened out. Figure 3 shown.

[0038] 4. Lasso regression model construction and verification:

[0039] The R package glmnet (v4.1.8) was used to perform lasso regression on gene expression levels, and the family = "binomial" parameter was set to build a lasso logistic regression model suitable for binary classification problems. The coefficients of the regression model were extracted by the coef function to view the contribution of each gene expression level to the classification results. The equation for predicting the probability of tuberculosis was: 6-mRNA = NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEM A7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439. The R package pROC (v1.18.5) was used to verify the classification performance of the model on the GEO dataset, and the ROC curve and AUC value were calculated to measure the prediction accuracy and discrimination ability of the model. The areas under the curve (AUC) of the datasets GSE19491, GSE28623, GSE73408, and GSE101705 are 0.6578, 0.6852, 0.782, and 0.8438, respectively. Figure 4 The above results indicate that the combination of this 6-mRNA signature exhibits a significant diagnostic effect.

[0040] 5. qPCR Validation of Candidate Biomarkers

[0041] Peripheral blood was collected from 30 patients with pulmonary tuberculosis and 30 healthy controls. All patients signed informed consent. The protocol was approved by the Ethics Committee of Shanghai Public Health Clinical Center. RNA was extracted from the blood using QIAamp RNABlood Mini Kit (Qiagen), and cDNA was synthesized from RNA using HiScript III RT SuperMix for qPCR (+gDNA wiper) (Vazyme). Subsequently, the target gene was quantified using the 2-△△Ct method using ChamQ Universal SYBR qPCR Master Mix (Vazyme). The specificity of the PCR product was evaluated by melting curve analysis. Real-time PCR was performed using NEDD4, PLTP, RNASEL, SEMA7A, TAPBP, and THBS1 primers. qRT-PCR primers were purchased from Jinweizhi (Suzhou, China), and the primer information is shown in Table 1. The six mRNAs screened in the early stage were validated by qRT-PCR, and the experimental data were statistically analyzed using GraphPad Prism software (version 9.5). The Student's t-test was used to compare continuous data. The validation results are shown in Table 1. Figure 5 shown.

[0042] Table 1 qRT-PCR primers

[0043]

[0044]

[0045] 6. ROC curve analysis of markers for diagnosis of tuberculosis

[0046] SPSS software (version 27.0.1) was used to evaluate the accuracy of the 6-mRNA diagnostic method. P < 0.05 indicated that the difference was statistically significant. ROC curve analysis results showed that ( Figure 6 ), the AUC of the prediction model constructed by the 6-mRNA combination was 0.79, and the 95% confidence interval (CI) was 0.712-0.869.

[0047] The above description is only a preferred embodiment of the present invention and is not any formal or substantial limitation of the present invention. It should be pointed out that a person skilled in the art can make several improvements and supplements without departing from the present invention, and these improvements and supplements should also be regarded as the protection scope of the present invention.

Claims

1. A combined marker for diagnosing pulmonary tuberculosis, characterized in that: Including NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1.

2. Use of the combined marker or its detection reagent according to claim 1 in the preparation of a product for diagnosing pulmonary tuberculosis.

3. The use according to claim 2, characterized in that The products include diagnostic kits, diagnostic systems or devices.

4. The use according to claim 3, characterized in that The product is a kit, which at least includes reagents for detecting the expression amounts of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids.

5. A method for constructing a tuberculosis diagnosis model based on LASSO regression analysis, characterized in that: The construction method includes the following steps: Step 1: Sequencing RNA samples from lung tissue removed during surgery from clinical tuberculosis patients; Step 2: Combine the sequencing results of step 1 with the tuberculosis blood sample miRNA dataset in the GEO database to screen for differentially expressed miRNAs; Step 3: The target mRNA of the differentially expressed miRNA was predicted using the miRTarbase and Tarbase databases, and the intersection with the differentially expressed genes, differentially expressed mRNAs and immune-related genes in blood samples was taken to construct a miRNA-mRNA interaction network, and finally 6 target mRNAs NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 were screened out; Step 4: A tuberculosis diagnostic model was constructed by LASSO regression analysis. The diagnostic model was: RS=NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439; NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 in the diagnostic model respectively represent their mRNA expression levels.

6. A tuberculosis diagnosis kit, characterized in that: The kit at least includes reagents for detecting the expression amounts of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids.

7. The kit according to claim 6, characterized in that The kit at least includes a detection reagent for specifically detecting the mRNA expression of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1, and the detection reagent includes qRT-PCR detection primers as shown in SEQ ID NOs: 3 to 14.

8. The kit according to claim 7, characterized in that The kit also includes qRT-PCR detection primers for detecting the expression of internal reference gene HPRT1 mRNA, and the sequences thereof are shown in SEQ ID NOs: 1-2.

9. The kit according to claim 6, characterized in that The kit is tested according to the following method: the risk score RS of the sample to be tested is calculated by the Lasso regression risk diagnosis model RS=NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439, and whether the sample suffers from tuberculosis is distinguished according to the risk score RS.

10. A tuberculosis diagnosis system, comprising a sample collection device, a sample detection device and a diagnosis device; wherein: The sample collection device is configured as a device for collecting a blood sample of a subject; the sample detection device is a device capable of detecting the expression levels of NEDD4, PLTP, RNASEL, SEMA7A, TAPBP and THBS1 proteins or their nucleic acids in the blood sample; the diagnostic device includes a data acquisition module and a diagnostic module, the data acquisition module is configured to acquire the data detected by the sample detection device, and the diagnostic module is configured to input the data into a diagnostic model: RS=NEDD4*-0.117407108+PLTP*0.00241744+RNASEL*1.097231674+SEMA7A*0.727327735+TAPBP*-0.004076635+THBS1*0.028823439, and judge the diagnostic result according to the diagnostic model output value RS.

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