A biomarker for diagnosing tuberculosis comorbid with diabetes mellitus

Abstract

The application screens a group of biomarkers, including three genes of MMP8, MMP9 and C1QA, finds that taking the genes as biomarkers, and through ROC curve analysis, it is found that whether combined diagnosis or single gene diagnosis has good diagnostic efficiency. Among them, when the three genes are combined for diagnosis, the AUC reaches 0.905; when single gene diagnosis, the performance of MMP8 (AUC=0.93) and C1QA (AUC=0.87) is more prominent.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a biomarker for diagnosing the comorbidity of tuberculosis and diabetes and its application. Background Technology

[0002] Diabetic patients are among the high-risk groups for tuberculosis, and their risk of developing active tuberculosis is approximately three times that of the general population. The increased susceptibility to tuberculosis in diabetic patients stems from immune mechanisms involving defects in bacterial recognition, phagocytic cell activity, and cell activation. These defects lead to impaired production of chemokines and cytokines, which play crucial roles in macrophage activation and the inflammatory response in tuberculosis. Furthermore, impaired immune responses may increase chronic inflammation, all of which promote Mycobacterium tuberculosis infection.

[0003] For patients suspected of having tuberculosis in early screening, further diagnostic testing will be conducted to confirm the diagnosis. Laboratory tests are an important means of diagnosing tuberculosis. A positive result for Mycobacterium tuberculosis is the gold standard for diagnosing tuberculosis. Sputum acid-fast bacillus smears are simple and effective, but the quality of sputum samples is difficult to guarantee, the positive rate is low, and it cannot effectively distinguish between Mycobacterium tuberculosis and non-tuberculous mycobacteria. With the continuous development of molecular biotechnology, new molecular diagnostic technologies have emerged. GeneXpert MTB / RIF, high-sensitivity Mycobacterium tuberculosis and rifampicin resistance gene detection methods, isothermal amplification technology, metagenomic sequencing, and other emerging methods are highly favored in clinical practice due to their high specificity and sensitivity (Research progress on molecular biological diagnostic methods for pediatric tuberculosis [J]. Journal of Tuberculosis and Lung Diseases, 2021, 2(01): 69-72.). In addition, immunological tests are also one of the important means of diagnosing tuberculosis. For diabetes, according to the "WS397-2021 Diabetes Screening and Diagnosis", typical symptoms of diabetes can be used as one of the diagnostic methods. Furthermore, if any of the following criteria are met: (1) FBG ≥ 7.0 mmol / L; (2) OGTT 2-hour blood glucose ≥ 11.1 mmol / L; (3) random blood glucose ≥ 11.1 mmol / L, diabetes can be diagnosed. Currently, venous plasma glucose is mostly used as the standard method for measuring and reporting blood glucose levels.

[0004] Furthermore, at present, the diagnostic criteria for comorbid tuberculosis and diabetes mellitus are that both tuberculosis and diabetes mellitus are diagnosed simultaneously. Diabetes mellitus can be diagnosed through the above-mentioned venous blood sampling method, which is simple and easy to perform. However, tuberculosis is difficult to diagnose due to atypical clinical symptoms and imaging manifestations. Studies have found that patients with comorbid tuberculosis and diabetes mellitus often present with cough and sputum, and the initial symptoms are relatively mild. Symptoms of tuberculosis intoxication such as fever, fatigue, weight loss, and hemoptysis are not obvious and are easily overlooked, which is not conducive to early diagnosis (Clinical characteristics and diagnosis and treatment analysis of pulmonary tuberculosis complicated with diabetes mellitus [J]. Journal of Clinical Pulmonology, 2014, 19(03): 558-560.). Zhao Li et al. found that in patients with simple tuberculosis and comorbid tuberculosis and diabetes, the common sites of lung lesions (anterior segment of the upper lobe and middle lobe of the right lung) and the morphology of lesions (mainly cavitation) were changed (CT imaging characteristics and diagnostic value of tuberculosis and diabetes patients [J]. Chinese Journal of CT and MRI, 2021, 19(07):55-56+96.).

[0005] Although research on the comorbidity of tuberculosis and diabetes has increased in recent years, existing studies mainly focus on the relationship between glycemic control and the efficacy of anti-tuberculosis drug treatment in patients with both tuberculosis and diabetes. Specific biomarkers and early diagnosis for the comorbidity of tuberculosis and diabetes remain lacking. Therefore, achieving early diagnosis and precision treatment of tuberculosis and diabetes has become a significant challenge that urgently needs to be addressed. Summary of the Invention

[0006] This invention has identified a biomarker for diagnosing the comorbidity of tuberculosis and diabetes, which exhibits strong diagnostic efficacy. Based on this, the invention was completed.

[0007] In a first aspect, the present invention provides a combination of biomarkers for diagnosing comorbid tuberculosis and diabetes, the combination of biomarkers comprising: MMP8 , MMP9, C1QA and MMP8+MMP9+C1QA ,in, When patient biological samples MMP8 When the gene expression level is higher than 119.63 pg / mL, the patient is considered to have comorbid tuberculosis and diabetes. When patient biological samples MMP9 When the gene expression level is higher than 123.86 pg / mL, the patient is considered to have comorbid tuberculosis and diabetes. When patient biological samples C1QA When the gene expression level is higher than 1.49 ng / mL, the patient is considered to have comorbid tuberculosis and diabetes. When patient biological samples MMP8+MMP9+C1QAWhen the gene expression levels are higher than 119.63 pg / mL, 123.86 pg / mL, and 1.49 ng / mL, respectively, the patient is considered to have comorbid tuberculosis and diabetes.

[0008] Furthermore, the patient's biological sample was selected from blood.

[0009] Furthermore, the patient's blood sample is at least one of peripheral blood, plasma, and / or serum.

[0010] Secondly, the present invention provides the use of the biomarker combination described in the first aspect in the preparation of a reagent for diagnosing patients with comorbid tuberculosis and diabetes, the reagent being capable of detecting… MMP8 , MMP9, C1QA or MMP8+MMP9+C1QA Gene expression levels.

[0011] Thirdly, the present invention provides a kit for diagnosing patients with comorbid tuberculosis and diabetes, the kit containing a detection... MMP8 , MMP9, C1QA and MMP8+MMP9+C1QA Reagents for gene expression levels.

[0012] Furthermore, the kit may be one or more of the following: nucleic acid detection kit, immunofluorescence kit, and / or gene chip detection kit.

[0013] Furthermore, the diagnostic methods of the kit include one or more of PCR, qPCR, linear probe and / or gene chip methods.

[0014] Beneficial effects This invention screened out a group of biomarkers, including MMP8 , MMP9 and C1QA Three genes were identified, and ROC curve analysis showed that using these genes as biomarkers, both combined and single-gene diagnostics demonstrated excellent diagnostic efficacy. Specifically, the AUC reached 0.905 when all three genes were used for combined diagnosis; for single-gene diagnosis, [the AUC was not specified]. MMP8 (AUC=0.93) and C1QA The results (AUC=0.87) were even more outstanding. These biomarkers, used to diagnose the comorbidity of tuberculosis and diabetes, have advantages such as simple sampling, rapid result reporting, and good patient compliance. Attached Figure Description

[0015] Figure 1This is a differential gene volcano plot, where A: tuberculosis with diabetes and healthy control group; B: tuberculosis with diabetes and diabetic non-tuberculosis group; C: tuberculosis with diabetes and non-diabetic tuberculosis group; D: non-diabetic tuberculosis and healthy control group; E: diabetic non-tuberculosis and healthy control group; F: diabetic non-tuberculosis and non-diabetic tuberculosis group.

[0016] Figure 2 This is a heatmap of differentially expressed genes, where A: tuberculosis with diabetes and healthy control group; B: tuberculosis with diabetes and diabetic non-tuberculosis group; C: tuberculosis with diabetes and non-diabetic tuberculosis group; D: non-diabetic tuberculosis and healthy control group; E: diabetic non-tuberculosis and healthy control group; F: diabetic non-tuberculosis and non-diabetic tuberculosis group.

[0017] Figure 3 This is a Venn diagram of intersecting genes.

[0018] Figure 4 For weighted gene co-expression network analysis, A: Cluster dendrogram: each branch represents a gene, and each module is displayed in a different color; B: Module-trait correlation heatmap: the numbers in the squares represent the correlation coefficient between the module and the disease, and the numbers in parentheses are the p-values; C: Consistent trait gene network diagram and hierarchical cluster diagram; D: MEgeenyellow module correlation scatter plot.

[0019] Figure 5 Gene expression analysis was performed on patients with tuberculosis and diabetes and healthy controls. In this study, A represents the expression level of the C1QA gene; B represents the expression level of the MMP9 gene; and C represents the expression level of the MMP8 gene.

[0020] Figure 6 The graph shows the gene and protein expression of C1QA, MMP8, and MMP9. A is the ELISA detection graph of the three genes; B is the qRT-PCR detection graph; and CD are the Western blot verification graphs.

[0021] Figure 7 To assess the efficacy in diagnosing comorbid tuberculosis and diabetes, A is a nomogram; B is the calibration curve of the nomogram; C is the ROC curve; and D is the DCA curve.

[0022] Figure 8 This is a graph showing the ROC curve analysis.

[0023] Figure 9Clinical laboratory results for patients in different study groups. Note: Continuous variables in the table are expressed as mean ± standard deviation (Mean ± SD), and comparisons between groups were performed using analysis of variance. Statistically significant differences between groups are indicated by different letters (a, b, c, etc.), signifying statistical significance after multiple comparisons (Tukey's HSD test). The same letter indicates no significant difference between groups. The significance level is p < 0.05.

[0024] Figure 10 The expression levels of CD248 and LINc00278 in clinical samples are shown by qRT-PCR, where A represents the expression level of CD248 and B represents the expression level of LINc00278.

[0025] Figure 11 CD248 expression level in clinical samples (ELISA) Detailed Implementation

[0026] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the embodiments described below can be combined with each other as long as they do not conflict with each other.

[0027] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the experimental materials used in the following embodiments can be purchased through conventional commercial channels unless otherwise specified.

[0028] Example 1: Screening for key genes in the comorbidity of tuberculosis and diabetes The clinical sample information of the patients is summarized in Table 1. The samples were whole blood. The patients were divided into four groups: diabetes mellitus with tuberculosis (Diabetes & TB), diabetes mellitus without tuberculosis (Diabetes & non TB), non-diabetic tuberculosis (nonDiabetes & TB), and a healthy control group (non Diabetes & non TB).

[0029] Table 1. Clinical characteristics of patients Differential gene analysis was performed on the expression data using the limma package in R, with the screening thresholds set as follows: |log FoldChange (logFC) > 1, False Discovery Rate (FDR) < 0.05. In the diabetes mellitus with tuberculosis group and the healthy control group, a total of 271 differentially expressed genes were identified, including 71 upregulated genes and 200 downregulated genes. Heatmaps and volcano plots are shown below. Figure 1 A and Figure 2 A. In the tuberculosis-associated diabetes group and the diabetes-non-tuberculosis group, a total of 191 differentially expressed genes were screened, including 90 upregulated genes and 100 downregulated genes. Figure 1 B and Figure 2 B. A total of 127 differentially expressed genes were identified between the tuberculosis-associated diabetes group and the non-diabetic tuberculosis group, including 6 upregulated genes and 121 downregulated genes. Figure 1 C and Figure 2 C. In a non-diabetic tuberculosis patient and healthy control group, a total of 85 differentially expressed genes were identified, including 48 upregulated genes and 37 downregulated genes. Figure 1 D and Figure 2 D. In a non-tuberculous diabetic control group and a healthy control group, a total of 28 differentially expressed genes were screened, including 16 upregulated genes and 12 downregulated genes, such as... Figure 1 E and Figure 2 E. In the diabetic non-tuberculosis group and the non-diabetic tuberculosis group, a total of 212 differentially expressed genes were screened, including 149 upregulated genes and 63 downregulated genes, such as... Figure 1 F and Figure 2 F. To further integrate differentially expressed genes, Venn diagrams (see...) were used. Figure 3 Intersection analysis was performed, and 38 differentially expressed genes were successfully screened out.

[0030] Key genes for Diabetes & TB were identified using WGCNA. A gene clustering dendrogram was generated using a soft threshold power of 8 (see [link to WGCNA]). Figure 4 A). Through analysis of the dataset, 21 gene modules were successfully identified, and a heatmap of the correlation between the disease and the modules was plotted using Spearman's correlation coefficient (see [link]). Figure 4 B). The results show that the MEgreenyellow module has the best correlation between the PTB and PTB & T2DM modules. The MEgreenyellow module shows PTB (coefficient: 0.63; p <0.001) and PTB & T2DM (coefficient: 0.63; p <0.001; Figure 4 There is a significant correlation between B) and the results of consistent feature gene networks and hierarchical clustering (see B). Figure 4 C). Pearson correlation analysis was used to study the relationship between characteristic gene modules and clinical characteristics. The results showed that the genetic significance of the MEgreenyellow module was highly significantly correlated with PTB & T2DM (coefficient: 0.63). p <0.0001; see Figure 4 D). The differentially expressed genes obtained above are then merged with the genes in this module, and the intersection is taken to obtain 5 overlapping genes: C1QA , MMP8 , MMP9 , CD248 and LINC00278 .

[0031] In different clinical groups C1QA , MMP8 , MMP9 , CD248 and LINC00278 There are differences in the relative expression levels of genes. Among them, CD248 and LINC00278 The expression of genes varied little across groups, and the differences between groups did not reach statistical significance (see [link to relevant documentation]). Figure 10 This suggests that its expression stability at the transcriptional level is poor, making it difficult to use as a reliable indicator to distinguish different disease states.

[0032] To further verify the expression of CD248 at the protein level, the protein concentration of CD248 in each group of clinical samples was detected by ELISA. The results are as follows: Figure 11 As shown, there was almost no difference in the protein expression level of CD248 among the groups, indicating that the slight difference at the transcriptional level failed to translate into a significant change at the protein level, further weakening its feasibility and reliability as a clinical diagnostic biomarker.

[0033] In summary, although CD248 and LINC00278 showed some correlation in the initial screening, they performed poorly in subsequent multidimensional validation, lacking the stability, reproducibility, and biological mechanism support required for diagnostic markers. In contrast, C1QA, MMP8, and MMP9 not only showed significant differences at the transcriptional level, but also clearly participated in key pathological processes such as immune inflammatory responses, complement activation, and matrix remodeling in previous functional enrichment analyses, demonstrating more explicit biological significance and diagnostic potential.

[0034] To further verify C1QA, MMP8 and MMP9 The expression level under comorbid conditions, in the tuberculosis and diabetes group and the healthy control group. C1QA, MMP8 and MMP9 Expression level analysis was performed. Results showed that in comorbid patients... C1QA MMP8 and MMP9 All significantly increased ( p < 0.05 (see) Figure 5 A, 5B, 5C). Further analysis using ELISA to detect the cutoff value when a single gene is used as a diagnostic biomarker... C1QA , MMP8 as well as MMP9 Comorbidities in patients can be identified when the concentrations in biological samples are higher than 1.49 ng / mL, 119.63 pg / mL, and 123.86 pg / mL, respectively.

[0035] Further through C1QA , MMP8 and MMP9 The expression of these values ​​was used to construct a nomogram for diagnosing the comorbidity of tuberculosis and diabetes (see [link]). Figure 7 A), in addition, through the calibration curve, ROC curve, and DCA curve of the nomogram (see A), Figure 7 As shown in B, 7C, and 7D, the model has excellent stability, and clinically, the probability of comorbidity in patients can be determined by gene expression.

[0036] MMP9 , MMP8 and C1QA The gene expression levels of the three genes showed significant differences in the comorbidity of tuberculosis and diabetes, which can serve as biomarkers for the clinical diagnosis of comorbidity of tuberculosis and diabetes.

[0037] Example 2: Clinical validation of the diagnostic efficacy of biomarkers in patients with comorbidities Study participants were recruited from inpatients and outpatients at Taicang First People's Hospital, Suzhou Fifth People's Hospital, and Shenzhen Third People's Hospital, including: 39 healthy controls (HC), 56 patients with pulmonary tuberculosis (PTB), 45 patients with type 2 diabetes mellitus (T2DM), and 27 patients with pulmonary tuberculosis with type 2 diabetes mellitus (PTB & T2DM). The demographic and clinical baseline characteristics of the participants are shown in Table 2. Patients included in this example were independent of those in Example 1.

[0038] Clinical laboratory tests were performed on the four groups of patients, and the differences were compared. The results are as follows: Figure 9 As shown. Among them, the neutrophil count was 4.32 ± 1.69 × 10⁻⁶. 9 ( / L) and monocyte count (0.59±0.23×10) 9The levels of fasting blood glucose (μmol / L) were significantly elevated in the PTB & T2DM groups. Regarding metabolic indicators, fasting blood glucose was significantly elevated in both the T2DM and PTB & T2DM groups (p<0.05). In terms of liver function indicators, total bilirubin was significantly lower in the PTB & T2DM groups (9.73±5.05 μmol / L). Alanine aminotransferase (ALT) was 26.98±25.86 U / L in the PTB & T2DM group, significantly higher than in the PTB group (18.33±14.78 U / L), while aspartate aminotransferase (AST) was 17.98±8.93 U / L in the PTB & T2DM group, significantly lower than in other groups. Alkaline phosphatase (107.34±48.73 U / L) and gamma-glutamyl transferase (49.02±38.37 U / L) were significantly elevated in the PTB & T2DM group, higher than in other groups.

[0039] Table 2. General baseline characteristics of patients in different study groups Note: Categorical variables (gender, smoking and drinking history, cardiovascular disease history, and hypertension history) in the table are expressed as "frequency (percentage)" and the significance of differences in categorical variables between groups is assessed using the Chi-Square Test or Fisher's exact test. Continuous variables (age, weight, height, BMI, systolic blood pressure, diastolic blood pressure, etc.) are expressed as "mean ± standard deviation" (Mean ± SD) and differences between groups are compared using ANOVA. The significance level is [not specified in the original text]. p < 0.05.

[0040] The peripheral blood samples of the above patients were used to assess MMP8 , C1QA and MMP9 Expression levels of three key genes. qRT-PCR analysis showed that... C1QA , MMP8 and MMP9 The relative expression level of [a substance] was significantly upregulated in the PTB, T2DM, and PTB & T2DM groups, and the mRNA expression level in the PTB & T2DM group was significantly higher than that in the PTB and T2DM groups. p <0.001) (see Figure 6 A). Additionally, the peripheral blood biological samples from the aforementioned patients... MMP8 , C1QA and MMP9 The content of [specific substances] was quantitatively detected by qRT-PCR, and the results are shown in Table 3. [The content of these substances in biological samples from patients with comorbid tuberculosis and diabetes was...] MMP8 , C1QA and MMP9 The levels of these substances were all higher than those in the biological samples of the healthy control group, the tuberculosis patient group, and the diabetes patient group. MMP8 , C1QA and MMP9 The content of [specific marker] was [specifically, a concentration of [specific marker]]. This result verifies the application of the biomarker provided by this invention in patients with comorbid tuberculosis and diabetes, i.e., when [specific marker] in the patient's biological sample [specifically, a concentration of [specific marker]]. C1QA , MMP8 as well as MMP9 When the gene content is higher than 1.49 ng / mL, 119.63 pg / mL, and 123.86 pg / mL, the patient can be diagnosed with comorbid tuberculosis and diabetes.

[0041] Table 3. Biological samples from patients in different study groups MMP8 , C1QA and MMP9 Gene expression level Note: Continuous variables in the table are expressed as "Mean ± Standard Deviation" (Mean ± SD), and comparisons between groups were performed using analysis of variance. Statistically significant differences between groups are indicated by different letters (a, b, c, etc.), signifying statistical significance after multiple comparisons (Tukey's HSD test). The same letter indicates no significant difference between groups. The significance level is [not specified in the original text]. p <0.05.

[0042] Western blot results further validated the above differences in the PTB & T2DM groups. MMP9 , MMP8 and C1QA The relative expression level of the protein in this group was significantly higher than that in the other groups. p <0.001)( Figure 6 BC).

[0043] Extracting key genes from sample expression data MMP8 , C1QA and MMP9 Expression values ​​were measured. ROC curves for single genes and multi-gene combinations were constructed using the pROC library in R. The diagnostic capabilities of three candidate genes in differentiating specific disease states were systematically evaluated, and the results are shown below. Figure 8 As shown. ROC analysis was performed on three genes in the tuberculosis and diabetes comorbidity group and the healthy control group. The combined model of the three genes had an AUC value of 0.84. Among the single genes, MMP8 The AUC is 0.93. MMP8 The AUC is 0.79. C1QA The AUC is 0.87.

[0044] Comprehensive analysis shows that, C1QA , MMP8 and MMP9These genes were significantly upregulated in patients with both PTB and T2DM, especially in the PTB & T2DM group. This indicates that these three genes are key characteristic genes of differential expression in the comorbidity of tuberculosis and diabetes, and can serve as biomarkers for the clinical diagnosis of comorbidity of tuberculosis and diabetes.

Claims

1. A combination of biomarkers for diagnosing comorbid tuberculosis and diabetes, the combination of biomarkers comprising: MMP8 , MMP9, C1QA and MMP8+MMP9+C1QA ,in, When the patient's biological sample MMP8 When the gene expression level is higher than 119.63 pg / mL, the patient is considered to have comorbid tuberculosis and diabetes. When the patient's biological sample MMP9 When the gene expression level is higher than 123.86 pg / mL, the patient is considered to have comorbid tuberculosis and diabetes. When the patient's biological sample C1QA When the gene expression level is higher than 1.49 ng / mL, the patient is considered to have comorbid tuberculosis and diabetes. When the patient's biological sample MMP8+MMP9+C1QA When the gene expression levels are higher than 119.63 pg / mL, 123.86 pg / mL, and 1.49 ng / mL, respectively, the patient is considered to have comorbid tuberculosis and diabetes.

2. The combination of biomarkers as described in claim 1, wherein the patient's biological sample is selected from blood.

3. The combination of biomarkers as described in claim 1, wherein the patient's biological sample is at least one of peripheral blood, plasma, and / or serum.

4. The use of the biomarker as described in claim 1 in the preparation of a reagent for diagnosing patients with comorbid tuberculosis and diabetes, wherein the reagent is for detecting... MMP8 , MMP9, C1QA or MMP8+MMP9+C1QA Reagents for gene expression levels.

5. A kit for diagnosing patients with comorbid tuberculosis and diabetes, said kit containing a detection... MMP8 , MMP9, C1QA and MMP8+MMP9+C1QA Reagents for gene expression levels.

6. The kit as described in claim 5, wherein the kit may be one or more of a nucleic acid detection kit, an immunofluorescence kit, and / or a gene chip detection kit.

7. The kit according to claim 5, wherein the diagnostic method of the kit includes one or more of PCR, qPCR, linear probe and / or gene chip methods.

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