Long Noncoding RNA and Predictive Model To Improve Diagnosis of Clinically Diagnosed Pulmonary Tuberculosis

Logistic regression and other statistical tools in diagnostic biomarker studies

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

The role of Mycobacterium tuberculosis exosomal miRNAs in host pathogen cross-talk as diagnostic and therapeutic biomarkers

Tuberculosis (TB) is a global threat, affecting one-quarter of the world's population. The World Health Organization (WHO) reports that 6 million people die annually due to chronic illnesses, a statistic that includes TB-related deaths. This high mortality is attributed to factors such as the emergence of drug-resistant strains and the exceptional survival mechanisms of Mycobacterium tuberculosis (MTB). Recently, microRNAs (miRNAs) have garnered attention for their crucial role in TB pathogenesis, surpassing typical small RNAs (sRNA) in their ability to alter the host's immune response. For instance, miR-155, miR-125b, and miR-29a have been identified as key players in the immune response to MTB, particularly in modulating macrophages, T cells, and cytokine production. While sRNAs are restricted to within cells, exo-miRNAs are secreted from MTB-infected macrophages. These exo-miRNAs modify the function of surrounding cells to favor the bacterium, perpetuating the infection cycle. Another significant aspect is that the expression of these miRNAs affects specific genes and pathways involved in immune functions, suggesting their potential use in diagnosing TB and as therapeutic targets. This review compiles existing information on the immunomodulatory function of exosomal miRNAs from MTB, particularly focusing on disease progression and the scientific potential of this approach compared to existing diagnostic techniques. Thus, the aim of the study is to understand the role of exosomal miRNAs in TB and to explore their potential for developing novel diagnostic and therapeutic methods.

Review on Long Non-Coding RNAs as Biomarkers and Potentially Therapeutic Targets for Bacterial Infections

The confrontation between humans and bacteria is ongoing, with strategies for combating bacterial infections continually evolving. With the advancement of RNA sequencing technology, non-coding RNAs (ncRNAs) associated with bacterial infections have garnered significant attention. Recently, long ncRNAs (lncRNAs) have been identified as regulators of sterile inflammatory responses and cellular defense against live bacterial pathogens. They are involved in regulating host antimicrobial immunity in both the nucleus and cytoplasm. Increasing evidence indicates that lncRNAs are critical for the intricate interactions between host and pathogen during bacterial infections. This paper emphatically elaborates on the potential applications of lncRNAs in clinical hallmarks, cellular damage, immunity, virulence, and drug resistance in bacterial infections in greater detail. Additionally, we discuss the challenges and limitations of studying lncRNAs in the context of bacterial infections and highlight clear directions for this promising field.

Neutrophil lncRNA ZNF100-6:2 is a potential diagnostic marker for active pulmonary tuberculosis

Active pulmonary tuberculosis (PTB) poses challenges in rapid diagnosis within complex clinical conditions. Given the close association between neutrophils and tuberculosis, we explored differentially expressed long non-coding RNAs (lncRNAs) in neutrophils as potential molecular markers for diagnosing active PTB. We employed a gene microarray to screen for lncRNA alterations in neutrophil samples from three patients with active PTB and three healthy controls. The results revealed differential expression of 1457 lncRNAs between the two groups, with 916 lncRNAs upregulated and 541 lncRNAs down-regulated in tuberculosis patients. Subsequent validation tests demonstrated down-regulation of lncRNA ZNF100-6:2 in patients with active PTB, which was restored following anti-tuberculosis treatment. Our findings further indicated a high diagnostic potential for lncRNA ZNF100-6:2, as evidenced by an area under the receiver operating characteristic (ROC) curve of 0.9796 (95% confidence interval: 0.9479 to 1.000; P < 0.0001). This study proposes lncRNA ZNF100-6:2 as a promising and novel diagnostic biomarker for active PTB.

The sound of silent RNA in tuberculosis and the lncRNA role on infection

Tuberculosis (TB) is one of the leading causes of death worldwide, and Diabetes Mellitus is one of the major comorbidities (TB/DM) associated with the disease. A total of 103 differentially expressed ncRNAs have been identified in the TB and TB/DM comparisons. A machine learning algorithm was employed to identify the most informative lncRNAs: ADM-DT, LINC02009, LINC02471, SOX2-OT, and GK-AS1. These lncRNAs presented substantial accuracy in classifying TB from HC (AUCs >0.85) and TB/DM from HC (AUCs >0.90) in the other three countries. Genes with significant correlations with the five lncRNAs enriched common pathways in Brazil and India for both TB and TB/DM. This suggests that lncRNAs play an important role in the regulation of genes related to the TB immune response.

Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma

High-grade neuroblastoma (HG-NB) exhibits a significantly diminished survival rate in comparison to low-grade neuroblastoma (LG-NB), primarily attributed to the mechanism of HG-NB is unclear and the lacking effective therapeutic targets and diagnostic model. Therefore, the current investigation aims to study the dysregulated network between HG-NB and LG-NB based on transcriptomics and metabolomics joint analysis. Meanwhile, a risk diagnostic model to distinguish HG-NB and LG-NB was also developed. Metabolomics analysis was conducted using plasma samples obtained from 48 HG-NB patients and 36 LG-NB patients. A total of 39 metabolites exhibited alterations, with 20 showing an increase and 19 displaying a decrease in HG-NB. Additionally, transcriptomics analysis was performed on NB tissue samples collected from 31 HG-NB patients and 20 LG-NB patients. Results showed that a significant alteration was observed in a total of 1,199 mRNAs in HG-NB, among which 893 were upregulated while the remaining 306 were downregulated. In particular, the joint analysis of both omics data revealed three aberrant pathways, namely the cAMP signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway, which were found to be associated with cell death. Notably, a diagnostic model for HG-NB risk classification was developed based on the genes MGST1, SERPINE1, and ERBB3 with an area under the receiver operating characteristic curve of 0.915. In the validation set, the sensitivity and specificity were determined to be 75.0% and 80.0%, respectively.

Long non-coding RNA expression in PBMCs of patients with active pulmonary tuberculosis

Purpose

Mycobacterium tuberculosis (Mtb) infection is the primary cause of the chronic infectious illness tuberculosis (TB). Long non-coding RNAs (lncRNAs) are functional RNA molecules that cannot be translated into proteins and play a crucial role in regulating the immune system's innate and adaptive responses. It has been demonstrated that the dysregulation of lncRNA expression is associated with various human diseases. However, the mechanism underlying the involvement of so many lncRNAs in the immune response to TB infection remains unclear. The objective of our current study was to identify a number of significantly differentially expressed lncRNAs in peripheral blood mononuclear cells (PBMCs) from TB patients and to select the most indicative lncRNAs as potential biomarkers for active pulmonary tuberculosis.

Methods

Microarray analysis was performed to determine the lncRNA and mRNA expression profiles in TB patients using a case-control model. The differentially expressed lncRNAs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to investigate potential roles and pathways associated with the pathogenesis of TB infection, and to screen lncRNAs specifically linked to TB infection. Using real-time fluorescence quantitative PCR (QRT-PCR), specific lncRNAs were identified in TB patients and latent infections.

Results

Our findings revealed that various signaling pathways were differentially expressed in TB-infected individuals, suggesting a potential role for lncRNAs in the immunological responses driven by TB infection. This study provides crucial guidelines for future functional research. Upregulated lncRNAs were mainly enriched in Neutrophil extracellular trap formation and Chemokine signaling pathways, while downregulated lncRNAs were enriched in Neuroactive ligand-receptor interaction and Cushing syndrome in TB PBMCs. Furthermore, we found that lnc-XPNPEP1-5, lnc-CASKIN2-2, lnc-HSPA13-6, lnc-CLIC5-1, and LINC02502 were significantly downregulated in TB-infected patients, while LINC00528, lnc-SLC45A4-3, and LINC00926 were significantly upregulated in TB patients and latent infections. These eight lncRNAs, identified as novel biological marker candidates for diagnosing TB infection, were validated by real-time fluorescence quantitative PCR (QRT-PCR).

Conclusion

The abnormally expressed lncRNAs identified in this research may provide crucial information for understanding the pathophysiological characteristics of TB patients and the dysfunction of PBMCs. Our findings reveal potential targets for early TB diagnosis and therapy, as well as offer new insights into the mechanisms underlying TB infection.

Advances of Long Non-Coding RNAs as Potential Biomarkers for Tuberculosis: New Hope for Diagnosis?

Tuberculosis (TB), one of the top ten causes of death globally induced by the infection of Mycobacterium tuberculosis (Mtb), remains a grave public health issue worldwide. With almost one-third of the world's population getting infected by Mtb, between 5% and 10% of these infected individuals are predicted to develop active TB disease, which would not only result in severe tissue damage and necrosis, but also pose serious threats to human life. However, the exact molecular mechanisms underlying the pathogenesis and immunology of TB remain unclear, which significantly restricts the effective control of TB epidemics. Despite significant advances in current detection technologies and treatments for TB, there are still no appropriate solutions that are suitable for simultaneous, early, rapid, and accurate screening of TB. Various cellular events can perturb the development and progression of TB, which are always associated with several specific molecular signaling events controlled by dysregulated gene expression patterns. Long non-coding RNAs (lncRNAs), a kind of non-coding RNA (ncRNA) with a transcript of more than 200 nucleotides in length in eukaryotic cells, have been found to regulate the expression of protein-coding genes that are involved in some critical signaling events, such as inflammatory, pathological, and immunological responses. Increasing evidence has claimed that lncRNAs might directly influence the susceptibility to TB, as well as the development and progression of TB. Therefore, lncRNAs have been widely expected to serve as promising molecular biomarkers and therapeutic targets for TB. In this review, we summarized the functions of lncRNAs and their regulatory roles in the development and progression of TB. More importantly, we widely discussed the potential of lncRNAs to act as TB biomarkers, which would offer new possibilities in novel diagnostic strategy exploration and benefit the control of the TB epidemic.

From tuberculosis bedside to bench: UBE2B splicing as a potential biomarker and its regulatory mechanism

Alternative splicing (AS) is an important approach for pathogens and hosts to remodel transcriptome. However, tuberculosis (TB)-related AS has not been sufficiently explored. Here we presented the first landscape of TB-related AS by long-read sequencing, and screened four AS events (S100A8-intron1-retention intron, RPS20-exon1-alternaitve promoter, KIF13B-exon4-skipping exon (SE) and UBE2B-exon7-SE) as potential biomarkers in an in-house cohort-1. The validations in an in-house cohort-2 (2274 samples) and public datasets (1557 samples) indicated that the latter three AS events are potential promising biomarkers for TB diagnosis, but not for TB progression and prognosis. The excellent performance of classifiers further underscored the diagnostic value of these three biomarkers. Subgroup analyses indicated that UBE2B-exon7-SE splicing was not affected by confounding factors and thus had relatively stable performance. The splicing of UBE2B-exon7-SE can be changed by heat-killed mycobacterium tuberculosis through inhibiting SRSF1 expression. After heat-killed mycobacterium tuberculosis stimulation, 231 ubiquitination proteins in macrophages were differentially expressed, and most of them are apoptosis-related proteins. Taken together, we depicted a global TB-associated splicing profile, developed TB-related AS biomarkers, demonstrated an optimal application scope of target biomarkers and preliminarily elucidated mycobacterium tuberculosis-host interaction from the perspective of splicing, offering a novel insight into the pathophysiology of TB.

Immune regulation and emerging roles of noncoding RNAs in Mycobacterium tuberculosis infection

Tuberculosis, caused by Mycobacterium tuberculosis, engenders an onerous burden on public hygiene. Congenital and adaptive immunity in the human body act as robust defenses against the pathogens. However, in coevolution with humans, this microbe has gained multiple lines of mechanisms to circumvent the immune response to sustain its intracellular persistence and long-term survival inside a host. Moreover, emerging evidence has revealed that this stealthy bacterium can alter the expression of demic noncoding RNAs (ncRNAs), leading to dysregulated biological processes subsequently, which may be the rationale behind the pathogenesis of tuberculosis. Meanwhile, the differential accumulation in clinical samples endows them with the capacity to be indicators in the time of tuberculosis suffering. In this article, we reviewed the nearest insights into the impact of ncRNAs during Mycobacterium tuberculosis infection as realized via immune response modulation and their potential as biomarkers for the diagnosis, drug resistance identification, treatment evaluation, and adverse drug reaction prediction of tuberculosis, aiming to inspire novel and precise therapy development to combat this pathogen in the future.

Long Non-coding RNAs in Tuberculosis: From Immunity to Biomarkers

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is the leading lethal infectious disease with 1.3 million deaths in 2020. Despite significant advances have been made in detection techniques and therapeutic approaches for tuberculosis, no suitable diagnostic tools are available for early and precise screening. Many studies have reported that Long non-coding RNAs (lncRNAs) play a regulatory role in gene expression in the host immune response against Mtb. Dysregulation of lncRNAs expression patterns associated with immunoregulatory pathways arose in mycobacterial infection. Meanwhile, host-induced lncRNAs regulate antibacterial processes such as apoptosis and autophagy to limit bacterial proliferation. In this review, we try to summarize the latest reports on how dysregulated expressed lncRNAs influence host immune response in tuberculosis infection. We also discuss their potential clinical prospects for tuberculosis diagnosis and development as molecular biomarkers.

High miRNA-378 expression has high diagnostic values for pulmonary tuberculosis and predicts adverse outcomes

BACKGROUND

Pulmonary tuberculosis (TB) is a chronic infectious disease. microRNA (miR)-378 is involved in TB diagnosis. This study explored the effects of miR-378 on TB patients.

METHODS

A total of 126 TB patients were selected, including 63 active TB and 63 latent TB, with 62 healthy subjects as controls. Serum miR-378 expression was detected. The diagnostic value of miR-378 in TB was analyzed using the ROC curve. Immune inflammatory factor levels were detected and their correlations with miR-378 expression were analyzed. The drug resistance of active TB patients was recorded after standard treatment. miR-378 expression in drug-resistant TB patients was detected. The effects of miR-378 on adverse outcome incidence were analyzed.

RESULTS

miR-378 expression was highly expressed in TB and the expression was higher in the active group than the latent group. Serum miR-378 expression > 1.490 had high sensitivity and specificity in TB diagnosis. miR-378 expression was correlated with TB clinical indexes. IL-4, IL-6, and IL-1β levels were highly expressed, while IFN-γ, TNF-α, and IL-12 levels were lowly expressed in TB patients. Serum miR-378 level in the active group was positively correlated with serum IL-4, IL-6, and IL-1β, and negatively correlated with serum IFN-γ, TNF-α, and IL-12 concentrations. miR-378 expression was downregulated in the TB treated, single (SDR TB) and multi-drug resistance (MDR TB) groups, the miR-378 expression in SDR TB and MDR TB groups was higher than the TB treated group and lower in the SDR TB group than the MDR TB group. High miR-378 expression predicted higher adverse outcome incidence.

CONCLUSIONS

High miR-378 expression assisted TB diagnosis and predicted adverse outcomes.

Screening and identification of differentially expressed long non-coding RNAs in multidrug-resistant tuberculosis

BACKGROUND

Efforts to eradicate tuberculosis are largely threatened by drug-resistant tuberculosis, particularly, multidrug-resistant tuberculosis (MDR-TB). Screening and identification potential biomarkers for MDR-TB is crucial to diagnose early and reduce the incidence of MDR-TB.

METHODS

To screen the differentially expressed long non-coding RNAs in MDR-TB, the lncRNA and mRNA expression profiles in serum derived from healthy controls (HCs), individuals with MDR-TB and drug-sensitive tuberculosis (DS-TB) were analyzed by microarray assay and 10 lncRNAs were randomly selected for further validation by reverse transcription-quantitative real-time PCR(RT-qPCR). The biological functions of differentially expressed mRNAs as well as relationships between genes and signaling pathways were investigated using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), respectively.

RESULTS

A total of 353 differentially expressed lncRNAs (312 upregulated) and 202 mRNAs (99 upregulated) were found in the MDR-TB group compared to HCs. And compared with the DS-TB group, 442 differentially expressed lncRNAs (115 upregulated) and 190 mRNAs (87 upregulated) were found in the MDR-TB group. The expression levels of lncRNA n335659 were found to differ significantly between each group by RT-qPCR. Compared with DS-TB group, the GO analysis showed that the differential mRNAs were mainly enriched in the processes associated with the detection of the chemical stimulus, the regulation of mRNA metabolic process and neutrophil activation in the MDR-TB group; the KEGG analysis indicated that the differential mRNAs between DS-TB and MDR-TB were mainly enriched in proteasome and Notch signaling pathway, which might reveal a fraction of the mechanism of MDR-TB. The discovery of the serum lncRNA n335659 might serve as a potential biomarker for MDR-TB and Notch signaling pathway provided a new clue for the investigation of the pathological mechanism of MDR-TB.

A LncRNA-miRNA-mRNA ceRNA regulatory network based tuberculosis prediction model

The high incidence of tuberculosis (TB) has brought serious social burdens and it is urgent to explore the mechanism of TB development. This study was conducted to analyze the role of lncRNA-miRNA-mRNA regulatory network and its contained nodes involved in TB to identify crucial biomarkers for early diagnosis of TB. Long-noncoding RNAs (lncRNAs), messenger RNA (mRNAs) and microRNAs (miRNAs) expression profiles of TB patients and healthy individuals were downloaded from the GSE34608 dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identified the key modules related to TB and the highly related mRNA-lncRNA pair in the module. Based on highly related mRNAs and lncRNAs in greenyellow module, lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network was constructed. The DE-mRNAs in the network were functionally enriched with Gene ontology (GO) and Gene set enrichment analysis (GSEA). Least absolute shrinkage and selection operator (LASSO) algorithm and receiver operating characteristic curve (ROC) were used to construct and evaluate the prediction model of TB. We identified 3267 DE-mRNAs, 484 DE-lncRNAs and 69 DE-miRNAs between the TB and healthy subjects, from which 8 DE-mRNAs, 14 DE-lncRNAs and 3 DE-miRNAs were used to construct the ceRNA network. The genes contained in the ceRNA network were mainly enriched in neutrophil mediated immune response, including neutrophil activation, degradation and signal transduction. ROC analysis revealed that has-miR-140-5p, has-miR-142-3p and the LASSO cox prediction model based on HMGA1 and CAPN1 have potential value for forecasting TB (AUC > 0.7). Hence, our study provides a new perspective from the lncRNA-miRNA-mRNA ceRNA regulatory network for TB diagnosis and treatment.

The Role of microRNAs and Long Non-Coding RNAs in the Regulation of the Immune Response to Mycobacterium tuberculosis Infection

Non-coding RNAs have emerged as critical regulators of the immune response to infection. MicroRNAs (miRNAs) are small non-coding RNAs which regulate host defense mechanisms against viruses, bacteria and fungi. They are involved in the delicate interplay between Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), and its host, which dictates the course of infection. Differential expression of miRNAs upon infection with M. tuberculosis, regulates host signaling pathways linked to inflammation, autophagy, apoptosis and polarization of macrophages. Experimental evidence suggests that virulent M. tuberculosis often utilize host miRNAs to promote pathogenicity by restricting host-mediated antibacterial signaling pathways. At the same time, host- induced miRNAs augment antibacterial processes such as autophagy, to limit bacterial proliferation. Targeting miRNAs is an emerging option for host-directed therapies. Recent studies have explored the role of long non-coding RNA (lncRNAs) in the regulation of the host response to mycobacterial infection. Among other functions, lncRNAs interact with chromatin remodelers to regulate gene expression and also function as miRNA sponges. In this review we attempt to summarize recent literature on how miRNAs and lncRNAs are differentially expressed during the course of M. tuberculosis infection, and how they influence the outcome of infection. We also discuss the potential use of non-coding RNAs as biomarkers of active and latent tuberculosis. Comprehensive understanding of the role of these non-coding RNAs is the first step towards developing RNA-based therapeutics and diagnostic tools for the treatment of TB.

Integrating serum microRNAs and electronic health records improved the diagnosis of tuberculosis

Background

To verify the differential expression of miR‐30c and miR‐142‐3p between tuberculosis patients and healthy controls and to investigate the performance of microRNA (miRNA) and subsequently models for the diagnosis of tuberculosis (TB).

Methods

We followed up 460 subjects suspected of TB, and finally enrolled 132 patients, including 60 TB patients, 24 non‐TB disease controls (TB‐DCs), and 48 healthy controls (HCs). The differential expression of miR‐30c and miR‐142‐3p in serum samples of the TB patients, TB‐DCs, and HCs were identified by reverse transcription–quantitative real‐time PCR. Diagnostic models were developed by analyzing the characteristics of miRNA and electronic health records (EHRs). These models evaluated by the area under the curves (AUC) and calibration curves were presented as nomograms.

Results

There were differential expression of miR‐30c and miR‐142‐3p between TB patients and HCs (p < 0.05). Individual miRNA has a limited diagnostic value for TB. However, diagnostic performance has been both significantly improved when we integrated miR‐142‐3p and ordinary EHRs to develop two models for the diagnosis of tuberculosis. The AUC of the model for distinguishing tuberculosis patients from healthy controls has increased from 0.75 (95% CI: 0.66–0.84) to 0.96 (95% CI: 0.92–0.99) and the model for distinguishing tuberculosis patients from non‐TB disease controls has increased from 0.67 (95% CI: 0.55–0.79) to 0.94 (95% CI: 0.89–0.99).

Conclusions

Integrating serum miR‐142‐3p and EHRs is a good strategy for improving TB diagnosis.

LncExpDB: an expression database of human long non-coding RNAs

Expression profiles of long non-coding RNAs (lncRNAs) across diverse biological conditions provide significant insights into their biological functions, interacting targets as well as transcriptional reliability. However, there lacks a comprehensive resource that systematically characterizes the expression landscape of human lncRNAs by integrating their expression profiles across a wide range of biological conditions. Here, we present LncExpDB (https://bigd.big.ac.cn/lncexpdb), an expression database of human lncRNAs that is devoted to providing comprehensive expression profiles of lncRNA genes, exploring their expression features and capacities, identifying featured genes with potentially important functions, and building interactions with protein-coding genes across various biological contexts/conditions. Based on comprehensive integration and stringent curation, LncExpDB currently houses expression profiles of 101 293 high-quality human lncRNA genes derived from 1977 samples of 337 biological conditions across nine biological contexts. Consequently, LncExpDB estimates lncRNA genes' expression reliability and capacities, identifies 25 191 featured genes, and further obtains 28 443 865 lncRNA-mRNA interactions. Moreover, user-friendly web interfaces enable interactive visualization of expression profiles across various conditions and easy exploration of featured lncRNAs and their interacting partners in specific contexts. Collectively, LncExpDB features comprehensive integration and curation of lncRNA expression profiles and thus will serve as a fundamental resource for functional studies on human lncRNAs.

Monokine induced by gamma interferon for detecting pulmonary tuberculosis: A diagnostic meta-analysis

BACKGROUNDS

Pulmonary tuberculosis (PTB) is an oldest-known and most formidable disease. The standard microbiology culture is time-wasting. Monokine induced by gamma interferon (MIG) has been reported as a new biomarker to auxiliarily detect PTB. In our study, we used meta-analysis to assess the diagnostic value of MIG for PTB.

METHODS

PubMed, Embase, Web of Science, and Cochrane Library were searched for relative records up to April 2, 2020. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve, and summary receiver operating characteristic curve were estimated.

RESULTS

Eight studies including 1487 participants were included. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of MIG for detecting PTB were 84%, 84%, 5.19, and 0.19, respectively. The diagnostic odds ratio and area under the curve were 27.88 and 0.90, respectively, indicating a good diagnostic ability of MIG. Meta-regression analysis showed that human immunodeficiency virus status might be a source of heterogeneity (P = .02).

CONCLUSIONS

Our results showed that MIG had a good diagnostic value for PTB.