High sensitivity and specificity of a 5-analyte protein and microRNA biosignature for identification of active tuberculosis

Combining metabolomics and machine learning to discover biomarkers for early-stage breast cancer diagnosis

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.

CXCL9/CXCL10 as biomarkers the monitoring of treatment responses in Pulmonary TB patients: a systematic review and meta-analysis

BACKGROUND

Tuberculosis (TB) remains a persistent threat to global public health and traditional treatment monitoring approaches are limited by their potential for contamination and need for timely evaluation. Therefore, new biomarkers are urgently required for monitoring the treatment efficacy of TB.

METHODS

This study aimed to elucidate the levels of CXCL10 and CXCL9 in pulmonary TB patients who underwent anti-TB treatment. The data was acquired from five databases, including PubMed, Ovid, Web of Science, Embase, and the Cochrane Library. A meta-analysis of CXCL10 data from all time points was conducted. Furthermore, a trend meta-analysis of temporal data of CXCL10 and CXCL9 from multiple time points was also performed.

RESULTS

It was revealed that patients who responded poorly to anti-TB treatment had higher serum levels relative to those who responded well (SMD: 1.23, 95% CI: -0.37-2.84) at the end of intensive treatment (2 months). Furthermore, heterogeneity was observed in these results, which might be because patients with a prior history of TB and different treatment monitoring methods than those selected in this study were also included. The analysis of alterations in CXCL10 and CXCL9 levels since the last collection time points indicated that their levels reduced with time.

CONCLUSION

In summary, the study revealed that reductions in CXCL10 levels during the first two months of anti-TB treatment are correlated with treatment responses. Furthermore, decreasing levels of CXCL9 during the treatment suggest that it may also serve as a biomarker with a similar value to CXCL10. Future in-depth studies are thus warranted to further probe the relevance of CXCL10 and CXCL9 in monitoring the treatment efficacy of TB.

miR-29 as diagnostic biomarkers for tuberculosis: a systematic review and meta-analysis

Background

The timely diagnosis of tuberculosis through innovative biomarkers that do not rely on sputum samples is a primary focus for strategies aimed at eradicating tuberculosis. miR-29 is an important regulator of tuberculosis pathogenesis. Its differential expression pattern in healthy, latent, and active people who develop tuberculosis has revealed its potential as a biomarker in recent studies. Therefore, a systematic review and meta-analysis were performed for the role of miR-29 in the diagnosis of tuberculosis.

Methods

EMBASE, PubMed, CNKI, Web of Science, and Cochrane Library databases were searched utilizing predefined keywords for literature published from 2000 to February 2024.Included in the analysis were studies reporting on the accuracy of miR-29 in the diagnosis of tuberculosis, while articles assessing other small RNAs were not considered. All types of study designs, including case-control, cross-sectional, and cohort studies, were included, whether prospectively or retrospectively sampled, and the quality of included studies was determined utilizing the QUADAS-2 tool. Publication bias was analyzed via the construction of funnel plots. Heterogeneity among studies and summary results for specificity, sensitivity, and diagnostic odds ratio (DOR) are depicted in forest plots.

Results

A total of 227 studies were acquired from the various databases, and 18 articles were selected for quantitative analysis. These articles encompassed a total of 2,825 subjects, primarily sourced from the Asian region. Patient specimens, including sputum, peripheral blood mononuclear cells, cerebrospinal fluid and serum/plasma samples, were collected upon admission and during hospitalization for tuberculosis testing. miR-29a had an overall sensitivity of 82% (95% CI 77, 85%) and an overall specificity of 82% (95% CI 78, 86%) for detecting tuberculosis. DOR was 21 (95% CI 16-28), and the area under the curve was 0.89 (95% CI 0.86, 0.91). miR-29a had slightly different diagnostic efficacy in different specimens. miR-29a showed good performance in both the diagnosis of pulmonary tuberculosis and extrapulmonary tuberculosis. miR-29b and miR-29c also had a good performance in diagnosis of tuberculosis.

Conclusion

As can be seen from the diagnostic performance of miR-29, miR-29 can be used as a potential biomarker for the rapid detection of tuberculosis.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=461107.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.

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.