Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children

Abstract

Background Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients. Method The expression profiles of cuproptosis regulators and immune characteristics in pediatric patients with ATB and LTBI were analyzed based on GSE39939 downloaded from the Gene Expression Omnibus. From the 52 ATB samples, we investigated the molecular subtypes based on differentially expressed cuproptosis-related genes (DE-CRGs) via consensus clustering and related immune cell infiltration. Subtype-specific differentially expressed genes (DEGs) were found using the weighted gene co-expression network analysis. The optimum machine model was then determined by comparing the performance of the eXtreme Gradient Boost (XGB), the random forest model (RF), the general linear model (GLM), and the support vector machine model (SVM). Nomogram and test datasets (GSE39940) were used to verify the prediction accuracy. Results Nine DE-CRGs (NFE2L2, NLRP3, FDX1, LIPT1, PDHB, MTF1, GLS, DBT, and DLST) associated with active immune responses were ascertained between ATB and LTBI patients. Two cuproptosis-related molecular subtypes were defined in ATB pediatrics. Single sample gene set enrichment analysis suggested that compared with Subtype 2, Subtype 1 was characterized by decreased lymphocytes and increased inflammatory activation. Gene set variation analysis showed that cluster-specific DEGs in Subtype 1 were closely associated with immune and inflammation responses and energy and amino acids metabolism. The SVM model exhibited the best discriminative performance with a higher area under the curve (AUC = 0.983) and relatively lower root mean square and residual error. A final 5-gene-based (MAN1C1, DKFZP434N035, SIRT4, BPGM, and APBA2) SVM model was created, demonstrating satisfactory performance in the test datasets (AUC = 0.905). The decision curve analysis and nomogram calibration curve also revealed the accuracy of differentiating ATB from LTBI in children. Conclusion Our study suggested that cuproptosis might be associated with the immunopathology of Mycobacterium tuberculosis infection in children. Additionally, we built a satisfactory prediction model to assess the cuproptosis subtype risk in ATB, which can be used as a reliable biomarker for the distinguishment between pediatric ATB and LTBI.

Cost-Effective Whole Transcriptome Sequencing Landscape and Diagnostic Potential Biomarkers in Active Tuberculosis

“…The six mRNA biomarkers indicated extraordinary performance in distinguishing active tuberculosis patients from healthy controls, with AUC values ranging from 0.912 to 0.995. Furthermore, we compared the diagnostic efficiency of these six mRNA biomarkers with other tuberculosis biomarkers; − ,− in comparison to existing tuberculosis biomarkers, our six mRNA biomarkers demonstrated superior or equivalent diagnostic accuracy in differentiating ATB patients from healthy controls (Figure A and Figure S12) and ATB patients from LTBI (Figure A and Figure S13). We next explored the connection between the TBMMRP and relevant clinical features; in contrast to the controls or the LTBI group, ATB patients had considerably higher estimated risk probabilities based on the TBMMRP model, but there were no notable changes between the two groups in terms of BCG-vaccinated or gender variations across various cohorts (Figures S14 and S15).…”

“…To create a clinically adaptable mRNA-based active tuberculosis model for estimating the risk probability of developing active 0.907−0.947, sensitivity: 81.25%, specificity: 87.75%) (Figure 5A), and a BIIR cohort (103 ATB and 69 LTBI) with an AUC of 1.000 (95% CI: 1.000−1.000, sensitivity: 100.00%, specificity: 100.00%) (Figure 5B). , 32 Xu et al, 19 Qiu et al, 21 Yi et al, 31 Chen et al, 22 Wu et al, 33 and Geng et al We assessed the diagnostic performance in four fully blinded external cohorts with various platforms. The discriminatory capacity of 13 ATB from the first external cohort and 17 LTBI in England from the BIIR-1 cohort was examined; the results showed the favorable performance of differentiating ATB from LTBI with an AUC of 0.991 (95% CI: 0.937−0.995, sensitivity: 92.30%, specificity: 94.12%) (Figure 5C).…”

“…21 Another study developed a good prediction model to evaluate the ATB and revealed that cuproptosis might be linked to M. tuberculosis infection in children. 22 Previous studies of genetic diagnostic biomarkers of TB essentially focused on gene expression chips with fewer data through high-throughput sequencing. However, the discovery of clinical diagnostic markers requires massive samples to do analysis of multiomics including transcriptome sequencing.…”

“…(B) AUC curve of six mRNA biomarkers and other tuberculosis diagnostic biomarkers in identifying active tuberculosis and latent tuberculosis infection. Li et al,32 Xu et al,19 Qiu et al,21 Yi et al,31 Chen et al,22 Wu et al,33 and Geng et al20…”

Identification of cuproptosis-related gene clusters and immune cell infiltration in major burns based on machine learning models and experimental validation

“…Based on previous research on cuproptosis, we identified 19 CRGs ( 11 , 12 ) After obtaining gene expression data, in R software 4.3.0, we used the “limma” package to correct the data. We identified differentially expressed CRGs (DE-CRGs) (P<0.05) between the major burn group and control group.…”