Serological biomarkers for monitoring response to treatment of pulmonary and extrapulmonary tuberculosis in children and adolescents

Santos, Danielle C.M. dos; Lovero, Kathryn L.; Schmidt, Christiane Mello; Barros, Ana Cláudia Mamede Wiering de; Quintanilha, Ana Paula; Barbosa, Ana Paula; Pone, Marcos V.S.; Pone, Sheila Moura; Araujo, Julienne Martins; Martins, Camila de Paula; Cosme, Estela Magalhães; Oliveira, Thais Raquelly Dourado de; Miceli, Ana Lúcia; Vieira, Maria Luíza; Queiroz, Adriano; Velarde, Luís Guillermo Coca; Kritski, Afrânio Lineu; March, Maria de Fátima Pombo; Sias, Selma Maria de Azevedo; SantÀAnna, Clemax C.; Riley, Lee W.; Cardoso, Claudete Aparecida Araújo

doi:10.1016/j.tube.2020.101960

Cited by 6 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Five inflammatory biomarkers (MIG, IP-10, MIF, CCL22 and CCL23) can be used to monitor the efficacy of EPTB treatment (Ambreen et al, 2021 ). Serum immunoglobulin response to cell wall products of Mycobacterium may be a valuable instrument for monitoring treatment response to PTB or EPTB in children and adolescents (Dos Santos et al, 2020 ). Although there have been advances in the diagnosis of EPTB, studies on immune-related serum biomarkers are still limited (Perumal et al, 2020 ; Liang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

Wang,

Jin,

Mao

et al. 2024

Front. Microbiol.

View full text Add to dashboard Cite

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717–0.805) and 0.833 (95% CI: 0.793–0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

show abstract