{"doi":"10.1038/s41598-021-93893-3","title":"Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB","abstract":"<jats:title>Abstract</jats:title><jats:p>Bacterial culture of <jats:italic>M. tuberculosis</jats:italic> (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change &gt; 1.2; p &lt; 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.</jats:p>","journal":"Scientific Reports","year":2021,"id":596650,"datarank":0.44166584687496613,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.0,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":461664,"name":"Jianjun Hu","orcid":"0000-0002-8725-6660","position":1,"is_corresponding":false},{"id":1528148,"name":"Pengchong Liu","orcid":null,"position":2,"is_corresponding":false},{"id":550963,"name":"Dan Cui","orcid":"0000-0002-0824-9092","position":3,"is_corresponding":false},{"id":1528151,"name":"Hongqin Di","orcid":null,"position":4,"is_corresponding":false},{"id":1528153,"name":"Shucai Wu","orcid":null,"position":5,"is_corresponding":false},{"id":747081,"name":"Zhihui Li","orcid":"0000-0002-7042-5966","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Microarray-based selection of a serum biomarker panel that can discriminate between latent and active pulmonary TB","abstract":"<jats:title>Abstract</jats:title><jats:p>Bacterial culture of <jats:italic>M. tuberculosis</jats:italic> (MTB), the causative agent of tuberculosis (TB), from clinical specimens is the gold standard for laboratory diagnosis of TB, but is slow and culture-negative TB cases are common. Alternative immune-based and molecular approaches have been developed, but cannot discriminate between active TB (ATB) and latent TB (LTBI). Here, to identify biomarkers that can discriminate between ATB and LTBI/healthy individuals (HC), we profiled 116 serum samples (HC, LTBI and ATB) using a protein microarray containing 257 MTB secreted proteins, identifying 23 antibodies against MTB antigens that were present at significantly higher levels in patients with ATB than in those with LTBI and HC (Fold change &gt; 1.2; p &lt; 0.05). A 4-protein biomarker panel (Rv0934, Rv3881c, Rv1860 and Rv1827), optimized using SAM and ROC analysis, had a sensitivity of 67.3% and specificity of 91.2% for distinguishing ATB from LTBI, and 71.2% sensitivity and 96.3% specificity for distinguishing ATB from HC. Validation of the four candidate biomarkers in ELISA assays using 440 serum samples gave consistent results. The promising sensitivity and specificity of this biomarker panel suggest it merits further investigation for its potential as a diagnostic for discriminating between latent and active TB.</jats:p>","is_dataset_classified":null,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"34267288","pmcid":null,"openalex_id":"https://openalex.org/W3182798518","authors":[],"funders":[],"total_grants":0,"fwci":0.8052,"citation_percentile":0.7533871,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":5},{"year":2023,"count":3},{"year":2024,"count":4},{"year":2026,"count":5}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.nature.com/articles/s41598-021-93893-3.pdf","host_type":"journal"},{"url":"https://www.nature.com/articles/s41598-021-93893-3.pdf","host_type":"publisher"},{"url":"https://www.nature.com/articles/s41598-021-93893-3","host_type":"publisher"},{"url":"https://doi.org/10.1038/s41598-021-93893-3","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/34267288","host_type":"repository"},{"url":"https://doaj.org/article/4365acb6423f4c63a76caac34a58c373","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8282789","host_type":"repository"}],"fields_of_study":["Tuberculosis Research and Epidemiology","vaccines and immunoinformatics approaches","Mycobacterium research and diagnosis","Adolescent","Adult","Aged","Antibodies, Bacterial","Antigens, Bacterial","Bacterial Proteins","Biomarkers","Female","Humans","Latent Tuberculosis","Male","Middle Aged","Mycobacterium tuberculosis","Protein Array Analysis","Protein Interaction Maps","Sensitivity and Specificity","Tuberculosis, Pulmonary","Young Adult"],"mesh_terms":["Adolescent","Adult","Aged","Antibodies, Bacterial","Antigens, Bacterial","Bacterial Proteins","Female","Humans","Male","Middle Aged","Mycobacterium tuberculosis","Sensitivity and Specificity","Tuberculosis, Pulmonary","Biomarkers","Protein Array Analysis","Young Adult","Latent Tuberculosis","Protein Interaction Maps"],"keywords":["Biomarker","Selection (genetic algorithm)","Microarray","Computational biology","Medicine","Computer science","Biology","Machine learning","Genetics","Gene expression","Gene"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-28T10:52:44.117432Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}