{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:11:46Z","timestamp":1787026306202,"version":"build-2736575974"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Apoptosis is associated with the pathogenesis of\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six biomarkers including\n                      <jats:italic>CASP1, TNFSF10, CASP4, CASP5, IFI16<\/jats:italic>\n                      , and\n                      <jats:italic>ATF3<\/jats:italic>\n                      were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values &amp;gt; 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      infection.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2024.1285493","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T23:28:09Z","timestamp":1705620489000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Xiaoting","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Litian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopu","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.110983","article-title":"Mycobacterium tuberculosis infection drives a type I IFN signature in lung lymphocytes","volume":"39","author":"Akter","year":"2022","journal-title":"Cell Rep."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1016\/j.biopha.2017.05.045","article-title":"Recent trends in ZikV research: A step away from cure","volume":"91","author":"Alam","year":"2017","journal-title":"BioMed. 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