{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:56:51Z","timestamp":1776056211360,"version":"3.50.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"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. Mol. Biosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Tuberculosis (TB) is a chronic infectious disease caused by\n                      <jats:italic>Mycobacterium tuberculosis<\/jats:italic>\n                      (\n                      <jats:italic>M.tb<\/jats:italic>\n                      ), and poses a significant threat to global human health. Dendritic cells (DCs) represent the most potent antigen-presenting cells and play a critical role in host defenses against\n                      <jats:italic>M.tb<\/jats:italic>\n                      infection.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed GEO datasets to identify differentially expressed circRNAs and mRNAs in\n                      <jats:italic>M.tb<\/jats:italic>\n                      -infected DCs. The downstream miRNAs were predicted using the circBank and CircInteractome databases, while the target mRNAs were predicted using TargetScan and miRanda. A ceRNA regulatory network consisting of 10 circRNAs, 41 miRNAs, and 145 mRNAs was constructed. Protein\u2013protein interaction (PPI) analysis identified 30 hub nodes, which were further subjected to Gene Ontology and KEGG enrichment analyses.Subsequently, the circRNA\/miRNA\/mRNA regulatory axis was assessed, followed by validation using qRT-PCR and evaluation through ROC curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Four core genes\u2014\n                      <jats:italic>STAT1<\/jats:italic>\n                      ,\n                      <jats:italic>BCL2<\/jats:italic>\n                      ,\n                      <jats:italic>TRAF6<\/jats:italic>\n                      , and\n                      <jats:italic>IL1A<\/jats:italic>\n                      \u2014were enriched in tuberculosis,JAK\u2013STAT,and NF-\u03baB pathways. Two ceRNA axes, circNFATC3\/miR-150-5p\/\n                      <jats:italic>STAT1<\/jats:italic>\n                      and circNFATC3\/miR-23a-3p\/\n                      <jats:italic>BCL2<\/jats:italic>\n                      , were validated by qRT-PCR, showing expression patterns consistent with high-throughput data. ROC analysis demonstrated strong diagnostic potential (AUC &amp;gt;0.7) for both axes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study constructed a ceRNA regulatory network in\n                      <jats:italic>M.tb<\/jats:italic>\n                      -infected DCs, identified key molecular modules, and proposed circRNA-associated axes as promising biomarkers for early TB diagnosis and potential therapeutic targets.However, these findings are limited to experimental models of DCs infection and require further validation in clinical samples and\n                      <jats:italic>in vivo<\/jats:italic>\n                      models.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmolb.2026.1764518","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T04:39:50Z","timestamp":1776055190000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deciphering the circRNA-Mediated ceRNA regulatory network in dendritic cells during H37Ra and BCG infection"],"prefix":"10.3389","volume":"13","author":[{"given":"Xiaohong","family":"Sun","sequence":"first","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangshuang","family":"Bao","sequence":"additional","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqi","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Basic Medical Sciences, Beihua University","place":["Jilin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1704045","DOI":"10.3389\/fimmu.2025.1704045","article-title":"Dendritic cell maturation assay for non-clinical immunogenicity risk assessment: best practices recommended by the european immunogenicity platform","volume":"16","author":"Ackaert","year":"2025","journal-title":"Front. 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