{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:59:12Z","timestamp":1785160752989,"version":"3.55.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Endoplasmic reticulum (ER) stress plays a crucial role in airway inflammation and other asthma-related processes. This study aims to identify potential ER stress-related biomarkers in asthma.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Sputum transcriptome data (GSE76262) from 118 moderate-to-severe asthma patients and 21 healthy controls were analyzed to identify ER stress-related differentially expressed genes (DEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction were performed. Machine learning algorithms (LASSO logistic regression and random forest) were applied to screen key gene signatures. The diagnostic performance was evaluated by ROC curve analysis. An OVA-induced murine asthma model with LPS or poly(I:C) co-administration was established to validate the Expression of the ER stress-related biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      By integrating bioinformatic analysis of the GEO dataset GSE76262 with machine learning approaches, we identified two ER stress-related key genes,\n                      <jats:italic>endoplasmic reticulum to nucleus signaling 1 (ERN1)<\/jats:italic>\n                      and\n                      <jats:italic>transmembrane protein 129 (TMEM129)<\/jats:italic>\n                      , whose combined signature can distinguish asthma from healthy controls with an AUC of 0.811. The diagnostic performance of the\n                      <jats:italic>ERN1\/TMEM129<\/jats:italic>\n                      signature was independently validated in an external cohort (GSE256534), achieving an AUC of 0.907. In human induced sputum,\n                      <jats:italic>ERN1<\/jats:italic>\n                      was upregulated while\n                      <jats:italic>TMEM129<\/jats:italic>\n                      was downregulated (\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001). These changes were validated in an ovalbumin-induced murine asthma model: lung inositol-requiring protein 1 (IRE1, protein encoded by\n                      <jats:italic>ERN1<\/jats:italic>\n                      ) levels were upregulated while\n                      <jats:italic>TMEM129<\/jats:italic>\n                      mRNA was downregulated versus saline controls. Co-exposure to lipopolysaccharides (LPS) or poly(I:C) potentiated airway inflammation and elevated the ER stress chaperone BiP\/GRP78; however, only IRE1 levels increased further, whereas\n                      <jats:italic>TMEM129<\/jats:italic>\n                      remained unchanged upon LPS and poly(I:C) co-administration.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      We identify\n                      <jats:italic>ERN1<\/jats:italic>\n                      and\n                      <jats:italic>TMEM129<\/jats:italic>\n                      as a two-gene ER stress signature associated with asthma and propose them as potential diagnostic biomarkers for asthma.\n                    <\/jats:p>\n                  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