{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:37:52Z","timestamp":1792802272064,"version":"4.5.0"},"reference-count":47,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Jishuitan Hospital Youcai Plan","award":["JSTYC202402"],"award-info":[{"award-number":["JSTYC202402"]}],"id":[{"id":"https:\/\/ror.org\/035t17984","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>A link exists between decorin (DCN) and endometriosis, nevertheless, the role of DCN in this condition remains unclear. This study aims to bioinformatically characterize DCN expression, diagnostic value, and related signaling in endometriosis using single-cell and bulk transcriptomic data, with experimental validation of expression level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Single-cell RNA sequencing (scRNA-seq) was analyzed using Scissor and ROGUE algorithms to identify key cell types associated with endometriosis. CIBERSORTx was used to construct a signature matrix for deconvolution of bulk RNA-seq data and estimation of immune and stromal subpopulation proportions. DCN expression was explored across datasets and validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in endometrial and endometriotic stromal cells. Its diagnostic value was assessed by receiver operating characteristic (ROC) curve analysis, and associated pathways were predicted by gene set enrichment analysis (GSEA). Potential DCN-targeted drugs were predicted via bioinformatic databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Stromal cells were identified as the key cell type with high heterogeneity and dominant DCN expression. Ten stromal subtypes were delineated, with Activated\/Myofibroblast-like Stromal Cells (AMSC), Fibrosis-Effector Stromal Cells (FESC), Stemness-Remodeling Stromal Cells (SRSC), and dStromal showing significantly altered proportions in endometriosis. DCN was significantly upregulated in endometriosis at both transcriptomic and cellular levels, with area under the curve (AUC) &gt; 0.8 in two independent datasets, supporting its diagnostic potential. GSEA indicated DCN correlates with complement\/coagulation cascades, TGF-\u03b2 signaling, and other pathways linked to tissue remodeling and inflammation. Seven candidate drugs targeting DCN were predicted as hypothesis-generating leads.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study provides bioinformatic and correlative evidence that DCN is abnormally upregulated in endometriotic stromal cells and exhibits favorable diagnostic value. DCN is associated with pathways relevant to endometriosis pathogenesis and represents a promising biomarker and therapeutic candidate pending future functional and mechanistic validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0349505","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:28:41Z","timestamp":1778866121000},"page":"e0349505","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Unraveling the significance of decorin in endometriosis development through single cell sequencing and experimental 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