{"paper_id":"035e0180-7468-4b94-b475-3b9a85642380","body_text":"Apoptosis          (2026) 31:142 \nhttps://doi.org/10.1007/s10495-026-02342-x\nErqing Huang and Jiang-Tian Li have contributed equally to this \nwork.\n \r Yi Liu\nliqun1994@hust.edu.cn\n \r Ling Zhang\nzhanglingxh@hust.edu.cn\n1 Department of Obstetrics and Gynecology, Union Hospital, \nTongji Medical College, Huazhong University of Science \nand Technology, Wuhan 430022, China\nAbstract\nEndometriosis is characterized by progressive fibrosis and limited therapeutic options. Cuproptosis, a copper-dependent \nform of regulated cell death, has been implicated in multiple pathological conditions, but its relevance to fibroblast-\nmediated fibrotic progression in endometriosis remains unclear. Single-cell RNA sequencing data from normal, eutopic, \nand ectopic endometrial tissues were analyzed to assess cuproptosis-related gene (CRG) activity and fibroblast heteroge -\nneity. Pseudotime analysis, cell–cell communication analysis and high-dimensional weighted gene co-expression network \nanalysis were performed to identify disease-associated fibroblast states and candidate fibrosis-related genes. Machine \nlearning approaches were applied to prioritize candidate hub genes. Functional validation was conducted in endometrial \nstromal cells, and a mouse model of endometriosis was used to assess the effects of tetrathiomolybdate (TTM), a copper \nchelator. Elevated CRG activity was enriched in a distinct fibroblast subpopulation with profibrotic transcriptional features. \nNetwork and machine learning analyses consistently prioritized AEBP1 as a candidate fibroblast-associated hub gene \nlinked to cuproptosis-related signatures. In vitro, CuCl 2 plus elesclomol treatment was associated with increased AEBP1 \nand fibrosis-related marker expression, accompanied by changes in β-catenin pathway-related proteins, whereas FDX1 or \nAEBP1 knockdown attenuated these effects. In vivo, TTM treatment reduced lesion burden, fibrotic marker expression \nand collagen deposition in ectopic lesions. Cuproptosis-related molecular alterations are associated with fibroblast activa -\ntion and fibrotic progression in endometriosis. Targeting copper metabolism may have therapeutic potential in limiting \nlesion fibrosis.\nKeywords Endometriosis · Cuproptosis · Fibroblasts · AEBP1 · Single-cell RNA sequencing (scRNA-seq) · Fibrosis · \nβ-catenin pathway\nReceived: 21 February 2026 / Accepted: 19 April 2026\n© The Author(s) 2026\nSingle-cell profiling and machine learning identify cuproptosis-related \nfibroblast subpopulations and fibrogenesis modulator AEBP1 in \nendometriosis\nErqing Huang1 · Jiang-Tian Li1 · Danhui Zuo1 · Ruijie Li1 · Qingyue Wu1 · Na Lin1 · Jinru Zhao1 · Huajing Wang1 · \nYi Liu1 · Ling Zhang1\nBackground\nEndometriosis (EMS) is defined as the presence of endo -\nmetrium-like tissue outside the uterus, a disorder associated \nwith severe pelvic pain and infertility [1–3]. Ectopic lesions \nexhibit high invasiveness, often inducing intra-abdominal \nfibrosis and adhesions that impair fertility and mental health \n[4]. While its exact pathogenesis remains unclear, core \npathological features have been identified: immunoinflam -\nmatory responses, neurogenesis, estrogen dependence with \nprogesterone resistance, and fibrosis [5]. Histologically, \nendometrial glands and stroma are surrounded by dense \nfibrous tissue—fibrosis itself arises from excessive extra -\ncellular matrix (ECM) accumulation, a process normally \n\n1 3\n  142  Page 2 of 24\nApoptosis          (2026) 31:142 \ncritical for tissue repair but dysregulated in EMS [6]. Despite \ngrowing recognition of these pathological hallmarks, the \nmolecular mechanisms driving EMS-associated fibrosis \nremain poorly characterized. This also leads to therapeutic \nlimitations. Current therapies, including hormonal drugs \nand surgical resection, alleviate pain and lesion burden but \nfail to address the underlying fibrotic process, resulting in \nelevated rates of adhesion recurrence post-treatment and \npersistent infertility. Myofibroblasts (MFBs) are recognized \nas the principal effector cells in endometriosis-associated \nfibrosis (EMS-associated fibrosis). Since their initial iden -\ntification in ectopic lesions in 1996, research has suggested \nvarious origins for myofibroblasts, including fibroblast-\nmyofibroblast transition (FMT), epithelial-to-mesenchymal \ntransition (EMT), endothelial-to-mesenchymal transition \n(EndoMT), mesothelial-to-mesenchymal transition (MMT), \nand differentiation of mesenchymal stem cells [7]. How -\never, the signals governing MFB differentiation in EMS are \ncurrently undetermined.\nA prospective yet insufficiently investigated avenue in \nthis context is cuproptosis—a recently characterized form \nof programmed cell death induced by intracellular copper \naccumulation, lipoylated tricarboxylic acid (TCA) cycle \nprotein aggregation, and mitochondrial impairment [8]. \nCopper homeostasis is essential for cellular function; how -\never, abnormal copper accumulation is linked to fibrosis in \nmultiple organs. For instance, copper-induced mitochon -\ndrial reactive oxygen species (ROS) facilitate myofibroblast \ndifferentiation in cardiac fibrosis [9, 10], whereas copper \nchelation mitigates liver fibrosis in Wilson’s disease [11, \n12]. Nonetheless, the significance of cuproptosis in EMS-\nassociated fibrosis has not been comprehensively investi -\ngated. Previous cuproptosis research in EMS is restricted \nto indirect correlations and does not include analysis of \ncuproptosis-specific events or their effects on stromal cell \nfunction. While MFBs drive ECM deposition, the specific \nfibroblast subpopulations that differentiate into MFBs and \nthe role of cuproptosis in regulating this transition remain \nunclear. Traditional bulk transcriptomics, long the mainstay \nof EMS research, cannot resolve cell-type-specific cupro -\nptosis responses or fibroblast–cuproptosis interactions, hin-\ndering the identification of fibroblast-specific therapeutic \ntargets.\nIn this study, we discovered a candidate fibrosis-associ -\nated gene, Adipocyte Enhancer-Binding Protein 1(AEBP1), \nassociated with endometriosis fibroblasts. AEBP1 has been \nimplicated in fibrotic regulation in several diseases, such \nas cardiac fibrosis and fibrosis within the pancreatic tumor \nmicroenvironment [13–15]. However, its expression, role, \nand regulatory mechanisms in EMS fibrosis remain unchar-\nacterized. It remains unclear whether AEBP1 is enriched \nin endometriosis-associated fibroblast states and whether it \nis associated with cuproptosis-related or copper-dependent \nprofibrotic changes in this disease.\nTo address these existing knowledge gaps, we employed \nan integrated approach combining single-cell RNA \nsequencing (scRNA-seq), high-dimensional weighted gene \nco-expression network analysis (hdWGCNA), machine-\nlearning analysis, and experimental validation. Our study \naimed to characterize cuproptosis-related fibroblast states \nin endometriosis, prioritize candidate fibrosis-associated \ngenes including AEBP1, and explore their potential asso -\nciation with profibrotic signaling. Furthermore, we used \nin vitro functional assays and an in vivo mouse model to \nevaluate whether cuproptosis-related molecular altera -\ntions were accompanied by changes in AEBP1 expression, \nfibrotic responses, and β-catenin pathway-related proteins. \nCollectively, these findings raise the possibility of a cupro -\nptosis-related regulatory network associated with fibrotic \nprogression in endometriosis and highlight AEBP1 as a can-\ndidate molecule for further study.\nMethods\nPatients and sample collection\nThis research was approved by the Ethics Committee of \nUnion Hospital, Tongji Medical College, Huazhong Uni -\nversity of Science and Technology. Informed written con -\nsent was acquired from patients before to the collection \nof human tissues, in compliance with the Declaration of \nHelsinki principles. Normal endometrial tissue specimens \nwere obtained from 15 women devoid of endometriosis \nwho underwent hysteroscopy and endometrial biopsy. The \npost-procedure pathology analysis verified that these sam -\nples originated from normal endometrium (n = 15). Paired \neutopic and ectopic endometrial specimens were obtained \nfrom the same 15 patients diagnosed with stage III or IV \nendometriosis. Pathological histopathological analysis con-\nfirmed that all collected endometrial tissues were in the pro-\nliferative phase. All individuals exhibited regular menstrual \ncycles, were neither pregnant nor nursing, had not utilized \nhormonal drugs within six months preceding surgery, and \npresented no indications of serious medical or surgical dis -\norders or associated complications.\nData collection\nSingle-cell RNA sequencing (scRNA-seq) data of endo -\nmetrial tissues were obtained from the Gene Expression \nOmnibus (GEO) database  (   h t  t p s  : / / w  w w  . n c b i . n l m . n i h . g o v / \ng e o /     ) . Two GEO datasets, GSE179640 and GSE5572238, \nwere acquired by using the “GEOquery” package in R \n\n1 3\nPage 3 of 24   142 \nApoptosis          (2026) 31:142 \nsoftware. Four normal control samples, five eutopic endo -\nmetrial samples, and five ectopic endometrium samples \nwere chosen for following analytical processes. A total of \n16 cuproptosis-related genes (CRGs) were systematically \nenrolled for bioinformatic analyses. These genes were iden-\ntified and compiled according to the core molecular machin-\nery of cuproptosis as originally characterized in the pivotal \nCell study [8] and supplemented by previously published \nwork on cuproptosis [16]. The complete list of the 16 CRGs \nis detailed in Supplemental Table S1. The bulk transcrip -\ntome dataset used for machine learning was sourced from \nthe merged datasets of GSE7305 and GSE11691 from GEO \n(https:/ /www.nc bi.nlm. nih.g ov/geo/), which includes 19 \nectopic endometrial tissue samples and 19 normal control \nendometrial samples. The comBat function from the “sva” \nR package was utilized for batch correction to mitigate \npotential batch effects across the two datasets.\nComprehensive processing of single cell datasets\nRaw scRNA-seq count matrices were processed using \nSeurat (version 4.2.2) in R. Cells expressing fewer than \n500 genes or more than 4000 genes were excluded, and \ngenes expressed in fewer than 3 cells were removed. Cells \nwith a high proportion of mitochondrial transcripts were \nalso excluded using a cutoff of 15%. Quality-control met -\nrics, including the distributions of nFeature_RNA, nCount_\nRNA, and percent.mt, are shown in Supplementary Fig. S1.\nAfter quality filtering, data were normalized using \nSCTransform. Principal component analysis (PCA) was \nthen performed on the scaled data (Supplementary Fig. S2). \nTo correct for batch effects and integrate samples from dif-\nferent datasets, Harmony (version 0.1.1) was applied to the \nPCA embeddings using sample identity as the batch vari -\nable. Batch correction performance and sample integration \nare shown in Supplementary Fig. S3. A shared nearest-\nneighbor graph was constructed using FindNeighbors, and \nclustering was performed using FindClusters with a resolu-\ntion of 0.5. Cell clusters were visualized by uniform mani -\nfold approximation and projection (UMAP). The clustering \nresults for each sample and the numbers of retained cells \nper sample are provided in the Supplementary Figs. S4–S6. \nCell-type annotation was performed according to canonical \nmarker genes and previously published endometrial single-\ncell references. Marker genes for each cluster were iden -\ntified using FindAllMarkers with the Wilcoxon rank-sum \ntest. Cell annotation was performed according to a prior \nstudy [17, 18]. The unique expression patterns of the identi-\nfied genes at the single-cell level were demonstrated using \nthe “scRNAtoolVis” package (version 0.1.0).\nCalculation of cuproptosis score\nTo estimate cuproptosis-related activity at the single-cell \nlevel, we used AUCell based on a predefined 16-gene \ncuproptosis-related gene (CRG) set derived from the semi -\nnal study by Tsvetkov et al. The calcAUC function from the \nAUCell package was used to calculate enrichment scores \nfor the predefined CRG set in each cell. The aucMaxRank \nparameter was set to 10% of the ranked gene list for each \ncell. According to the distribution of AUCell scores, cells \nwith an AUC score > 0.025 were defined as the CRG-high \ngroup, whereas the remaining cells were classified as the \nCRG-low group. PCA was performed as an orthogonal \nanalysis to examine whether AUCell-based CRGs stratifica-\ntion was associated with broader transcriptomic divergence \n(Supplementary Fig. S7). The resulting score was used for \ndownstream subgroup comparison and functional analyses. \nIn this study, the AUCell-derived score was interpreted as a \ntranscriptome-based indicator of cuproptosis-related activ -\nity rather than direct evidence of canonical cuproptotic cell \ndeath. To illustrate the distribution of cuproptosis scores \namong groups and enable subsequent comparison studies, \nthe grouped cuproptosis scores were visualized using the \n“ggplot2” program in R software.\nEnrichment analysis\nThe Seurat package’s “FindMarkers” function was used to \ndetermine the differentially expressed genes (DEGs) unique \nto each cell subcluster. Strict filtering criteria were used to \ndefine significant DEGs: only genes with an absolute log2 \nfold change (|log2FC|) > 0.25 and an adjusted P-value < 0.05 \nwere deemed statistically significant. In single-cell tran -\nscriptome research, these criteria are frequently used to \nminimize false-positive results while balancing the identifi-\ncation of physiologically significant changes. Following the \nidentification of significant DEGs, two forms of functional \nenrichment analyses—gene set enrichment analysis (GSEA) \nand gene ontology (GO) enrichment analysis—were carried \nout to investigate the possible biological roles of these genes \nacross cell subgroups. The “clusterProfiler” package (ver -\nsion 4.0.1) in R software, a thoroughly tested instrument \nfor functional annotation and enrichment analysis in omics \nresearch, was used for all enrichment studies [19].\nCell–cell communication analysis of fibroblasts \nstratified by CRG activity\nCell communication modulates target cell function by ini -\ntiating a sequence of physiological and biochemical alter -\nations through cell signal transduction, resulting in the \ntarget cell’s comprehensive biological effects. Intercellular \n\n1 3\n  142  Page 4 of 24\nApoptosis          (2026) 31:142 \ncommunication, facilitated by interactions between cell sur-\nface ligands and receptors, coordinates diverse cell types \nduring development and is essential for numerous biological \nactivities [20]. The “CellChat” tool (version 1.6.1) was uti -\nlized to detect and compare potential interactions between \nfibroblasts and other cell populations within cuproptosis-\nrelated gene (CRG) groupings. All analyses adhered to the \npackage’s prescribed pipelines with default configurations, \nassuring alignment with conventional techniques for infer -\nring cell–cell communication in single-cell data.\nTrajectory analysis of fibroblast populations\nAn unsupervised pseudo-temporal analysis was conducted \nusing the “Monocle” program (version 2.24.0) with the \nDDR-Tree technique and default parameters to investigate \nthe trajectory of fibroblasts in the high score group of CRGs \nin endometriosis. Subsequently, the “plot_cell_trajectory,” \n“plot_genes_in_pseudotime,” and “plot_genes_branched_\nheatmap” were utilized to generate plots that graphically \nrepresent the dynamic expression of module genes along \nthe pseudotime trajectories of high fibroblasts in CRGs. The \ndifferentiation status of cell subpopulations was evaluated \nalongside the pseudotime trajectory of cells to determine the \nextent of differentiation among cell subtypes.\nHigh dimensional weighted gene co-expression \nnetwork analysis\nHigh-dimensional weighted gene co-expression network \nanalysis (hdWGCNA) was used to identify important genes \nlinked with fibroblasts in the high group of CRGs among \nendometriosis samples. A correlation matrix of gene expres-\nsion, weighted gene co-expression networks, and module \nidentification were performed. Module-trait connection \nresearch revealed modules highly correlated with high \ngroupings of CRGs, and hub genes within these major mod-\nules were determined based on their intra-module connec -\ntivity. The first 120 hub genes were regarded as the principal \ngenes.\nScreening of EMS fibroblasts biomarkers by \nmachine learning techniques\nTo identify robust fibroblast-associated candidate genes, \nthe top 120 genes from the fibroblast-related blue module \nidentified by hdWGCNA were subjected to machine-learn -\ning analysis using the merged bulk transcriptomic dataset \n(GSE7305 and GSE11691), which included 19 ectopic \nendometrial samples and 19 normal control endometrial \nsamples. Three independent algorithms were applied, \nincluding random forest (RF), least absolute shrinkage and \nselection operator (LASSO), and support vector machine-\nrecursive feature elimination (SVM-RFE).\nFor RF analysis, the randomForest package was used \nwith 500 trees. Model stability was assessed using the \nout-of-bag error rate, and variables were ranked accord -\ning to MeanDecreaseGini. Genes with higher importance \nscores were considered RF-selected candidate features. \nFor LASSO analysis, the glmnet package was used, and \nthe optimal penalty parameter (λ) was selected by tenfold \ncross-validation using the cv.glmnet function. Genes with \nnon-zero coefficients at the selected λ value were retained \nas candidate features. For SVM-RFE analysis, candidate \nsubsets with different numbers of variables were evaluated \nby cross-validation, and the subset with the lowest cross-\nvalidation RMSE was selected as the optimal model.\nTo improve robustness and minimize model-specific \nbias, genes identified by the three independent algorithms \nwere intersected, and the overlapping genes were defined \nas candidate hub genes for subsequent analyses and experi-\nmental validation.\nIsolation and culture of primary endometrial \nstromal cells\nPrimary ectopic endometrial stromal cells (EESCs) were \nextracted from ectopic endometrial tissues of ten individu -\nals diagnosed with ovarian endometriosis, adhering to the \ndesignated protocol: Recently obtained tissues were washed \nwith PBS, chopped with scissors, and thereafter incubated \nwith preheated 0.1% type II collagenase (Sigma-Aldrich, St. \nFig. 1  Single-cell RNA-sequencing analysis identifies a fibroblast-\nenriched CRG-high state with profibrotic transcriptional features in \nendometriosis. A Uniform manifold approximation and projection \n(UMAP) plots showing the distribution of the major cell populations \nin control (Con), ectopic, and eutopic endometrial samples after inte -\ngration of the single-cell RNA-sequencing dataset. Annotated cell \ntypes include B cells, endothelial cells, epithelial cells, fibroblasts, \nmonocytes, NK cells, smooth muscle cells, and T cells. B Histogram \nof AUCell scores for the predefined cuproptosis-related gene (CRG) \nsignature across all cells. Cells with an AUC score > 0.025 were clas-\nsified as the CRG-high group (17,977 cells), whereas the remaining \ncells were classified as the CRG-low group. C UMAP plot showing \nAUCell-derived CRG scores across all cells. Brighter colors indicate \nhigher CRG activity. D Stacked bar plot showing the proportions of \nCRG-high and CRG-low fibroblasts in control, ectopic, and eutopic \nendometrial samples. Percentages are shown within the bars. E UMAP \nplots showing the distribution of CRG-high cells in control, ectopic, \nand eutopic samples. High-score cells are highlighted in yellow. F, \nG Gene set enrichment analysis (GSEA) of differentially expressed \ngenes between CRG-high and CRG-low fibroblasts, showing enrich -\nment of fibrosis-related biological processes, including collagen fibril \norganization and extracellular matrix organization. The normalized \nenrichment score (NES), adjusted P value, and false discovery rate \n(FDR) are indicated in each plot. CRGs, cuproptosis-related genes; \nUMAP, uniform manifold approximation and projection; GSEA, gene \nset enrichment analysis; AUC, area under the curve; FDR, false dis -\ncovery rate\n\n1 3\nPage 5 of 24   142 \nApoptosis          (2026) 31:142 \n \n\n1 3\n  142  Page 6 of 24\nApoptosis          (2026) 31:142 \nLouis, MO) in a shaker at 37 °C for 45 min. The mixture \nwas filtered in succession through sterile sieves with hole \nsizes of 150 μm and 38 μm to exclude epithelial cells and \nundigested tissues, followed by centrifugation at 1000 rpm \nfor 5 min. A red blood cell lysis buffer was introduced and \nmixed, subsequently followed by a second centrifugation to \nisolate primary endometrial stromal cells. Normal endome-\ntrial stromal cells (NESCs) and EESCs were cultivated in \nDMEM/F12 media enriched with 20% fetal bovine serum \n(FBS) in a 5% CO 2 incubator at 37 °C. Cells utilized for \nexperimentation were subcultured no more than three times. \nThe purity of isolated endometrial stromal cells (ESCs) was \nconfirmed using immunofluorescence, which identified the \nexpression of the epithelial marker E-cadherin (Abcam, \nab40772, 1:50) and the mesenchymal marker vimentin \n(Abcam, ab92547, 1:50).\nCell culture and transfection assay\nHuman endometrial stromal cells (ThESCs) were obtained \nfrom the American Type Culture Collection (ATCC; catalog \nno. CRL-4003) and cultured in Dulbecco’s Modified Eagle \nMedium (DMEM) enriched with 10% fetal bovine serum \n(FBS; Gibco, Carlsbad, CA, USA). Cells were cultivated in a \nhumidified incubator at 37 °C with 5% carbon dioxide (CO2). \nThe AEBP1 overexpression plasmid and its corresponding \nempty control plasmid, small interfering RNAs (siRNAs) \ndirected against FDX1 and AEBP1, along with non-target -\ning negative control siRNAs, were chemically produced by \nDianJun Biotechnology Co., Ltd. (Shanghai, China). The \nminor interfering sequences of FDX1 siRNA#:sense,  G C \nA A G U A G A G A U C C U G G A A T T; antisense,  U U C C A G G \nA U C U C U A C U U G C T T; AEBP1 siRNA#: sense,  C C A C A \nC U G G A C U A C A A U G A T T; antisense,  U C A U U G U A G U \nC C A G U G U G G T T. Cell transfection was conducted with \nthe jetPRIME transfection reagent (Polyplus-transfection, \nIllkirch, France) in strict adherence to the manufacturer’s \nprescribed methodology. Post-transfection, the transfection \nefficiency was assessed using western blot analysis to verify \nthe overexpression of AEBP1 or the knockdown of FDX1/\nAEBP1. Subsequent functional studies were conducted only \nafter confirming adequate transfection efficiency, in accor -\ndance with pre-established experimental criteria.\nProtein extraction and western blotting analysis\nProtein from tissues and cells was extracted using RIPA \nbuffer (Beyotime, Shanghai, PR China) supplemented with \nPMSF (Sigma-Aldrich, St. Louis, MO) to inhibit protein \nbreakdown. Proteins were denatured at 95 °C for 10 min and \nsubsequently kept at − 80 °C until required. For Western blot \nanalysis, 30 μg of protein per sample was resolved using \n12% SDS-PAGE and subsequently transferred to PVDF \nmembranes (Millipore, MA, USA). Membranes were incu -\nbated with 5% skim milk in TBST (0.05% Tween-20) at \nroom temperature for 1 h to minimize nonspecific binding. \nPrimary antibodies against AEBP1(ab168355; Abcam), \nα-SMA (14395-1-AP; Proteintech), CTGF (25474-1-AP; \nProteintech), β-catenin (M7A19; Selleck), c-myc (343250; \nZenbio), β-actin (20536-1-AP; Proteintech), LIAS (11577-\n1-AP, Proteintech), FDX1 (12592-1-AP; Proteintech), \nLipoic Acid (for Lip-DLAT) (ab58724; Abcam) were incu-\nbated with membranes overnight at 4 °C in the refrigerator. \nOn the following day, membranes were subjected to three \nwashes with TBST (5 min each) and subsequently incubated \nwith goat anti-rabbit HRP secondary antibody (1:400; Pro -\nteintech, Wuhan, PR China) at room temperature for one \nhour. Following three more TBST washes (5 min each), \nprotein bands were detected using ECL solution and sub -\nsequently photographed. Western blot quantified from three \nindependent experiments. Band intensities were assessed \nusing ImageJ.\nImmunofluorescence (IF) staining\nEndometrial stromal cells were fixed in 4% paraformal -\ndehyde at 25 °C for 30 min, followed by permeabiliza -\ntion with PBS containing 0.1% Triton X-100 at 25 °C for \n10 min. Non-specific binding sites were obstructed using \n1% bovine serum albumin in PBS at 37 °C for one hour. \nCells were then incubated with primary antibodies against \nAEBP1 (ab168355; Abcam, 1:100), α-SMA (14395-1-AP; \nProteintech, 1:200), β-catenin (M7A19; Selleck, 1:200) and \nFDX1 (12592-1-AP; Proteintech, 1:100) at 25 °C for 1 h. \nImmuno-signals were detected using fluorescence-conju -\ngated secondary antibodies (1:4000; Proteintech). Nuclei \nwere stained with 4′,6-diamidino-2-phenylindole dihydro -\nchloride (DAPI) for a duration of 10 min. Ultimately, pic-\ntures were obtained by fluorescence confocal microscopy \nand analyzed using Image Pro Plus 6.0 software.\nFig. 2  Cell–cell communication analysis reveals stronger incoming \nand outgoing profibrotic signaling in CRG-high fibroblasts. A, B Cir-\ncle plots showing inferred incoming communication patterns received \nby CRGs.Low_Fibroblasts and CRGs.High_Fibroblasts, respectively, \nfrom other cell populations in ectopic endometrial tissue. C, D Cir-\ncle plots showing inferred outgoing communication patterns sent by \nCRGs.Low_Fibroblasts and CRGs.High_Fibroblasts, respectively, to \nother cell populations. In the circle plots, line thickness reflects the \ninferred communication strength. E, F Bar plots showing the top \nligand–receptor pairs associated with CRGs.Low_Fibroblasts and \nCRGs.High_Fibroblasts, respectively. Salmon bars denote signaling \nfrom fibroblasts to other cells, whereas turquoise bars denote signal -\ning from other cells to fibroblasts. Total communication probability is \nshown on the x-axis. CRGs, cuproptosis-related genes; TGFB1, trans-\nforming growth factor beta 1\n\n1 3\nPage 7 of 24   142 \nApoptosis          (2026) 31:142 \nIHC, HE and Masson’s trichrome staining\nAll tissues were immediately fixed in 4% buffered for -\nmalin to preserve tissue morphology. Subsequent paraffin \nembedding, tissue sectioning (5-μm thickness), and IHC, \nHE, and Masson’s trichrome staining procedures were per -\nformed by Biosciences Biotechnology Co., Ltd. (Wuhan, \nChina).\n \n\n1 3\n  142  Page 8 of 24\nApoptosis          (2026) 31:142 \n \n\n1 3\nPage 9 of 24   142 \nApoptosis          (2026) 31:142 \nAssessment of mitochondrial membrane potential \nand ROS\nMitochondrial membrane potential was assessed using \nthe JC-1 assay according to the manufacturer’s instruc -\ntions. After the indicated treatments, cells were incubated \nwith JC-1 working solution (5 μg/mL) for 15 min at 37 °C, \nwashed with buffer, and analyzed by flow cytometry. The \nproportion of cells with increased green fluorescence was \nused as an indicator of reduced mitochondrial membrane \npotential.\nIntracellular mitochondrial ROS levels were assessed \nusing MitoSOX Red (Beyotime Biotechnology, catalog no. \nS0061) according to the manufacturer’s protocol. After treat-\nment, cells were incubated with MitoSOX working solution \n(5 μM) for 30 min at 37 °C, counterstained with DAPI, and \nimaged by fluorescence microscopy. Fluorescence intensity \nwas quantified using ImageJ from three independent fields \nper group.\nAnimal model and treatment\nExperiments with C57BL/6 mice received approval from \nthe Institutional Animal Care and Use Committee of Tongji \nMedical College, Huazhong University of Science and \nTechnology (HUST), and adhered to applicable regula -\ntory standards. Female C57BL/6 mice, aged 6 to 8 weeks \nand weighing 18 to 20 g, were acquired from BIONT Bio -\ntechnology Co., Ltd. in Beijing, China. A total of 40 mice \nwere randomly allocated into three groups: the donor group \n(n = 10), the negative control endometriosis group (Control \nEMS, n = 15), and the TTM treated endometriosis group \n(TTM, n = 15). Mice were anesthetized with pentobarbi -\ntal sodium at a dosage of 60 mg/kg. To create the endo -\nmetriosis model, the uterus of a single donor mouse was \nsectioned into 2–3 mm fragments, which were subsequently \nimplanted onto the abdomen walls of two recipient mice. \nEach mouse received one endometrial fragment sutured \nonto each side of the abdominal wall. Three weeks post-\nestablishment of the surgical model, mice received 50 mg/\nkg TTM (HY-128530, MCE, China) via oral gavage. TTM \nwas solubilized in a solvent comprising 5% DMSO, 40% \nPEG300, 5% Tween80, and 50% water, and subsequently \ndiluted to a final concentration of 10 mg/ml. The control \ngroup of mice was administered the identical solvent devoid \nof TTM. At the end of treatment, mice were euthanized, and \nectopic lesions were harvested for lesion measurement, pro-\ntein extraction, histology, immunohistochemistry, and Mas-\nson’s trichrome staining.\nStatistical analysis\nAll statistical analyses and data visualization were con -\nducted using R software (version 4.2.2). Data are presented \nas mean ± SD unless otherwise specified. For compari -\nsons between two groups, a two-tailed unpaired Student’s \nt-test was used. For comparisons among three or more \ngroups, one-way analysis of variance (ANOV A) followed \nby Tukey’s multiple-comparisons test was applied. A P \nvalue < 0.05 was considered statistically significant. Sta -\ntistical significance was indicated as follows: * P < 0.05, \n**P < 0.01, ***P < 0.001, ****P < 0.0001.\nResults\nSingle-cell RNA sequencing reveals \ncuproptosis-related genes (CRGs) groups in \nendometriosis\nTo identify genes predominantly indicative of cupropto -\nsis alteration, we performed an in-depth study of single-\ncell sequencing data from normal, eutopic, and ectopic \nendometrial tissues. Following quality screening, 44,597 \nhigh-quality cells were carefully selected for further exami-\nnation. The principal component analysis (PCA) reduction \nplot revealed no significant variations in cell cycles. After \nHarmony-based integration, cells from different samples \nshowed improved mixing in low-dimensional space, sug -\ngesting that major batch-driven separation was reduced. \nThe distribution of eight distinct cell clusters was illus -\ntrated using Uniform Manifold Approximation and Pro -\njection (UMAP) (Fig. 1A). Subsequently, utilizing the \nAUC score > 0.025, all cells were allocated an AUC score \nfor CRGs and classified into high-cuproptosis AUC and \nlow-cuproptosis AUC groups (Fig. 1B). Cells exhibiting a \ngreater quantity of cuproptosis-related genes (CRGs) were \npredominantly characterized by lighter-colored fibroblasts \nand smooth muscle cells (Fig. 1C). Since the occurrence \nof fibrosis in endometriosis is mainly associated with the \nFig. 3 Identification of an endometriosis-associated fibroblast popula-\ntion with distinct transcriptional and pathway features. A UMAP plots \nshowing fibroblast subclustering in control, ectopic, and eutopic sam -\nples. Eight fibroblast clusters were identified. B Stacked bar plot show-\ning the relative proportions of the eight fibroblast clusters in control, \nectopic, and eutopic samples. C Stacked bar plot showing fibroblast \nsubtype composition after integrating fibroblast clusters 2 and 4 as \nEMS_fibroblasts. D Dot plot showing the expression of representative \nshared genes across fibroblast clusters 2 and 4. Dot size indicates the \npercentage of cells expressing each gene, and dot color indicates the \naverage expression level. E Ridge plot showing the top enriched Hall-\nmark pathways in EMS_fibroblasts. F Heatmap showing differentially \nexpressed genes across fibroblast subclusters. Genes were grouped into \nexpression clusters (C1–C7) by unsupervised clustering. EMS_fibro -\nblasts, fibroblast populations enriched in endometriosis samples and \ncharacterized by shared profibrotic transcriptional features; UMAP, \nuniform manifold approximation and projection\n\n1 3\n  142  Page 10 of 24\nApoptosis          (2026) 31:142 \n \n\n1 3\nPage 11 of 24   142 \nApoptosis          (2026) 31:142 \nfunction of fibroblasts, we focused primarily on the propor-\ntions of fibroblasts with high and low cuproptosis scores \namong normal, eutopic, and ectopic endometrial fibroblasts. \nWe found that the proportion of cells with high cupropto -\nsis scores was significantly higher in ectopic and eutopic \nfibroblasts compared to normal fibroblasts (Fig. 1D). This \nsuggests that cuproptosis-related genes or pathways in \nendometriosis may be involved in certain processes of \nfibrosis. We further visualized the distribution of fibroblasts \nwith high cuproptosis scores in the clustering map using \nUMAP plots, which revealed a marked increase in yellow-\ncolored cells (representing high cuproptosis scores) among \nectopic fibroblasts (Fig. 1E). To further explore the spe -\ncific functions of fibroblasts with high cuproptosis scores, \nwe performed Gene Set Enrichment Analysis (GSEA) on \nthe differentially expressed genes between fibroblasts with \nhigh and low cuproptosis scores. The results showed that the \nfunctions of fibroblasts in the high cuproptosis score group \nwere significantly enriched in “collagen fibril organization” \nand “extracellular matrix organization” (Fig. 1F, G). This \nmay indicate that cuproptosis-related or copper-dependent \nsignaling is associated with fibroblast activation and fibrotic \nprogression in endometriosis.\nComparative cell–cell communication analysis \nbetween CRG-high and CRG-low fibroblasts\nThe availability of a single-cell dataset afforded us a distinc-\ntive chance to examine cell–cell communication facilitated \nby ligand-receptor interactions. To clarify the cell–cell com-\nmunication network between fibroblasts and other cell types \nin ectopic endometrial tissues, we conducted an analysis \nutilizing CellChat, which is founded on established ligand–\nreceptor pairings and their cofactors.\nSubsequently, in the cell communication analysis, we \nclassified fibroblasts into “CRGs.Low_Fibroblasts” and \n“CRGs.High_Fibroblasts” and compared the intensity \nof communication signals they received from and sent to \nother cell types. We found that CRGs.High_Fibroblasts \nmay receive more communication signals from epithelial \ncells, endothelial cells, and monocytes (Fig. 2A, B). The \ntransformation of epithelial cells and endothelial cells into \nfibroblasts and myofibroblasts is widely recognized as one \nof the mechanisms underlying fibrosis formation in endo -\nmetriosis, and numerous studies have also elaborated on the \nrole of the mononuclear phagocyte system in the immune \nmicroenvironment of endometriosis. Given the well-estab -\nlished involvement of these cell types in endometriosis-\nassociated fibrosis, the increased signal reception from \nthem by CRGs.High_Fibroblasts may imply a functional \nlink between cuproptosis and fibrotic signaling cascades. \nAdditionally, CRGs.High_Fibroblasts tend to emit stronger \ncommunication signals to other cells, which is reflected by \nthicker arrows in the circle plot (Fig. 2C, D). This suggests \nthat cuproptosis-high fibroblasts may exhibit enhanced \nintercellular communication capacity, potentially integrat -\ning pro-fibrotic signals from epithelial cells, endothelial \ncells, and the mononuclear phagocyte system to facilitate \nfibroblast activation and subsequent fibrotic progression in \nendometriosis.\nSubsequently, we compared the significantly activated \nreceptor-ligand pairs between the two groups of fibroblasts. \nWe found that COL1A1-related receptor-ligand pairs were \npresent in both groups (Fig. 2E). As a well-recognized fibro-\nsis marker, the expression of COL1A1 often indicates the \nactivation of fibrotic processes. Interestingly, CRGs.High_\nFibroblasts exhibited higher levels of TGFβ1 and Wnt \nrelated signals emitted by other cell types (Fig. 2F). This \nsuggests that, distinct from CRGs.Low_Fibroblasts, CRGs.\nHigh_Fibroblasts may be preferentially regulated by TGFβ1 \nand Wnt signaling axes—two classical pathways implicated \nin fibroblast proliferation, differentiation, and extracellular \nmatrix deposition. The enhanced crosstalk via these pro-\nfibrotic pathways might further reinforce the pro-fibrotic \nphenotype of CRGs.High_Fibroblasts, potentially amplify -\ning the fibrotic cascade in endometriosis.\nFig. 4 Identification of fibroblast-associated hub genes by hdWGCNA \nand machine-learning analysis. A Scale-free topology analysis used to \ndetermine the optimal soft-thresholding power for high-dimensional \nweighted gene co-expression network analysis (hdWGCNA). A soft-\nthresholding power of 5 was selected for network construction. B Mod-\nule eigengene (ME) plots showing 13 fibroblast-associated co-expres-\nsion modules identified by hdWGCNA, together with representative \ngenes within each module. C UMAP feature plots showing the spatial \ndistribution of the 13 module scores across fibroblast populations. D \nProtein–protein interaction (PPI) network constructed from represen -\ntative genes in the fibroblast-associated module. E Random forest error \ncurve showing model stability as the number of trees increases. F Ran-\ndom forest variable-importance plot showing candidate genes ranked \nby MeanDecreaseGini. G LASSO regression analysis showing cross-\nvalidation results for feature selection and determination of the optimal \npenalty parameter. H Support vector machine-recursive feature elimi-\nnation (SVM-RFE) analysis showing the relationship between the \nnumber of variables and model error. I Venn diagram showing overlap \namong candidate genes identified by random forest (RF), LASSO, \nand SVM-RFE analyses. J Heatmap showing the mean expression of \nthe three overlapping hub genes (AEBP1, COL6A3, and C1S) across \nfibroblast subpopulations. K Feature plots showing the distribution of \nAEBP1, COL6A3, and C1S in control, ectopic, and eutopic fibroblast \npopulations. hdWGCNA, high-dimensional weighted gene co-expres-\nsion network analysis; PPI, protein–protein interaction; RF, random \nforest; LASSO, least absolute shrinkage and selection operator; SVM-\nRFE, support vector machine-recursive feature elimination; UMAP, \nuniform manifold approximation and projection\n\n1 3\n  142  Page 12 of 24\nApoptosis          (2026) 31:142 \nFibroblasts 1\nFibroblasts 2\nFibroblasts 5EMS_Fibroblasts Fibroblasts 7\nFibroblasts 4 Fibroblasts 6\nCluster\n5\n0\n-5\n-10\n-10 0 10\nCon Ectopic EutopicOrig.sample\n1\n2\n-10 0 10\n5\n0\n-5\n-10\nComponent 1\nComponent 2\nComponent 2\nComponent 1\n-10 01 0\nState\n 12 34 5\n5\n0\n-5\n-10 Component 2\n2\n1\n5\n0\n-5\n-10 Component 2\n-10 0 10\nComponent 1\nPseudotime\n0 10 20 30\nA B\nC D\n01 2\n0\n1\n2\n3\n4\nPseudo-time\n12\nPseudo-time\n0\n1\n2\n3\n4\n01 2\n01 2\n0\n1\n2\n3\n4 AEBP1 expressionCOL6A3 expressionC1S expression\nF\nG\n-10 0 10\nComponent 1\nE\nPseudo-time\nSFRP4\nCOL6A2\nCOL6A1\nCOL6A3\nCOL1A2\nC1S\nPCOLCE\nFBN1\nIGFBP5\nCOL1A1\nCOL3A1\nLUM\nMMP2\nSFRP1\nMFAP4\nOGN\nCOL14A1\nTNXB\nIGFBP6\nCTSK\nDCN\nAEBP1\nIGFBP4\nSPARCL1\nSERPINF1\nC1R\nPTGDS\nCCDC80\nSFRP2\nCFD\n3\n2\n1\n0\n-1\n-2\nCell TypeCell Type\nPre-branch\nCell fate 1\nCell fate 2\nCluster\n1\n2\n3\nH\n2\n1\n2\n1\n2\n1 1\n2\nState\n \n\n1 3\nPage 13 of 24   142 \nApoptosis          (2026) 31:142 \nIdentified characteristic fibroblasts of \nendometriosis\nSubsequently, we conducted the UMAP analysis again to \nhierarchically cluster the fibroblasts. Subclustering of fibro-\nblasts revealed 8 different subtypes (Fig. 3A). We next \nquantified the distribution of the eight fibroblast subtypes \nacross normal, ectopic, and eutopic samples. Subtype 4 was \nmarkedly enriched in ectopic lesions, whereas subtype 2 \nwas significantly expanded in both ectopic and eutopic tis -\nsues compared with normal controls (Fig. 3B). Given their \nshared origin from endometriosis patients, we amalgamated \nthese two fibroblast clusters and designated them as EMS-\nfibroblast (Fig. 3C).\nTo substantiate this classification, we systematically \nexamined the differentially expressed genes (DEGs) across \nall fibroblast subtypes and highlighted the top 10 most \nsignificant shared DEGs between subtypes 2 and 4. These \nshared transcriptional features provide supportive evidence \nfor defining EMS-associated fibroblasts. Notably, several \nkey fibrosis-associated genes, including TGFB1, MMP2, \nand ACTA2, were prominently upregulated, implicating this \npopulation in fibrotic remodeling (Fig. 3D). Consistently, \nHallmark pathway analysis revealed that EMS_Fibroblasts \nwere significantly enriched in pathways related to cell \ncycle progression, TGF-β signaling, and estrogen response \n(Fig. 3E). Collectively, these data identify EMS_Fibroblasts \nas a distinct fibroblast population enriched in endometriosis, \ncharacterized by coordinated activation of proliferative and \npro-fibrotic programs. This cell population may play a cen-\ntral role in driving lesion progression and fibrotic remod -\neling through the integration of TGF-β signaling, estrogen \nresponsiveness, and cell cycle regulation.\nThe genes were subsequently analyzed by unsupervised \nclustering, leading to the emergence of unique gene groups. \nFurthermore, we categorized genes exhibiting analogous \nexpression patterns, as indicated by the clustering outcomes. \nAdditionally, specific differential genes of seven fibroblast \nclusters were depicted in a heatmap (Fig. 3F). These sub -\ntype-specific molecular features may serve as functional \nhallmarks, facilitating a deeper understanding of the dis -\ntinct roles of each fibroblast subpopulation in endometriosis \ndevelopment and fibrotic progression.\nIdentification of fibroblast-associated gene modules \nand hub genes using hdWGCNA and machine \nlearning\nWe employed high-dimensional weighted gene co-expres -\nsion network analysis (hdWGCNA) to identify the key \nmolecular characteristics associated with fibroblasts in the \ncontext of endometriosis. The co-expression network con -\nstruction revealed that a scale-free topology fitness index \nof 0.90 was achieved with a soft threshold power (β) of 5, \nwhich optimized the connectivity within the cell network \n(Fig. 4A). This analysis identified 13 co-expression modules \n(Fig. 4B), among which the blue module exhibited the stron-\ngest association with fibroblast activity (Fig. 4C).Within the \nblue module, highly connected genes were prioritized based \non intramodular connectivity, and the top 120 genes were \ndefined as key fibroblast-associated candidates.We further \nanalyzed through protein–protein interaction (PPI) network \nanalysis using the STRING database (Fig. 4D).\nTo identify robust hub genes, we integrated multiple \nmachine learning approaches using bulk transcriptomic \ndatasets (GSE7305 and GSE11691).We initially analyzed \nthe combined dataset comprising 19 EMS tissue samples \nand 19 normal endometrial samples. The random forest \napproach revealed six genes with a gene relevance score \nexceeding 2 (Fig. 4E, F). The LASSO method revealed six \ngenes of significant importance (Fig. 4G). The SVM-RFE \nalgorithm found four genes of considerable significance \n(Fig. 4H). We derived the intersection of the genes identi -\nfied by these three machine learning algorithms. Utilizing \nthe LASSO regression technique, random forest algorithm, \nand SVM-RFE algorithm, we identified three pivotal genes, \nAEBP1, COL6A3, and C1S, that demonstrated correlation \nwith endometriosis fibroblasts (Fig. 4I).\nWe next examined the expression patterns of these \nthree genes across fibroblast subclusters. Heatmap analy -\nsis showed that AEBP1, COL6A3, and C1S were relatively \nenriched in EMS-associated fibroblasts, with AEBP1 show-\ning the most prominent expression pattern (Fig. 4J). Feature \nplots further confirmed that these genes were preferentially \nexpressed in fibroblast populations from ectopic and eutopic \ntissues, particularly within the EMS-associated fibroblast \ncluster (Fig. 4K). Based on its expression pattern and con -\nsistent identification across multiple analytical approaches, \nFig. 5 Pseudotime analysis reveals a profibrotic differentiation trajec -\ntory of EMS-associated fibroblasts. A Monocle trajectory plot showing \nfibroblast differentiation states colored by fibroblast subtype. EMS_\nfibroblasts are preferentially distributed in later pseudotime regions. \nB Monocle trajectory plot colored by sample origin (control, ectopic, \nand eutopic), showing differential localization of fibroblasts from dif-\nferent tissue sources along the trajectory. C Monocle trajectory plot \ncolored by cell state, showing multiple differentiation states during \nfibroblast progression. D Monocle trajectory plot colored by pseudo -\ntime, illustrating the transition from early to late differentiation states. \nE–G Dynamic expression of AEBP1, COL6A3, and C1S along pseu -\ndotime. Smoothed curves indicate the overall expression trend during \nfibroblast differentiation. H Branched heatmap showing genes dynam-\nically regulated across branch-dependent cell fates. Genes related \nto extracellular matrix remodeling and fibrosis, including COL6A1, \nCOL6A2, COL6A3, C1S, FBN1, IGFBP5, COL1A1, COL3A1, DCN, \nand AEBP1, are enriched in late-stage branches. EMS_fibroblasts, \nendometriosis-associated fibroblasts\n\n1 3\n  142  Page 14 of 24\nApoptosis          (2026) 31:142 \nAEBP1 was prioritized for subsequent validation as a candi-\ndate fibrosis-associated marker in endometriosis.\nPseudotime analysis reveals a profibrotic \ndifferentiation trajectory of EMS-associated \nfibroblasts\nPseudotime analysis uncovered a continuous, branched \ndifferentiation trajectory for fibroblasts in endometriosis. \nDistinct fibroblast subclusters segregated along separate \ntrajectory branches, with EMS-associated fibroblasts pre -\ndominantly enriched in the relatively late-stage regions of \nthe trajectory—consistent with a disease-linked activated \nstate (Fig. 5A). Fibroblasts isolated from control, eutopic, \nand ectopic tissue also exhibited distinct distribution pat -\nterns: ectopic and eutopic fibroblasts preferentially local -\nized to specific trajectory branches and later pseudotime \nstates, indicating altered differentiation dynamics in endo -\nmetriosis-associated fibroblasts (Fig. 5B). In alignment with \nthese findings, multiple distinct cell states were identified \nacross different branches, supporting the occurrence of pro-\ngressive, heterogeneous state transitions during fibroblast \ndifferentiation (Fig. 5C). Pseudotime-based color mapping \nD\nFDX1\nEctopicEutopicNormal\nA\nAEBP1\nGAPDH \nLip-DLAT\nFDX1\nNC EU EC\nLIAS\nLIASB\nEctopicEutopicNormal\nAEBP1C\nEctopicEutopicNormal\n50μm\n50μm\n50μm 50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\nFDX1 LIAS Lip-DLAT AEBP1\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5Relative proteine xpression\nNC\nEU\nEC\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nns\n/uni2731/uni2731/uni2731/uni2731\nFDX1 LIAS AEBP1\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\nRelativeI HCS core\n(n=15)\nNC\nEU\nEC\nns\n/uni2731/uni2731/uni2731/uni2731\nns\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nFig. 6 Clinical endometriosis speci-\nmens exhibit altered cuproptosis-\nrelated molecular signatures and \nincreased AEBP1 expression. A, \nB Representative immunohisto-\nchemistry (IHC) staining of FDX1 \nand LIAS in ectopic, eutopic, and \nnormal endometrial tissues. C Rep-\nresentative IHC staining of AEBP1 \nin ectopic, eutopic, and normal \nendometrial tissues, with quanti-\nfication of relative IHC scores at \nright. D Representative western \nblots and densitometric quantifica-\ntion of FDX1, LIAS, Lip-DLAT, \nand AEBP1 in normal control \n(NC), eutopic (EU), and ectopic \n(EC) tissues. GAPDH was used as \nthe loading control. Human tissue \nsamples included NC (n = 15), EU \n(n = 15), and EC (n = 15). Data are \npresented as mean ± SD. Statisti-\ncal analysis was performed using \none-way ANOV A followed by \nTukey’s multiple-comparisons \ntest. ns, not significant; *P < 0.05; \n**P < 0.01; ****P < 0.0001. \nScale bars = 50 μm. FDX1, fer-\nredoxin 1; LIAS, lipoic acid \nsynthetase; Lip-DLAT, lipoylated \ndihydrolipoamide S-acetyl-\ntransferase; AEBP1, adipocyte \nenhancer-binding protein 1; IHC, \nimmunohistochemistry\n \n\n1 3\nPage 15 of 24   142 \nApoptosis          (2026) 31:142 \nfurther illustrated a gradual progression from early to late \ncellular states toward distinct branch termini (Fig. 5D).\nNotably, AEBP1, COL6A3, and C1S showed progres -\nsive upregulation along pseudotime, reflecting the grad -\nual acquisition of profibrotic properties during fibroblast \nAEBP1\nCTGF\nα-SMA\nT ubulin\nConC u+ele TTM\nFDX1\nLIAS\nT ubulin\nConC u+eleT TM\nLip-DL AT\nCon Cu+ele TTM\n50μm 50μm 50μ m\n50μm\n50μm 50μm\n50μm 50μm\n50μm\nConCu+eleTTM\nMitoSOX DAPI Merge\nα-SMA DAPI Merge AEBP1 DAPI Merge\nNCTTM Cu+ele\nα-SM AA EBP1\n0\n1\n2\n3\n4\nRela ti ve fl u or esce nt in te ns it y\n( n =3 )\nCo n\nCu+ele\nTT M\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nFDX1 LIAS Lip-DL AT\n0\n1\n2\n3R el a ti v ep rote in ex pre s s io n\nCo n\nCu+ele\nTT M\n/uni2731\n/uni2731\n/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nAEBP1 α-SM A CTGF\n0\n1\n2\n3\n4\n5Re lative protei ne xp re ssio n\nCon\nCu+ele\nTTM\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731\n/uni2731\n/uni2731\n/uni2731/uni2731\nc-myc\nβ-catenin\nTubulin\nConC u+eleT TM\nβ- ca te ni nc -m yc\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5R el a ti v e p ro t e i n e x p re ss io n\nCon\nCu+ele\nTTM\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\n0.0\n0.5\n1.0\n1.5\n2.0\nRelative Mi toSO Xl evel\n(n=3)\nCon\nCu+ele\nTTM\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\nCon\nCu+ele\nTTM\nA\nB\nC\nD\nEF\nJC-1 green fluorescence\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\n50μm\nJC-1 green fluorescence\nC o n\nCu+ele T TM\n0\n10\n20\n30\n40\n50\nJC-1 G r eenF luorescence(% )\n(n=3)\nCon\nCu+ele\nTTM\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731\n \n\n1 3\n  142  Page 16 of 24\nApoptosis          (2026) 31:142 \ndifferentiation (Fig. 5E–G). Consistent with this, branch-\nspecific heatmap analysis revealed that a panel of extracellu-\nlar matrix- and fibrosis-related genes—including COL6A1, \nCOL6A2, COL6A3, C1S, FBN1, IGFBP5, COL1A1, \nCOL3A1, DCN, and AEBP1—were enriched in late-stage \ntrajectory branches, further corroborating the profibrotic \ntransition of EMS-associated fibroblasts during disease pro-\ngression (Fig. 5H).\nClinical endometriosis exhibits altered cuproptosis-\nrelated molecular signatures and elevated AEBP1 \nexpression\nThe principal factors regulating cuproptosis are FDX1 and \nLIAS. FDX1 can convert divalent copper into the more \nhazardous monovalent copper and is also implicated in the \nregulation of lipoic acid modification of proteins. LIAS-\nencoded lipoic acid synthase (LIAS), an enzyme containing \nan iron-sulfur cluster, interacts with FDX1. Their connec -\ntion facilitates the normal progression of protein acylation \n[21]. Consequently, LIAS and FDX1 are typically consid -\nered diagnostic markers for cuproptosis, with their expres -\nsion levels diminishing during the process of cuproptosis. \nLip-DLAT, the lipoylated form of DLAT, is one of the major \nlipoylated tricarboxylic acid (TCA) cycle proteins impli -\ncated in cuproptosis-related processes and can serve as an \nadditional readout of cuproptosis-related molecular altera -\ntions. Therefore, we examined the expression of FDX1, \nLIAS, and Lip-DLAT to evaluate cuproptosis-related \nchanges, together with AEBP1 as a fibrosis-associated \nmarker.\nTo further characterize cuproptosis-associated molecular \nalterations in clinical endometriosis specimens, we assessed \nthe expression levels of FDX1, LIAS and AEBP1 in normal \ncontrol (NC), eutopic (EU) and ectopic (EC) endometrial \ntissues using immunohistochemistry (IHC) and Western \nblotting. IHC staining demonstrated that the expression \nof FDX1 and LIAS was notably downregulated in ecto -\npic endometrial tissues, while AEBP1 expression was sig -\nnificantly upregulated, particularly within ectopic lesions \n(Fig. 6A–C). Western blot analysis further validated the \ndecreased expression of FDX1, LIAS, Lip-DLAT as well as \nthe increased abundance of AEBP1 in EC tissues (Fig. 6D). \nAdditionally, altered Lip-DLAT expression was detected \nin endometriotic lesions, suggesting that copper-dependent \nmitochondrial alterations may be present in endometriosis. \nCollectively, these findings indicate that ectopic endome -\ntrial tissues display aberrant cuproptosis-related or copper-\ndependent mitochondrial stress signatures, accompanied by \nenhanced expression of the fibrosis-related marker AEBP1.\nCuproptosis-related changes are associated with \nincreased AEBP1 expression and fibrotic activation \nin primary ectopic endometrial stromal cells\nTo further investigate the association between cuproptosis-\nrelated changes and profibrotic activation in endometrial \nstromal cells, cells were treated with CuCl 2 (50 μM) and \nelesclomol (10 nM) for 24 h, with or without the copper \nchelator TTM. Western blot analysis showed that CuCl 2/\nelesclomol cotreatment reduced the expression of cupro -\nptosis regulators FDX1, LIAS and Lip-DLAT, while TTM \npartially restored their expression levels (Fig. 7A). JC-1 \nstaining revealed decreased mitochondrial membrane \npotential in the CuCl 2 plus elesclomol group, which was \nsignificantly attenuated by TTM (Fig. 7B). MitoSOX stain-\ning further confirmed increased mitochondrial ROS produc-\ntion following CuCl 2/elesclomol treatment, an effect that \nwas reversed by TTM (Fig. 7C).\nWe next evaluated whether cuproptosis-related changes \nwere coupled with profibrotic activation. Immunofluores -\ncence staining showed markedly increased expression of \nα-SMA and AEBP1 in the CuCl 2 plus elesclomol group, \nwhereas TTM treatment significantly reduced their fluo -\nrescence intensity (Fig. 7D). Western blot analysis further \nverified that CuCl 2/elesclomol cotreatment upregulated \nprofibrotic proteins (AEBP1, α-SMA, CTGF) and activated \nβ-catenin/c-Myc signaling, and these effects were notably \nsuppressed by TTM (Fig. 7E, F). Taken together, these find-\nings suggest that cuproptosis-related alterations are associ -\nated with increased AEBP1 expression and fibrotic marker \nFig. 7  Copper and copper ionophore treatment is associated with \ncuproptosis-related molecular alterations, increased AEBP1 expres -\nsion, and profibrotic activation in primary ectopic endometrial stro -\nmal cells. Primary ectopic endometrial stromal cells were treated with \nCuCl2 (50 μM) plus elesclomol (10 nM) for 24 h, with or without \ntetrathiomolybdate (TTM), as indicated. A Representative western \nblots and densitometric quantification of FDX1, LIAS, and Lip-DLAT. \nTubulin was used as the loading control. B Representative JC-1 flow \ncytometry plots and quantification of JC-1 green fluorescence. An \nincreased proportion of green fluorescence indicates reduced mito -\nchondrial membrane potential. C Representative MitoSOX staining \nimages and quantification of mitochondrial reactive oxygen species \n(ROS). Nuclei were counterstained with DAPI. D Representative \nimmunofluorescence staining of α-SMA and AEBP1, with quantifi -\ncation of relative fluorescence intensity. E Representative western \nblots and densitometric quantification of AEBP1, α-SMA, and CTGF. \nF Representative western blots and densitometric quantification of \nβ-catenin and c-Myc. Western blot quantification was derived from \nthree independent experiments. MitoSOX fluorescence was quantified \nfrom three independent microscopic fields per group. Data are pre -\nsented as mean ± SD. Statistical analysis was performed using one-way \nANOV A followed by Tukey’s multiple-comparisons test. * P < 0.05; \n**P < 0.01; *** P < 0.001; **** P < 0.0001. Scale bars = 50 μm. TTM, \ntetrathiomolybdate; α-SMA, alpha-smooth muscle actin; CTGF, con -\nnective tissue growth factor; DAPI, 4′,6-diamidino-2-phenylindole; \nROS, reactive oxygen species\n\n1 3\nPage 17 of 24   142 \nApoptosis          (2026) 31:142 \nexpression in endometrial stromal cells, accompanied by \nchanges in β-catenin pathway-related proteins.\nFDX1 knockdown attenuates cuproptosis-related \nalterations and profibrotic responses in endometrial \nstromal cells\nE\nB\nDC\nCTGF\nα-SMA\nT ubulin\nAEBP1 β-catenin\nTu bulin\nc-myc\nCu+ele\nsi FDX1si NC\n- + -+\nAEBP1 FDX1 DAPI Merge\nsi NCsi FDX1 Cu+el e\nCu+ele\n+\nsi FDX1\nFDX1\nTu buli n\nCu+el e\nsi FDX1si NC\n- + -+\nLIAS\nLip-DLA T\nMitoSOXD API Merge\nsi NCsi FDX1 Cu+el e\nCu+ele\n+\nsi FDX1\nsi NC\nsi NC+C u+el e\nsi FD X1+ Cu +ele\nsi FDX1\nFDX1 LI AS Lip-DL AT\n0.0\n0.5\n1.0\n1.5Re lative protei ne xp re ssio n\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nsi NCs i NC+Cu+el e\nsi FDX1+Cu+ele si FDX1\nJC-1 green fluorescence\nJC-1 green fluorescence\nsi N C\nsi NC\n+C u+el\ne\ns i FDX1+Cu\n+e le\ns i F\nD X1\n0\n10\n20\n30\n40\n50\nJC-1 Gr eenF luoresc e nce(%)\n(n=3 )\nsi NC\nsi NC+Cu+ele\nsi FDX1+Cu+ele\nsi FDX1\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n0\n1\n2\n3\n4\nRelati ve Mi t o SO Xl evel\n(n=3)\nsi NC\nsi NC+Cu+ele\nsi FDX1+Cu+ele\nsi FDX1\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nAEBP1 FDX1\n0\n1\n2\n3\nRe l a ti ve fl uor e sc en ti n t en sity\n(n=3 )\nsi NC\nsi NC+Cu+ele\nsi FDX1+Cu+ele\nsi FDX1\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731\nAEBP1 α-SMA CTGF\n0\n1\n2\n3\n4\nRe la t i v ep rote in expression\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nβ- cate ni nc -m yc\n0\n1\n2\n3\n4Re l a tive protei ne xp re ssio n\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\n/uni2731/uni2731/uni2731/uni2731\nsi NC\nsi NC+Cu+ele\nsi FDX1+Cu+ele\nsi FDX1\nCu+ele\nsi FDX1si NC\n- + -+\ns i N\nC\nsi NC+Cu+\ne le\nsi FDX1+\nCu+ele s i FDX1\nsi NC\nsi NC+Cu+ele\nsi FDX1+Cu+ele\nsi FDX1\nA\nF\n50μm\n50μm\n50μm\n50μm 50μm\n50μm\n50μm\n50μm 50μm\n50μm\n50μm\n50μm\n50μm 50μm 50μm 50μm\n50μm 50μm 50μm 50μm\n50μm 50μm 50μm 50μm\n50μm 50μm 50μm 50μm\n \n\n1 3\n  142  Page 18 of 24\nApoptosis          (2026) 31:142 \nFDX1 is a well-established key regulatory factor in the \nprocess of cuproptosis. On one hand, during the occur -\nrence of cuproptosis, FDX1 catalyzes the binding of toxic \ncopper(I) to lipoylated proteins in the tricarboxylic acid \n(TCA) cycle, which leads to impairment of cellular respira-\ntion and subsequent induction of cuproptosis, manifested as \na consumptive decrease in FDX1 expression. On the other \nhand, cuproptosis fails to occur following the knockdown \nof FDX1. Therefore, in this study, we induced cuproptosis \nin the human endometrial stromal cell line (ThESCs) while \nsimultaneously performing FDX1 knockdown.\nIn the aforementioned bioinformatics analysis, we iden -\ntified AEBP1 as a characteristic fibrosis marker of endo -\nmetrial stromal cells. We observed that the expression \nof AEBP1 was downregulated after FDX1 knockdown, \nwhich indicates that the expression of AEBP1 is regulated \nby FDX1 to a certain extent. Simultaneously, after knock -\ning down FDX1, the expression of FDX1, LIAS, and Lip-\nDLAT was markedly reduced (Fig. 8A). In parallel, JC-1 \nstaining revealed that cotreatment with CuCl 2 and elesclo -\nmol enhanced green fluorescence intensity, indicative of \nreduced mitochondrial membrane potential. This effect was \nnotably mitigated by FDX1 knockdown (Fig. 8B). Consis-\ntently, MitoSOX staining demonstrated that the elevation \nin mitochondrial ROS levels induced by CuCl 2/elesclomol \ncotreatment was partially reversed following FDX1 silenc -\ning (Fig. 8C).\nNext, we performed immunofluorescence analysis after \nFDX1 knockdown, confirming that AEBP1 expression lev-\nels significantly decreased when cuproptosis was induced \nconcurrently with FDX1 knockdown (Fig. 8D). Western \nblot analysis further showed that the expression of the \nfibrosis-related proteins AEBP1, α-SMA, and CTGF was \nelevated in the CuCl2 plus elesclomol group but was attenu-\nated following FDX1 knockdown (Fig. 8E). Similarly, the \nexpression of β-catenin and its downstream target c-myc \nwas also increased after CuCl2 plus elesclomol treatment and \nwas reduced by FDX1 silencing (Fig. 8F). Taken together, \nthese findings indicate that FDX1 is closely involved in \ncuproptosis-associated molecular alterations and elevated \nAEBP1 expression, enhanced profibrotic marker levels, and \nmodulated expression of β-catenin pathway-related proteins \nin endometrial stromal cells.\nAEBP1 is associated with profibrotic responses and \nthe β-catenin pathway-related protein expression in \nendometrial stromal cells\nPrior research indicates that β-catenin pathway is involved \nin the progression of several fibrosis-associated disease and \nhas also been implicated in endometriosis-related fibro -\nsis [22–24]. The involvement of the β-catenin pathway in \nendometriosis has been substantiated by numerous prior \ninvestigations [25]. AEBP1, a fibrosis-associated factor, \nhas been demonstrated in studies to have a regulatory role; \nparticularly, the silencing of AEBP1 can mitigate β-catenin-\nmediated renal fibrosis [26]. Consequently, we silenced \nAEBP1 in ThESCs with AEBP1-specific small interfering \nRNA (siAEBP1). Immunofluorescence results indicated \na reduction in the expression of α-SMA under CuCl 2 plus \nelesclomol treatment when AEBP1 was silenced (Fig. 9A). \nConsistently, Western blot analysis showed that the elevated \nexpression of AEBP1, α-SMA, and CTGF induced by CuCl2 \nplus elesclomol was attenuated following AEBP1 silencing \n(Fig. 9B). Conversely, AEBP1 overexpression was accom -\npanied by increased expression of AEBP1, α-SMA, and \nCTGF, showing a pattern comparable to that observed after \nCuCl2 plus elesclomol treatment (Fig. 9C).\nWe next examined β-catenin pathway-related changes \nafter modulation of AEBP1. Immunofluorescence stain -\ning showed that CuCl 2 plus elesclomol increased AEBP1 \nand β-catenin signals, whereas AEBP1 knockdown reduced \nboth signals (Fig. 9D). Western blot analysis further con -\nfirmed that the increased expression of β-catenin and its \ndownstream target c-myc induced by CuCl2 plus elesclomol \nwas attenuated after AEBP1 silencing (Fig. 9E). In contrast, \nAEBP1 overexpression was accompanied by increased \nexpression of β-catenin and c-myc, with levels similar to \nthose observed in the CuCl2 plus elesclomol group (Fig. 9F).\nFig. 8  FDX1 knockdown attenuates copper ionophore-associated \nmitochondrial alterations and profibrotic responses in endometrial \nstromal cells. ThESCs were transfected with negative-control siRNA \n(siNC) or FDX1 siRNA (siFDX1), followed by treatment with CuCl 2 \nplus elesclomol (Cu + ele) or vehicle, as indicated. A Representative \nwestern blots and densitometric quantification of FDX1, LIAS, and \nLip-DLAT. Tubulin was used as the loading control. B Representative \nJC-1 flow cytometry plots and quantification of JC-1 green fluores -\ncence. C Representative MitoSOX staining images and quantifica -\ntion of mitochondrial ROS levels. Nuclei were counterstained with \nDAPI. D Representative immunofluorescence staining of AEBP1 and \nFDX1, with quantification of relative fluorescence intensity. E Rep-\nresentative western blots and densitometric quantification of AEBP1, \nα-SMA, and CTGF. F Representative western blots and densitometric \nquantification of β-catenin and c-Myc. Western blot quantification was \nderived from three independent experiments. MitoSOX fluorescence \nwas quantified from three independent microscopic fields per group. \nData are presented as mean ± SD. Statistical analysis was performed \nusing one-way ANOV A followed by Tukey’s multiple-comparisons \ntest. **P < 0.01; ****P < 0.0001. Scale bars = 50 μm. si NC, negative-\ncontrol small interfering RNA; si FDX1, FDX1-specific small inter -\nfering RNA; Cu + ele, CuCl2 plus elesclomol; α-SMA, alpha-smooth \nmuscle actin; CTGF, connective tissue growth factor; ROS, reactive \noxygen species\n\n1 3\nPage 19 of 24   142 \nApoptosis          (2026) 31:142 \nTTM attenuates fibrotic progression of ectopic \nlesions in vivo\nWe created an endometriosis animal model by implanting \nmouse endometrial tissue fragments into the peritoneal \ncavity of C57 mice. To further examine whether copper-\ndependent alterations are associated with fibrotic progres -\nsion in ectopic lesions in vivo, we created endometriosis \n \n\n1 3\n  142  Page 20 of 24\nApoptosis          (2026) 31:142 \nmodel mice and administered TTM (50 mg/kg), previously \nidentified in studies as a cuproptosis inhibitor (Fig. 10A). \nVariations in average cyst sizes and lesion weights were \nnoted between the two treatment groups (Fig. 10B). In com-\nparison to the control EMS group, TTM treatment markedly \nsuppressed the development of abdominal wall endome -\ntriotic lesions (Fig. 10C). We then identified cuproptosis \nindicators, fibrosis markers, and molecules associated with \nthe β-catenin pathway in the ectopic lesions of the murine \nmodel. The results of Western blotting indicated that TTM \ntreatment was associated with increased expression of \nFDX1, LIAS, and Lip-DLAT, suggesting a partial rever -\nsal of cuproptosis-related molecular alterations in ectopic \nlesions (Fig. 10D). Concurrently, the expression levels of the \nfibrosis markers α-SMA, CTGF, and AEBP1 were markedly \nreduced (Fig. 10E). The expression of β-catenin and c-myc \nwas diminished subsequent to TTM therapy (Fig. 10F). \nSubsequently, we assessed the expression of four pivotal \nmolecules (AEBP1, CTGF, α-SMA and β-catenin) using \nimmunohistochemistry staining, and the findings were con-\ngruent with those derived from Western blotting (Fig. 10G). \nMasson staining demonstrated a considerable reduction in \ncollagen fiber deposition following TTM treatment. A sub -\nstantial area of blue-stained collagen fibers was noted in the \nectopic lesions of the control group, while the proportion of \nblue-stained collagen fibers in the TTM group was mark -\nedly reduced (Fig. 10H). Taken together, these findings sug-\ngest that TTM treatment is associated with reduced fibrotic \nburden in ectopic lesions in vivo, accompanied by reversal \nof cuproptosis-related molecular changes and decreased \nexpression of β-catenin pathway-related and fibrosis-related \nproteins.\nDiscussion\nCopper is an essential trace metal element in organisms, \nwhich acts as a cofactor or structural component of enzymes \nand participates in various life activities, including cellular \nfree radical scavenging, connective tissue synthesis, pig -\nment formation, immune regulation, and neurotransmit -\nter synthesis [27, 28]. In the fibrotic process of multiple \norgans, tissue copper ion levels are consistently elevated. \nCopper iron overload induces the production of mitochon -\ndrial reactive oxygen species (ROS), thereby promoting \nthe expression of fibrosis-related genes and the differentia -\ntion of myofibroblasts [29]. Specifically, copper accumula-\ntion in cardiomyocyte mitochondria triggers mitochondrial \ndamage, cytochrome c release, and cell apoptosis—events \nthat further contribute to cardiac injury and exacerbate car -\ndiac fibrosis [30]. In patients with Wilson’s disease, abnor-\nmal copper ion accumulation occurs within mitochondria. \nNotably, treatment with copper chelators leads to signifi -\ncant improvements in mitochondrial structure and func -\ntion, accompanied by the alleviation of liver fibrosis [31, \n32]. In patients with renal failure, plasma copper ion levels \nare markedly increased, indicating that copper ions accu -\nmulate to a certain extent in the body when renal function \nis impaired. Additionally, in a rat model of renal fibrosis \ninduced by unilateral ureteral obstruction (UUO), admin -\nistration of tetrathiomolybdate (TTM) significantly reduces \ncopper ion concentrations in renal tissue and ameliorates \nfibrosis [27]. These observations provide a biological ratio-\nnale for investigating whether copper-dependent stress \nresponses are also involved in endometriosis-associated \nfibrosis.\nCopper accumulation is a primary catalyst of cupropto -\nsis. Excessive intracellular copper accumulation beyond \nhomeostatic control induces cuproptosis through processes \nsuch as increasing the aggregation of lipoylated TCA cycle \nproteins, triggering overproduction of mitochondrial ROS, \nand interrupting cellular respiration [33, 34]. Our single-\ncell transcriptomic profiling identified that cells harboring \nelevated CRG scores were predominantly distributed in \nfibroblasts and smooth muscle cells. Specifically, fibro -\nblasts with heightened CRG activity displayed marked \nenrichment of extracellular matrix and collagen-associated \ntranscriptional programs. Given that fibroblast activation is \na pivotal mediator of fibrotic remodeling in endometriosis, \nthese results indicate that cuproptosis-related molecular \nsignatures together with copper-dependent mitochondrial \nFig. 9 AEBP1 modulates profibrotic marker expression and β-catenin \npathway-related proteins in endometrial stromal cells. ThESCs were \ntransfected with AEBP1-specific siRNA (siAEBP1) or AEBP1 overex-\npression plasmid (ovAEBP1), followed by treatment with CuCl 2 plus \nelesclomol (Cu + ele) or vehicle, as indicated. A Representative immu-\nnofluorescence staining of AEBP1 and α-SMA in siNC, siAEBP1, \nCu + ele, and siAEBP1 + Cu + ele groups, with quantification of relative \nfluorescence intensity. B Representative western blots and densitomet-\nric quantification of AEBP1, α-SMA, and CTGF in siNC, siAEBP1, \nCu + ele, and siAEBP1 + Cu + ele groups. C Representative western \nblots and densitometric quantification of AEBP1, α-SMA, and CTGF \nin ovNC, Cu + ele, and ovAEBP1 groups. D Representative immu -\nnofluorescence staining of AEBP1 and β-catenin in siNC, siAEBP1, \nCu + ele, and siAEBP1 + Cu + ele groups, with quantification of relative \nfluorescence intensity. E Representative western blots and densitomet-\nric quantification of β-catenin and c-Myc in siNC, siAEBP1, Cu + ele, \nand siAEBP1 + Cu + ele groups. F Representative western blots and \ndensitometric quantification of β-catenin and c-Myc in ovNC, Cu + ele, \nand ovAEBP1 groups. Tubulin was used as the loading control for all \nwestern blot analyses. Western blot quantification was derived from \nthree independent experiments. Data are presented as mean ± SD. \nStatistical analysis was performed using one-way ANOV A followed \nby Tukey’s multiple-comparisons test. ns, not significant; ** P < 0.01; \n***P < 0.001; **** P < 0.0001. Scale bars = 50 μm. si NC, negative-\ncontrol small interfering RNA; si AEBP1, AEBP1-specific small \ninterfering RNA; ov NC, empty vector control; ovAEBP1, AEBP1 \noverexpression plasmid; α-SMA, alpha-smooth muscle actin; CTGF, \nconnective tissue growth factor\n\n1 3\nPage 21 of 24   142 \nApoptosis          (2026) 31:142 \nstress may contribute to a profibrotic stromal phenotype. \nImportantly, however, the current data do not establish \nthat canonical cuproptosis occurs in endometriotic tissues; \nrather, they support an association between CRG-related \ntranscriptional programs, copper-dependent mitochondrial \nstress, and fibrosis-relevant fibroblast phenotypes.\nAlthough multi-omics approaches have substantially \nadvanced the understanding of endometriosis heterogeneity, \n \n\n1 3\n  142  Page 22 of 24\nApoptosis          (2026) 31:142 \nthe relationship between cuproptosis-related signaling and \nfibrosis-associated stromal states has remained poorly defined \n[17, 18, 35]. Prior research on cuproptosis in EMS have pre-\ndominantly concentrated on conventional bulk transcrip -\ntome-based bioinformatics techniques. In this investigation, \nwe initially conducted cuproptosis scoring for all cells at the \nsingle-cell level utilizing cuproptosis-related genes (CRGs). \nBy integrating single-cell analysis with network-based and \nmachine-learning approaches, our study extends previous \ntranscriptome-based observations and identifies a fibroblast-\nassociated CRG-high state linked to fibrotic remodeling.\nClinically, significant fibrosis is seen in endometriotic \nlesions, resulting in tissue or organ adhesions, anatomical dis-\narray, and in extreme instances, compromised fertility. AEBP1 \nhas been thoroughly investigated in various fibrotic diseases \n[15, 36–38]; specifically, endogenous AEBP1 expression is \nelevated in patients with cardiac hypertrophy and heart failure, \nand AEBP1 knockdown has been demonstrated to diminish the \ncontractile ability of cardiac fibroblasts and the expression of \nthe α-SMA gene, thereby underscoring AEBP1’s pivotal role in \ncardiac fibrosis [14]. AEBP1 is significantly expressed in can-\ncer-associated fibroblasts (CAFs) of patients with pancreatic \nductal adenocarcinoma (PDAC), where it facilitates fibrosis \nin the tumor microenvironment (TME) and enhances the inva-\nsion and migration of PDAC cells; elevated AEBP1 expres-\nsion correlates strongly with unfavorable prognosis in PDAC \n[13]. These studies collectively indicate that AEBP1 plays a \nsignificant role in fibrogenesis. Our data extend these obser-\nvations to endometriosis and support AEBP1 as a candidate \nfibrosis-associated mediator in EMS-associated fibroblasts. In \nsubsequent experiments, copper and its ionophore treatment \nwas accompanied by increased AEBP1 expression together \nwith elevated profibrotic markers, increased mitochondrial \nROS, and reduced mitochondrial membrane potential, whereas \nTTM or FDX1 knockdown attenuated these changes. These \nfindings support the possibility that AEBP1 participates in \ncopper-dependent profibrotic responses in endometrial stromal \ncells.\nCell–cell communication analysis further suggested that \nCRG-high stromal cells participate in stronger profibrotic \nsignaling interactions, including TGFβ and Wnt-related com-\nmunication. This observation is notable because β-catenin sig-\nnaling has previously been implicated in fibroblast activation \nand fibrosis in endometriosis and other organs. Together, these \ndata raise the possibility that copper-dependent stress responses \nmay influence not only stromal-cell intrinsic programs but also \nfibrosis-relevant intercellular signaling. Consistent with this \nmechanistic framework, manipulating copper-dependent stress \nin endometrial stromal cells coincided with modified expres-\nsion of β-catenin and c-Myc proteins. As β-catenin signaling is \nknown to regulate fibroblast activation and extracellular matrix \nproduction, these observations point to a plausible connection \nbetween stromal phenotypes with high CRG activity and profi-\nbrotic signaling driven by β-catenin.\nFurthermore, considering our earlier identification of \nAEBP1 as a candidate fibrosis-associated marker of EMS \nfibroblasts, we noticed that AEBP1 knockdown attenuated the \nincreases in profibrotic markers and β-catenin/c-Myc-related \nprotein expression observed under copper ionophore treatment. \nThese results support a potential association among cupropto-\nsis-related signaling, AEBP1 expression, and β-catenin path-\nway activity. However, the present data do not yet define a \ndirect linear mechanism or establish AEBP1 as the sole media-\ntor linking these processes, and additional mechanistic studies \nwill be required to clarify causality.\nFrom a translational perspective, our findings indicate that \nAEBP1 merits further investigation as a fibrosis-associated \nbiomarker candidate in endometriosis, and targeting copper \nmetabolism may represent a promising antifibrotic therapeu-\ntic strategy. That said, our study did not assess circulating \nAEBP1 levels, its diagnostic performance, or stratification of \nfibrosis severity; thus, these translational implications should \nbe regarded as preliminary. Additionally, while TTM mitigated \nfibrotic phenotypes in our in vivo models, its therapeutic effects \nshould be attributed to the attenuation of copper-dependent \nmolecular perturbations, rather than definitive blockade of core \ncuproptosis signaling. Future work should validate AEBP1 \nin larger and independent cohorts, define more precisely how \nAEBP1 relates to β-catenin pathway activity, and determine \nFig. 10 Tetrathiomolybdate attenuates lesion growth and fibrosis in a \nmouse model of endometriosis. A Schematic diagram of the mouse \nendometriosis model and treatment protocol. Uterine fragments from \ndonor C57BL/6 female mice were implanted onto the abdominal wall \nof recipient mice. Three weeks after model establishment, recipient \nmice received saline or tetrathiomolybdate (TTM, 50 mg/kg, intra -\ngastric administration) for 2 weeks and were sacrificed on day 45. B \nRepresentative image of excised ectopic lesions from control EMS and \nTTM-treated mice. C Representative in situ images of abdominal wall \nendometriotic lesions in control EMS and TTM-treated mice. White \narrows indicate lesion sites. D Representative western blots and den -\nsitometric quantification of FDX1, LIAS, and Lip-DLAT in ectopic \nlesions. E Representative western blots and densitometric quantifica -\ntion of AEBP1, α-SMA, and CTGF in ectopic lesions. F Representative \nwestern blots and densitometric quantification of β-catenin and c-Myc \nin ectopic lesions. β-actin was used as the loading control. G Repre-\nsentative immunohistochemistry staining of AEBP1, CTGF, α-SMA, \nand β-catenin in ectopic lesions from control EMS and TTM-treated \nmice; IgG staining served as the negative control. Quantification of \nrelative IHC scores is shown below. H Representative Masson’s tri -\nchrome staining of ectopic lesions and quantification of relative col -\nlagen deposition. For the animal study, recipient mice were assigned \nto control EMS (n = 15) and TTM-treated EMS (n = 15) groups; donor \nmice, n = 10. IHC and Masson quantification were performed on six \nlesions per group (n = 6). Data are presented as mean ± SD. Statistical \nanalysis was performed using a two-tailed unpaired Student’s t-test. \n**P < 0.01; *** P < 0.001; **** P < 0.0001. Scale bars = 50 μm. EMS, \nendometriosis; TTM, tetrathiomolybdate; IHC, immunohistochem -\nistry; α-SMA, alpha-smooth muscle actin; CTGF, connective tissue \ngrowth factor\n\n1 3\nPage 23 of 24   142 \nApoptosis          (2026) 31:142 \nwhether modulation of copper metabolism has reproducible \nantifibrotic effects in more physiologically relevant preclinical \nmodels of endometriosis.\nSeveral limitations of this study should be acknowledged. \nFirst, the single-cell analyses were based on a limited num-\nber of publicly available samples, although integration and \nquality-control procedures were applied to reduce technical \nbias. Second, the CRG score was derived from a predefined \n16-gene signature and AUCell-based inference, which reflects \npathway-related transcriptional activity rather than direct evi-\ndence of cell death. Third, although we assessed FDX1, LIAS, \nLip-DLAT, mitochondrial ROS, and mitochondrial membrane \npotential, these measurements do not by themselves conclu-\nsively demonstrate canonical cuproptosis in vivo. Finally, while \nour perturbation experiments support a regulatory relationship \nbetween AEBP1 and β-catenin-related changes, additional \nstudies, including rescue experiments and direct interrogation \nof pathway activity, will be needed to establish causality.\nCollectively, our data support an interpretive framework \nlinking cuproptosis-related molecular changes and copper-\ndependent mitochondrial stress to fibroblast activation and \nfibrotic remodeling in endometriosis. We further identify \nAEBP1 as a promising candidate mediator that warrants fur-\nther in-depth investigation. These findings advance our under-\nstanding of stromal heterogeneity in endometriosis and lay the \ngroundwork for exploring copper metabolism as a potential \nantifibrotic therapeutic target.\nConclusion\nIn conclusion, this study integrates single-cell transcriptomic \nanalysis with experimental validation to investigate the asso-\nciation between cuproptosis-related molecular alterations and \nfibrotic progression in endometriosis. By leveraging single-\ncell RNA sequencing, pseudotime trajectory analysis, and \nhdWGCNA, we identified a fibroblast subpopulation associ-\nated with relatively high cuproptosis-related gene activity and \nprofibrotic transcriptional features. Machine learning–based \nprioritization further highlighted AEBP1 as a candidate fibro-\nblast-associated gene linked to cuproptosis-related signatures.\nFunctional experiments showed that copper ionophore treat-\nment was accompanied by changes in fibrosis-related marker \nexpression in endometrial stromal cells, together with altered \nβ-catenin/c-Myc-related protein expression. In vivo, tetrathio-\nmolybdate attenuated lesion fibrosis and reduced the expres-\nsion of fibrosis-related markers, supporting the possibility that \nmodulation of copper metabolism may have antifibrotic effects \nin endometriosis.\nCollectively, our findings support an association between \ncuproptosis-related molecular alterations or copper-depen -\ndent mitochondrial stress and fibroblast-mediated fibrosis in \nendometriosis, and identify AEBP1 as a candidate molecule for \nfurther investigation. These results also provide a rationale for \nfurther exploring copper metabolism as a potential antifibrotic \ntarget in this disease.\nSupplementary Information  The online version contains \nsupplementary material available at  h t t  p s : /  / d o  i . o  r g / 1 0 . 1 0 0 7 / s 1 0 4 9 5 - 0 \n2 6 - 0 2 3 4 2 - x     .  \nAcknowledgements Not applicable.\nAuthor contributions L Zhang and Y Liu proposed the idea and re -\nviewed the manuscript, E Huang and J Li drafted and revised the initial \nmanuscript. E Huang, D Zuo, and R Li performed the experiments. J \nLi, Q Wu, N Lin, J Zhao and H Wang analyzed the data. All authors \nread and approved the final manuscript.\nFunding This study was financially supported by the National Natural \nScience Foundation of China (numbers: 82371681, U24A20658), Hu-\nbei Provincial Natural Science Foundation of China (2024AFB675), \nand National Key R&D Program of China (2023YFC2705505).\nData availability The public datasets used in this paper are available \non the NCBI website (https:/ /www.nc bi.nlm. nih.g ov/geo/).\nDeclarations\nConflict of interest The authors declare that they have no conflict of \ninterest.\nEthical approval and consent to participate The collection of endome-\ntriosis samples from human subjects was approved by the Ethics Com-\nmittee of Tongji Medical College, Huazhong University of Science \nand Technology and followed the tenets of the Declaration of Helsinki. \nThe patients provided their written informed consent to participate in \nthis study.\nOpen Access   This article is licensed under a Creative Commons \nAttribution-NonCommercial-NoDerivatives 4.0 International License, \nwhich permits any non-commercial use, sharing, distribution and \nreproduction in any medium or format, as long as you give appropri -\nate credit to the original author(s) and the source, provide a link to the \nCreative Commons licence, and indicate if you modified the licensed \nmaterial. You do not have permission under this licence to share \nadapted material derived from this article or parts of it. The images or \nother third party material in this article are included in the article’s Cre-\native Commons licence, unless indicated otherwise in a credit line to \nthe material. If material is not included in the article’s Creative Com -\nmons licence and your intended use is not permitted by statutory regu-\nlation or exceeds the permitted use, you will need to obtain permission \ndirectly from the copyright holder. To view a copy of this licence, visit \nhttp:// creativ ecommon s.org /licenses/by-nc-nd/4.0/.\nReferences\n1. Seli E, Berkkanoglu M, Arici A (2003) Pathogenesis of endome-\ntriosis. Obstet Gynecol Clin North Am 30:41–61.  h t t  p s : /  / d o  i . o  r g / \n1 0 . 1 0 1 6 / s 0 8 8 9 - 8 5 4 5 ( 0 2 ) 0 0 0 5 2 - 9       \n2. Olive DL, Schwartz LB (1993) Endometriosis. 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