{"paper_id":"d5ceface-3c18-4dd9-b076-41d5dfcde998","body_text":"RESEARCH Open Access\n© The Author(s) 2025. Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International \nLicense, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate \ncredit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. \nYou do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party \nmaterial in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Discover Oncology         (2025) 16:1088 \nhttps://doi.org/10.1007/s12672-025-02887-4\n*Correspondence:\nJie Yang\njie.yang.work@vip.163.com\n1Department of Pharmacy, \nChangning Maternity and Infant \nHealth Hospital, East China Normal \nUniversity, No.786 Yuyuan Road, \nChangning District,  \nShanghai 200051, China\n2Department of Pharmacy, \nShanghai Geriatric Medical Center, \nShanghai 201104, China\n3Department of Pharmacy, \nZhongshan Hospital, Fudan \nUniversity, Shanghai 200032, China\nUnraveling the interrelationship between \nbreast cancer and endometriosis based \non multi-omics analysis\nJie Yang1*, Ping-Ting Li2 and Sheng-Ying Xi3\nDiscover Oncology\nAbstract\nBackground Endometriosis and breast cancer are significant global health burdens \naffecting women worldwide. Both conditions share notable characteristics including \nestrogen dependence, progressive growth patterns, recurrence tendencies, and \nmetastatic potential. Despite these biological parallels, the molecular mechanisms \nconnecting these conditions remain incompletely characterized. This study aimed to \nidentify shared gene signatures and underlying molecular processes in breast cancer \nand endometriosis.\nMethods Expression matrices for both conditions were obtained from the Gene \nExpression Omnibus (GEO), UCSC Xena, and the Molecular Taxonomy of Breast \nCancer International Consortium. Common differentially expressed genes (DEGs) \nwere identified using the limma package. Comprehensive analyses included Gene \nOntology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway \nenrichment, machine learning-based diagnostic and prognostic model development, \npotential therapeutic compound screening, tumor immune microenvironment (TIME) \ncharacterization, and hub gene identification with subsequent validation.\nResults The analysis identified 47 common DEGs between breast cancer and \nendometriosis. Functional assessment of these genes revealed their involvement \nin critical biological processes including cell cycle regulation, oxidative stress \nresponse, and secretory granule and recycling endosome dynamics. Integration of \ncomprehensive genomic and clinical data led to the development of a prognostic \nmodel for breast cancer and a diagnostic model for endometriosis.\nConclusion This study provides molecular insights into shared pathogenic \nmechanisms underlying breast cancer and endometriosis, highlighting common \nphysiological pathways and key regulatory genes. These findings offer novel \nperspectives for understanding disease pathogenesis and potential therapeutic \ninterventions for both conditions.\nKeywords Breast cancer, Endometriosis, Multi-omics analysis, Machine learning, Hub \ngenes\n\nPage 2 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \n1 Introduction\nEndometriosis and breast cancer are major global health challenges for women. Endo -\nmetriosis affects 5–10% of reproductive-age women, with over 176 million cases world -\nwide [1, 2]. Characterized by ectopic endometrial tissue growth, this condition manifests \nas pelvic pain, dysmenorrhea, and infertility [ 3]. These symptoms occur in 50–80% of \nwomen with pelvic pain and up to 50% of those experiencing fertility difficulties [ 2, 4]. \nThe pathogenesis primarily involves retrograde menstruation, wherein endometrial \nfragments flow into the peritoneal cavity, where they implant and infiltrate pelvic struc -\ntures [5, 6]. Additional contributing factors include obstructed menstrual flow, extended \nestrogen exposure (from early menarche or late menopause), genetic predisposition, \nimmune dysfunction, and lifestyle factors. As an estrogen-dependent chronic inflam -\nmatory disorder, molecular alterations in estrogen signaling and inflammatory pathways \nfacilitate both implantation and proliferation of abnormal endometrial tissue [7].\nDiagnosis of endometriosis typically involves pelvic examination and ultrasound imag-\ning, though laparoscopy with histopathological confirmation remains the gold standard \ndespite risks including trauma, adhesion formation, and potential impacts on fertility \n[8]. The biomarker CA125, while elevated in advanced disease, lacks sensitivity for early \ndetection. The absence of reliable peripheral blood or endometrial tissue biomarkers, \ncoupled with the requirement for invasive surgical procedures, often delays diagnosis \nby 7–11 years, hampering timely intervention [ 9]. Addressing these limitations is essen -\ntial for developing non-invasive diagnostic approaches and elucidating the fundamental \nmechanisms of endometriosis.\nBreast cancer accounted for 11.7% of all global cancer cases in 2020, with approxi -\nmately 2.3 million new diagnoses, representing a leading cause of mortality among \nwomen [ 10]. Risk factors include advancing age, genetic predisposition, history of \nbenign breast disease, endogenous hormone exposure, fertility issues, obesity, and radia-\ntion exposure [ 11]. Diagnostic evaluation comprises comprehensive clinical assessment \nand detailed imaging (mammography, breast ultrasound), typically confirmed by core \nbiopsy before treatment planning [ 12]. Research has classified breast cancer into four \nmajor molecular subtypes through gene clustering analysis [ 13]: luminal, human epider-\nmal growth factor receptor 2 (HER2)-enriched, basal-like, and normal breast-like. At the \nRNA level, subtype differentiation primarily depends on estrogen receptor (ER) activ -\nity, ER-associated genes, proliferation drivers, and to a lesser extent, HER2 and genes \nwithin the HER2 amplicon on chromosome 17 [14]. Treatment strategies based on diag-\nnostic findings typically include surgery, radiotherapy, chemotherapy, targeted therapy, \nand endocrine treatment [ 12, 15]. The heterogeneity of breast cancer is reflected in its \nmultiple clinically relevant mutations, with molecular characterization of metastatic dis-\nease and subsequent targeted therapy assessed through next-generation sequencing and \nmutation analysis, potentially improving prognosis and survival.\nEndometriosis and breast cancer share several significant characteristics and risk \nfactors, including estrogen dependence, progressive growth patterns, invasiveness, \nrecurrence, and metastatic potential [ 16]. Elevated estrogen levels in ectopic lesions \nof endometriosis patients [ 17] and endogenous hormone exposure both contribute to \nincreased breast cancer risk. The infertility associated with endometriosis often results in \nnulliparity or delayed childbearing, established risk factors for breast cancer [ 18]. More-\nover, common treatments for endometriosis, such as progestins and oral contraceptives, \n\nPage 3 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nmay influence breast health [19]. While research has established a significant association \nbetween endometriosis and increased risk of epithelial ovarian cancer [ 5], evidence link-\ning endometriosis to breast cancer progression remains inconclusive. Further investiga -\ntion is needed to elucidate the underlying pathological connections and identify shared \ngenetic markers between these conditions, potentially revealing common drug targets \nand improving treatment strategies for both diseases.\nThe development of biomarkers for endometriosis and breast cancer that combine \nhigh sensitivity with precise specificity remains inadequate. Understanding the biolog -\nical pathways and molecular networks underlying these diseases is essential for effec -\ntive screening, prevention, diagnosis, and treatment. In this study, we analyzed datasets \nfrom the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and \nthe Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) to \ninvestigate the relationship between shared differentially expressed genes in both dis -\neases and their impact on endometriosis diagnosis and breast cancer prognosis. Using \nmachine learning algorithms, we identified 11 signature genes predictive of endometri -\nosis and constructed a three-gene model for breast cancer prognosis. This model was \nvalidated with both internal and external datasets, confirming its stability and reliability \nin predicting outcomes for breast cancer patients. Our findings suggest potential novel \nbiomarkers for endometriosis diagnosis and breast cancer prognostication, while also \nhighlighting possible therapeutic targets.\n2 Materials and methods\n2.1 Data acquisition\nDatasets for endometriosis and breast cancer were obtained from multiple platforms. \nTwo endometriosis datasets, GSE51981 [20] and GSE35287 [21], were acquired from the \nNCBI GEO. The GSE51981 dataset, generated using the Affymetrix Human Genome \nU133 Plus 2.0 array (GPL570), contained 77 samples from endometriosis patients and 71 \nsamples from healthy controls. The GSE35287 dataset, used for external validation, was \nproduced with the Affymetrix Human Gene 1.0 ST Array (GPL6244) and included 40 \nendometriosis and 40 normal samples.\nBreast cancer datasets from TCGA and METABRIC were obtained from cBioPortal \n[22] and UCSC Xena [ 23]. These datasets were generated using the Illumina platform, \nwith TCGA comprising 1050 tumor and 98 normal samples, and METABRIC containing \n1980 tumor samples, which served as external validation cohorts.\n2.2 Data preprocessing\nData from GEO were processed according to previously described methods using the \n“GEOquery” R package [ 24]. Gene probes were annotated with gene symbols, and \nprobes lacking symbols or matching multiple symbols were excluded. For duplicate gene \nsymbols, the maximum expression value was retained.\n2.3 DEGs screening and Functional Analysis\nDEGs were identified using the “limma” package [25] from the TCGA-breast cancer and \nGSE51981 datasets. Genes with an absolute Log Fold Change (LogFC) greater than 1 and \nadjusted P-value below 0.05 were considered statistically significant. Common DEGs \nwere visualized with a Venn diagram, and their expression patterns were displayed in a \n\nPage 4 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nheatmap generated using R. Functional enrichment of these genes was analyzed through \nGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) path -\nways using Metascape [ 26], with a minimum overlap of 3 and enrichment factor of 1.5. \nEnrichment results with a P-value below 0.01 were considered statistically significant.\n2.4 Characteristic genes in endometriosis\nTo identify distinctive genes associated with endometriosis, three complementary \nmachine learning techniques were employed: Random Forests (RF), Least Absolute \nShrinkage and Selection Operator (LASSO) logistic regression, and Support Vector \nMachine-Recursive Feature Elimination (SVM-RFE). These methods were selected for \ntheir distinctive strengths: LASSO for feature selection and regularization to prevent \noverfitting, SVM-RFE for effective ranking of gene features, and RF for robust handling \nof complex interactions. The RF technique was implemented using the “randomForest” \npackage [27]. LASSO logistic regression was conducted with the “glmnet” package [ 28], \nselecting the minimal lambda as optimal. Optimization parameters were cross-verified \nwith a tenfold factor, ensuring minimal criteria for partial likelihood deviation. Genes \ncommonly identified across all models were selected for further analysis. A diagnos -\ntic column line graph predicting endometriosis occurrence was generated using the \n“rms” package. The GSE35287 dataset served as the validation set, with model effective -\nness evaluated through receiver operating characteristic (ROC) curves and area under \nthe curve (AUC). The predictive power and clinical utility of the model were further \nassessed using the consistency index (C-index) and decision curve analysis (DCA) based \non the calibration curve.\n2.5 Establishing prognostic markers in breast cancer\nThe prognostic relevance of common DEGs was initially assessed through univariate \nCox regression analysis, with significance defined at p < 0.05. The prognostic gene set \nwas refined using the stepwise Akaike information criterion (stepAIC) method imple -\nmented in the “MASS” package. Individual patient risk scores were derived using the \nfollowing equation:\nRisk score =\n∑ N\ni=1\n(E xpi× Coei)\nwhere E xpi and Coei are the normalized expression levels and corresponding regres -\nsion coefficients of the candidate genes, respectively. Patients were stratified into high- \nand low-risk categories based on the median risk score as the cutoff value. The efficacy \nof the gene signature was evaluated through Kaplan–Meier survival plots and ROC \ncurve analyses using the ‘survminer’ , ‘survival’ , and ‘survivalROC’ packages. The prognos-\ntic independence of the risk score from other clinical variables in breast cancer patients \nwas determined through both univariate and multivariate Cox regression analyses.\n2.6 Prognostic characteristics of the tumor microenvironment\nThis study compared genomic alterations, gene expression patterns, immune microenvi-\nronment composition, hypoxia status, tumor stemness scores, and biological functions \nbetween risk groups. We used “maftools” and cBioPortal to analyze gene mutations. The \nabundance of immune cells in each patient sample was determined by single-sample \n\nPage 5 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \ngene set enrichment analysis (ssGSEA), using marker genes for 28 distinct immune cell \ntypes as reference [ 29]. Hypoxia scores were obtained from cBioPortal, and drug sensi -\ntivities were predicted using “oncoPredict” [30].\nTumor stemness was evaluated using 26 gene sets from StemChecker [ 31], employ -\ning ssGSEA via the GSVA method to derive stemness enrichment scores. Differential \ngene expression analysis was performed to compare high- and low-risk groups. Gene set \nvariation analysis (GSVA) was performed using hallmark gene sets from MSigDB v7.5. \nThe resulting enrichment scores, reflecting pathway activity in individual samples, were \ncompared between risk groups using the Wilcoxon rank-sum test. DEGs were identi -\nfied using thresholds of |logFC| > 1 and FDR < 0.05. These genes underwent GO/KEGG \npathway analysis using Metascape.\n2.7 Statistical evaluation methods\nAll statistical analyses were performed using R (version 4.3.1). Prognostic outcomes \nand survival rates across patient subgroups were analyzed using Kaplan-Meier survival \nplots and the log-rank test. Normality of data distribution was evaluated using the Sha -\npiro-Wilk test. Due to significant deviation from normal distribution in most variables, \nnon-parametric statistical methods were selected for between-group comparisons. The \nWilcoxon rank-sum test was used for two-group comparisons, while the Kruskal-Wallis \ntest was applied for analyses involving multiple groups. The prognostic significance of \nclinical characteristics within high- and low-risk groups was determined using both uni -\nvariate and multivariate Cox regression analyses, conducted via the “survival” package in \nR.\n2.8 qRT-PCR methodology\nTotal mRNA was isolated from cellular samples using TRIpure reagent (ELK Biotechnol-\nogy). Reverse transcription was performed using EntiLink™ 1 st Strand cDNA Synthesis \nSuper Mix with the following temperature profile: 5 min at 25 °C, 30 min at 42 °C, and \n5 min at 85 °C. Quantitative real-time PCR (qRT-PCR) was conducted using a real-time \nPCR system (Applied Life Technologies, USA), with relative expression levels calculated \nusing the 2^-ΔΔCT method. Specific primers were used for targeted gene amplification:\nH-ACTIN\nForward: GTCCACCGCAAATGCTTCTA\nReverse: TGCTGTCACCTTCACCGTTC\nH-SHCBP1\nForward: GGTGCTGGTATAGAAATCTACCCT\nReverse: GTTTCACCAAGACAACACCATAAC\nH-PMAIP1\nForward: GTGCTACTCAACTCAGGAGATTTG\nReverse: TCTTTCTTCAAATTGATGAAACGT\nH-LTF\nForward: TGCAAATTTGATGAATATTTCAGTC\nReverse: CATTGTTATTTCCATCAGTGTTCTG\n\nPage 6 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \n2.9 Western blotting\nCells were lysed using Aspen buffer for total protein extraction. Proteins were separated \nby SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% \nskim milk and incubated with primary antibodies: SHCBP-1 (No:12672-1-AP , 1:1000, \nProteintech), PAMIP (No: PA5-19977, 1:500, Thermofisher), LTF (No:10933-1-AP , \n1:1000, Proteintech), and GAPDH (Cat No. ab181602, 1:10000, Abcam). After washing, \nthe membranes were incubated with secondary antibodies (1:10000, Aspen). Protein \nbands were visualized, scanned, and documented. For both PCR and western blotting \n(WB) experiments, each gene was analyzed in duplicate and all experiments were per -\nformed in triplicate. Neither PCR nor WB procedures were conducted under blind con -\nditions. Statistical analysis was performed using SPSS. Differences between groups were \nassessed using one-way ANOVA and Student’s T-test, with P < 0.05 considered statisti -\ncally significant.\n3 Results\n3.1 Identification of common genes associated with endometriosis and breast cancer\nDifferential expression analysis identified 1,600 DEGs between breast cancer and normal \ntissue samples in the TCGA-breast cancer cohort, and 179 DEGs between endometrio -\nsis and normal tissues in the GSE51981 cohort (Fig.  1A, B). Further analysis revealed 47 \ncommon genes associated with both endometriosis and breast cancer in these cohorts. \n(Fig.  1C). Expression profiles of these 47 genes were characterized for both cohorts \n(Fig.  1D, E). GO/KEGG pathway analysis demonstrated enrichment in biological pro -\ncesses including chromosome segregation, cell cycle regulation, positive regulation \nof cell cycle phase transition, oxidative stress response, muscle cell development, and \nsecretory granule and recycling endosome dynamics (Fig.  1F, G).\n3.2 Selection of endometriosis’s signature genes using machine learning algorithm\nEndometriosis biomarkers were identified using three machine learning algorithms: RF, \nSVM-RFE, and LASSO regression. The RF model identified 22 genes (Fig.  2A), SVM-\nRFE identified 43 genes (Fig.  2B), and LASSO analysis yielded 18 genes (Fig.  2C, D). \nIntersection of these results revealed 11 robust core biomarkers (OLFM4, APOBEC3B, \nBPIFB1, CPM, MSRB3, EZH2, SCGB3A1, F13A1, PTGER3, FOS, and RCAN1) (Fig.  2E). \nUsing the ‘rms’ package, we constructed a diagnostic column line graph for endome -\ntriosis (Fig.  2F). A calibration curve showed minimal deviation between predicted and \nactual risk, confirming the model’s accuracy (Fig.  3A, B). DCA demonstrated that this \nmodel provided significant net benefit compared to alternative strategies (Fig.  3C, D). \nThe model exhibited high AUC values in both the training (GSE51981) and external vali-\ndation (GSE35287) sets, with scores of 0.896 and 0.988, respectively (Fig.  3E, F). These \nfindings corroborated the superior predictive performance of the diagnostic model.\n3.3 Development and evaluation of breast cancer prognostic models\nWe developed a prognostic model for breast cancer using univariate Cox regression \nanalysis, which initially identified five genes with significant prognostic impact (p < 0.05). \nFurther refinement through stepAIC analysis yielded three key prognostic genes. The \nrisk score was calculated as: Risk score = (0.2857) × SHCBP1 + (−0.1610) × PMAIP1 \n+ (−0.0534) × LTF (Supplementary Fig. 1). The median risk score served as the cutoff  \n\nPage 7 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \npoint to stratify patients into high- and low-risk groups, and was applied consistently in \nthe external validation cohort (METABRIC) to assess model generalizability. Based on \nmedian signature values, 525 patients were categorized into high- or low-risk groups. \nIn the TCGA cohort, the low-risk (LR) group demonstrated significantly longer overall \nsurvival (OS) than the high-risk (HR) group (median duration 215.0 months vs. 115.0 \nmonths, p < 0.0001, Fig.  4A). Lower risk scores consistently correlated with improved \nsurvival (Fig.  4C). The model’s robustness was confirmed in the independent META -\nBRIC cohort, where LR patients also exhibited superior OS (median time = 167.0 months \nvs. 145.0 months, P = 0.02, Fig.  4B). These validation findings confirmed the efficacy of \nthe model across multiple datasets. The distribution of risk scores and survival status in \nFig. 1 Differential expression analysis. A Volcano graph of the normal group and breast cancer group in differ -\nential analysis. B Volcano diagram for difference analysis of normal group and endometriosis. C Venn Figure for \nintersected genes in differentially expressed genes of breast cancer and endometriosis. D Heat map of differential \nanalysis between breast cancer and normal group. E Heat map of differential analysis between endometriosis and \nnormal group. F, G The GO terms and KEGG pathway enrichment analysis of common DEGs. GO, Gene Ontology; \nKEGG, Kyoto Encyclopedia of Genes and Genomes\n \n\nPage 8 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nthe METABRIC cohort is shown in Fig. 4D. Both univariate and multivariate Cox regres-\nsion analyses confirmed that the prognostic risk score was independent of other clini -\ncal characteristics including age, stage, TNM classification, and radiation therapy in the \nTCGA-breast cancer cohort (Fig. 4E, F).\n3.4 Association between cancer hallmarks and risk groups\nWe examined correlations between risk scores and immune responses by measuring \nenrichment scores for immune cell subsets and their associated activities through ssG -\nSEA. The LR group showed greater infiltration by eosinophils, mast cells, natural killer \n(NK) cells, neutrophils, and plasmacytoid dendritic cells (Fig.  5A). In contrast, the HR \ngroup displayed elevated levels of activated CD4 and CD8 T cells, effector memory CD4 \nFig. 2 Detection of diagnostic markers using machine-learning algorithms in endometriosis. A Based on RF algo-\nrithm to screen biomarkers. B Based on SVM-RFE to screen biomarkers. C, D LASSO logistic regression algorithm to \nscreen diagnostic markers. E Venn diagram showed the intersection of diagnostic markers obtained by the three \nalgorithms. F Nomogram is used to predict the occurrence of Endometriosis\n \n\nPage 9 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nT cells, γδ T cells, and regulatory T cells (Fig.  5A). Expression of immune checkpoint \ninhibitors varied significantly with risk scores. Patients in the LR category exhibited \nincreased expression of NRP1, CD200, and CD44, while those in the HR group showed \nelevated levels of CD276, IDO1, PDCD1LG2, and TNFRSF9 (Fig.  5B). Cancer stem \ncell assessment using 26 stemness gene sets revealed higher enrichment scores in the \nHR group (Fig.  5C). Additionally, HR patients demonstrated elevated hypoxia scores \n(Fig. 5D) and higher non-synonymous tumor mutation burden (TMB) (Fig. 5E). Analysis \nof the 15 most frequently mutated genes revealed distinct mutation patterns between \nrisk groups (Fig.  5F), with significant differences observed for PIK3CA (22% in HR vs. \n44% in LR) and TP53 (50% in HR vs. 17% in LR) (Fig.  5G). Further genomic analyses \nFig. 3 Verification of nomogram model for endometriosis. A, B Construction of the calibration curve for assessing \nthe predictive efficiency of the nomogram model in both A GSE51981 and B GSE35287. C, D Decision curve analy-\nsis of risk prediction nomogram for endometriosis in both C GSE51981 and D GSE35287. E, F ROC curve validation \nof risk prediction nomogram for endometriosis in both E GSE51981 and F GSE35287\n \n\nPage 10 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nshowed that the HR group had significantly higher fraction genome altered (FGA) and \ndistinctive copy number variation (CNV) patterns compared to the LR group (Fig.  5H, \nI).\n3.5 Efficacy of prognostic signature in predicting drug sensitivity\nWe evaluated associations between our prognostic model and drug responsiveness by \nmeasuring IC 50 values for various therapeutic agents in breast cancer samples. Differ -\nences in IC 50 values indicated varying drug sensitivities correlated with risk groups \n(Fig.  6A). Higher IC 50 values for Lapatinib, Temsirolimus, and Vinorelbine in the HR \ngroup indicated resistance to these agents, whereas lower IC 50 values for Cisplatin, \nFig. 4 Construction and validation of a prognosis signature for breast cancer. A, B Overall survival in the low- and \nhigh-risk score group patients in A TCGA- breast cancer and B METABRIC. C, D Distribution of risk score according \nto the survival status and time in C TCGA- breast cancer and D METABRIC. E Univariate analysis for the clinico -\npathologic characteristics and risk score in TCGA- breast cancer. F Multivariate analysis for the clinicopathologic \ncharacteristics and risk score in TCGA- breast cancer. StepAIC: stepwise Akaike information criterion\n \n\nPage 11 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nPaclitaxel, and Rapamycin suggested sensitivity (Fig.  6B-G). These findings highlighted \nthe potential utility of Cisplatin, Paclitaxel, and Rapamycin in treating chemotherapy-\nresistant breast cancer.\n3.6 Biological characteristics between risk groups\nAnalysis of the prognostic gene model revealed distinct biological characteristics \nbetween risk groups. Differential expression analysis identified 91 genes, visualized in \na volcano plot (Fig.  7A). A protein-protein interaction (PPI) network constructed using \nthe Metascape database with the MCODE plug-in (minimum interaction score of 0.7) \nidentified two critical functional modules (Fig.  7B). GO/KEGG pathway analysis linked \nthese genes to diverse biological processes including cell cycle phase transition, mitotic \ncell cycle regulation, immune response, epithelial cell differentiation, inflammatory \nresponse, neuronal apoptotic regulation, supramolecular fiber organization, and cortical \nFig. 5 Dissection of tumor microenvironment based on prognosis signature. A The box plot of 28 infiltrated \nimmune cell types was calculated by ssGSEA. B Box plot of expression levels of immune checkpoint-associated \ngenes. C Box plot displaying the differences of 26 ssGSEA stemness scores between low risk and high-risk group. D \nViolin plot of significantly increased hypoxic score in high-risk patients. E Comparison of tumor mutation burden \n(TMB). F Oncoplot of mutation, deletion, insertion, and frameshift. G Comparison of different mutation sites of \nTP53 and PIK3CA. H The score of fraction of genome altered (FGA) in different risk groups. I Copy number variation \n(CNV) patterns in different risk cohorts. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001\n \n\nPage 12 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nactin cytoskeleton dynamics (Fig.  7C-D). GSVA demonstrated significant associations \nbetween the HR group and DNA damage repair and cell cycle-related functions (Fig. 7E).\n3.7 Validation of breast cancer prognostic gene expression through qRT-PCR and WB\nExpression levels of key prognostic genes were validated using both qRT-PCR and WB \nin breast cancer and control samples. Results confirmed significantly higher expression \nof SHCBP1 and PMAIP1 in breast cancer samples, while LTF expression was markedly \ndecreased (Fig.  8A-E). These findings reinforced the potential utility of these genes as \nbiomarkers for predicting breast cancer outcomes.\nFig. 6 Efficacy of prognosis signature in predicting drug sensitivity. A Bubble plot of the relationship between \ndrugs and model genes. Boxplots of the comparison of IC50 of drugs between high- and low-risk groups, and cor-\nrelation between the IC50 and riskscore in TCGA- breast cancer cohort: B Lapatinib; C Temsirolimus; D Vinorelbine; \nE Cisplatin; F Paclitaxel; G Rapamycin\n \n\nPage 13 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \n4 Discussion\nEndometriosis, a chronic gynecological disorder dependent on estrogen, exhibits traits simi-\nlar to malignant cells despite its benign classification, including local and distant metastasis \nwith resultant tissue damage [2]. This condition shares several risk factors with breast cancer, \nincluding endogenous estrogen exposure, reproductive characteristics, obesity, and hormone \nreplacement therapy. Our study explored these associations, suggesting that identification \nof common differential genes and construction of prognostic risk models for breast cancer \ncould elucidate shared underlying mechanisms and potentially reveal novel biomarkers for \nbreast cancer prognosis.\nFig. 7 Biologic functions underlying the breast cancer prognostic model. A Volcano plot showed DEGs (FDR < 0.05 \nand |log2FC|> 1) between high risk and low-risk group. B PPI network of differentially expressed genes between \nhigh risk and low-risk group based on the Metascape website. C, D The GO terms and KEGG pathway enrichment \nanalysis of differentially expressed genes. E Heatmap of GSVA analysis shows different biological functions be -\ntween high risk and low-risk group. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes\n \n\nPage 14 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nAccurate diagnosis of endometriosis remains challenging, often resulting in delays and \nmisdiagnoses [32], highlighting the need for precise clinical diagnostic tools to initiate timely \ntreatment. This investigation employed three machine learning algorithms—RF, LASSO \nlogistic regression, and SVM-RFE—to identify eleven robust core biomarkers: OLFM4, \nAPOBEC3B, BPIFB1, CPM, MSRB3, EZH2, SCGB3A1, F13A1, PTGER3, FOS, and RCAN1. \nThese biomarkers demonstrated high diagnostic accuracy for endometriosis in a diagnostic \ncolumn line graph, outperforming other strategies and indicating significant clinical utility.\nOLFM-4, an extracellular matrix protein highly expressed in human endometrium [33], \nis downregulated in endometriosis compared to controls [9]. This protein may stabilize the \nendometrium and modulate inflammation through negative regulation of M2 macrophages \n[34]. APOBEC3B, a member of the cytidine deaminases superfamily [ 35], contributes to \nDNA mutation by converting cytosine to uracil, potentially increasing the mutational burden \nin endometriosis [36, 37] and is associated with poorer outcomes in ER-positive breast can-\ncer due to its elevated expression [38–40]. EZH2, a component of the polycomb repressive \ncomplex 2 (PRC2), mediates transcriptional silencing through histone H3 methylation [41, \n42]. Hypoxic conditions enhance EZH2 expression, amplifying activity in pathways such as \nWnt/β-catenin that are critical in the epithelial-to-mesenchymal transition observed in both \nbreast cancer [43] and endometriosis [44].\nBPIFB1 expression is stimulated by estrogen, and elevated levels correlate with negative \nprognosis in luminal A breast cancer [45, 46]. MSRB3, a protein repair enzyme, is associated \nwith apoptotic cell death in various cancers, including breast cancer [47]. FOS, an immediate \nresponse gene, plays a crucial role in estrogen-driven proliferation of endometrial cells [48]. \nPTGER3, a receptor with high affinity for prostaglandin E2 (PGE2), is upregulated in endo-\nmetriosis and implicated in tumor-associated angiogenesis, influencing clinical outcomes in \nvarious cancers [8, 49]. RCAN1 functions as a tumor suppressor, inhibiting cellular growth \nand angiogenesis in breast cancer [ 50]. Secretoglobin family 3  A member 1 (SCGB3A1) \nenhances stem cell characteristics and aggressiveness in breast cancer cells [51]. Carboxy-\npeptidase M (CPM), found on tumor-associated macrophages, may serve as a cancer bio-\nmarker [52]. Factor XIII A chain (F13A1) participates in fibrin network stabilization and \nFig. 8 The expression of genes was verified by qRT-PCR and West-blotting. A The expression of SCHBP1 between \nbreast cancer group and control group. B The expression of PMAIP1 between breast cancer group and control \ngroup. C The expression of LTF between breast cancer group and control group. D Protein expression levels of \nSCHBP1, PMAIP1 and LTF in breast cancer group 1 and control group. E Protein expression levels of SCHBP1, \nPMAIP1 and LTF in breast cancer group 2 and control group. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001\n \n\nPage 15 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \npotentially facilitates tumor matrix formation and progression [53]. These genes may play \nkey roles in the development of both diseases and could serve as targets for future therapies.\nThrough univariate Cox regression analysis combined with stepAIC, we constructed a \nprognostic model incorporating three key genes: SHCBP1, PMAIP1, and LTF. This model \neffectively stratified breast cancer patients into high- and low-risk groups. The HR group \ndemonstrated significantly reduced OS compared to the LR group in both the TCGA-breast \ncancer and METABRIC cohorts. The model’s reliability was further validated in the META-\nBRIC study. Within the TCGA-breast cancer cohort, model-derived risk scores emerged as \nindependent prognostic factors, remaining significant regardless of age, stage, TNM classifi-\ncation, or radiation treatment status.\nSHCBP1, a member of the SHC protein family, plays vital roles in cell proliferation, migra-\ntion, adhesion, and cell cycle regulation, contributing significantly to carcinogenesis [54, 55]. \nIn breast cancer, elevated SHCBP1 expression correlates with advanced clinical stages and \nshorter survival times [54, 56–58]. PMAIP1, a pro-apoptotic member of the BCL-2 protein \nfamily, interacts with the p53 pathway to enhance apoptosis [59–62]. It functions as a tumor \nsuppressor and shows elevated expression in breast cancer samples [63], with critical impor-\ntance in paclitaxel response in triple-negative breast cancer [ 64]. High PMAIP1 mRNA \nexpression represents a positive prognostic marker for relapse-free and OS across diverse \nbreast cancer molecular subtypes [64]. LTF, a multifunctional glycoprotein belonging to the \ntransferrin family, exhibits significant anti-tumor properties through mechanisms including \ninhibition of tumor cell proliferation and promotion of apoptosis or necrosis [65–68]. Pan-\ncancer analysis confirms that low LTF expression in tumors supports its classification as a \ntumor suppressor gene [69].\nFurther analysis revealed distinct patterns in immune cell infiltration and immune check-\npoint expression between risk groups. The LR group exhibited increased infiltration by \neosinophils, mast cells, and NK cells. Conversely, the HR group showed greater presence of \nactivated CD4 and CD8 T cells, alongside elevated stemness enrichment scores and hypoxia \nscores, suggesting more aggressive tumor characteristics. Despite this activation pattern, the \nLR group maintained higher total CD8 + T cell levels with reduced immunosuppressive M2 \nmacrophage presence—potentially explaining enhanced immunotherapy responsiveness. \nPharmacogenomic analyses revealed higher predicted Lapatinib IC50 values in the HR group, \nindicating potential HER2-targeted therapy resistance. The HR group also demonstrated \nincreased non-synonymous mutation burden and aneuploidy, reflecting underlying genomic \ninstability.\nSeveral limitations exist regarding sample size and clinical annotation depth. Future inves-\ntigations require larger cohorts with comprehensive clinical and longitudinal data to enhance \nmodel generalizability and better account for clinical heterogeneity. Collaborations are being \nestablished to access well-annotated prospective datasets. Subsequent studies will imple-\nment network-based analyses with experimental validation to elucidate shared gene func-\ntions between pathologies. Advanced statistical approaches, including causal inference and \npropensity score matching, will address potential confounders. While METABRIC provided \nvaluable validation, cohort heterogeneity, processing variations, and treatment history differ-\nences necessitate further validation through prospective multi-center studies.\nThis study identified common genes between endometriosis and breast cancer, facilitating \nthe development of diagnostic and prognostic models. Our diagnostic model, based on 11 \ncore biomarkers, accurately predicted endometriosis onset. The prognostic model, utilizing \n\nPage 16 of 18\nYang et al. Discover Oncology         (2025) 16:1088 \nthree genes, effectively stratified breast cancer patients into distinct risk categories that cor-\nrelated with specific clinical outcomes and biological behaviors. These risk groups exhib-\nited unique immune cell profiles and genomic features, enhancing our understanding of the \nmolecular dynamics underlying both conditions. These insights are essential for advancing \npersonalized diagnostic and treatment approaches.\nSupplementary Information\nThe online version contains supplementary material available at https://doi.org/10.1007/s12672-025-02887-4.\nSupplementary Material 1\nAcknowledgements\nNot applicable.\nAuthor contributions\nAll authors contributed to the study conception and design. Writing - original draft preparation: [Jie Yang and Ping-Ting Li]; \nWriting - review and editing: [Jie Yang and Sheng-Ying Xi]; Conceptualization: [Jie Yang]; Methodology: [Jie Yang and Ping-Ting \nLi]; Formal analysis and investigation: [Sheng-Ying Xi]; Resources: [Jie Yang]; Supervision: [Jie Yang], and all authors commented \non previous versions of the manuscript. All authors read and approved the final manuscript.\nFunding\nThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\nData availability\nDatasets for endometriosis and breast cancer were obtained from multiple platforms. Two endometriosis datasets, \nGSE51981 and GSE35287, were obtained from the NCBI GEO, and Breast cancer datasets from TCGA and METABRIC were \nretrieved from cBioPortal and UCSC Xena.\nDeclarations\nCompeting interests\nThe authors declare no competing interests.\nEthical approval\nEthics approval not required.\nConsent to participate\nNot applicable.\nConsent to publish\nNot applicable.\nReceived: 10 February 2025 / Accepted: 2 June 2025\nReferences\n1. Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN, Viganò P . Endometriosis. Nat Rev Dis Primers. 2018.  h t t p s : / / d o i . o r \ng / 1 0 . 1 0 3 8 / s 4 1 5 7 2 - 0 1 8 - 0 0 0 8 - 5     .   \n2. Taylor HS, Kotlyar AM, Flores VA. 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