Multiomic insight into the involvement of cell aging related genes in the pathogenesis of endometriosis

Our study, following the STROBE-MR guidelines 15 , uses a Mendelian Randomization (MR) approach to examine the causal associations between cell aging-related genes and endometriosis. The study’s approach is based on a detailed review of genetic information, supported by multi-omics SMR and HEIDI tests. These advanced analytical tools assess the potential associations between genetic variants related to cell aging and the risk profile for endometriosis. Colocalization analysis pinpoints genetic determinants with potential dual effects on cell aging mechanisms and endometriosis progression. Figure  1 illustrates our study design and research approach concisely. Fig. 1 The flow diagram of the process in this SMR Mendelian randomization analysis. The flow diagram of the process in this SMR Mendelian randomization analysis. Fig. 2 Causal associations between the methylation levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the association between representative loci methylation levels and endometriosis. This figure displays a subset of representative CpG sites associated with endometriosis, for which the corresponding gene expression is also causally associated with endometriosis. ( B ) Map of colocalization evidence between cg21506229 (MAP3K5) and endometriosis. ( C ) Manhattan plot showing the distribution of gene methylation loci on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Causal associations between the methylation levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the association between representative loci methylation levels and endometriosis. This figure displays a subset of representative CpG sites associated with endometriosis, for which the corresponding gene expression is also causally associated with endometriosis. ( B ) Map of colocalization evidence between cg21506229 (MAP3K5) and endometriosis. ( C ) Manhattan plot showing the distribution of gene methylation loci on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Fig. 3 Causal associations between the expression levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the significant association between gene expression and endometriosis; ( B ) Map of colocalization evidence between MAP3K5 and endometriosis. ( C ) Manhattan plot showing the distribution of gene locus on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Causal associations between the expression levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the significant association between gene expression and endometriosis; ( B ) Map of colocalization evidence between MAP3K5 and endometriosis. ( C ) Manhattan plot showing the distribution of gene locus on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Fig. 4 Causal associations between the protein levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the significant association between protein abundance and endometriosis; ( B ) Map of colocalization evidence between EGLN1 and endometriosis. ( C ) Manhattan plot showing the distribution of genes corresponding to the proteins on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Causal associations between the protein levels of cell aging-related genes and endometriosis. ( A ) Forest plot depicting the significant association between protein abundance and endometriosis; ( B ) Map of colocalization evidence between EGLN1 and endometriosis. ( C ) Manhattan plot showing the distribution of genes corresponding to the proteins on chromosomes. The red dashed line corresponds to the significance threshold of p_SMR multi = 0.05. Fig. 5 SMR locus plot and SMR effect plot for MAP3K5. ( A ) SMR effect plot of MAP3K5 in eQTL; ( B ) SMR effect plot for MAP3K5 at cg21506299 in mQTL; ( C ) SMR effect plot for MAP3K5 at cg24999105 in mQTL; ( D ) The SMR locus plot for MAP3K5 in eQTL; ( E ) The SMR locus plot for MAP3K5 in mQTL. SMR locus plot and SMR effect plot for MAP3K5. ( A ) SMR effect plot of MAP3K5 in eQTL; ( B ) SMR effect plot for MAP3K5 at cg21506299 in mQTL; ( C ) SMR effect plot for MAP3K5 at cg24999105 in mQTL; ( D ) The SMR locus plot for MAP3K5 in eQTL; ( E ) The SMR locus plot for MAP3K5 in mQTL. The summary statistics of the primary discovery dataset was sourced from the Catalog database (ID: GCST90269970) with a case-control sample size of 21,779 cases and 449,087 controls of European ancestries 16 .For validation, we utilized GWAS summary statistics from the FinnGen R10 cohort (ID: N14_ENDOMETRIOSIS) with a case-control sample size of 16,588 cases and 111,583 controls of European ancestries 17 . Further validation was conducted using summary statistics from the UK Biobank (ID: 615: Endometriosis) with a case-control sample size of 4036 cases and 210,927 controls of European ancestries 18 . A total of 949 cell aging-related genes were identified from the CellAge, a comprehensive database that provides detailed information on genes and pathways associated with cell aging 19 (Table S1 ). To explore the genetic regulation of gene expression, we procured blood eQTL summary data from eQTLGen 14 , encompassing genetic expression data from 31,684 individuals. The blood mQTL summary data were obtained from a meta-analysis of two European cohorts 20 , including the Brisbane Systems Genetics Study comprising 614 participants and the Lothian Birth Cohorts with 1366 individuals. For the assessment of protein abundance, blood pQTL summary data were obtained from Benjamin et al. 21 , which included a cohort of 54,219 UK Biobank participants. We conducted an assessment of the tissue-specific expression of target genes using eQTL data retrieved from the Genotype-Tissue Expression (GTEx) database, probing into the potential causal effects of these genes on endometriosis. The GTEx v8 dataset encompasses a rich collection of 17,382 samples obtained from 838 donors, representing 52 different tissues and two cell lines. In our analysis of endometriosis, we specifically utilized eQTL data pertaining to the Uterus. We utilized the SMR software tool (version 1.3.1) to perform SMR and HEIDI tests, employing SMR to evaluate the association between methylation, gene expression, and protein abundance of cell aging-related genes and endometriosis. The SMR approach yields enhanced statistical power over traditional MR analysis when the exposure and outcome are derived from two large, independent cohorts, based on top cis-QTLs. Top cis-QTLs were selected using a ± 1000 kb window centered around the corresponding gene and a P-value threshold of 5.0 × 10 − 8 . SNPs with allele frequency differences exceeding the specified threshold (set at 0.2 for this study) between any pairwise datasets, including LD reference samples, QTL summary data, and outcome summary data, were excluded. The maximum proportion of SNPs with allele frequency differences greater than 0.2 was set at 0.05 for mQTLs, eQTLs, and pQTLs. Furthermore, we employed a multi-SNP based SMR analysis method that considers all SNPs within the QTL probe window area, with P-values below the default threshold of 5E − 8 and LD r2 values below the default of 0.9 with the top associated SNPs 20 . To differentiate between pleiotropy and linkage, we employed the heterogeneity in the dependent instrument (HEIDI) test. A P-HEIDI value below 0.05 was deemed suggestive of potential pleiotropy, resulting in the exclusion of the variant from subsequent analyses. Associations meeting the criteria (P-value < 0.05 and Multi-SNP-based P-value  0.05) were considered for colocalization analysis in QTLs datasets. Beyond exploring the causal associations between genes (methylation level, gene and protein expression level) and endometriosis, we further investigated the causal associations between gene methylation level and gene expression. These analyses aimed to determine whether the expression of a target gene is influenced by methylation at a specific CpG site, or whether gene expression regulates the abundance of its encoded protein. The intersecting results from the SMR analysis of mQTLs-GWAS and eQTLs-GWAS were considered key signals of interest between mQTLs and eQTLs. Additionally, we explored the causal associations between key eQTLs (as exposure) and pQTLs (as outcome), with a focus on key results from the integrated analysis of mQTL-eQTL. We conducted colocalization analysis using the R package ‘coloc’ to identify shared causal variants between cis-QTLs related to cell aging genes (mQTLs, eQTLs, and pQTLs) and endometriosis. When colocalization is observed between GWAS signals and QTLs, it indicates that the genetic variants identified could affect phenotypes through their impact on underlying gene functions. The colocalization analysis reported five distinct posterior probabilities corresponding to five mutually exclusive hypotheses: (1) H0: No traits in the region are genetically associated with the SNP; (2) H1: Only trait 1 is genetically associated with the SNP; (3) H2: Only trait 2 is genetically associated with the SNP; (4) H3: Both traits are associated with the SNP, but through different causal variants; (5) H4: Both traits are associated with the SNP, sharing a causal variant. The colocalization region windows for mQTL-GWAS, eQTL-GWAS, and pQTL-GWAS were set at ± 500 kb, ± 1000 kb, and ± 1000 kb, respectively 22 – 24 . To allow for colocalization of QTLs with less significant P-values, colocalization was successful for signals where the prior probability of colocalization (P12) = 5 × 10 − 5 and the posterior probability of H4 (PPH4) > 0.5 25 . All statistical analyses were performed using R (version 4.3.0). The R packages ‘ggplot2’ and ‘ggrepel’ were utilized for the construction of Manhattan plots, while ‘forestplot’ was employed for forest plot generation. The plotting codes for SMRLocusPlot and SMREffectPlot were sourced from Zhu et al. 12 . Table 1 SMR analysis results for mQTL–eQTL. Expo ID Outco gene p SMR p SMR multi p FDR p HEIDI OR SMR (95% CI) cg23803022 PMVK 2.51E−14 2.51E−14 7.89E−13 5.83E−04 0.59 (0.51–0.67) cg20065217 PMVK 9.02E−20 9.02E−20 4.49E−18 4.35E−06 0.66 (0.6–0.72) cg16318349 PMVK 1.92E−23 6.33E−22 1.22E−21 0.017 1.45 (1.35–1.56) cg17748504 EGLN1 9.97E−14 9.97E−14 2.96E−12 5.87E−05 2.03 (1.69–2.45) cg26313511 ZNF148 1.80E−09 1.80E−09 3.21E−08 0.032 1.55 (1.34–1.78) cg18011163 ZNF148 1.87E−12 1.87E−12 4.86E−11 0.017 1.42 (1.29–1.57) cg02970696 ZNF148 1.22E−13 1.22E−13 3.59E−12 0.133 1.39 (1.27–1.52) cg24451117 MAP3K5 4.04E−18 4.04E−18 1.77E−16 0.686 0.19 (0.13–0.28) cg21506299 MAP3K5 1.11E−12 1.11E−12 2.95E−11 0.514 0.13 (0.08–0.23) cg24999105 MAP3K5 1.75E−22 1.75E−22 1.05E−20 0.191 0.26 (0.2–0.34) cg27539060 MAP3K5 4.94E−09 4.94E−09 8.28E−08 0.129 0.1 (0.04–0.21) cg16581840 MAP3K5 3.38E−08 3.38E−08 4.98E−07 0.178 14.04 (5.5–35.88) cg10599345 MAP3K5 2.64E−14 2.64E−14 8.3E−13 0.765 0.16 (0.1–0.25) cg15804973 MAP3K5 4.95E−41 6.14E−38 7.51E−39 0.01 2.79 (2.4–3.24) cg10983013 PSMB1 2.07E−90 1.36E−86 1.25E−87 2.19E−56 0.87 (0.86–0.88) cg26224077 PSMB1 5.98E−13 5.98E−13 1.64E−11 1.37E−05 2.5 (1.95–3.22) cg16625770 FGFR1 1.58E−24 1.63E−21 1.08E−22 1.30E−21 2.19 (1.88–2.54) cg12614213 FGFR1 1.48E−08 1.48E−08 2.3E−07 1.66E−05 0.22 (0.13–0.38) cg15321288 FGFR1 2.79E−44 2.60E−37 4.8E−42 3.39E−23 1.68 (1.56–1.81) cg09886946 KL 3.77E−07 3.77E−07 4.69E−06 1.18E−05 0.65 (0.56–0.77) cg21545902 KL 4.48E−15 4.45E−14 1.51E−13 6.24E−06 0.81 (0.77–0.86) SMR analysis results for mQTL–eQTL. Table 2 SMR analysis results for eQTL–pQTL. Expo gene Outco protein p SMR p SMR multi p FDR p HEIDI OR SMR (95% CI) EGLN1 Egl nine homolog 1 7.32E−22 1.55E−20 5.87E−20 0.001 0.55 (0.48–0.62) PSMB1 Proteasome subunit beta type-1 2.6E−102 8.4E−100 1.7E−99 3.14E−09 0.38 (0.35–0.42) FGFR1 Fibroblast growth factor receptor 1 3.79E−15 2.97E−16 2.03E−13 1.61E−05 1.5 (1.36–1.66) SMR analysis results for eQTL–pQTL. The SMR analysis utilized comprehensive statistical data from previous studies, all of which had obtained ethical approval and informed consent.

Our multi-omics Mendelian randomization study has identified significant associations between cell aging-related CpG site methylation levels and the risk of endometriosis. A total of 196 CpG sites, corresponding to 78 genes, demonstrated associations with the endometriosis (P-SMR < 0.05, P-SMR multi  0.05) (Table S2 ), among which 72 of the identified sites (29 genes) were colocalized with this condition (PPH4 > 0.5). Figure  2 a shows the significant causal relationship of some CpG sites with endometriosis, and these sites’ corresponding gene expressions also have a significant causal relationship with endometriosis. Notably, CpG sites near MAP3K5 (cg21506299) showed a positive association with endometriosis risk (OR 1.15, 95% CI 1.04–1.28), while another site near MAP3K5 (cg16581840) exhibited a negative association (OR 0.82, 95% CI 0.71–0.95). Figure  2 B for the association between cg21506299 and endometriosis. A Manhattan plot depicted the spatial arrangement of gene methylation loci along chromosomes (Fig.  2 C). The results were further validated using the FinnGen R10 (26 CpG sites in 16 genes were validated) and UKB 615 (25 CpG sites in 19 genes were validated) endometriosis cohorts. Notably, the CpG sites associated with DNMT3A (cg08485187), ATG7 (cg11277834), HMGA1 (cg25207224), IRF7 (cg27271532, cg15780465, cg03886085), ETV6 (cg14830166, cg18697143), CRISPLD2 (cg10444486), and BCL2 (cg24408313) demonstrated significant associations in these cohorts. Comprehensive details of the validation outcomes are delineated in Tables S3 and S4 . The causal association between cell aging-related gene expression and endometriosis was also investigated, with the complete SMR analysis results of GWAS-eQTLs presented in Table S5 . A total of 18 genes were found to be associated with endometriosis (P-value < 0.05 & Multi-SNP-based P-value  0.05), where the expression levels of 9 genes (PMVK, CD28, THRB, ZNF148, PSMB1, FGFR1, HNRNPA1, MAP2K1, RPS6KB1) were positively associated with the risk of endometriosis, and the expression levels of remaining 9 genes were negatively associated with the risk (Fig.  3 A). 6 of these genes exhibited substantial evidence of shared genetic variation influence (PPH4 > 0.5) (Fig.  3 B for the association between MAP3K5 and endometriosis). A Manhattan plot depicted the spatial arrangement of gene locus along chromosomes (Fig.  3 C). The validation in the FinnGen R10 dataset did not support their association with endometriosis (Table S6 ). However, the THRB gene (OR = 3.57 (1.41–9.04), P-SMR = 0.007, P-SMR multi = 0.007) was confirmed in the UKB 615 endometriosis cohort, demonstrating significant associations (P-SMR < 0.05, P-SMR multi  0.05), with validation results presented in Table S7 . The study explored the causal association between cell aging-related proteins and endometriosis (Table S8 ). A total of 7 proteins (S100A6, EGLN1, PSMB1, FGFR1, ENG, WIF1, APEX1) were associated with endometriosis (P-value < 0.05 & Multi-SNP-based P-value  0.05) (Fig.  4 A), with 3 of them exhibiting evidence of colocalization with this disease (Fig.  4 B for the association between EGLN1 and endometriosis). Except for PSMB1 and APEX1, the abundance of the other proteins showed a positive association with the risk of endometriosis. A Manhattan plot depicted the spatial arrangement of genes corresponding to the proteins along chromosomes (Fig.  4 C). The ENG-encoded protein was validated in both the FinnGen R10 (OR 1.38, 95% CI 1.07–1.79, P-SMR = 0.015, P-SMR multi = 0.013) and UKB 615 (OR  1.98, 95% CI 1.21–3.25, P-SMR = 0.007, P-SMR multi = 0.015) endometriosis cohorts, demonstrating significant associations with endometriosis risk (P-SMR < 0.05, P-SMR multi  0.05). The validation results can be found in Tables S9 – S10 . Based on the SMR analysis of cell aging-related blood eQTLs, mQTLs, and endometriosis GWAS, key findings suggest a potential causal association between endometriosis and several genes, including PMVK, EGLN1, ZNF148, MAP3K5, PSMB1, FGFR1, and KL. Further SMR analysis with blood mQTLs as the exposure and eQTLs as the outcome revealed significant regulatory effects of methylation at specific CpG sites within ZNF148 (cg02970696) and MAP3K5 (cg24451117, cg21506299, cg24999105, cg27539060, cg16581840, cg10599345) genes (P-SMR < 0.05, P-SMR multi  0.05), with results presented in Table  1 . Comprehensive results of the mQTL-eQTL SMR analysis are presented in Table S11 . Integrating the SMR analysis results from cell aging-related mQTL-GWAS, eQTL-GWAS, and the mQTL-eQTL analysis, along with the pQTL-GWAS SMR analysis, the proteins encoded by EGLN1, PSMB1, and FGFR1 were significantly validated in the pQTL-GWAS SMR analysis. However, subsequent SMR analysis integrating eQTL and pQTL data did not confirm the regulatory effects of EGLN1, PSMB1, and FGFR1 expression levels on protein abundance (Table  2 ), with complete results in Table S12 . Based on the analysis, MAP3K5, associated with CpG sites cg21506299 and cg24999105, is potentially causally associated with endometriosis. Both the gene and its CpG sites showed significant results in blood mQTL and eQTL SMR analyses and received robust colocalization evidence in the mQTL and eQTL to GWAS Colocalization analyses (PPH4 > 0.5). The SMR analysis of mQTL-eQTL successfully validated the regulatory effect of CpG site methylation on gene expression. In brief, it indicated that higher methylation at cg21506299 and cg24999105 is positively associated with endometriosis risk, while lower MAP3K5 expression is inversely related to risk, suggesting that methylation at these sites may suppress gene expression (Fig. 2 ). SMR effect plots and locus plots were used to visualize the genetic association between endometriosis and MAP3K5 and its methylation loci cg21506299 and cg24999105 (Fig.  5 ). In summary, it is hypothesized that higher methylation levels at CpG sites cg21506299 and cg24999105 may downregulate MAP3K5 expression, increasing the risk of endometriosis. Further exploration of tissue-specific validation was conducted to discover the causal association between the tissue expression of key genes and endometriosis risk. Utilizing eQTLs of cell aging related genes at the uterine tissue level alongside endometriosis GWAS data, a SMR analysis was performed to corroborate findings from the integrated blood cis-QTLs analysis (P-SMR < 0.05, P-SMR multi  0.05). The integrated analysis outcomes were not substantiated in the tissue eQTLs SMR analysis (Table S13 ).

In conclusion, this study presents evidence supporting a causal association between cell aging-related genes and the development of endometriosis. Our research indicates that MAP3K5, along with its associated pathways, may serve as promising therapeutic targets for endometriosis treatment. Further investigation is warranted to confirm these findings and to evaluate the feasibility of targeting cellular aging pathways as a novel approach to managing this debilitating condition.

SASP plays a crucial role in inflammation and tissue deterioration commonly seen in aging-related diseases. In the context of endometriosis, cellular aging may contribute to the formation of an inflammatory environment through SASP, thereby exacerbating disease progression 4 . Therefore, the role of cell aging underscores the importance of exploring therapeutic strategies that target senescent cells or modulate SASP to improve outcomes in endometriosis and other age-related conditions. Our SMR analysis has significant causal associations between cell aging-related genes and endometriosis. Utilizing an integrative approach that combined GWAS, eQTLs, mQTLs, and pQTLs, identified MAP3K5 as a potential therapeutic target. Notably, our findings suggest a mechanistic association between methylation at specific CpG sites and the regulation of MAP3K5, which may consequently influence endometriosis risk. In the mQTLs analysis, we identified 196 CpG sites in 78 genes associated with endometriosis, indicating the potential role of epigenetic modifications in the pathogenesis of the disorder. Methylation is an epigenetic modification that can silence gene expression and has been implicated in various diseases, including cancer and aging. The identification of specific CpG sites associated with endometriosis risk provides potential epigenetic biomarker for diagnostic and therapeutic interventions 26 – 28 . Notably, our research shows that different CpG sites within the MAP3K5 gene exhibited varying effects on endometriosis risk, suggesting a complex regulatory role of methylation in disease pathology. MAP3K5, also known as apoptosis signal-regulating kinase 1 (ASK1), functions as a stress-responsive kinase involved in regulating cell fate, inflammation, and cellular senescence 29 , 30 . The activation of MAP3K5 promotes the production of the SASP through pathways such as p38 MAPK, thereby creating a pro-inflammatory environment that may stimulate the growth of endometrial cells, potentially leading to endometriosis 31 . It also triggers cell cycle arrest and cell death in response to stressors like oxidative stress and DNA damage 32 , 33 . The dysregulation of these processes is key to the pathogenesis of endometriosis. Choi et al. showed that MAP3K5 (ASK1), implicated in endometriosis, might be a target of Dienogest, influencing cellular responses to endoplasmic reticulum stress and potentially reducing pathological cell growth and invasion associated with the disease 34 . Shi et al. showed that in patients with ovarian endometriosis (OEM), the expression of MAP3K5 (ASK1) is upregulated and negatively associated with the number of retrieved oocytes. This suggests that ASK1 may be involved in regulating the function of reproductive cells related to endometriosis 35 . Our study hypothesizes that the higher methylation levels at cg21506299 and cg24999105 may downregulate the expression of MAP3K5, thereby increasing the risk of endometriosis. These findings collectively suggest that the importance of MAP3K5 as a key regulatory gene. In the eQTLs analysis, we identified 18 genes associated with endometriosis, with THRB confirmed as a validated risk factor. The THRB gene encodes the thyroid hormone receptor beta, which is integral to the regulation of gene expression and metabolic processes 36 . Our research has shown a positive association between THRB and the risk of endometriosis. Although the role of THRB in endometriosis has not been extensively studied, evidence suggests that its reduction is associated with increased vitality and motility of endometrial cancer cells, potentially through its impact on the mTOR-4EBP1/eIF4G signaling pathway related to progesterone resistance 37 . Furthermore, THRB is also correlated with immune cell infiltration and macrophage activity 38 , 39 . These findings suggest that THRB could be instrumental in further research as a potential novel therapeutic target in endometriosis. In the pQTLs analysis, we identified 7 proteins associated with endometriosis, with ENG demonstrating a positive correlation with the risk of the disorder. Its status as a risk factor was further confirmed in the FinnGen R10 and UK Biobank cohorts. ENG encodes endoglin, a protein pivotal to angiogenesis and cell proliferation 40 . In endometriosis, characterized by abnormal angiogenesis, ENG has been identified as a critical factor. Hayrabedyan et al. 41 showed that ENG is not only a marker of active angiogenesis but also actively participates in the process of blood vessel formation in endometriosis. Its close connection with the VEGF signaling pathway suggests that it may influence the formation of the vascular wall and the migration of endothelial and vascular smooth muscle cells. Our findings, consistent with this study, highlight the potential role of ENG in the progression of endometriosis. The multi-omics approach employed in this study offers several advantages over traditional MR analysis. By integrating data from various omic layers, we can assess causal associations between exposures and outcomes more comprehensively, providing a nuanced understanding of the biological pathways involved 13 . The inclusion of mQTLs, eQTLs, and pQTLs allows for the investigation of the causal effects of gene expression, methylation, and protein abundance on endometriosis risk, respectively. However, this study is not without limitations. The reliance on summary data from large-scale omics studies may introduce potential biases. Additionally, the absence of pQTL data for some genes restricts our ability to fully explore the causal associations between protein abundance and endometriosis risk.

Endometriosis is a prevalent gynecological disorder affecting approximately 5–10% of women of reproductive age globally, leading to significant impairment in their quality of life and fertility, characterized by the ectopic growth of endometrium-like tissue planted outside the uterine cavity 1 , 2 . Recent research has underscored the potential role of cell aging in the disease’s development and progression, suggesting that this biological process may be a key factor in endometriosis pathogenesis 3 – 8 . Cell aging, characterized by cell cycle arrest, the senescence-associated secretory phenotype (SASP), and increased vulnerability to apoptosis, is a hallmark of aging and has been implicated in various age-related diseases 9 , 10 . The role of cell aging in the pathogenesis of endometriosis has garnered increasing attention. Several studies have identified specific cell aging-related genes that are dysregulated in endometriosis 6 , 7 . For example, SIRT1, a key regulator of cellular metabolism and longevity, has been shown to be upregulated in endometriotic tissues and promote epithelial-mesenchymal transition and cell proliferation 5 . Furthermore, the NLRP3 inflammasome is intricately linked to cell aging through mechanisms involving inflammation, oxidative stress, mitochondrial dysfunction, and SASP 11 . Specifically, cell aging is thought to contribute to the maintenance of endometriosis by creating a pro-inflammatory environment through SASP, which can sustain lesion development and inflammation (38879630). This process is further supported by studies showing that senescent cells in endometriotic lesions exhibit increased expression of pro-inflammatory cytokines like IL-1β, which can accelerate cellular aging and exacerbate endometriosis progression 4 . Given the growing evidence of the involvement of cell aging in endometriosis, it is imperative to identify specific cell aging-related genes that contribute to the pathogenesis of the disease. This knowledge could lead to the development of novel therapeutic targets and improve the diagnosis and treatment of endometriosis. To investigate the causal associations between cell aging and endometriosis, a multi-omic summary Mendelian randomization (SMR) approach was employed. This technique is predicated on the assumption that genetic variants, which are randomly assigned at conception, serve as instrumental variables that are not confounded by environmental and behavioral factors 12 , 13 . The SMR method approach integrates data from genome-wide association studies (GWAS), expression quantitative trait loci (eQTLs), methylation QTL (mQTLs), and protein QTL (pQTLs) to assess the causal associations between exposures and outcomes 14 . This multi-omics summary MR methodology allows for a comprehensive assessment of the genetic regulation of gene expression, methylation, and protein abundance, thereby providing a more nuanced understanding of the biological pathways involved in endometriosis. In conclusion, this study employed SMR approach to investigate causal associations between cell aging-related genes and endometriosis. Our findings have the potential to uncover novel therapeutic targets and contribute to the development of precision medicine strategies for the treatment of endometriosis.

Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 1