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. 2025 Sep 5;104(36):e44074. doi: 10.1097/MD.0000000000044074

Integration of summary data from GWAS and eQTLs studies predicts causality of S1PR1 and breast cancer

Huiwen Shi a, Haibing Wang b, Mingkui Li c, Guangfeng Wang b, Lujia Li b, Li Wei c,*
PMCID: PMC12419331  PMID: 40922373

Abstract

Breast cancer is a major health threat to women, with limited effective indicators for early screening and prognosis. The role of sphingosine 1-phosphate receptor 1 (S1PR1) in breast cancer remains controversial. This study aims to explore the potential causal relationship between S1PR1 and breast cancer risk, considering estrogen receptor (ER) status. Summary-level data for genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) from European ancestry was collected. A summary-data-based Mendelian randomization (SMR), multi-SNP-based SMR, heterogeneity in dependent instruments (HEIDI) test, 2-sample MR analysis, and Bayesian colocalization method were conducted. Potential targets for S1PR1 were predicted based on DrugBank and ChEMBL databases. Elevated S1PR1 expression in blood was significantly associated with a heightened risk of overall breast cancer (odds ratio (OR): 1.15, 95% CI: 1.02–1.29; PSMR = .019) and ER+ breast cancer (OR: 1.20, 95% CI: 1.04–1.38; PSMR = .010), as demonstrated by SMR analysis. A protective association was identified between S1PR1 expression in the brain cortex and the risk of ER+ breast cancer (OR: 0.89, 95% CI: 0.84–0.99; PSMR = .032). No significant association was found regarding breast cancer survival (PSMR > .05). The MR analysis corroborated these findings, indicating an increased risk for both overall breast cancer (OR: 1.10, 95% CI: 1.02–1.20; P = .019) and ER+ breast cancer (OR: 1.16, 95% CI: 1.05–1.28; P = .003). Colocalization analysis revealed no evidence of shared genetic polymorphisms between S1PR1 expression and breast cancer risk or ER status (PP.H4 < 0.8), yet these studies were probably underpowered. Our finding revealed that the S1PR1 gene might act as a potential target for diagnosing the risk of breast cancer, especially for ER+ breast cancer.

Keywords: Bayesian colocalization, breast cancer, Mendelian randomization, S1PR1

1. Introduction

Breast cancer is one of the most common malignant tumors and the leading cause of 2.3 million new cases and 685,000 deaths in 2020.[1] The risk factors for breast cancer mainly include race, ethnicity, family history of cancer, prevalence of reproductive and hormonal risk factors, lifestyles, and genetic traits.[1,2] Factors such as an early age at menarche, a later age at menopause, an advanced age at the first birth, fewer children, less breastfeeding, menopausal hormone therapy, oral contraceptives, alcohol consumption, excess body weight, physical inactivity, and BRCA1 and BRCA2 mutations have been identified.[3–5] Moreover, the high heterogeneity of breast cancer molecular subtypes at morphological and molecular levels leads to different clinical outcome prognoses.[6] It has been found that breast cancer patients with estrogen receptor-positive (ER+) had a better prognosis compared with those with estrogen receptor-negative (ER−).[7] Therefore, more predictors are required to identify patients with breast cancer, provide a feasible treatment strategy, and predict the prognosis of breast cancer.

Previous research has yielded insights into the genetic etiology of breast cancer. For example, 3 genome-wide significant loci associated with breast cancer, rs11552449 (DCLRE1B), rs3747479 (MRPS30), and rs73134739 (ATG10), could significantly change the promoter activities of their target genes to promote breast tumorigenesis.[8] rs61938093 (NTN4) decreases NTN4 promoter activity, promoting the progression of breast cancer.[9] However, there remains a gap in the understanding of genetic factors, especially genetic variants that contribute to the risk of breast cancer. Therefore, exploring more risk-associated variations in genes is necessary for preventing breast cancer.

Sphingosine 1-phosphate receptor 1 (S1PR1) is one of the subtypes of G protein-coupled receptors and plays an essential role in adaptive immune cell trafficking, vascular development, and homeostasis.[10] S1PR1 exerts an important role in tumorigenesis that links to angiogenesis, lymphangiogenesis, immune cell infiltration, and metastasis.[11–15] S1PR1 acts as a novel and promising therapeutic target in cancer treatment.[16,17] However, there are contradictory results in breast cancer. A recent study has indicated that S1PR1 contributes to breast cancer metastasis,[18] and high S1PR1 expression contributes to the progression of invasive breast cancer and poor prognosis in patients.[19,20] Inversely, S1PR1 expression inhibits the progression of multiple categories of BCA patients,[21] and it has also been reported that low expression of S1PR1 links to vasculogenic mimicry and poor prognosis in breast cancer patients.[22] Therefore, it is required to explore the relationship between S1PR1 and the incidence and prognosis of breast cancer.

Mendelian randomization (MR) has emerged as a potent epidemiological tool for inferring causal relationships between exposures and outcomes due to the advancement of genome-wide association studies (GWAS).[23,24] By utilizing genetic variants, particularly single nucleotide polymorphisms (SNPs), as instrumental variables (IVs), MR can effectively overcome the biases and reverse causation inherent in conventional observational studies.[23,24] This study investigates the causal relationship between S1PR1 gene expression and the risk and prognosis of breast cancer, utilizing expression quantitative trait loci (eQTLs) from the eQTLGen, GTEx v8, and BrainMeta v2 datasets, combined with breast cancer and survival GWAS data from the breast cancer association consortium (BCAC). We will first employ summary-data-based MR (SMR) analysis to investigate the association between S1PR1 expression and breast cancer risk and survival outcomes. Subsequently, MR analysis will be performed to enhance the validation and robustness of our findings. Additionally, colocalization analysis will assess whether the observed associations are driven by shared causal genetic variants. Finally, the therapeutic potential of S1PR1 will be examined by identifying candidate medicines and assessing their present stage in clinical development. This research seeks to clarify the function of S1PR1 in breast cancer, aiming to offer new insights into its potential as a biomarker for prognosis and as a therapeutic target.

2. Materials and methods

2.1. Study overview

In this study, we employed SMR and MR analyses to investigate the causal effects of S1PR1 expression on breast cancer risk and survival outcomes, integrating GWAS and eQTL summary statistics. Colocalization analysis further identified shared causal variants among these traits. Finally, we assessed S1PR1’s druggability and clinical development status. The present analysis utilized open-access GWAS summary statistics from previously published studies that had secured ethical approvals and documented informed consent, precluding the need for additional ethical review.

2.2. Data source

We obtained S1PR1 eQTL summary statistics from 3 independent sources. The discovery dataset came from eQTLGen with 31,684 peripheral blood samples.[25] For replication, we used the genotype-tissue expression (GTEx) project v8 data covering multiple tissues with sample sizes ranging from 73 to 670,[26] along with BrainMeta v2 data containing 2865 cerebral cortex samples.[27] All data information is shown in Table 1. We utilized GWAS results from the breast cancer association consortium (bcac.ccge.medschl.cam.ac.uk) for the outcome data derived from European-ancestry females.[28] The breast cancer dataset comprised 228,951 individuals, including 22,977 cases, divided into ER+ (175,475 individuals, 69,501 cases) and ER− (127,442 individuals, 21,468 cases) subgroups. The breast cancer survival dataset included 96,661 individuals (7697 cases), categorized into ER+ (64,171 individuals, 4116 cases) and ER− (16,172 individuals, 2125 cases) subgroups.

Table 1.

Detailed information of eQTL and GWAS summary data in this study.

Phenotype Consortium Sample size Population ancestry Reference
Exposures
 eQTL eQTLGen 31,684 European (majority) 34475573
 eQTL GTEx v8 73–670 European (majority) 32913098
 eQTL BrainMeta v2 2443 European 29891976
Outcomes
 Breast cancer BCAC 228,951 (122,977 cases) European 29059683
 Breast cancer (ER+) BCAC 175,475 (69,501 cases) European 29059683
 Breast cancer (ER−) BCAC 127,442 (21,468 cases) European 29059683
 Breast cancer survival BCAC 96,661 (7697 cases) European 30787463
 Breast cancer survival (ER+) BCAC 64,171 (4116 cases) European 30787463
 Breast cancer survival (ER−) BCAC 16,172 (2125 cases) European 30787463
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BCAC = breast cancer association consortium, eQTL = expression quantitative trait locus, ER = estrogen receptor, GWAS = genome-wide association study.

2.3. IVs selection

MR analysis was conducted with the following 3 assumptions: The robust association of the IVs with the exposure; SNPs are not associated with confounders; the association of IVs with outcome only through the exposure.[29] For MR analysis, cis-eQTLs genetic variants were selected as IVs at gene regions (±1 million base pairs (Mb)) with a P < 5 × 10−8, and the cutoff for linkage disequilibrium (LD) was R2 < 0.1 at a 1000 kb window to ensure independence between IVs, minor allele frequency > 0.01, and F-statistic > 10. An available online tool (https://sb452.shinyapps.io/power/) was used to calculate the statistical power of MR analysis. A power >80% was considered enough statistical power.[30]

2.4. SMR and HEIDI analysis

SMR analysis is a method to explore the pleiotropic association between gene expression level and a complex trait by integrating GWAS and eQTLs summary data based on the principles of MR.[31] The HEIDI test can distinguish causality and pleiotropy from linkage. In the present study, SMR analysis was performed using SMR software (https://yanglab.westlake.edu.cn/software/gsmr/). The 1000 genomes european reference was used for linkage disequilibrium estimation. The top cis-eQTLs were identified within a 1 Mb window centered around the target gene and chosen as IV, adhering to a stringent significance threshold of P < 5 × 10−8. For the HEIDI test, we excluded the SNPs with extremely high LD (r2 > 0.9) and extremely low LD (r2 < 0.05) with the top SNP. P(HEIDI) < .01 indicates that the association is most likely attributable to linkage. Moreover, multiple SNPs with LD (r2 < 0.1) are considered IVs to conduct SMR analysis to reduce false positive rates. A P-value < .05 indicates a statistically significant association between S1PR1 expression and the risk and survival of breast cancer.

2.5. Two-sample MR analysis

In the process of deriving IVs from outcome data, due to the absence of SNPs associated with S1PR1 expression in the eQTLGen discovery dataset, we utilized proxy SNPs in high LD (r2 > 0.80). To maintain consistency, rigorous data harmonization was applied, ensuring that the allelic effects on both the exposure and outcome were precisely aligned. The inverse-variance weighted (IVW) method was the primary analytical approach for conducting 2-sample MR analysis, which calculates the weighted average of Wald ratio estimates with a forced intercept of zero.[32] In the absence of heterogeneity, a fixed-effects IVW model was utilized, while conversely, a multiplicative random-effects IVW model was employed when heterogeneity was present. Moreover, weighted median estimator (WME), MR-Egger regression, and MR-robust adjusted profile score (MR-RAPS) analysis were performed as supplements.[33] WMR is a method to provide a consistent estimate effect even when more than 50% of selected genetic variants are invalid instruments.[34] MR-Egger regression is employed to assess and correct for horizontal pleiotropy, while MR-RAPS is used to provide robust estimates in the presence of weak instruments and potential pleiotropy.[33,35] Moreover, the MR-Egger intercept test was used to test the pleiotropy, and Cochran’s Q statistic was used to quantify the heterogeneity of IVs.[36] To further augment the robustness of our findings, we employed the leave-one-out sensitivity analysis to evaluate the influence of individual SNPs on the overall results. This was accomplished by systematically excluding each SNP and reanalyzing the data using the IVW method. In conjunction, we applied the MR-Steiger filtering test to identify SNPs indicative of reversed causation. The above analyses were conducted by the “TwoSampleMR” and “MR-RAPS” packages in R (4.2.3). For 2-sample MR analysis, a P-value < .05 was considered a significant association.

2.6. Colocalization analysis

Bayesian colocalization analysis is a method to test whether GWAS summary data and cis-eQTLs share the same genetic variant.[37] This analysis is based on the posterior probability of 5 distinct hypotheses: H0, no association with GWAS or QTL within loci; H1, association with GWAS only; H2, association with QTL only; H3, association with GWAS and QTL but not colocalized; H4, GWAS and QTL are significantly related and driven by the same causal variant loci.[38] Bayesian colocalization analysis was performed by the “coloc” R package. All cis-eQTLs (gene ± 1 Mb) data from eQTLGen and the accompanying outcome GWAS data are provided, unfiltered for linkage disequilibrium and P-value. Colocalization between GWAS and QTL was indicated by a posterior probability of hypothesis 4 (PP.H4) ≥ .80.

2.7. Exploring the druggability and clinical development conditions

We further explored the potential molecular drugs targeting S1PR1 based on the DrugBank (https://go.DrugBank.com/) and ChEMBL (https://www.ebi.ac.uk/chembl/) online databases. Subsequently, we assessed the status of these molecular drugs in relation to clinical trials by consulting the ClinicalTrials.gov online database (https://classic.clinicaltrials.gov/).

3. Results

3.1. SMR analysis

A detailed flowchart of this study is shown in Figure 1. Here, we first conducted the SMR analysis to explore the association between the expression of S1PR1 and breast cancer (ER+ and ER−) and breast cancer survival (ER+ and ER−). SMR analyses were performed using the top cis-eQTLs SNPs (rs1922987, rs10875360, rs1999134, rs61782147, rs7367938, rs74106743, and rs17123757) as IVs for S1PR1 (Table S1, Supplemental Digital Content, https://links.lww.com/MD/P758). The statistical power for the outcome of breast cancer or ER+ breast cancer was >80% (Table S2, Supplemental Digital Content, https://links.lww.com/MD/P758). Utilizing data from the eQTLGen consortium, the analysis revealed an OR of 1.15 (95% CI: 1.02–1.29; PSMR = .019) for overall breast cancer, indicating a significant elevation in risk associated with higher S1PR1 expression. Notably, for ER+ breast cancer, a more pronounced association was observed, with an OR of 1.20 (95% CI: 1.04–1.38; PSMR = .010) (Fig. 2, Table S3, Supplemental Digital Content, https://links.lww.com/MD/P758). Conversely, for replication in the BrainMeta v2 Consortium, a protective association was observed between S1PR1 expression and ER + breast cancer risk (95% CI: 0.84–0.99; PSMR = .032). However, the association between the expression of S1PR1 and the risk of breast cancer survival was not observed (PSMR > .05). The HEIDI test demonstrated that the associations between S1PR1 and the breast cancer outcome were not due to LD (P > .01) (Fig. 2). Furthermore, the multi-SNP-based SMR analyses also suggested a significant association between the S1PR1 expression level and the risk of ER+ breast cancer (PSMR_multi = .027; PSMR_multi = .032) based on the eQTLGen and BrainMeta Consortium, while no significant association was observed for ER− breast cancer (PSMR_multi > .05) (Fig. 2, Table S3, Supplemental Digital Content, https://links.lww.com/MD/P758).

Figure 1.

Figure 1.

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The flowchart of the study design. BC = breast cancer, BCAC = breast cancer association consortium, eQTLs = expression quantitative trait loci, ER = estrogen receptor, GWAS = genome-wide association studies, HEIDI =heterogeneity in dependent instruments, IVs = instrumental variables, IVW = inverse-variance weighted, LD = linkage disequilibrium, MAF =minor allele frequency, Mb = million base pairs, MR= Mendelian randomization, PP.H4 = posterior probability for hypothesis 4, RAPS = robust adjusted profile score, SMR = summary-data-based MR, SNP = single nucleotide polymorphism, WME = weighted median estimator.

Figure 2.

Figure 2.

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Forest plot illustrating the SMR analysis of S1PR1 expression on breast cancer risk, survival, and ER subtypes. CHR = chromosome, 95% CI = 95% confidence interval, eQTLs = expression quantitative trait loci, ER = estrogen receptor, HEIDI = heterogeneity in dependent instrument, OR = odds ratio, P = pval, SMR = summary-data-based Mendelian randomization, SMR_multi = multi-SNP-based SMR.

3.2. MR analysis

We further conducted an MR analysis to discover the causal effect of S1PR1 expression levels on breast cancer or breast cancer survival. In the present study, 4 genome-wide significant SNPs for S1PR1 were selected as IVs with the F-statistics > 10 (Table S1, Supplemental Digital Content, https://links.lww.com/MD/P758). And the statistical power for the outcome of ER+ breast cancer was >80% (Table S2, Supplemental Digital Content, https://links.lww.com/MD/P758). Employing the IVW method, we observed a statistically significant association, indicating an increase in breast cancer risk (OR: 1.10, 95% CI 1.02–1.20; P = .019). A further stratification of the analysis revealed a similar pattern for ER+ breast cancer (OR: 1.16, 95% CI: 1.05–1.28; P = .003), suggesting that higher expression of S1PR1 correlates with an elevated risk of developing ER+ breast cancer. The results of this analysis align harmoniously with those derived from the SMR analysis. Moreover, results from WMR and MR-RAPS methods corroborated the directionality and significance of the IVW findings, further enhancing the robustness of our results (Fig. 3). In contrast, for ER− breast cancer, the IVW method and additional analyses revealed no significant association (P > .05). With respect to breast cancer survival, no evidence was found to support significant associations for overall breast cancer survival, nor for ER+ and ER− breast cancer survival (P > .05). No pleiotropy or heterogeneity was observed in these results (P > .05). Moreover, in light of the leave-one-out results, which suggest potential instability of the MR results, as not all horizontal lines consistently fall on one side of the center line (Fig. S1, Supplemental Digital Content, https://links.lww.com/MD/P757), we applied stricter criteria to exclude SNPs in LD (R2 < 0.01, windows = 10,000 kb). We observed that the significant associations between S1PR1 expression and both breast cancer and ER+ breast cancer remained consistent (P < .05), further strengthening the validity of our results (Table S4, Supplemental Digital Content, https://links.lww.com/MD/P758). The MR-Steiger filtering method ensured the directionality of the IVs related to S1PR1 expression[39] (Table S5, Supplemental Digital Content, https://links.lww.com/MD/P758).

Figure 3.

Figure 3.

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Forest plot illustrating the MR analysis of S1PR1 expression on breast cancer risk, survival, and ER subtypes. 95% CI = 95% confidence interval, ER = estrogen receptor, IVW (fixed) = inverse-variance weighted (fixed effect), MR = Mendelian randomization, nSNPs = number of single-nucleotide polymorphisms, OR = odds ratio, RAPS = robust adjusted profile score.

3.3. Bayesian colocalization analysis

Next, as shown in Figure S2A to C (Supplemental Digital Content, https://links.lww.com/MD/P757), the colocalization analysis was performed by integrating the GWAS and transcript expression datasets of the S1PR1 in eQTLGen cis-eQTLs at gene regions (±1 Mb). The results showed that S1PR1 expression had no significant variant shared genetic effects with breast cancer or ER+/ER− breast cancer with PP.H4 < .8 (Table 2).

Table 2.

Colocalization analysis of S1PR1 expression with breast cancer and ER subtypes.

Outcome nSNPs PP.H0 PP.H1 PP.H2 PP.H3 PP.H4
Breast cancer 6475 1.51E−28 1.30E−27 0.102756 0.8841246 0.0131194
ER+ breast cancer 6476 1.22E−27 2.34E−28 0.8336529 0.1594487 0.0068985
ER− breast cancer 5076 1.03E−27 2.58E−28 0.70362 0.1753183 0.1210617
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eQTL = expression quantitative trait locus, ER = estrogen receptor, nSNPs = number of single-nucleotide polymorphisms, PP.H = posterior probability for hypothesis, where PP.H4 > 0.8 indicates strong evidence for shared causal variants.

3.4. Identification of the potential target for S1PR1

We also investigated the potential target drugs for S1PR1 based on the DrugBank and ChEMBL online databases. Several potential drugs were identified (Tables S6 and S7, Supplemental Digital Content, https://links.lww.com/MD/P758). For instance, Siponimod, Ozanimod, and Fingolimod are either approved or under investigation. Etrasimod has received approval for the treatment of moderately to severely active ulcerative colitis in adults, while Amiselimod is currently classified as an experimental drug. Additionally, we searched the ClinicalTrials.gov website for further insights into the clinical development of these drugs. A significant number of small molecule drugs targeting S1PR1 are presently in the experimental phase.

4. Discussion

The present study adopted an MR design and used publicly available GWAS and eQTLs data to evaluate the causal effect of S1PR1 gene expression on breast cancer and breast cancer survival and its subtypes. Using SMR and HEIDI methods, we found the positive association between S1PR1 expression and overall breast cancer and ER+ breast cancer, while there is no evidence about S1PR1 in relation to ER− breast cancer or breast cancer survival. Two-sample MR results evaluated using the IVW method were consistent with SMR findings and robustly validated through the WMR and MR-RAPS methods. However, colocalization results indicated that the association between S1PR1 expression and the risk of breast cancer and subtypes was not due to a shared genetic variant. Ultimately, we explored S1PR1’s druggability potential and current clinical development status.

The discrepancies observed between MR and colocalization results might be attributed to several factors: The colocalization analysis indicated a high posterior probability (PP.H3 = .884) for breast cancer, suggesting that distinct causal variants exist in LD, which undermines the assumptions of the IVs used in MR. The result raises concerns regarding the robustness of the MR findings due to the potential influence of LD. In our study, the strong association indicated by a high probability (PP.H2 = .8337) for ER+ breast cancer with S1PR1 gene expression contrasts with the lack of a significant correlation in this region (P > .0001, Fig. S2, Supplemental Digital Content, https://links.lww.com/MD/P757). The finding implies limited power to detect colocalization, reflecting greater skepticism inherent in the colocalization approach, as strong association signals from both traits are necessary to identify a shared causal variant.[40] Furthermore, GWAS findings can be elucidated by eQTLs only when the causative mutation affects mRNA production rather than protein type.[41,42] Furthermore, the phenotype of many diseases strictly manifests in certain tissues. Hence, colocalization results depend on the tissue types of the expression dataset.[43–46]

S1PR1 is a driver of multiple diseases that belongs to the S1PR subfamily, comprising 5 members (S1PR1-5), and plays a prominent role in regulating embryonic cell development, cell survival, migration, immune cell transport, and vascular development.[10] A next-generation whole-exome sequencing analysis reveals that somatic mutations in S1PR1 have been observed in patients with mantle cell lymphoma.[47] A bioinformatics analysis based on the cancer genome atlas has indicated that the high expression of S1PR1 was associated with poor overall survival in bladder cancer patients.[48] It has also been found that there was high expression of S1PR1 in esophageal squamous cell carcinoma.[49] Similarly, high expression of S1PR1 is associated with poor survival and a worse response to chemotherapy in gastric cancer patients.[50] Previous studies have shown that high expression of S1PR1 is associated with recurrence and shorter disease-specific survival times in ER+ breast cancer.[51] While another study has found that S1PR1 connects to survival benefit in breast cancer.[21] In this study, we have provided causal evidence of the association between S1PR1 and the risk of breast cancer, especially ER+ breast cancer.

Increasing evidence has demonstrated that S1PR1 is involved in multiple tumor progressions and acts as a therapeutic target by regulating angiogenesis,[52] vascular normalization,[53] vascular invasion,[54] lymphangiogenesis,[55] and tumor cell migration and proliferation.[56,57] Cancer-related cognitive impairment (CRCI) and sensory peripheral neuropathy are the major neurotoxicities affecting clinical efficacy,[58,59] and cisplatin-induced high expression of S1PR1 led to cognitive impairment.[58] These findings suggested that S1PR1 is a therapeutic target for targeting CRCI treatment. Moreover, microtubule targeting agents are anticancer therapies for breast cancer and other solid tumors. A pharmacogenomic study highlights S1PR1 as a drug target for prevention and treatment of MTA-induced neuropathy.[59] Therefore, we also investigated the potential drugs against S1PR1. Siponimod, ozanimod, fingolimod, ponesimod, etrasimod, and amiselimod were selected as the potential target drugs based on the DrugBank and ChEMBL online databases. Four S1PR1 modulators, including fingolimod, siponimod, ozanimod, and ponesimod, showed antitumor effects on melanoma cell lines.[60] Recently, fingolimod, siponimod, and ozanimod have been widely used for antitumor therapy in some tumors. For example, siponimod (BAF312) is against S1PR1 by inhibiting triple-negative breast cancer growth and angiogenesis by regulating the S1PR1/p-STAT3/VEGFA axis in vitro and in vivo.[20] A therapeutic drug for multiple sclerosis, FTY720 (fingolimod), has shown antitumor effects in breast cancer by targeting S1PR1.[61,62] A monoclonal antibody S1PR1 also reveals the antitumor effects in breast cancer by inhibiting cell proliferation and enhancing breast cell sensitivity to carboplatin.[63] Similar to FTY720, ozanimod, an FDA-approved functional S1PR1 antagonist, showed attenuation effects on cisplatin-induced CRCI.[58] Etrasimod has been approved for clinically treating ulcerative colitis and eosinophilic oesophagitis, which may have a longer-term benefit in reducing the risk of gastrointestinal cancers.[64] The above studies highlight the antitumor therapy of S1PR1 in breast cancer.

Our study has several notable strengths. SMR, HEIDI, 2-sample MR, and colocalization analyses were performed to evaluate the causal effects of S1PR1 expression and the risk and prognosis of breast cancer, as well as whether S1PR1 expression and breast cancer shared the same loci variation. The design of this study can effectively avoid reverse causation and reduce confounders, as well as be less susceptible to population stratification bias. Although this study highlights the association between S1PR1 and breast cancer, there are a few limitations that need to be acknowledged. First, our analyses were limited to individuals of European ancestry, and further research is necessary to determine if our results apply to other ancestry groups. Second, despite the causal association between S1PR1 and the risk of breast cancer with or without ER+, the expression of S1PR1 between breast cancer and normal as well as between ER+ breast cancer and ER- breast cancer was not directly found due to a lack of relevant gene expression data, so we need more experimental evidence to verify our results. Third, based on the MR analysis, we can not adequately rule out the possibility of pleiotropy. Finally, colocalization analysis was performed using the coloc R package. Although the solution is generally effective, the limitation is that top SNP selection for the conditional analysis can create biases.[65]

5. Conclusion

Taken together, this work presents robust evidence that blood-derived S1PR1 expression correlates with an increased risk of breast cancer, especially in ER+ subtypes, as validated by both SMR and MR analyses. No notable correlations were detected regarding ER− breast cancer or breast cancer survival. Although colocalization analysis found no common genetic variations between S1PR1 expression and breast cancer risk or ER status, these studies may have had insufficient statistical power to detect such effects. Moreover, prospective pharmacological agents aimed at S1PR1, such as Siponimod, Ozanimod, and Fingolimod, underscore its therapeutic potential. These findings identify S1PR1 as a possible biomarker for breast cancer risk and a novel therapeutic target, providing significant insights for future precision medicine approaches, especially for ER + breast cancer. Future investigations involving bigger and more diverse populations are necessary to corroborate these findings and to further examine the underlying processes of S1PR1 in breast cancer. Furthermore, clinical trials targeting S1PR1 may provide critical insights into its therapeutic potential and facilitate the development of novel therapy methods for breast cancer care.

Acknowledgments

We sincerely appreciate the participants and investigators of the IEU Open GWAS Project and GWAS data. We acknowledge the eQTLGen, GTEx v8, BrainMeta v2, and IEU consortiums for their contributions.

Author contributions

Conceptualization: Lujia Li, Li Wei.

Formal analysis: Huiwen Shi, Haibing Wang.

Methodology: Mingkui Li, Guangfeng Wang.

Resources: Mingkui Li, Guangfeng Wang.

Supervision: Lujia Li, Li Wei.

Visualization: Huiwen Shi, Haibing Wang.

Writing – original draft: Huiwen Shi.

Writing – review & editing: Lujia Li, Li Wei.

Supplementary Material

medi-104-e44074-s001.docx (24.5KB, docx)
medi-104-e44074-s002.docx (472.7KB, docx)

Abbreviations:

BC
breast cancer
BCAC
breast cancer association consortium
CRCI
cancer-related cognitive impairment
eQTLs
expression quantitative trait loci
ER
estrogen receptor
ER−
estrogen receptor-negative
ER+
estrogen receptor-positive
GTEx
genotype-tissue expression
GWAS
genome-wide association studies
HEIDI
heterogeneity in dependent instruments
IVs
instrumental variables
IVW
inverse-variance weighted
LD
linkage disequilibrium
Mb
million base pairs
MR
Mendelian randomization
MR-RAPS
MR-robust adjusted profile score
MTA
microtubule targeting agents
OR
odds ratio
PP.H4
the posterior probability of hypothesis 4
S1PR1
sphingosine 1-phosphate receptor 1
SMR
summary-data-based Mendelian randomization
SNPs
single nucleotide polymorphisms
WME
weighted median estimator.

This work was supported by the Qingdao Medical Research Guidance Program (Grant No. 2020-WJZD195).

Ethical approval was waived because this study used the data from publicly available databases.

The authors declare that they have no conflicts of interest.

The GWAS data are available in the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/) or original study. The eQTLs data of S1PR1 can be found in the eQTLGen Consortium (https://eqtlgen.org/index.html) GTEx v8, and BrainMeta v2.

Supplemental Digital Content is available for this article.

How to cite this article: Shi H, Wang H, Li M, Wang G, Li L, Wei L. Integration of summary data from GWAS and eQTLs studies predicts causality of S1PR1 and breast cancer. Medicine 2025;104:36(e44074).

HS and HW contributed to this article equally.

Contributor Information

Huiwen Shi, Email: calvinoshi@126.com.

Haibing Wang, Email: xin.yuan712@163.com.

Mingkui Li, Email: li.lujia@163.com.

Guangfeng Wang, Email: xin.yuan712@163.com.

Lujia Li, Email: li.lujia@163.com.

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