Revealing putative causal genes by establishing the causality between different lymphomas and immune cells

Revealing putative causal genes by establishing the causality between different lymphomas and immune cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Revealing putative causal genes by establishing the causality between different lymphomas and immune cells Jingxuan Lian, Xinghong Zhang, Wenjie Chen, Zheshen Lin, Ming Lu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4664711/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The immune microenvironment not only plays a pivotal role in the pathogenesis of lymphoma but also serves as a critical determinant influencing disease progression and treatment resistance. However, there remains a dearth of comprehensive investigations exploring the causal relationship between various immune cell types and different lymphomas. Method: In this study, we employed common bidirectional two-sample mendelian randomization (MR) and linked disequilibrium score regression (LDSC) to investigate the causal relationship and genetic correlation between immune cells and various lymphomas. Additionally, we utilized the Mendelian randomization-based method of summary data-based MR (SMR), which incorporated genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data from immune cells to identify genes associated with lymphoma. Furthermore, colocalization analysis and genetic correlation analysis were conducted for further validation of our findings. Results: The two-sample mendelian randomization approach was employed to identify the immune cell types that exhibit a causal relationship with different lymphomas. Additionally, the genetic correlation between these immune cells and malignant lymphomas was further analyzed using the linked disequilibrium score regression method, thereby enhancing the reliability of our findings. The SMR and colocalization analyses revealed several genes associated with these immune cells, thereby providing additional support for their putative role in the pathogenesis of lymphoma. Conclusions: Our study elucidates the intricate interplay between immune cells by employing genetic methodologies, thus offering insights for potential therapeutic targets and risk predictors in different subtypes of lymphoma treatments. The tumor immune microenvironment lymphoma immune cells genome wide association studies single-nucleotide polymorphisms Mendelian randomization summary data-based Mendelian randomization (SMR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lymphoma, comprising more than 90 subtypes of malignant lymphocyte neoplasms, is conventionally classified broadly as non-Hodgkin or Hodgkin lymphoma. Each year, approximately 82,000 new cases of lymphoma are diagnosed in the United States.[ 1 ]. The prevailing belief is that malignant lymphocytes in lymphoma primarily recruit and sustain a microenvironment consisting of other immune cells as well as stromal elements, which facilitate the promotion of malignant cell growth and survival.[ 2 ]. The biology and clinical behavior of malignant lymphoma are not solely determined by the intrinsic characteristics of tumor cells, but are also significantly influenced by their dynamic interaction with the non-malignant microenvironment[ 3 ]. The tumor immune microenvironment (TIME) is a recently proposed concept that has been demonstrated to exhibit a robust association with the clinical prognosis of cancer patients[ 4 ]. The tumor microenvironment (TME) predominantly consists of a diverse array of immune cell populations, encompassing both innate and adaptive immune cells such as myeloid cells and lymphocytes[ 5 , 6 ]. It is noteworthy that TIME also plays a pivotal role in determining the immune response state within the tumor microenvironment (TME), which predominantly relies on the composition and activity of infiltrating immune cells, alongside various influencing factors such as cell surface expression of immune checkpoint molecules and alterations in associated extracellular matrix[ 5 ]. The composition of immune cells within the tumor immune microenvironment (TIME) exhibits inter-tumoral variability and demonstrates significant associations with clinical outcomes across diverse cancer types[ 7 ]. The immune microenvironment plays a crucial role in the pathogenesis, disease progression, and therapy resistance of lymphomas. In diffuse large B-cell lymphoma (DLBCL), the composition of various immune effectors and cells can serve as prognostic biomarkers and independent indicators for different immunotherapies[ 8 , 9 ]. The presence of myeloid-derived suppressor cells (MDSCs) has been observed in the peripheral blood of patients with Hodgkin and Non-Hodgkin lymphoma (NHL), demonstrating a positive correlation with disease aggressiveness and significant prognostic value[ 10 , 11 ]. The tumor microenvironment (TME) is widely recognized to play a crucial role in various processes, including lymphoma progression, treatment response, drug resistance, and prognosis. Targeting components of the TME holds promise for uncovering novel insights into precise lymphoma management. Furthermore, by employing Mendelian randomization in epidemiological etiological inference, we establish a methodological foundation to elucidate potential causal relationships between distinct immune cells and malignant lymphoma as well as its subtypes. In this study, we conducted a comprehensive bidirectional Mendelian randomization analysis to elucidate the causal associations between diverse immune cell types and malignant lymphoma along with its different subtypes. Methods 2.1 Study design description The bidirectional Mendelian randomization (MR) design, as depicted in Fig. 1 , provides a concise overview of the investigation into the association between 731 immune cell types and malignant lymphomas, encompassing their diverse subtypes. First, Two MR analyses were conducted using aggregated statistics from genome-wide association studies (GWAS) to explore the reciprocal relationship between immune cells and malignant lymphoma, as well as its distinct subtypes. Subsequently, linked disequilibrium score regression (LDSC) was utilized to further investigate the genetic correlation of immune cells causally associated with two or more lymphomas as indicated by the previous MR Results. Second, we regarded malignant lymphoma and its subtypes as exposures with subsequent effects on the 731 immune cell types. Thrid, by employing SMR analysis, we performed a joint analysis of GWAS and eQTL aggregate statistics to identify functionally relevant genes at the loci identified in GWAS. 2.2 Data sources for malignant lymphoma and its different subtypes of GWAS Regarding the diverse types of malignant lymphoma GWAS database utilized in this study, it was primarily sourced from FinnGen – an extensive project involving genetic data collection and analysis from over 500,000 participants within the Finnish Biobank[ 12 ].Our study aimed to investigate the causal relationship between six distinct subtypes of lymphoma, including DLBCL, follicular lymphoma, and non-follicular lymphoma, and a total of 731 immune cells. 2.3 Immunity-wide GWAS data sources The aggregated GWAS statistics for all 731 immune traits can be accessed publicly through the GWAS catalog, spanning from GCST0001391 to GCST0002121[ 13 ]. The study encompassed a total of 731 immunophenotypes. Specifically, the B cells, cDCs, mature T cells, monocytes, bone marrow cells, TBNK (T cells, B cells, natural killer cells), and Treg cells were characterized by MFI, AC, and RC features. Conversely, the cDCs and TBNK panels constituted the MP features. The genome-wide association analysis of immune traits employed data from a non-overlapping cohort of 3,757 Europeans. A comprehensive set of genetic variants comprising 20,143,392 SNPs and 1,6888,858 indels was examined using either high-density genotyping arrays or reference panel based on Sardinian sequences, and tested for association after adjusting for covariates (i.e., sex, age and age 2 )[ 14 ]. 2.2 Genetic instrumental variants (IVs) selection In strict accordance with the three core assumptions of MR Study designs (1. there is a strong association between instrumental variables (IVs) and exposure factors; 2. IVs are independent of confounding factors in the expose-outcome relationship; (3) Genetic variation can only affect results through exposure, and not through other ways[ 15 ]. To identify instrumental variables for various exposure factors, single nucleotide polymorphisms (SNPs) at the genome-wide significance threshold (P < 5 × 10 − 6) were extracted from the immune traits genome-wide association study (GWAS). Linkage disequilibrium (LD), with an r 2 value of 0.01 and a clumping distance of 500, was calculated using the reference panel provided by the 1000 Genomes Project. Additionally, SNPs not present in the outcome GWAS were removed, and proxy SNPs were not utilized in this study. The remaining SNPs were used for Mendelian randomization analysis. F statistics were computed to assess the strength of genetic instrumental variables, considering only those SNPs with an F statistic > 10 as non-weak instruments. 2.3 Statistical analysis The main analyses involved three stages: two-sample Mendelian analysis, primary SMR analyses and colocalization analyses. The data were harmonized to exclude SNPs with ambiguous alleles and palindromic SNPs. The primary MR analyses were conducted using the multiplicative random-effects inverse-variance weighted (IVW) method, which provides precise estimates under the assumption of all SNPs being valid instruments. Estimates from different sources were combined using fixed-effects meta-analysis, and heterogeneity among associations was assessed using the I 2 statistic for different data sources and Cochran's Q value for SNP estimates within each association. The I 2 statistic was calculated to assess the heterogeneity of each outcome from different data sources, and the I 2 values 75% were considered to indicate low moderate,and high heterogeneity, respectively. Sensitivity analyses including weighted median, MR-Egger, and MR pleiotropy residual sum and outlier (MR-PRESSO) analyses were performed to detect potential unbalanced pleiotropy (horizontal pleiotropy) and examine consistency of associations[ 16 ]. Additionally, MR-PRESSO can effectively correct the outliers in the instrumental variables (IVs) and provide an estimate that is consistent with IVW after removing these outliers, as indicated by a significant p-value 10 indicated a sufficiently strong instrument. Power analysis was conducted using an online tool. To account for multiple testing, the Benjamini-Hochberg correction controlling false discovery rate was applied. Associations with nominal p-values 0.05 and < 0.1 was considered suggestive, while those with Benjamini-Hochberg adjusted p-values < 0.05 were deemed significant. Summary-data-based MR (SMR) The Summary-data-based Mendelian randomization (SMR) method was employed to generate effect estimates when utilizing expression quantitative trait loci (eQTLs) as instrumental variables, enabling the investigation of the association between gene expression levels and outcomes of interest using summary-level data from genome-wide association studies (GWAS) and eQTL studies. Allele harmonization and analysis were conducted using version 1.03 of the SMR software. Detailed information regarding the SMR method has been previously reported[ 18 ]. The associations with HEIDI test P < 0.05 may be ascribed to linkage rather than pleiotropy, where the same variant independently regulates both outcomes and exposures; thus, such associations should be excluded from the analysis. In SMR analysis, cis-eQTL genetic variation is used as an instrumental variable (IVs) of gene expression. We used lymphocyte eQTL data for SMR analysis. eQTL data comes from the V8 version of GTEx summary data. Detailed information on sample collection and processing is provided elsewhere. EQTL data can be downloaded from https://cnsgenomics.com/data/SMR/#eQTLsummarydata . Colocalization analysis The colocalization approach serves as a means to evaluate the presence of shared causal variations between two features within a given genomic region. To enhance the precision of our findings, we conducted an additional Bayesian test for colocalization of two traits using the coloc R package ( https://chr1swallace.github.io/coloc/ , version 5.1.0) in order to estimate the posterior probability of shared variants. The basic hypothesis for colocalization in the same genomic location is: H0: neither trait has a causal genetic variant; H1: only trait 1 has a causal genetic variant; H2: only trait 2 has a causal genetic variant; H3: both traits have a causal genetic variant, but not the same variant; H4: both traits share the same causal variant. For each leading SNP in the gastrointestinal disease GWAS database under investigation, all SNPs within a 100 kb range upstream and downstream from the leading SNP were retrieved for co-localization analysis, aiming to assess the posterior probability of H4 (PP.H4). A PP.H4 value greater than 0.75 was considered as a robust threshold indicating evidence supporting co-localization between GWAS and QTL associations. Linked disequilibrium score regression We used linked disequilibrium score regression (LDSC) and assessed genome-wide genetic associations between different immune cells that were causally associated with six types of lymphoma. Genetic correlation analyses were performed according to the standard analysis process of LDSC. We performed LDSC using well-imputed HapMap3 variants and pre-computed LD scores of European ancestry from the 1000 Genomes Project Phase3. We did not constrain the intercepts in LDSC analysis, which could not only account for residual confounding but also indicate whether there was potential sample overlap between two GWAS studies. Results 3.1 Analysis and comparison of causal immune cells in different types of lymphoma After conducting MR analysis and applying BH correction, we identified a series of distinct immune cell types that exhibited causal relationships with six lymphomas (Fig. 2 A-F). Supplementary Table 1 provides detailed information on these specific immune cell types. Our findings demonstrated that Hodgkin lymphoma was causally associated with 11 immune cells, DLBCL with 19 immune cells, other and unspecified non-Hodgkin lymphomas with 19 immune cells, mature T/NK-cell lymphomas with 14 immune cells, non-follicular lymphoma with 18 immune cells, and follicular lymphoma with 15 immune cells. Moreover, both the MR-Egger truncation test and the MR-PRESSO global test effectively ruled out pleiotropy at the level of causal immune cell associations across different lymphomas. Sensitivity analyses further confirmed the robustness of our observed causal relationships. Comprehensive information can be found in Supplementary Table 2. The heterogeneity analysis revealed that the majority of results exhibited homogeneity, while those with heterogeneity demonstrated only mild levels (I 2 < 25%). A small subset of findings displayed moderate heterogeneity (25%<I 2 <75%), and Significant heterogeneity was observed in a limited number of findings (mature T/NK-cell lymphomas: CD3 on CD4, I 2 = 75.7%; non-follicular lymphoma: HLA DR on CD33dim HLA DR + CD11b + , I 2 = 75.7%, IgD on IgD + CD38br, I 2 = 76%), thus indicating the robustness of our analytical findings. Additionally, to investigate whether there existed a causal relationship between lymphomas and aforementioned immune cell types, we conducted inverse Mendelian randomization analysis as an extension to our study. The corresponding results are provided in Supplementary Table 3. Our analysis revealed a unidirectional association between these specific immune cell types and lymphoma rather than a bidirectional relationship. 3.2 Analysis and comparison of causal immune cells in different types of lymphoma To further explore the causal relationship between immune cells and various subtypes of lymphoma at the genetic level, we conducted an architectural analysis and employed a Manhattan plot (Fig. 3 A) to identify effector genes. Our findings exhibited significant correlations in Hodgkin's lymphoma between CD247, CMTM6, CD25, ENTPD1, MBL2, and CD40. In non-Hodgkin lymphoma, including unspecified types, FCGR2A, ENTPD1, LYZ, CIITA, and rs709589 were found to be significantly associated. Notably, LYZ displayed a pronounced association specifically with follicular lymphoma but was also observed in non-follicular lymphoma. Additionally, robust associations of CD247, HLADR-DQ, and CIITA were evident. Furthermore, in DLBCL cases, CD247, FCGR2A, LYZ, and CD40 demonstrated significant associations. In mature T/NK cell lymphomas, a significant association was observed between CD247 and ENTPD1. Figure 3 B highlights the co-occurrence of immune cells that are causally linked to different types of lymphoma. A total of 28 immune cells were identified as being causally associated with two or more distinct lymphomas. Among these, 20 immune cells were associated with two lymphomas, seven immune cells were associated with three lymphomas, and notably, one immune cell was implicated in four lymphoma subtypes (including unspecified non-Hodgkin lymphoma, DLBCL, follicular lymphoma, and non-follicular lymphoma). Figure 3 C further illustrates the specific directionality exhibited by these immune cells in relation to the six aforementioned lymphomas. Surprisingly, all immune cells exhibit consistent directional activity across the six different lymphomas, including CD28 − CD25 ++ CD8br % CD8br, which is identified as a risk factor for the four lymphomas. 3.3 LDSC results causally associated with lymphoma Furthermore, to delve deeper into the correlation between different immune cell populations and lymphoma, we conducted LDSC analysis. Detailed results can be found in the supplementary materials Table 4. Notably, the association of CD28 − CD25 ++ CD8br%CD8br immune cells with all four lymphoma types is particularly noteworthy (p = 0.0343 with other and unspecified non-Hodgkin lymphoma, p = 0.0294 with follicular lymphoma, p = 0.0306 with non-follicular lymphoma, p = 0.0247 with DLBCL). This finding significantly strengthens the reliability of our previous Mendelian randomization (MR) results. Moreover, certain immune cell populations, such as CD8br%leukocyte and CD25 on secreting Treg cells, were observed to be associated with three types of lymphoma simultaneously. 3.4 Immune cell SMR results and LDSC results causally associated with lymphoma The associations between immune cells from the lymphoid tissue that were causal for different lymphomas were obtained by SMR analysis (Fig. 4 A). Through SMR analysis, we identified genes that exhibited a significant association with lymphoma-associated immune cells (P FDR 0.05). Detailed genetic information can be found in supplementary materials Table 5. As showed in Fig. 4 . we focused on immune cells (CD28 − CD25 ++ CD8br%CD8br) that were causally associated with all four lymphomas, and found WARS2(beta SMR[SE] =-0.13 [0.04] , P SMR =8.37×10 − 04 , P FDR =0.026, P HEID =0.77, Fig. 4 A), PTPN7(beta SMR[SE] = 0.19 [0.05] , P SMR =9.81×10 − 04 , P FDR =0.026, P HEID =0.69, Fig. 4 B). Additionally, we have also identified associations between several genes (HLA-DRB5, TSPAN32, SLC25A11, MRPL28, L3MBTL2, etc.) and diverse immune cell populations, suggesting their potential involvement in the regulation of multiple immune cell types simultaneously. Discussion Based on a large amount of published genetic data, we conducted an exploration of the causal relationship between immune cell traits and different types of lymphoma. To the best of our knowledge, this study represents the first mendelian randomization analysis that integrates two-sample MR and SMR approaches to investigate the causal association between multiple immunophenotypes and various lymphomas. Additionally, we employed LDSC analyses to further enhance the robustness and reliability of our findings. The innovative study provides important insights into the interactions between the immune system and lymphomas, offering valuable insights that could inform future prevention and treatment strategies. Our study unveiled distinct immune cell populations with causal relationships among different subtypes of lymphoma. To further investigate the causal relationship between these immune cells and different lymphomas, we employed a Manhattan plot analysis to examine the genetic architecture of the immune cells associated with these six lymphomas and identify the effector genes linked to them. Our findings revealed a robust association between CD247 and Hodgkin lymphoma, non-follicular lymphoma, DLBCL, as well as mature T/NK cell lymphomas. Notably, CD247 exhibited predominantly low expression levels in natural killer (NK) and T cells. Interestingly, previous studies conducted in 2015 also reported reduced expression of CD247 in NK/T-cell lymphoma[ 19 ]. The subsequent discovery made by scholars unveiled a significant association between the high expression of CD247 in DLBCL and enhanced overall survival[ 20 ]. Our studies consistently support the aforementioned perspective. However, investigations regarding the involvement of CD247 in the other two lymphomas have not been conducted thus far. Consequently, our findings suggest that CD247 may also exert an influence on Hodgkin lymphoma and non-follicular lymphoma. Furthermore, our study revealed a significant correlation between ENTPD1 (ectonucleoside triphosphate diphosphate hydrolase-1) and Hodgkin lymphoma as well as other unspecified types of non-Hodgkin lymphoma. The present study demonstrated that inhibiting ENTPD1 can enhance the anticancer efficacy of ceritinib in triple negative breast cancer, melanoma cells, and non-small cell lung cancer[ 21 ]. Our findings demonstrate a significant and robust correlation between ENTPD1 and Hodgkin lymphoma, suggesting that the inhibition of ENTPD1 may also exhibit antitumor effects in this disease. Furthermore, our study identifies CIITA, LYZ, MBL2, and CD40 as potential therapeutic targets for various types of lymphoma treatment[ 22 – 25 ]. We conducted an analysis on the immune cells that exhibited overlap and a causal relationship with different subtypes of lymphoma. Our findings unveiled that seven specific immune cell types were causally associated with three distinct lymphoma subtypes, thereby indicating their potential as therapeutic targets for lymphoma treatment. Additionally, CD28 − CD25 ++ CD8br%CD8br immune cells demonstrated significant causal relationships with unspecified types of non-Hodgkin lymphoma, DLBCL, follicular lymphoma and non-follicular lymphoma, thereby serving as risk factors for these four types of lymphomas. However, there is currently a dearth of existing literature regarding the application of this specific immune cell in various types of lymphomas. To strengthen the robustness of our findings, we conducted additional LDSC analysis and identified a genetic association between CD28 − CD25 ++ CD8br%CD8br immune cells and the four lymphomas, thereby reinforcing confidence in our initial Mendelian analysis results. Through SMR analysis, we identified WARS2 and PTPN7 as key regulators of CD28 − CD25 ++ CD8br%CD8br immune cells and various lymphomas. PTPN7, a member of the non-receptor protein tyrosine phosphatase (PTPN) family primarily involved in tyrosine phosphorylation, has been identified as being associated with immune-heat tumors in various cancers, including breast cancer. This discovery positions it as a promising predictive biomarker for immunotherapy[ 26 ]. The latest study has unveiled a notable upregulation of WARS2 in hepatocellular carcinoma cells, indicating its potential as an immunotherapeutic target against liver cancer. However, the current literature lacks any reports on the involvement of this gene in lymphoma[ 27 ]. Furthermore, through the implementation of SMR analysis, we have successfully identified additional genes that exert a regulatory influence on immune cell function, encompassing previously unexplored genes in the context of lymphoma such as PSPHP1 and AGAP6. Consequently, we propose that these newly discovered gene loci exhibit promising potential as innovative therapeutic targets. This study employed a two-sample Mendelian Randomization (MR) analysis based on published findings from a large-scale genome-wide association study (GWAS) cohort comprising approximately 150,000 individuals, ensuring robust statistical power. In this investigation, we utilized traditional two-sample Mendelian analysis, SMR analysis, and LDSC analysis to identify immune cell types that exhibit causal relationships with various lymphomas through diverse analytical approaches. However, it is important to acknowledge certain inherent limitations in our study design. Firstly, given the extensive number of analyses conducted, we primarily relied on the results obtained using the inverse variance weighting (IVW) method of the random effects model as our primary reference point to mitigate false positive outcomes. Although this approach yields more conservative results compared to other methods, there remains a possibility that some immune cell types demonstrating causal associations with different lymphoma subtypes may have been somewhat overlooked. Secondly, due to limited access to individual-level data availability for further demographic stratification was not feasible within this survey. Lastly, since our study predominantly relied on European databases for data collection and analysis purposes caution should be exercised when generalizing these findings to other ethnic groups. Conclusion In summary, we conducted comprehensive MR, LDSC, and SMR analyses to investigate the causal relationship between immune cells and various subtypes of malignant lymphoma. Through SMR analysis, we confirmed associated gene loci that are causally linked to different lymphomas, enabling us to provide a detailed risk profile for each subtype. These findings offer potential therapeutic targets and risk predictors for future treatment strategies targeting diverse types of lymphoma. Declarations Ethical Approval This study did not involve any experimentation on humans or animals. Consent for publication All authors contributed to the article and approved the submitted version and agree to publish this study. Funding Currently, this study has not received any funding. Data Availability The original contributions presented in the study are included in the article/supplementary material. Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We want to acknowledge the participants and investigators of the FinnGen study. Author contributions LJX designed the study. CWJ and ZXH wrote the article. ZXH collected and processed the data. LM was responsible for the Figures. LJX and LZS were responsible for revising the article. LR provided helpful guidance and made great contributions in the process of manuscript revision. All authors contributed to the article and approved the submitted version. References Lewis WD, Lilly S, Jones KL (2020) Lymphoma: Diagnosis and Treatment. Am Fam Physician 101 (1):34-41 Ansell SM, Lin Y (2020) Immunotherapy of lymphomas. J Clin Invest 130 (4):1576-1585. doi:10.1172/JCI129206 de Jong D, Enblad G (2008) Inflammatory cells and immune microenvironment in malignant lymphoma. 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Eur J Epidemiol 30 (7):543-552. doi:10.1007/s10654-015-0011-z Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32 (5):377-389. doi:10.1007/s10654-017-0255-x Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50 (5):693-698. doi:10.1038/s41588-018-0099-7 Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, Yang J (2016) Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 48 (5):481-487. doi:10.1038/ng.3538 Liao Z, Zhou L, Wang C, He Z, Wang X, Luo X, Chen S, Yang L, Tan H, Li Y (2015) Characteristics of TCRzeta, ZAP-70, and FcvarepsilonRIgamma gene expression in patients with T- and NK/T-cell lymphoma. DNA Cell Biol 34 (3):201-207. doi:10.1089/dna.2014.2693 Li Z, Duan Y, Ke Q, Wang M, Cen H, Zhu X (2022) Gene set-based identification of two immune subtypes of diffuse large B cell lymphoma for guiding immune checkpoint blocking therapy. Front Genet 13:1000460. doi:10.3389/fgene.2022.1000460 Schakel L, Mirza S, Winzer R, Lopez V, Idris R, Al-Hroub H, Pelletier J, Sevigny J, Tolosa E, Muller CE (2022) Protein kinase inhibitor ceritinib blocks ectonucleotidase CD39 - a promising target for cancer immunotherapy. J Immunother Cancer 10 (8). doi:10.1136/jitc-2022-004660 Sanchez-Espiridion B, Martin-Moreno AM, Montalban C, Medeiros LJ, Vega F, Younes A, Piris MA, Garcia JF (2012) Immunohistochemical markers for tumor associated macrophages and survival in advanced classical Hodgkin's lymphoma. Haematologica 97 (7):1080-1084. doi:10.3324/haematol.2011.055459 Dufva O, Polonen P, Bruck O, Keranen MAI, Klievink J, Mehtonen J, Huuhtanen J, Kumar A, Malani D, Siitonen S, Kankainen M, Ghimire B, Lahtela J, Mattila P, Vaha-Koskela M, Wennerberg K, Granberg K, Leivonen SK, Meriranta L, Heckman C, Leppa S, Nykter M, Lohi O, Heinaniemi M, Mustjoki S (2020) Immunogenomic Landscape of Hematological Malignancies. Cancer Cell 38 (3):380-399 e313. doi:10.1016/j.ccell.2020.06.002 Nielsen KR, Steffensen R, Bendtsen MD, Rodrigo-Domingo M, Baech J, Haunstrup TM, Bergkvist KS, Schmitz A, Boedker JS, Johansen P, Dybkaeaer K, Boeogsted M, Johnsen HE (2015) Inherited Inflammatory Response Genes Are Associated with B-Cell Non-Hodgkin's Lymphoma Risk and Survival. PLoS One 10 (10):e0139329. doi:10.1371/journal.pone.0139329 Chen Z, Simon-Molas H, Cretenet G, Valle-Argos B, Smith LD, Forconi F, Schomakers BV, van Weeghel M, Bryant DJ, van Bruggen JAC, Peters FS, Rathmell JC, van der Windt GJW, Kater AP, Packham G, Eldering E (2022) Characterization of metabolic alterations of chronic lymphocytic leukemia in the lymph node microenvironment. Blood 140 (6):630-643. doi:10.1182/blood.2021013990 Wang F, Wang X, Liu L, Deng S, Ji W, Liu Y, Wang X, Wang R, Zhao X, Gao E (2022) Comprehensive analysis of PTPN gene family revealing PTPN7 as a novel biomarker for immuno-hot tumors in breast cancer. Front Genet 13:981603. doi:10.3389/fgene.2022.981603 Zheng Q, Yang Q, Zhou J, Gu X, Zhou H, Dong X, Zhu H, Chen Z (2021) Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions. Cancer Cell Int 21 (1):330. doi:10.1186/s12935-021-02033-4 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4664711","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322669289,"identity":"6c13abd2-9746-45c2-b5ad-ae5c5e924d17","order_by":0,"name":"Jingxuan Lian","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingxuan","middleName":"","lastName":"Lian","suffix":""},{"id":322669290,"identity":"49f0a68e-0354-4b7f-aea1-28f18fb488e0","order_by":1,"name":"Xinghong Zhang","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinghong","middleName":"","lastName":"Zhang","suffix":""},{"id":322669291,"identity":"e0dfa3b2-be74-4d80-93b9-a9a628eb4206","order_by":2,"name":"Wenjie Chen","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Chen","suffix":""},{"id":322669292,"identity":"7a73d8ea-cee6-4c0e-87a8-0b2aeac8e340","order_by":3,"name":"Zheshen Lin","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zheshen","middleName":"","lastName":"Lin","suffix":""},{"id":322669293,"identity":"9adfd981-ed96-4593-b3a6-2cc016f9660a","order_by":4,"name":"Ming Lu","email":"","orcid":"","institution":"Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Lu","suffix":""},{"id":322669294,"identity":"fa6888d8-d929-4750-8185-afc4dda271ed","order_by":5,"name":"Rong Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYLCCBwZAgr35wIGECgk5eaK0JIC08BxLPPDhjIWxYQNRWkCEhI/xwZltFYkMBwioNm/vPfwiocAuTz6CLeEw7zyJBMYG5oePbuDRInPmXJpFgkFyseHt5gOHebdJ5LEzsBkb5+DRIiGRY2aQYMCcuHHOsQSQlmLGBh42aSK01CdunJFjcJh3jkRiwwHCWowfJBgcTpwvkWNwcGYDMVp4zpgBA/l44gaeYwkHPhyTMDZsJuQX9h7jDx/+VCfOb28+/CGhpk5Onr354WN8WoCATQJEGhyA8ZnxKwcr+QAi5RsIqxwFo2AUjIIRCgBnGlHZs2sdHwAAAABJRU5ErkJggg==","orcid":"","institution":"Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-07-01 01:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4664711/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4664711/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61078477,"identity":"e5b3b611-5b4d-4647-8a7b-4a61be300942","added_by":"auto","created_at":"2024-07-25 10:06:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":217623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDescription of the study design in this bidirectional MR study. \u003c/strong\u003e\u0026nbsp;Sketch of the study design. The red represented the forward MR analyses, with 731 immune cells as exposure and malignant lymphoma and its different subtypes as the outcome. The blue represented the reverse MR analyses, with malignant lymphoma and its different subtypes as exposure and 731 immune cells as the outcome. The immune cells previously examined for their causal association with various lymphomas were further subjected to SMR and LDSC analysis, as suggested by Brown. MR, Mendelian randomization; SNPs, single-nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/8a29b1ae220f5a9104718aaa.png"},{"id":61078028,"identity":"444a9cc6-371d-473c-8d74-a9e12bf348ad","added_by":"auto","created_at":"2024-07-25 09:58:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1321728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune cells exhibiting a causal association with various types of lymphomas by IVW.\u003c/strong\u003e (A) The immune cells exhibiting a causal association with Hodgkin's lymphoma; (B) The immune cells exhibiting a causal association with DLBCL; (C) The immune cells exhibiting a causal association with other and unspecified types of non-Hodgkin lymphoma; (D) The immune cells exhibiting a causal association with mature T/NK-cell lymphomas; (E) The immune cells exhibiting a causal association with non-follicular lymphoma; (F) The immune cells exhibiting a causal association with follicular lymphoma.IVW. inverse variance weighted\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/6b907d4a951c83220138a6c0.png"},{"id":61078947,"identity":"543f30b8-7a6a-479f-b9a3-c4573aa5b2e6","added_by":"auto","created_at":"2024-07-25 10:14:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":482987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis and comparison of causal immune cells.\u003c/strong\u003e (A) Summary of the association between immune cells and gene loci with different subtypes of lymphoma; (B) Overlap of different causal immune cells in six lymphomas; (C) Direction of action of different immune cells on six lymphomas. Dotted lines are protective factors, solid lines are risk factors, and different colors represent different cell panels. HL, Hodgkin lymphoma, Other-HL, other and unspecified types of non-Hodgkin lymphoma.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/9e1b815e34f2dd1b3d29d3cd.png"},{"id":61078029,"identity":"7a3f86b2-ea35-4cdb-8331-73c343f44ba9","added_by":"auto","created_at":"2024-07-25 09:58:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1206885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExploration of potential lymphoma therapeutic genes through SMR analysis. A\u003c/strong\u003e. SMR and colocalization results from eQTL of diverse immune cells causally associated with lymphoma. positive correlation is indicated by β\u0026gt;0, while a negative correlation is indicated by β\u0026lt;0. Ratios were computed based on the expected value of the causal estimate (β coefficient). Co-localization was determined by PP.H4 between eQTL and lymphoma, with a PP.H4 threshold of \u0026gt;0.75 considered as strong evidence for co-localization. The displayed results are limited to those with PP.H4 values of 0.70 or higher; \u003cstrong\u003eB.\u003c/strong\u003e The associations of PTPN7 and WARS2 with CD28-CD25++ CD8br%CD8br were investigated using eQTL data obtained from SMR analysis of lymphomas\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/0605c221a495e3ae4874aa2a.png"},{"id":66243724,"identity":"bcf06ad6-85c6-4642-ad20-6c6be4d01b60","added_by":"auto","created_at":"2024-10-09 07:17:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3976785,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/c9b4346e-d400-4b0e-a941-2554f0fee68d.pdf"},{"id":61078027,"identity":"824cd028-198c-4a23-a105-506e73f7d998","added_by":"auto","created_at":"2024-07-25 09:58:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68717,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4664711/v1/5f876c29a675c6e877377bfe.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing putative causal genes by establishing the causality between different lymphomas and immune cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLymphoma, comprising more than 90 subtypes of malignant lymphocyte neoplasms, is conventionally classified broadly as non-Hodgkin or Hodgkin lymphoma. Each year, approximately 82,000 new cases of lymphoma are diagnosed in the United States.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevailing belief is that malignant lymphocytes in lymphoma primarily recruit and sustain a microenvironment consisting of other immune cells as well as stromal elements, which facilitate the promotion of malignant cell growth and survival.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The biology and clinical behavior of malignant lymphoma are not solely determined by the intrinsic characteristics of tumor cells, but are also significantly influenced by their dynamic interaction with the non-malignant microenvironment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The tumor immune microenvironment (TIME) is a recently proposed concept that has been demonstrated to exhibit a robust association with the clinical prognosis of cancer patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The tumor microenvironment (TME) predominantly consists of a diverse array of immune cell populations, encompassing both innate and adaptive immune cells such as myeloid cells and lymphocytes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is noteworthy that TIME also plays a pivotal role in determining the immune response state within the tumor microenvironment (TME), which predominantly relies on the composition and activity of infiltrating immune cells, alongside various influencing factors such as cell surface expression of immune checkpoint molecules and alterations in associated extracellular matrix[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The composition of immune cells within the tumor immune microenvironment (TIME) exhibits inter-tumoral variability and demonstrates significant associations with clinical outcomes across diverse cancer types[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe immune microenvironment plays a crucial role in the pathogenesis, disease progression, and therapy resistance of lymphomas. In diffuse large B-cell lymphoma (DLBCL), the composition of various immune effectors and cells can serve as prognostic biomarkers and independent indicators for different immunotherapies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The presence of myeloid-derived suppressor cells (MDSCs) has been observed in the peripheral blood of patients with Hodgkin and Non-Hodgkin lymphoma (NHL), demonstrating a positive correlation with disease aggressiveness and significant prognostic value[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The tumor microenvironment (TME) is widely recognized to play a crucial role in various processes, including lymphoma progression, treatment response, drug resistance, and prognosis. Targeting components of the TME holds promise for uncovering novel insights into precise lymphoma management. Furthermore, by employing Mendelian randomization in epidemiological etiological inference, we establish a methodological foundation to elucidate potential causal relationships between distinct immune cells and malignant lymphoma as well as its subtypes. In this study, we conducted a comprehensive bidirectional Mendelian randomization analysis to elucidate the causal associations between diverse immune cell types and malignant lymphoma along with its different subtypes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design description\u003c/h2\u003e \u003cp\u003eThe bidirectional Mendelian randomization (MR) design, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, provides a concise overview of the investigation into the association between 731 immune cell types and malignant lymphomas, encompassing their diverse subtypes. First, Two MR analyses were conducted using aggregated statistics from genome-wide association studies (GWAS) to explore the reciprocal relationship between immune cells and malignant lymphoma, as well as its distinct subtypes. Subsequently, linked disequilibrium score regression (LDSC) was utilized to further investigate the genetic correlation of immune cells causally associated with two or more lymphomas as indicated by the previous MR Results. Second, we regarded malignant lymphoma and its subtypes as exposures with subsequent effects on the 731 immune cell types. Thrid, by employing SMR analysis, we performed a joint analysis of GWAS and eQTL aggregate statistics to identify functionally relevant genes at the loci identified in GWAS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources for malignant lymphoma and its different subtypes of GWAS\u003c/h2\u003e \u003cp\u003eRegarding the diverse types of malignant lymphoma GWAS database utilized in this study, it was primarily sourced from FinnGen – an extensive project involving genetic data collection and analysis from over 500,000 participants within the Finnish Biobank[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].Our study aimed to investigate the causal relationship between six distinct subtypes of lymphoma, including DLBCL, follicular lymphoma, and non-follicular lymphoma, and a total of 731 immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Immunity-wide GWAS data sources\u003c/h2\u003e \u003cp\u003eThe aggregated GWAS statistics for all 731 immune traits can be accessed publicly through the GWAS catalog, spanning from GCST0001391 to GCST0002121[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The study encompassed a total of 731 immunophenotypes. Specifically, the B cells, cDCs, mature T cells, monocytes, bone marrow cells, TBNK (T cells, B cells, natural killer cells), and Treg cells were characterized by MFI, AC, and RC features. Conversely, the cDCs and TBNK panels constituted the MP features. The genome-wide association analysis of immune traits employed data from a non-overlapping cohort of 3,757 Europeans. A comprehensive set of genetic variants comprising 20,143,392 SNPs and 1,6888,858 indels was examined using either high-density genotyping arrays or reference panel based on Sardinian sequences, and tested for association after adjusting for covariates (i.e., sex, age and age\u003csup\u003e2\u003c/sup\u003e)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Genetic instrumental variants (IVs) selection\u003c/h2\u003e \u003cp\u003eIn strict accordance with the three core assumptions of MR Study designs (1. there is a strong association between instrumental variables (IVs) and exposure factors; 2. IVs are independent of confounding factors in the expose-outcome relationship; (3) Genetic variation can only affect results through exposure, and not through other ways[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To identify instrumental variables for various exposure factors, single nucleotide polymorphisms (SNPs) at the genome-wide significance threshold (P \u0026lt; 5 × 10 − 6) were extracted from the immune traits genome-wide association study (GWAS). Linkage disequilibrium (LD), with an r\u003csup\u003e2\u003c/sup\u003e value of 0.01 and a clumping distance of 500, was calculated using the reference panel provided by the 1000 Genomes Project. Additionally, SNPs not present in the outcome GWAS were removed, and proxy SNPs were not utilized in this study. The remaining SNPs were used for Mendelian randomization analysis. F statistics were computed to assess the strength of genetic instrumental variables, considering only those SNPs with an F statistic \u0026gt; 10 as non-weak instruments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe main analyses involved three stages: two-sample Mendelian analysis, primary SMR analyses and colocalization analyses. The data were harmonized to exclude SNPs with ambiguous alleles and palindromic SNPs. The primary MR analyses were conducted using the multiplicative random-effects inverse-variance weighted (IVW) method, which provides precise estimates under the assumption of all SNPs being valid instruments. Estimates from different sources were combined using fixed-effects meta-analysis, and heterogeneity among associations was assessed using the I\u003csup\u003e2\u003c/sup\u003e statistic for different data sources and Cochran's Q value for SNP estimates within each association. The I\u003csup\u003e2\u003c/sup\u003e statistic was calculated to assess the heterogeneity of each outcome from different data sources, and the I\u003csup\u003e2\u003c/sup\u003evalues \u0026lt; 25%, 25–75%, and \u0026gt; 75% were considered to indicate low moderate,and high heterogeneity, respectively. Sensitivity analyses including weighted median, MR-Egger, and MR pleiotropy residual sum and outlier (MR-PRESSO) analyses were performed to detect potential unbalanced pleiotropy (horizontal pleiotropy) and examine consistency of associations[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, MR-PRESSO can effectively correct the outliers in the instrumental variables (IVs) and provide an estimate that is consistent with IVW after removing these outliers, as indicated by a significant p-value \u0026lt; 0.05 demonstrating the presence of directional pleiotropy.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Instrument strength was quantified by estimating the F-statistic, where an F-statistic \u0026gt; 10 indicated a sufficiently strong instrument. Power analysis was conducted using an online tool. To account for multiple testing, the Benjamini-Hochberg correction controlling false discovery rate was applied. Associations with nominal p-values \u0026lt; 0.05 and Benjamini-Hochberg adjusted p-values \u0026gt; 0.05 and \u0026lt; 0.1 was considered suggestive, while those with Benjamini-Hochberg adjusted p-values \u0026lt; 0.05 were deemed significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSummary-data-based MR (SMR)\u003c/h2\u003e \u003cp\u003eThe Summary-data-based Mendelian randomization (SMR) method was employed to generate effect estimates when utilizing expression quantitative trait loci (eQTLs) as instrumental variables, enabling the investigation of the association between gene expression levels and outcomes of interest using summary-level data from genome-wide association studies (GWAS) and eQTL studies. Allele harmonization and analysis were conducted using version 1.03 of the SMR software. Detailed information regarding the SMR method has been previously reported[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The associations with HEIDI test P \u0026lt; 0.05 may be ascribed to linkage rather than pleiotropy, where the same variant independently regulates both outcomes and exposures; thus, such associations should be excluded from the analysis. In SMR analysis, cis-eQTL genetic variation is used as an instrumental variable (IVs) of gene expression. We used lymphocyte eQTL data for SMR analysis. eQTL data comes from the V8 version of GTEx summary data. Detailed information on sample collection and processing is provided elsewhere. EQTL data can be downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cnsgenomics.com/data/SMR/#eQTLsummarydata\u003c/span\u003e\u003cspan address=\"https://cnsgenomics.com/data/SMR/#eQTLsummarydata\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eThe colocalization approach serves as a means to evaluate the presence of shared causal variations between two features within a given genomic region. To enhance the precision of our findings, we conducted an additional Bayesian test for colocalization of two traits using the coloc R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chr1swallace.github.io/coloc/\u003c/span\u003e\u003cspan address=\"https://chr1swallace.github.io/coloc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 5.1.0) in order to estimate the posterior probability of shared variants. The basic hypothesis for colocalization in the same genomic location is: H0: neither trait has a causal genetic variant; H1: only trait 1 has a causal genetic variant; H2: only trait 2 has a causal genetic variant; H3: both traits have a causal genetic variant, but not the same variant; H4: both traits share the same causal variant. For each leading SNP in the gastrointestinal disease GWAS database under investigation, all SNPs within a 100 kb range upstream and downstream from the leading SNP were retrieved for co-localization analysis, aiming to assess the posterior probability of H4 (PP.H4). A PP.H4 value greater than 0.75 was considered as a robust threshold indicating evidence supporting co-localization between GWAS and QTL associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLinked disequilibrium score regression\u003c/h2\u003e \u003cp\u003eWe used linked disequilibrium score regression (LDSC) and assessed genome-wide genetic associations between different immune cells that were causally associated with six types of lymphoma. Genetic correlation analyses were performed according to the standard analysis process of LDSC. We performed LDSC using well-imputed HapMap3 variants and pre-computed LD scores of European ancestry from the 1000 Genomes Project Phase3. We did not constrain the intercepts in LDSC analysis, which could not only account for residual confounding but also indicate whether there was potential sample overlap between two GWAS studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003e3.1 Analysis and comparison of causal immune cells in different types of lymphoma\u003c/h2\u003e\u003cp\u003eAfter conducting MR analysis and applying BH correction, we identified a series of distinct immune cell types that exhibited causal relationships with six lymphomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F). Supplementary Table\u0026nbsp;1 provides detailed information on these specific immune cell types. Our findings demonstrated that Hodgkin lymphoma was causally associated with 11 immune cells, DLBCL with 19 immune cells, other and unspecified non-Hodgkin lymphomas with 19 immune cells, mature T/NK-cell lymphomas with 14 immune cells, non-follicular lymphoma with 18 immune cells, and follicular lymphoma with 15 immune cells. Moreover, both the MR-Egger truncation test and the MR-PRESSO global test effectively ruled out pleiotropy at the level of causal immune cell associations across different lymphomas. Sensitivity analyses further confirmed the robustness of our observed causal relationships. Comprehensive information can be found in Supplementary Table\u0026nbsp;2. The heterogeneity analysis revealed that the majority of results exhibited homogeneity, while those with heterogeneity demonstrated only mild levels (I\u003csup\u003e2\u003c/sup\u003e \u0026lt; 25%). A small subset of findings displayed moderate heterogeneity (25%\u0026lt;I\u003csup\u003e2\u003c/sup\u003e\u0026lt;75%), and Significant heterogeneity was observed in a limited number of findings (mature T/NK-cell lymphomas: CD3 on CD4, I\u003csup\u003e2\u003c/sup\u003e = 75.7%; non-follicular lymphoma: HLA DR on CD33dim HLA DR\u003csup\u003e+\u003c/sup\u003e CD11b\u003csup\u003e+\u003c/sup\u003e, I\u003csup\u003e2\u003c/sup\u003e = 75.7%, IgD on IgD\u003csup\u003e+\u003c/sup\u003e CD38br, I\u003csup\u003e2\u003c/sup\u003e = 76%), thus indicating the robustness of our analytical findings. Additionally, to investigate whether there existed a causal relationship between lymphomas and aforementioned immune cell types, we conducted inverse Mendelian randomization analysis as an extension to our study. The corresponding results are provided in Supplementary Table\u0026nbsp;3. Our analysis revealed a unidirectional association between these specific immune cell types and lymphoma rather than a bidirectional relationship.\u003c/p\u003e\u003ch2\u003e3.2 Analysis and comparison of causal immune cells in different types of lymphoma\u003c/h2\u003e\u003cp\u003eTo further explore the causal relationship between immune cells and various subtypes of lymphoma at the genetic level, we conducted an architectural analysis and employed a Manhattan plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) to identify effector genes. Our findings exhibited significant correlations in Hodgkin's lymphoma between CD247, CMTM6, CD25, ENTPD1, MBL2, and CD40. In non-Hodgkin lymphoma, including unspecified types, FCGR2A, ENTPD1, LYZ, CIITA, and rs709589 were found to be significantly associated. Notably, LYZ displayed a pronounced association specifically with follicular lymphoma but was also observed in non-follicular lymphoma. Additionally, robust associations of CD247, HLADR-DQ, and CIITA were evident. Furthermore, in DLBCL cases, CD247, FCGR2A, LYZ, and CD40 demonstrated significant associations. In mature T/NK cell lymphomas, a significant association was observed between CD247 and ENTPD1. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB highlights the co-occurrence of immune cells that are causally linked to different types of lymphoma. A total of 28 immune cells were identified as being causally associated with two or more distinct lymphomas. Among these, 20 immune cells were associated with two lymphomas, seven immune cells were associated with three lymphomas, and notably, one immune cell was implicated in four lymphoma subtypes (including unspecified non-Hodgkin lymphoma, DLBCL, follicular lymphoma, and non-follicular lymphoma). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC further illustrates the specific directionality exhibited by these immune cells in relation to the six aforementioned lymphomas. Surprisingly, all immune cells exhibit consistent directional activity across the six different lymphomas, including CD28\u003csup\u003e−\u003c/sup\u003e CD25\u003csup\u003e++\u003c/sup\u003e CD8br\u003csup\u003e%\u003c/sup\u003eCD8br, which is identified as a risk factor for the four lymphomas.\u003c/p\u003e\u003ch2\u003e3.3 LDSC results causally associated with lymphoma\u003c/h2\u003e\u003cp\u003eFurthermore, to delve deeper into the correlation between different immune cell populations and lymphoma, we conducted LDSC analysis. Detailed results can be found in the supplementary materials Table\u0026nbsp;4. Notably, the association of CD28\u003csup\u003e−\u003c/sup\u003eCD25\u003csup\u003e++\u003c/sup\u003e CD8br%CD8br immune cells with all four lymphoma types is particularly noteworthy (p = 0.0343 with other and unspecified non-Hodgkin lymphoma, p = 0.0294 with follicular lymphoma, p = 0.0306 with non-follicular lymphoma, p = 0.0247 with DLBCL). This finding significantly strengthens the reliability of our previous Mendelian randomization (MR) results. Moreover, certain immune cell populations, such as CD8br%leukocyte and CD25 on secreting Treg cells, were observed to be associated with three types of lymphoma simultaneously.\u003c/p\u003e\u003ch2\u003e3.4 Immune cell SMR results and LDSC results causally associated with lymphoma\u003c/h2\u003e\u003cp\u003eThe associations between immune cells from the lymphoid tissue that were causal for different lymphomas were obtained by SMR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Through SMR analysis, we identified genes that exhibited a significant association with lymphoma-associated immune cells (P\u003csub\u003eFDR\u003c/sub\u003e \u0026lt; 0.05, P\u003csub\u003eHIED\u003c/sub\u003e \u0026gt; 0.05). Detailed genetic information can be found in supplementary materials Table\u0026nbsp;5. As showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. we focused on immune cells (CD28\u003csup\u003e−\u003c/sup\u003eCD25\u003csup\u003e++\u003c/sup\u003e CD8br%CD8br) that were causally associated with all four lymphomas, and found WARS2(beta\u003csub\u003eSMR[SE]\u003c/sub\u003e=-0.13\u003csub\u003e[0.04]\u003c/sub\u003e, P\u003csub\u003eSMR\u003c/sub\u003e=8.37×10\u003csup\u003e− 04\u003c/sup\u003e, P\u003csub\u003eFDR\u003c/sub\u003e=0.026, P\u003csub\u003eHEID\u003c/sub\u003e=0.77, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), PTPN7(beta\u003csub\u003eSMR[SE]\u003c/sub\u003e = 0.19\u003csub\u003e[0.05]\u003c/sub\u003e, P\u003csub\u003eSMR\u003c/sub\u003e=9.81×10\u003csup\u003e− 04\u003c/sup\u003e, P\u003csub\u003eFDR\u003c/sub\u003e=0.026, P\u003csub\u003eHEID\u003c/sub\u003e=0.69, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, we have also identified associations between several genes (HLA-DRB5, TSPAN32, SLC25A11, MRPL28, L3MBTL2, etc.) and diverse immune cell populations, suggesting their potential involvement in the regulation of multiple immune cell types simultaneously.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a large amount of published genetic data, we conducted an exploration of the causal relationship between immune cell traits and different types of lymphoma. To the best of our knowledge, this study represents the first mendelian randomization analysis that integrates two-sample MR and SMR approaches to investigate the causal association between multiple immunophenotypes and various lymphomas. Additionally, we employed LDSC analyses to further enhance the robustness and reliability of our findings. The innovative study provides important insights into the interactions between the immune system and lymphomas, offering valuable insights that could inform future prevention and treatment strategies.\u003c/p\u003e \u003cp\u003eOur study unveiled distinct immune cell populations with causal relationships among different subtypes of lymphoma. To further investigate the causal relationship between these immune cells and different lymphomas, we employed a Manhattan plot analysis to examine the genetic architecture of the immune cells associated with these six lymphomas and identify the effector genes linked to them. Our findings revealed a robust association between CD247 and Hodgkin lymphoma, non-follicular lymphoma, DLBCL, as well as mature T/NK cell lymphomas. Notably, CD247 exhibited predominantly low expression levels in natural killer (NK) and T cells. Interestingly, previous studies conducted in 2015 also reported reduced expression of CD247 in NK/T-cell lymphoma[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The subsequent discovery made by scholars unveiled a significant association between the high expression of CD247 in DLBCL and enhanced overall survival[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our studies consistently support the aforementioned perspective. However, investigations regarding the involvement of CD247 in the other two lymphomas have not been conducted thus far. Consequently, our findings suggest that CD247 may also exert an influence on Hodgkin lymphoma and non-follicular lymphoma. Furthermore, our study revealed a significant correlation between ENTPD1 (ectonucleoside triphosphate diphosphate hydrolase-1) and Hodgkin lymphoma as well as other unspecified types of non-Hodgkin lymphoma. The present study demonstrated that inhibiting ENTPD1 can enhance the anticancer efficacy of ceritinib in triple negative breast cancer, melanoma cells, and non-small cell lung cancer[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our findings demonstrate a significant and robust correlation between ENTPD1 and Hodgkin lymphoma, suggesting that the inhibition of ENTPD1 may also exhibit antitumor effects in this disease. Furthermore, our study identifies CIITA, LYZ, MBL2, and CD40 as potential therapeutic targets for various types of lymphoma treatment[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe conducted an analysis on the immune cells that exhibited overlap and a causal relationship with different subtypes of lymphoma. Our findings unveiled that seven specific immune cell types were causally associated with three distinct lymphoma subtypes, thereby indicating their potential as therapeutic targets for lymphoma treatment. Additionally, CD28\u003csup\u003e\u0026minus;\u003c/sup\u003e CD25\u003csup\u003e++\u003c/sup\u003e CD8br%CD8br immune cells demonstrated significant causal relationships with unspecified types of non-Hodgkin lymphoma, DLBCL, follicular lymphoma and non-follicular lymphoma, thereby serving as risk factors for these four types of lymphomas. However, there is currently a dearth of existing literature regarding the application of this specific immune cell in various types of lymphomas. To strengthen the robustness of our findings, we conducted additional LDSC analysis and identified a genetic association between CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD25\u003csup\u003e++\u003c/sup\u003e CD8br%CD8br immune cells and the four lymphomas, thereby reinforcing confidence in our initial Mendelian analysis results. Through SMR analysis, we identified WARS2 and PTPN7 as key regulators of CD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD25\u003csup\u003e++\u003c/sup\u003e CD8br%CD8br immune cells and various lymphomas. PTPN7, a member of the non-receptor protein tyrosine phosphatase (PTPN) family primarily involved in tyrosine phosphorylation, has been identified as being associated with immune-heat tumors in various cancers, including breast cancer. This discovery positions it as a promising predictive biomarker for immunotherapy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The latest study has unveiled a notable upregulation of WARS2 in hepatocellular carcinoma cells, indicating its potential as an immunotherapeutic target against liver cancer. However, the current literature lacks any reports on the involvement of this gene in lymphoma[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, through the implementation of SMR analysis, we have successfully identified additional genes that exert a regulatory influence on immune cell function, encompassing previously unexplored genes in the context of lymphoma such as PSPHP1 and AGAP6. Consequently, we propose that these newly discovered gene loci exhibit promising potential as innovative therapeutic targets.\u003c/p\u003e \u003cp\u003eThis study employed a two-sample Mendelian Randomization (MR) analysis based on published findings from a large-scale genome-wide association study (GWAS) cohort comprising approximately 150,000 individuals, ensuring robust statistical power. In this investigation, we utilized traditional two-sample Mendelian analysis, SMR analysis, and LDSC analysis to identify immune cell types that exhibit causal relationships with various lymphomas through diverse analytical approaches. However, it is important to acknowledge certain inherent limitations in our study design. Firstly, given the extensive number of analyses conducted, we primarily relied on the results obtained using the inverse variance weighting (IVW) method of the random effects model as our primary reference point to mitigate false positive outcomes. Although this approach yields more conservative results compared to other methods, there remains a possibility that some immune cell types demonstrating causal associations with different lymphoma subtypes may have been somewhat overlooked. Secondly, due to limited access to individual-level data availability for further demographic stratification was not feasible within this survey. Lastly, since our study predominantly relied on European databases for data collection and analysis purposes caution should be exercised when generalizing these findings to other ethnic groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we conducted comprehensive MR, LDSC, and SMR analyses to investigate the causal relationship between immune cells and various subtypes of malignant lymphoma. Through SMR analysis, we confirmed associated gene loci that are causally linked to different lymphomas, enabling us to provide a detailed risk profile for each subtype. These findings offer potential therapeutic targets and risk predictors for future treatment strategies targeting diverse types of lymphoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any experimentation on humans or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the article and approved the submitted version and agree to publish this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, this study has not received any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the FinnGen study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLJX designed the study. CWJ and ZXH wrote the article. ZXH collected and processed the data. LM was responsible for the Figures. LJX and LZS were responsible for revising the article. LR provided helpful guidance and made great contributions in the process of manuscript revision. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLewis WD, Lilly S, Jones KL (2020) Lymphoma: Diagnosis and Treatment. Am Fam Physician 101 (1):34-41\u003c/li\u003e\n \u003cli\u003eAnsell SM, Lin Y (2020) Immunotherapy of lymphomas. J Clin Invest 130 (4):1576-1585. doi:10.1172/JCI129206\u003c/li\u003e\n \u003cli\u003ede Jong D, Enblad G (2008) Inflammatory cells and immune microenvironment in malignant lymphoma. 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Cancer Cell Int 21 (1):330. doi:10.1186/s12935-021-02033-4\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"The tumor immune microenvironment, lymphoma, immune cells, genome wide association studies, single-nucleotide polymorphisms, Mendelian randomization, summary data-based Mendelian randomization (SMR) ","lastPublishedDoi":"10.21203/rs.3.rs-4664711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4664711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe immune microenvironment not only plays a pivotal role in the pathogenesis of lymphoma but also serves as a critical determinant influencing disease progression and treatment resistance. However, there remains a dearth of comprehensive investigations exploring the causal relationship between various immune cell types and different lymphomas.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eIn this study, we employed common bidirectional two-sample mendelian randomization (MR) and linked disequilibrium score regression (LDSC) to investigate the causal relationship and genetic correlation between immune cells and various lymphomas. Additionally, we utilized the Mendelian randomization-based method of summary data-based MR (SMR), which incorporated genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data from immune cells to identify genes associated with lymphoma. Furthermore, colocalization analysis and genetic correlation analysis were conducted for further validation of our findings.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The two-sample mendelian randomization approach was employed to identify the immune cell types that exhibit a causal relationship with different lymphomas. Additionally, the genetic correlation between these immune cells and malignant lymphomas was further analyzed using the linked disequilibrium score regression method, thereby enhancing the reliability of our findings. The SMR and colocalization analyses revealed several genes associated with these immune cells, thereby providing additional support for their putative role in the pathogenesis of lymphoma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur study elucidates the intricate interplay between immune cells by employing genetic methodologies, thus offering insights for potential therapeutic targets and risk predictors in different subtypes of lymphoma treatments.\u003c/p\u003e","manuscriptTitle":"Revealing putative causal genes by establishing the causality between different lymphomas and immune cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 09:58:37","doi":"10.21203/rs.3.rs-4664711/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5ea74ff-4c1f-47c3-af8f-4c55ccb72d64","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-09T07:08:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 09:58:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4664711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4664711","identity":"rs-4664711","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}