Study on the Gene Regulatory Effects in Leukemia Based on Multi-omics Mendelian Randomization, Network Pharmacology, and Functional Validation

Study on the Gene Regulatory Effects in Leukemia Based on Multi-omics Mendelian Randomization, Network Pharmacology, and Functional Validation | 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 Article Study on the Gene Regulatory Effects in Leukemia Based on Multi-omics Mendelian Randomization, Network Pharmacology, and Functional Validation Guanjun Chen, Yulan Li, Zhenyu Song, Tianjun Huang, Zumiao Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7510513/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Leukemia is a type of malignant hematological disease, for which conventional treatments have limited efficacy, and there is an urgent need for novel therapeutic targets.This study investigates the causal effects of gene expression and plasma proteins on four subtypes of leukemia using a multi-omics integration strategy. Methods: Based on publicly available GWAS data from European populations, two-sample Mendelian randomization (MR) was employed to analyze the causal relationships between cis-expression quantitative trait loci (cis-eQTL, eQTLGen consortium, n=31,684) and cis-plasma protein quantitative trait loci (cis-pQTL, deCODE consortium, n=4,907) with four types of leukemia (FinnGen database, n≈345,000). Co-significant genes were screened through intersection analysis, and their associations were validated using the SMR method. At the experimental level, functional validation of key genes (CLEC1B/IGFLR1) was conducted in MEC-1/K562 leukemia cell lines: after siRNA silencing, silencing efficiency was detected by quantitative real-time PCR (qRT-PCR), proliferation and apoptosis were assessed using Cell Counting Kit-8 (CCK-8) and flow cytometry, and invasion ability was examined by Transwell assay. Targeted drugs were screened based on HEB and ITCM databases, and molecular docking simulations were performed using the CBDock2 platform. Results: MR analysis identified CLEC1B (chronic lymphocytic leukemia) and IGFLR1 (chronic myeloid leukemia) as significant causal genes (P<0.05). Cell experiments confirmed that silencing CLEC1B inhibited CLL cell proliferation and promoted apoptosis (P<0.05), and silencing IGFLR1 significantly inhibited CML cell proliferation, promoted apoptosis, and suppressed CML cell invasion (P<0.05). Molecular docking revealed that Coumestrol has strong binding potential with IGFLR1 (Vina score = -7.2). Conclusion: IGFLR1 and CLEC1B are potential targets for leukemia therapy, and their corresponding small molecule compounds provide new directions for drug development. This study integrates genetic and experimental evidence, laying the foundation for precision therapy in leukemia. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology QTL Mendelian randomization Leukemia IGFLR1 CLEC1B Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Leukemia refers to a malignant hematological neoplasm characterized by abnormal clonal expansion of leukemia cells in the bone marrow [1] . The prevalence of leukemia varies significantly across different regions, with the highest age-standardized prevalence in North America and Western Europe exceeding 40 per 100,000 people, while in Southeast Asia it is approximately 19.33 per 100,000, and in East Asia it is about 20.57 per 100,000 [2] . Clinically, leukemia can be classified into four categories: acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL) [3] . The limitations of conventional treatment methods have prompted the development of new therapeutic strategies for leukemia. Although chemotherapy and radiotherapy have certain efficacy in killing cancer cells, these methods often cause severe damage to the patient's body and may not completely eradicate the lesions [4] . With the advancement of science and technology, gene therapy has gradually entered the field of leukemia treatment as an emerging therapeutic approach. This approach not only enhances treatment specificity but also reduces damage to normal cells and minimizes side effects [5] . For example, in many clinical trials, CAR-T cell therapy has demonstrated significant efficacy, particularly in patients with B-cell acute lymphoblastic leukemia (B-ALL), with approximately 90% achieving complete remission, and a subset maintaining long-term disease-free survival during follow-up [6, 7] . Numerous preclinical and clinical studies have evaluated the application of CRISPR/Cas9 in leukemia treatment. In animal models, studies have shown that knocking out the BCR-ABL gene using CRISPR technology can significantly reduce tumor burden and improve hematopoietic function [8, 9] . Furthermore, small interfering RNA (siRNA) and antisense oligonucleotides (ASO), as important tools in gene therapy, have shown promising prospects in the treatment of leukemia in recent years. ASOs are designed to target specific mutant genes, such as TP53, and achieve the desired gene silencing effect after delivery via lipid nanoparticles (LNP). Dose-response studies in clinical trials have indicated that appropriate doses of siRNA or ASO can significantly improve efficacy and reduce side effects, demonstrating the potential of these strategies in leukemia treatment [10, 11] . However, some targeted drugs developed for leukemia and other tumors may induce resistance to conventional treatments or even lead to disease recurrence [12, 13] . Therefore, to improve treatment outcomes and avoid resistance and recurrence, there is an urgent need to identify new therapeutic targets. In recent years, the multi-omics integration approach has gained increasing attention in leukemia research, as it can reveal the complex biological characteristics of the disease by integrating various omics data, including genomic, transcriptomic, and proteomic data. Expression quantitative trait loci (eQTL) refer to genetic loci in the genome associated with gene expression levels; by analyzing associations between genetic variants and gene expression, they reveal the regulatory effects of genetic variation on gene expression. mRNA expression of genes involved in the development and progression of complex diseases correlates with genetic variations, and eQTL analysis provides critical insights into the genetics of complex diseases [14–16] . Protein quantitative trait loci (pQTL) denote specific genomic regions associated with variations in protein expression levels; they provide genetic evidence for causal relationships between potential variants, affected genes, protein levels, and related physiological effects, serving as a valuable resource for drug development [17] . Mendelian randomization (MR) is a research methodology that employs genetic variants associated with specific exposures as instrumental variables to estimate causal relationships between target exposures and expected outcomes [18] . In drug-target MR analyses, eQTL associated with gene expression levels are frequently utilized as instrumental variables to investigate the effects of druggable genes. Specifically, cis-acting expression quantitative trait loci (cis-eQTL) located in proximal genomic regions of target genes are typically selected due to their strong association with gene expression [19] . Concurrently, MR analysis of pQTL and leukemia GWAS summary data detects the impact of protein levels on outcomes, further validating the significance of these genes. Furthermore, summary-data-based Mendelian randomization (SMR) integrates GWAS data with eQTL study data to effectively identify genes whose expression levels are associated with complex traits due to pleiotropy [20] . Building on this, in the present study, we combined eQTL and pQTL to perform genome-wide association MR analyses for four common types of leukemia; integrating SMR analysis, we identified genes closely associated with leukemia. Additionally, we validated these findings through in vitro cell experiments. Finally, we explored potential drugs targeting these genes using network pharmacology. These genes may play pivotal roles in leukemia pathogenesis and progression, and our research offers valuable insights for developing innovative therapeutic strategies. 2. Materials and Methods 2.1 Research Design This study was based on summary data from genome-wide association studies (GWAS). Ethical approval was waived since participant consent had been obtained during the original studies. Figure 1 illustrates the detailed framework of the study design. We first obtained cis-eQTL for genes from the eQTLGen Consortium as exposures. Using a two-sample MR approach, we investigated causal relationships between these eQTLs and four leukemia outcomes. Concurrently, we performed pQTL-level causal analyses for four leukemias using large-scale plasma protein data from the deCODE Genetics Consortium. Results from eQTL and pQTL analyses were intersected. Subsequently, SMR analysis was employed to validate associations between the intersected genes and four leukemias, aiming to identify genes significantly associated with leukemia.In parallel, we validated these findings using leukemia cell models through CCK-8 assay, flow cytometry, and Transwell experiments. Finally, potential targeted drugs for the identified genes were screened using HEB and ITCM databases, followed by molecular docking simulations of the selected compounds. 2.2 Data Sources This study employed a two-sample MR design to investigate the causal relationships of both eQTL and pQTL with leukemia risk, utilizing publicly available GWAS summary statistics. Summary statistics for eQTLs of 31,684 genes were obtained from the eQTLGen Consortium [ 21 ] , which integrates data from 37 individual studies comprising predominantly European-ancestry participants. Genetic instruments (cis-pQTLs) for plasma proteins were derived from a large-scale proteomics study by the deCODE Genetics Consortium. This study quantified plasma protein levels using the aptamer-based SomaScan platform in 35,559 individuals of European descent, identifying cis-pQTLs for 4,907 distinct proteins. GWAS datasets for different leukemia subtypes were sourced from the FinnGen project ( https://r11.finngen.fi/ ), a Finnish biobank initiative collecting and analyzing genomic and health data from 500,000 participants [ 22 ] . Each leukemia dataset included approximately 345,115 individuals, comprising 1,520 diagnosed cases. In brief, eQTLs and pQTLs served as the exposures, while leukemia status was the outcome. Leukemia diagnoses were defined according to the International Classification of Diseases (ICD-10) codes: C910 for ALL, C920 for AML, C911 for CLL, and C921 for CML. Detailed information on the datasets used for the MR analyses is presented in Table 1 and Supplementary Table S1 . Table 1 Dataset Information Datasets Sources Population Gender ICD-10 coding Sample size Exposure cis-eQTL eQTLGen European Males and Females - 31684 cis-pQTL deCODE European Males and Females - 4907 Outcome Acute lymphocytic leukaemia FinnGen European Males and Females C910 345331 Acute myeloid leukaemia FinnGen European Males and Females C920 345438 Chronic lymphocytic leukaemia FinnGen European Males and Females C911 345961 Chronic myeloid leukaemia FinnGen European Males and Females C921 345251 2.3 MR Analysis 2.3.1 Instrumental Variable Selection and Data Processing This study employed single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which must satisfy three core assumptions for MR analysis: Assumption 1 (Relevance): IVs must be strongly associated with the exposure; Assumption 2 (Independence): IVs must not associate with confounders; Assumption 3 (Exclusion restriction): IVs affect the outcome solely through the exposure. We constructed a directed acyclic graph (Fig. 2 ) incorporating IVs (SNPs), exposures (eQTL, pQTL) and outcomes (ALL, AML, CLL, CML) to illustrate these assumptions. GWAS summary data underwent sequential SNP filtering to meet these assumptions: First, SNPs were extracted from cis-eQTL/cis-pQTL data using a stringent genome-wide significance threshold (P < 5.0×10⁻⁸). Subsequent clumping analysis based on 1000 Genomes European data ensured SNP independence with a linkage disequilibrium (LD) threshold of r² < 0.001 within 10,000 kb windows [ 23 ] . Palindromic SNPs with incompatible alleles between exposures and outcomes (e.g., A/G vs. A/C) were removed. Forward-strand alleles were inferred using allele frequencies where available; otherwise, palindromic SNPs were excluded. Finally, SNPs with F-statistics < 10 were excluded to avoid weak instrument bias, calculated as: (F = β²/se²) [ 24 ] 。 2.3.2 MR analysis study design We employed the inverse-variance weighted (IVW) method as the primary analytical approach, synthesizing Wald ratio effect estimates from individual SNPs. Secondary MR methods included MR-Egger, weighted median, simple mode, and weighted mode. When no heterogeneity or horizontal pleiotropy was detected, the IVW fixed-effects model served as the principal analytical method. In the presence of heterogeneity, results from both IVW random-effects models and the weighted median method were integrated for interpretation. Where horizontal pleiotropy among SNPs was identified, the MR-Egger method was adopted as the primary analytical approach. Non-primary MR methods served as sensitivity analyses to evaluate the robustness of core models.Final results were considered statistically significant at P < 0.05. 2.3.3 Sensitivity Analyses Heterogeneity testing: Cochran's Q test assessed between-instrument heterogeneity (P 0.05 indicating no significant bias). Complementary validation: Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis identified and removed outlier SNPs with pleiotropic effects [ 25 ] . 2.3.4 Statistical Software and Data Visualization All statistical analyses were performed using R software (version 4.1.2) with the "TwoSampleMR" package and core R libraries. Visualization included: 1) Scatter plots with regression curves illustrating individual SNP effects on exposures and outcomes, 2) Forest plots of final causal estimates, 3) MR analysis scatter plots with fitted curves. 2.4 SMR Analysis We intersected the aforementioned results to identify genes demonstrating causal associations with leukemia in both eQTL and pQTL analyses, followed by SMR analysis of these candidate genes. SMR is an innovative methodology that utilizes summary statistics from GWAS and eQTL studies to identify pleiotropic associations between specific gene expression levels and complex traits. SMR analyses were performed using the SMR software package (version 1.3.1; https://yanglab.westlake.edu.cn/software/smr/#Download ). SNPs significantly associated with cis-eQTLs (P < 0.05) were incorporated in the SMR analysis. 2.5 Experimental Validation Given the limited number of SNPs associated with cis-eQTLs and cis-pQTLs, potential bias in predicted outcomes necessitated experimental validation. Consequently, in vitro cell-based assays were conducted to validate candidate genes. 2.5.1 Gene Silencing and Validation Target gene silencing: Leukemia cells (MEC-1 and K562) were transiently transfected with gene-specific siRNAs (Engreen Biosystem Co) using Lipofectamine™ 3000 (Thermo Fisher Scientific), with parallel negative control siRNA (si-NC) transfection groups. Cells were harvested 48 hours post-transfection for subsequent analyses. Silencing efficiency validation: Gene expression levels were quantified via quantitative real-time PCR (qRT-PCR). Total RNA was extracted using TRIzol™ reagent (Invitrogen) and reverse-transcribed to cDNA with the PrimeScript™ RT reagent kit (Takara Bio). Amplification was performed on the QuantStudio™ 5 system (Applied Biosystems) using SYBR Green qPCR Master Mix (Servicebio Technology, Wuhan) with primer sequences detailed in Table 2 . β-actin served as the endogenous control, and silencing efficiency was calculated using the2-ΔΔCTmethod. Table 2 Primer sequences Gene Primer sequences(5'→3') IGFLR1 F: GGCCGCCTTGAATACTGGAA R: GGTCATTGAGTCCGCAGTTTT CLEC1B F: AGCGCAATTACCTACAAGGTG R: CTTCCCATGTTAAGTTGTGCCT β-actin F: CATGTACGTTGCTATCCAGGC R: CTCCTTAATGTCACGCACGAT 2.5.2 Cell Proliferation and Apoptosis Assays CCK-8 proliferation/apoptosis analysis: Silenced groups (si-IGFLR1, si-CLEC1B), control and si-NC cells were seeded in 96-well plates at 5×10³ cells/well (n = 6). At 12, 24, 36 and 48 hours post-transfection, CCK-8 reagent (Biosharp, China) was added. After 2-hour incubation at 37°C, OD450 nm was measured using a microplate reader (BioTek Synergy H1). Inhibition rate was calculated as: % Inhibition = [(ODcontrol - ODsilenced) / (ODcontrol - ODblank)] × 100%. Flow cytometric apoptosis detection: After 48-hour transfection, cells were stained using Annexin V-FITC/PI Apoptosis Kit (Meilunbio, Dalian). Apoptotic cells were analyzed on Attune™ NxT Flow Cytometer (Thermo Fisher), with FlowJo v10.8 quantifying early (Annexin V+/PI−) and late (Annexin V+/PI+) apoptotic populations. 2.5.3 Cell Invasion Assessment Transwell invasion assay: Transfected cells (1×10⁵/insert) in serum-free medium were seeded into Transwell® chambers (8 µm pore, Corning), with 10% FBS medium in lower chamber. Following 24-hour incubation at 37°C, invaded cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Invading cells were counted in three random 20× fields. Dye was solubilized for OD 570nm measurement. Experiments were triplicated. 2.6 Potential Drug Identification 2.6.1 Potential Drug Screening In order to identify prospective therapeutic agents that specifically target CLEC1B and IGFLR1, we carried out an exhaustive compound screening using the HEB and ITCM databases [ 26 , 27 ] . 2.6.2 Molecular Docking Analysis To assess the feasibility of CLEC1B as a therapeutic target for CLL, we conducted molecular docking simulations between associated drugs( Cetylic acid, Citric acid and Nicotine) and CLEC1B. Similarly, for IGFLR1, a potential target in CML treatment, we performed molecular docking simulations with Coumestrol. The three-dimensional spatial structures of CLEC1B and IGFLR1 were sourced from the Protein Data Bank (PDB) ( https://www1.rcsb.org/ ) and the AlphaFold Protein Structure Database ( https://alphafold.com/ ). Concurrently, the molecular structures of the relevant drugs were extracted from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). The molecular docking simulations were conducted using the CBDock2 platform, employing a structure-based blind docking approach as previously described [ 28 ] . The protein-ligand complexes generated from the docking simulations were saved in the .pdb file format. Visualization of the molecular docking results was achieved using the 3D visualization tools provided by CBDock2. Additionally, the interactions between the protein and the ligand were analyzed using LigPlot + v2.2.8. 2.7 Statistical Analysis All statistical analyses and graphical representations were performed using GraphPad Prism v10.1.2 (GraphPad Software, San Diego, CA). Continuous data are presented as mean ± standard deviation (mean ± SD). Normality-assessed data used two-tailed t-test or ANOVA; non-parametric data employed Mann-Whitney U test. Statistical significance was defined at P < 0.05. 3. Results 3.1 MR Analysis Findings 3.1.1 eQTL MR Results As shown in Supplementary Figure S1 and S7, MR analysis using the IVW method revealed potential causal associations between eQTLs and four leukemia subtypes. Specifically, 1,693 genes showed significant associations with ALL (P < 0.05), driven by 34,666 significant SNPs; 1,615 genes were associated with AML (P < 0.05), involving 34,688 SNPs; 1,920 genes demonstrated significant links with CLL (P < 0.05), corresponding to 40,575 SNPs; and 1,649 genes were related to CML (P < 0.05), encompassing 34,704 SNPs. Detailed information on the selected instrumental variables is provided in Supplementary Table S3 . 3.1.2 pQTL MR Results pQTL MR analysis further uncovered protein-level causal associations with leukemia subtypes (Supplementary Figure S2 and S8). Results demonstrated: in ALL, 16 plasma proteins exerted significant causal effects (P < 0.05), driven by 143 SNPs; in AML, 20 plasma proteins showed significant associations (P < 0.05), involving 149 SNPs; in CLL, 30 plasma proteins exhibited significant links (P < 0.05), containing 227 SNPs; and in CML, 43 plasma proteins demonstrated significant effects (P < 0.05), mediated by 303 SNPs. Comprehensive details of selected instrumental variables are documented in Supplementary Table S3 . 3.1.3 Intersection Analysis Identifies Co-Significant Genes By integrating eQTL and pQTL results (Figs. 3 and 4 ), we identified 12 co-significant genes (P < 0.05) across four leukemia subtypes: (1) ALL: MST1 - eQTL: OR = 1.92 [95% CI = 1.17–3.14], P = 0.009; pQTL: OR = 0.75 [95% CI = 0.61–0.92], P = 0.006; (2) AML: TCL1A - eQTL: OR = 2.26 [95% CI = 1.46–3.52], P = 0.0003; pQTL: OR = 3.06 [95% CI = 1.59–5.86], P = 0.0008; (3) CLL: CLEC1B - eQTL: OR = 0.84 [95% CI = 0.74–0.95], P = 0.005; pQTL: OR = 1.96 [95% CI = 1.19–3.24], P = 0.008; CREB3L4 - eQTL: OR = 0.61 [95% CI = 0.41–0.9], P = 0.013; pQTL: OR = 0.64 [95% CI = 0.46–0.9], P = 0.009;DOCK9 - eQTL: OR = 0.61 [95% CI = 0.44–0.85], P = 0.003; pQTL: OR = 0.24 [95% CI = 0.09–0.6], P = 0.003; THG1L - eQTL: OR = 0.81 [95% CI = 0.7–0.93], P = 0.003; pQTL: OR = 0.38 [95% CI = 0.19–0.74], P = 0.005; (4) CML: CD48 - eQTL: OR = 0.45 [95% CI = 0.26–0.77], P = 0.004; pQTL: OR = 0.39 [95% CI = 0.19–0.77], P = 0.007; IGFLR1 - eQTL: OR = 1.65 [95% CI = 1.1–2.47], P = 0.016; pQTL: OR = 2.72 [95% CI = 1.3–5.69], P = 0.008; ISOC1 - eQTL: OR = 0.55 [95% CI = 0.37–0.81], P = 0.002; pQTL: OR = 4.87 [95% CI = 2.05–11.58], P = 0.0003; NMT1 - eQTL: OR = 2.43 [95% CI = 1.12–5.26], P = 0.025; pQTL: OR = 26.11 [95% CI = 2.23–305.87], P = 0.009; RAF1 - eQTL: OR = 0.26 [95% CI = 0.08–0.8], P = 0.02; pQTL: OR = 53.78 [95% CI = 4.61–627.32], P = 0.001; ST3GAL6 - eQTL: OR = 0.53 [95% CI = 0.31–0.9], P = 0.02; pQTL: OR = 0.58 [95% CI = 0.38–0.87], P = 0.01. These findings demonstrate significant associations between gene expression and disease risk, further validating the crucial role of gene regulation in leukemogenesis. Table 3 provides comprehensive OR values, 95% CIs, and P-values for all identified gene loci. Notably, several genes (MST1, CLEC1B, ISOC1, RAF1) exhibited discordant effect directions between transcriptomic and proteomic levels. For example, MST1 showed increased disease risk with higher mRNA expression (eQTL: OR = 1.92, 95% CI 1.17–3.14, P = 0.009), yet demonstrated reduced risk with elevated protein levels (pQTL: OR = 0.75, 95% CI 0.61–0.92, P = 0.006); CLEC1B exhibited protective effects at the transcript level (eQTL: OR = 0.84, 95% CI 0.74–0.95, P = 0.005), but risk effects at the protein level (pQTL: OR = 1.96, 95% CI 1.19–3.24, P = 0.008). Table 3 Corresponding OR values, 95% CI, and P-values of co-significant genetic loci Types of leukemia Gene eQTL analysis results pQTL analysis results ALL MST1 OR = 1.92 [1.17–3.14], P = 0.009 OR = 0.75 [0.61–0.92], P = 0.006 AML TCL1A OR = 2.26 [1.46–3.52], P = 0.0003 OR = 3.06 [1.59–5.86], P = 0.0008 CLL CLEC1B OR = 0.84 [0.74–0.95], P = 0.005 OR = 1.96 [1.19–3.24], P = 0.008 CREB3L4 OR = 0.61 [0.41–0.90], P = 0.013 OR = 0.64 [0.46–0.90], P = 0.009 DOCK9 OR = 0.61 [0.44–0.85], P = 0.003 OR = 0.24 [0.09–0.60], P = 0.003 THG1L OR = 0.81 [0.70–0.93], P = 0.003 OR = 0.38 [0.19–0.74], P = 0.005 CML CD48 OR = 0.45 [0.26–0.77], P = 0.004 OR = 0.39 [0.19–0.77], P = 0.007 IGFLR1 OR = 1.65 [1.10–2.47], P = 0.016 OR = 2.72 [1.30–5.69], P = 0.008 ISOC1 OR = 0.55 [0.37–0.81], P = 0.002 OR = 4.87 [2.05–11.58], P = 0.0003 NMT1 OR = 2.43 [1.12–5.26], P = 0.025 OR = 26.11 [2.23–305.87], P = 0.009 RAF1 OR = 0.26 [0.08–0.80], P = 0.020 OR = 53.78 [4.61–627.32], P = 0.001 ST3GAL6 OR = 0.53 [0.31–0.90], P = 0.020 OR = 0.58 [0.38–0.87], P = 0.010 3.1.4 Sensitivity Analyses We conducted comprehensive sensitivity analyses including heterogeneity testing, horizontal pleiotropy assessment, and MR-Steiger directionality analysis. Results for co-significant genes are summarized in Table 4 , with full data in Supplementary Tables S5, S6 and Figures S3 - S6 . Cochran's Q test indicated heterogeneity for CREB3L4-pQTL (P < 0.05). The IVW random-effects model yielded OR = 0.64 [95% CI = 0.46–0.90], P = 0.009, while the weighted median method showed OR = 0.75 [95% CI = 0.52–1.09], P = 0.13. Given this lack of concordant statistical evidence, we concluded that the CREB3L4-pQTL association with CLL lacks significance and will exclude it from further analyses. No significant heterogeneity was detected in other results. MR-Egger regression intercept analysis revealed no evidence of horizontal pleiotropy among selected SNPs. MR-PRESSO testing detected no outliers in significant gene instruments (Supplementary Table S4 ). Furthermore, scatter plots demonstrated robust regression relationships (Supplementary Figures S3 and S4). Finally, symmetrical funnel plots supported the reliability of MR analyses by confirming the integrity of statistical inferences (Supplementary Figures S5 and S6). Associations between genes and leukemia estimated by multiple MR methods are documented in Supplementary Table S2 , with heterogeneity and pleiotropy test results in Supplementary Tables S5 and S6 respectively. Table 4 Qualitative and pleiotropy test results of co-significant genes in Parazacco spilurus subsp. spilurus, along with MR-Steiger causal direction test Exposures Outcomes Exposed database Q from IVW Q from MR-Egger Pval_Q from IVW Pval_Q from MR-Egger MR-Steiger Pval of MR-Egger interception MST1 ALL eQTL 1.33 1.25 0.97 0.94 TRUE 0.79 pQTL 27.92 27.87 0.36 0.31 TRUE 0.84 TCL1A AML eQTL 12.63 12.54 0.25 0.18 TRUE 0.81 pQTL 12.1 12.1 0.28 0.21 TRUE 0.94 CLEC1B CLL eQTL 43.61 43.24 0.53 0.5 TRUE 0.54 pQTL 1.3 0.84 0.94 0.93 TRUE 0.54 CREB3L4 CLL eQTL 4.13 3.58 0.9 0.89 TRUE 0.48 pQTL * 23.55 22.99 0.04 0.03 TRUE 0.6 DOCK9 CLL eQTL 8.72 5.87 0.65 0.83 TRUE 0.12 pQTL 1.63 1.48 0.44 0.22 TRUE 0.81 THG1L CLL eQTL 20.88 20.85 0.86 0.83 TRUE 0.87 pQTL 1.22 0.91 0.87 0.82 TRUE 0.61 CD48 CML eQTL 14.34 14.3 0.42 0.35 TRUE 0.84 pQTL 12.89 12.88 0.38 0.3 TRUE 0.93 IGFLR1 CML eQTL 26.75 26.09 0.37 0.35 TRUE 0.44 pQTL 3.17 2.99 0.79 0.7 TRUE 0.69 ISOC1 CML eQTL 51.46 51.23 0.11 0.09 TRUE 0.68 pQTL 2.77 2.19 0.91 0.9 TRUE 0.47 NMT1 CML eQTL 11.42 11.11 0.41 0.35 TRUE 0.61 pQTL 1.83 0.22 0.4 0.64 TRUE 0.42 RAF1 CML eQTL 2.78 2.16 0.95 0.95 TRUE 0.46 pQTL 1.98 1.32 0.58 0.52 TRUE 0.5 ST3GAL6 CML eQTL 20.13 20.04 0.79 0.74 TRUE 0.78 pQTL 22.55 22.53 0.6 0.55 TRUE 0.9 Note : * refer to existence of heterogeneity between SNPs , # refer to existence of pleiotropy between SNPs. 3.1.5 SMR verification results To validate the functional relevance of MR-identified genes, we performed SMR analysis. As demonstrated in Fig. 5 and Supplementary Table S7 , cis-eQTLs of CLEC1B and IGFLR1 showed significant associations with CLL and CML occurrence, respectively (P < 0.05), indicating their potential functional impacts. Specifically, CLEC1B cis-eQTLs suggested protective effects against CLL, whereas IGFLR1 cis-eQTLs were significantly associated with increased CML risk. Figure 6 visually summarizes the per-SNP analyses for these two genes through forest plots and scatter plots of eQTL and pQTL associations. Integrative analysis revealed: both CLEC1B gene expression (eQTL-MR) and SMR analysis demonstrated protective effects (OR 1), suggesting potential disruption by post-translational modifications(PTMs). In contrast, IGFLR1 exhibited consistent risk effects across all levels: gene expression (eQTL-MR, OR > 1), SMR analysis (OR > 1), and protein quantification (pQTL-MR, OR > 1), indicating a concerted pathogenic mechanism. 3.2 Experimental Validation Results To experimentally validate the causal roles of CLEC1B in CLL and IGFLR1 in CML identified by MR, comprehensive in vitro studies were conducted using human leukemia cell lines MEC-1 (CLL model) and K562 (CML model). Gene-specific silencing was achieved via siRNA transfection, followed by phenotypic assays for proliferation, apoptosis, and invasion. 3.2.1 Gene Silencing Efficiency Validation As shown in Figs. 7 A-B, transient siRNA transfection significantly reduced target gene expression: si-CLEC1B decreased CLEC1B mRNA to 7.23% ± 1.38% of control in MEC-1 cells (P = 0.002); si-IGFLR1 reduced IGFLR1 expression to 20% ± 6.13% in K562 cells (P < 0.001). 3.2.2 Cell Proliferation and Apoptosis Assays CCK-8 assays (Figs. 7 C-D) demonstrated that CLEC1B knockdown (Group si-CLEC1B) significantly inhibited MEC-1 proliferation in a time-dependent manner. Specifically, inhibition rates at 12h, 24h, 36h, and 48h were 55.27% ± 2.44%, 73.70% ± 4.22%, 81.85% ± 2.19%, and 91.00% ± 3.44%, respectively. Significant differences between adjacent time points were observed (12h vs 0h: P < 0.0001; 24h vs 12h: P < 0.0001; 36h vs 24h: P = 0.004; 48h vs 36h: P = 0.001). Similarly, IGFLR1 knockdown (Group si-IGFLR1) suppressed K562 proliferation with increasing inhibition rates: 21.84% ± 1.49% at 12h, 33.11% ± 4.47% at 24h, 43.78% ± 4.54% at 36h, and 54.87% ± 4.57% at 48h. Adjacent time point comparisons showed significant differences (12h vs 0h: P < 0.0001; 24h vs 12h: P = 0.001; 36h vs 24h: P = 0.002; 48h vs 36h: P = 0.001). These findings indicate that both CLEC1B and IGFLR1 critically regulate leukemia cell proliferation in a time-dependent manner. Flow cytometry analysis (Figs. 7 E-J) confirmed that silencing either CLEC1B or IGFLR1 significantly suppressed proliferation and induced apoptosis. Specifically, CLEC1B-silenced MEC-1 cells (Group si-CLEC1B) showed 4-fold higher apoptosis versus controls (P < 0.001), while IGFLR1-knockdown K562 cells (Group si-IGFLR1) exhibited 3-fold increased apoptosis (P < 0.001), both with concomitant proliferation inhibition. Collectively, these results demonstrate that CLEC1B and IGFLR1 play essential roles in leukemia cell survival, and their silencing effectively induces apoptosis while inhibiting proliferation. 3.2.3 Cell Invasion Capacity Assessment Using Transwell invasion assays, we found that CLEC1B knockdown did not significantly affect the invasive capacity of MEC-1 cells, whereas IGFLR1 knockdown markedly inhibited the invasion of K562 cells. As shown in Fig. 8 , the number of invading MEC-1 cells in the si-CLEC1B group showed no statistical difference compared to controls; conversely, si-IGFLR1-treated K562 cells exhibited significantly reduced transmigration (P < 0.01). Absorbance measurements yielded results consistent with this trend. These results indicate that IGFLR1 plays a critical role in leukemia cell invasion, while CLEC1B may not be directly involved in this biological process. 3.3 Molecular Docking Analysis of CLEC1B and IGFLR1 For CLEC1B, the drug screening identified three effective compounds—Cetylic acid, Citric acid, and Nicotine—and molecular docking simulations were performed to evaluate CLEC1B's potential as a therapeutic target for CLL (Figs. 9 A, B, C). The potential drug screening targeting IGFLR1 identified two promising substances: Coumestrol and Nickel. Molecular docking analysis was performed to evaluate IGFLR1 as a viable therapeutic target for CML (Fig. 9 D). The docking experiment uncovered a binding pocket (C1) with a Vina score of -7.2, indicating a strong affinity between Coumestrol and IGFLR1. The 3D molecular docking diagram provided insight into the binding orientation of Coumestrol within the IGFLR1 protein. The calculated volume of the binding pocket was 2058 ų, signifying a relatively large binding site. The coordinates of the binding pocket's center were (0, -4, -4), with the docking dimensions measuring (20, 28, 20) in the x, y, and z axes, respectively. Furthermore, the 2D molecular docking diagram elucidated several critical interactions, including hydrogen bonds and hydrophobic contacts. These findings emphasize the promising potential of Coumestrol as a candidate molecule for targeting IGFLR1 in future therapeutic investigations (Table 5 ). Table 5 Molecular Docking Analysis of CLEC1B, IGFLR1and Drugs. Complexes Vina score Cavity volume (Å3) Center (x, y, z) Docking size (x, y, z) Cetylic acid - CLEC1B -4.8 107 28, 0, 12 23, 23, 23 Citric acid - CLEC1B -5.1 107 28, 0, 12 17, 17, 17 Nicotine - CLEC1B -5.0 107 28, 0, 12 17, 17, 17 Coumestrol - IGFLR1 -7.2 2058 0, -4, -4 20, 28, 20 4. Discussion Gene therapies show considerable potential in leukemia treatment, particularly for targeted interventions addressing specific genetic mutations. Recent studies have established leukemia stem cells as pivotal drivers in leukemogenesis, progression, and relapse [ 29 ] , rendering therapies targeting these cells a critical research focus. Additionally, the substantial heterogeneity of leukemia and paucity of biomarkers make precision medicine an urgent unmet need. Emerging genomic and immunotherapeutic approaches offer promising avenues to address this challenge [ 30 , 31 ] . Our study employed two-sample MR to investigate causal effects of gene expression and plasma proteins across four leukemias, elucidating biomarker-disease causal relationships. By integrating multi-omics data, these findings provide novel mechanistic insights and potential therapeutic directions for precision leukemia treatment. We identified CLEC1B and IGFLR1 as potential therapeutic targets, with experimental validation of their biological functions establishing foundations for targeted therapies. Our findings underscore complex gene-disease causal architectures, advancing leukemia research frontiers. CLEC1B encodes a critical type II transmembrane receptor featuring a carbohydrate-recognition domain (CRD) that mediates intercellular interactions and signal transduction [ 32 ] . Its CRD exhibits specific binding to multiple ligands, enabling essential roles in immune responses and platelet activation [ 33 ] . CLEC1B's cell-type-specific expression patterns across immune subsets have garnered substantial research interest. Expression in dendritic cells and macrophages critically regulates immune responses, modulating immune cell infiltration and activity within tumor microenvironments [ 32 , 34 ] . Mechanistically, CLEC1B may drive M2 polarization of TAMs [ 34 , 35 ], suppressing T-cell activity to promote tumor growth and metastasis. Furthermore, CLEC1B expression is regulated by epigenetic modifications [ 34 , 36 ] that alter transcriptional activity, potentially impacting tumor immune evasion and immunotherapy responses. Notably, CLEC1B expression correlates positively with PD-L1 levels [ 37 , 38 ] , suggesting it may potentiate immune checkpoint activation within tumor microenvironments, thereby modulating therapeutic efficacy. Intriguingly, we report the first evidence of eQTL-pQTL effect direction dissociation in leukemia for CLEC1B, with protective effects at the transcript level (OR 1). We posit potential mechanisms: First, PTMs—chemical alterations occurring after protein synthesis—critically regulate cellular signaling, gene expression, and metabolic homeostasis. Advances in mass spectrometry and bioinformatics have revealed diverse PTM types that contribute to both physiological processes and disease pathogenesis (e.g., cancer, neurodegeneration) [ 39 ] . Our findings indicate pathological hijacking of PTMs within the leukemia microenvironment. While CLEC1B expression enhances NK-cell cytotoxicity and antitumor immunity [ 32 ] (consistent with eQTL OR 1). Genomic instability in leukemia cells may disrupt PTM regulation. As an immunoregulatory factor in hematologic malignancies [ 32 ] , CLEC1B PTMs could be dysregulated by aberrant B-cell receptor signaling, driving protein-level risk effects. Technical limitations in quantifying PTMs for pQTL studies may introduce bias, as plasma measurements may not capture intracellular dynamics [ 41 ] . Secondly, tissue-specific biological differences may be amplified since eQTLs derive from specific tissues (e.g., blood/bone marrow) [ 42 ] , whereas pQTLs reflect plasma proteins [ 43 ] . In leukemia, plasma protein levels represent secretory phenotypes, while eQTLs capture intracellular gene regulation. As a soluble receptor, CLEC1B plasma concentrations may be confounded by tumor-secreted factors (e.g., exosomes) [ 44 ] , potentially obscuring protective effects observed in eQTLs. Finally, genetic instrument robustness in MR may be compromised by population heterogeneity (ancestral origins/substructure), causing effect direction divergence [ 45 ] . In vitro experiments demonstrated that CLEC1B silencing promoted apoptosis and suppressed proliferation in MEC-1 cells, suggesting its high-expression state confers pro-survival/proliferative advantages increasing leukemia risk. This aligns with pQTL-MR risk effects (OR > 1), indicating elevated protein levels may promote oncogenesis. These insights elucidate CLEC1B's dual roles in leukemia and provide rationale for targeted therapies (e.g., CLEC1B inhibitors), highlighting the necessity of addressing molecular-level discordance in precision medicine. On the other hand, we provide the first evidence establishing a causal relationship between IGFLR1 and CML, offering novel molecular insights into CML pathogenesis. The association of IGFLR1 expression with CML cell proliferation, apoptosis, and invasion underscores its potential as a therapeutic target and provides rationale for clinical translation. Substantial evidence indicates oncogenic roles for IGFLR1 across multiple cancers. For instance, elevated IGFLR1 mRNA in tumor versus matched adjacent tissues suggests involvement in tumorigenesis and progression. Moreover, IGFLR1 expression correlates with advanced clinical stage and pathological grade, predicting poor prognosis in clear cell renal cell carcinoma (ccRCC) [ 46 ] . The immunomodulatory function of IGFLR1 within tumor microenvironments is increasingly recognized. IGFLR1 upregulation associates with altered tumor-infiltrating immune profiles, particularly suppressing CD8 + T cells and CXCL13 + BHLHE40 + TH1-like cells with potent anti-tumor activity [ 47 , 48 ] . In stage II/III colorectal cancer, membrane/cytoplasmic IGFLR1 co-expresses with FOXP3 cells, CD8 + T cells, and IFN response pathway components, suggesting utility in tumor classification and prognostication [ 49 ] . While these findings align with our observations, current research primarily focuses on solid tumors. The specific functions and immunomodulatory potential of IGFLR1 in hematologic malignancies—particularly within leukemia microenvironments—remain largely unexplored and warrant dedicated investigation. Molecular docking revealed strong binding potential between coumestrol and IGFLR1 (Vina score = -7.2).This finding identifies novel candidate molecules for drug development targeting IGFLR1, facilitating future screening efforts. Prior studies indicate promising therapeutic potential for targeted small molecules in leukemia, particularly for gene-specific interventions [ 30 ] . Future investigations should characterize compound-gene interactions to develop effective therapeutic strategies. Although current studies have demonstrated the importance of CLEC1B and IGFLR1 in the tumor microenvironment, many key scientific questions remain to be answered. Their context-dependent regulation by intrinsic/extrinsic factors warrants investigation into microenvironmental dynamics.Mechanisms underlying their roles in immune evasion require elucidation. Future work should delineate their immune evasion pathways and interactions with other immunomodulators to define holistic impacts on leukemogenesis. Our study has the following advantages: First, we establish the first integrated pipeline combining genome-wide MR (eQTL/pQTL), SMR, functional assays to identify leukemia-associated genes (CLEC1B and IGFLR1), providing more robust causal inference than observational studies. This offers a novel paradigm for candidate gene prioritization. Crucially, wet-lab validation significantly enhanced findings reliability. Second, comprehensive profiling across four major leukemia subtypes. This enabled assessment of pleiotropic causal genes. Third, utilization of publicly available GWAS summary statistics provided cost-efficient genetic insights. Finally, restriction to European ancestry minimized confounding from population stratification. Limitations of our study: First, incomplete functional validation of all candidate genes limits biological interpretation. Second, sample size constraints may affect generalizability across subtypes. Third, absence of clinical validation precludes assessment of translational potential. Future studies should incorporate preclinical models and larger cohorts to enhance reliability. In conclusion, we establish causal roles for CLEC1B and IGFLR1 in leukemia and identify actionable drug candidates. Integration of genetics and experimental data provides foundations for precision leukemia therapy. Future investigations should dissect their roles in disease progression and treatment response to advance clinical translation. 5. Conclusions This study integrates multi-omics Mendelian randomization (cis-eQTL/cis-pQTL) with functional validation to establish, for the first time, causal driver effects of CLEC1B in CLL and IGFLR1 in CML pathogenesis: Genetic evidence demonstrates significant associations between their expression levels and leukemia risk; Functional assays confirm that silencing CLEC1B inhibited proliferation and induced apoptosis in CLL cells, while IGFLR1 knockdown suppressed proliferation, induced apoptosis, and significantly impaired invasion capacity in CML cells. Molecular docking identified lead compounds targeting these genes: coumestrol (IGFLR1, Vina score = -7.2), providing novel therapeutic candidates for precision leukemia therapy. Declarations Author Contribution Supervision and writing (review & editing): GJ.C.; Conceptualization and writing (original draft): GJ.C. and ZY.S.; Revision: CJ.Z.; Funding acquisition: CJ.Z.; Investigation, Visualization, and Validation: ZY.S.; Formal analysis and Methodology: YL.L.and TJ.H.; Resources and Writing (review & editing):YL.L.and TJ.H.. Declaration of authors' contributions All authors made substantial contributions to the manuscript, including conception and design, preparing the manuscript, and approving the final version. Supervision and writing (review & editing): Guanjun Chen; Conceptualization and writing (original draft): Guanjun Chen and Zhenyu Song; Revision: Chunjiang Zhu; Funding acquisition: Chunjiang Zhu; Investigation, Visualization, and Validation: Zhenyu Song; Formal analysis and Methodology: Yulan Li and Tianjun Huang; Resources and Writing (review & editing):Yulan Li and Tianjun Huang. 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Supplementary Files SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable31.xlsx SupplementaryTable32.xlsx SupplementaryTable4.xlsx SupplementaryTable5.xlsx SupplementaryTable6.xlsx SupplementaryTable7.xlsx SupplementaryFigure1ivwforesteqtl.pdf SupplementaryFigure2ivwforestpqtl.pdf SupplementaryFigure3ScatterPlotseQTL.pdf SupplementaryFigure4ScatterPlotspQTL.pdf SupplementaryFigure5FunnelPlotseQTL.pdf SupplementaryFigure6FunnelPlotspQTL.pdf SupplementaryFigure7ForestPlotseQTL.pdf SupplementaryFigure8ForestPlotspQTL.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor invited by journal 05 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 01 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":1442595,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Flowchart\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/000673e08fc748a16dc84b57.jpeg"},{"id":92187636,"identity":"00968945-d672-45c8-81d9-fbfc238c51bd","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34534,"visible":true,"origin":"","legend":"\u003cp\u003eDirected acyclic graph of MR analysis principles\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/b47098f771ff6cbb60d696c2.jpeg"},{"id":92187631,"identity":"3c32fb6d-494f-491d-acf3-0a99d291216d","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"png","order_by":3,"title":"Figure 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(A)ALL QTL Gene Intersection; (B)AML QTL Gene Intersection; (C)CLL QTL Gene Intersection; (D)CML QTL Gene Intersection\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/207da060a41440068c47bca7.png"},{"id":92187616,"identity":"9f7fa047-678a-45c6-9ffb-513232f98792","added_by":"auto","created_at":"2025-09-25 14:33:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7753262,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of genes with co-significant eQTL and pQTL. (A) Forest plot of eQTL-MR results; (B) Forest plot of pQTL-MR results.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/97bf4ba6f04e4f2a6b6a2de8.png"},{"id":92187614,"identity":"b57e7743-766b-46f0-9050-738f00bfc929","added_by":"auto","created_at":"2025-09-25 14:33:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100792,"visible":true,"origin":"","legend":"\u003cp\u003eThe SMR results demonstrate the association between gene expression and leukemia risk\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/e710b64cb419744e9a6bd9af.png"},{"id":92187639,"identity":"0e5d09bb-cd27-4c2f-9406-8c5774512f51","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1317325,"visible":true,"origin":"","legend":"\u003cp\u003eSNP forest plots and scatter plots of CLEC1B and IGFLR1. (A) (B) respectively show the forest plot and scatter plot of each SNP for CLEC1B and CLL eQTL; (C) (D) respectively show the forest plot and scatter plot of each SNP for CLEC1B and CLL pQTL; (E) (F) respectively show the forest plot and scatter plot of each SNP for IGFLR1 and CML eQTL; (G) (H) respectively show the forest plot and scatter plot of each SNP for IGFLR1 and CML pQTL\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/ab23829ce14eb7da59b89305.png"},{"id":92188414,"identity":"c6753a8c-5f21-4239-a7f7-819b1f215e8b","added_by":"auto","created_at":"2025-09-25 14:41:23","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1099916,"visible":true,"origin":"","legend":"\u003cp\u003e(A) and (B) demonstrate the effects of transient siRNA transfection in MEC-1 and K562 cells, respectively (n=3); (C) and (D) show the time-dependent growth inhibition induced by CLEC1B and IGFLR1 knockdown in MEC-1 and K562 cells, respectively (n=6); (E)-(G) present the flow cytometry apoptosis detection results for the control group, NC group, and si-CLEC1B group in MEC-1 cells; (H)-(J) display the flow cytometry apoptosis detection results for the control group, NC group, and si-IGFLR1 group in K562 cells (n=3)\u003c/p\u003e\n\u003cp\u003e（Note：****P＜0.0001，***P＜0.001，**P＜0.01，*P＜0.05）\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/3561b837d99179b8eaec40d1.jpeg"},{"id":92187634,"identity":"8062600d-65f5-409f-b0c1-f449a96ad253","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1350894,"visible":true,"origin":"","legend":"\u003cp\u003e(A)-(C) demonstrate the invasion conditions of the MEC-1 cell control group, NC group, and siRNA-treated group; (D)-(F) show the invasion conditions of the K562 cell control group, NC group, and siRNA-treated group; (G)(H) respectively display the trend of absorbance value changes after MEC-1 and K562 cell transmigration (n=3); (I)(J) quantify the number of transmigrated cells (n=3).\u003c/p\u003e\n\u003cp\u003e（Note：****P＜0.0001，***P＜0.001，**P＜0.01，*P＜0.05）\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/0e9b1940ca4f68583e91c87f.jpeg"},{"id":92187628,"identity":"a39861fe-2818-4b55-a4db-8ae485e96bd6","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":669330,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking analysis of IGFLR1, CLEC1B and drugs, including 3D chemical structures of the drugs and 3D and 2D molecular docking diagrams. (A) Cetylic acid - CLEC1B (B) Citric acid - CLEC1B (C) Nicotine - CLEC1B (D) Coumestrol - IGFLR1.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/cab7055f7e14b7bd140aeaaa.png"},{"id":92189260,"identity":"9f702744-91e7-4b33-9ca1-9639620c7ff7","added_by":"auto","created_at":"2025-09-25 14:58:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16970887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/0b9991b5-ccf2-4fb8-9e15-9a525bbf5eae.pdf"},{"id":92187611,"identity":"26968b9b-4333-4a5b-bb6e-53be73d794cb","added_by":"auto","created_at":"2025-09-25 14:33:20","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9800,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/88bb54937f05427f54e02359.xlsx"},{"id":92187643,"identity":"4e686db2-23c8-46b5-b4ff-f334f042db73","added_by":"auto","created_at":"2025-09-25 14:33:23","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8037723,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/5aa4150b01939c3a2686305a.xlsx"},{"id":92187629,"identity":"6ef6abf8-e976-427c-b602-896ab97312e9","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25092301,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable31.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/d1ef79bd6734ed56272994a7.xlsx"},{"id":92187653,"identity":"af0e4230-4230-46e9-a403-b6041ccb2672","added_by":"auto","created_at":"2025-09-25 14:33:23","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11539546,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable32.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/b9196c795b3b58a8f8bb86b6.xlsx"},{"id":92187633,"identity":"3dcf64dc-de49-430f-b74e-36aa2c3eddfa","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":427370,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/577793f5711ff4b55661fb71.xlsx"},{"id":92187621,"identity":"0dca2acd-ace6-4e1a-ac84-62c1f2f52f47","added_by":"auto","created_at":"2025-09-25 14:33:21","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2340472,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/c43b2c21fc8caafce183ae00.xlsx"},{"id":92187626,"identity":"ea13cc36-ab27-497e-baa1-9c9519ad1bdc","added_by":"auto","created_at":"2025-09-25 14:33:22","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1342429,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/261448e3b7164d810ebe77a5.xlsx"},{"id":92187613,"identity":"49e97665-7019-448b-bb50-117e676a850a","added_by":"auto","created_at":"2025-09-25 14:33:20","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11216,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/91dbdbb63a939d9cd4a6e580.xlsx"},{"id":92188408,"identity":"489a5262-98ef-4e66-af28-f5ed998da576","added_by":"auto","created_at":"2025-09-25 14:41:22","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":885051,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1ivwforesteqtl.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/d0469ccee072cfc6fe511346.pdf"},{"id":92188405,"identity":"5c6024f6-dd47-46d8-b8ab-c1b27dae6f59","added_by":"auto","created_at":"2025-09-25 14:41:22","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":27640,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2ivwforestpqtl.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/7766996ce8709bf6c5f183df.pdf"},{"id":92187660,"identity":"55116f98-8862-49b5-b31a-5c8b10dbadf7","added_by":"auto","created_at":"2025-09-25 14:33:23","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":44436724,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3ScatterPlotseQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/2268549713ccc577e64bbab2.pdf"},{"id":92187674,"identity":"1046ab11-8500-47b1-91f1-9bfd5a4b6dd4","added_by":"auto","created_at":"2025-09-25 14:33:25","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":89934336,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4ScatterPlotspQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/e4bffd92bdce44b4e27a9fe1.pdf"},{"id":92187671,"identity":"1c12629f-a065-4975-a9d5-bec37e0536bf","added_by":"auto","created_at":"2025-09-25 14:33:24","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":36078891,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure5FunnelPlotseQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/d98f4f9f86d20ba7b75f2d12.pdf"},{"id":92188422,"identity":"47e68e77-cb71-4d38-ad14-12d77480f060","added_by":"auto","created_at":"2025-09-25 14:41:24","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":68852440,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure6FunnelPlotspQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/7d2182c17d929c575c1844c3.pdf"},{"id":92188420,"identity":"d9cb0fe2-fa61-4c1c-89ca-b23645d8dd9c","added_by":"auto","created_at":"2025-09-25 14:41:24","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":42537111,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure7ForestPlotseQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/6f491c8afc108d63140307cc.pdf"},{"id":92187673,"identity":"e40b2948-fb13-47b7-bfc5-12cd0dd567b8","added_by":"auto","created_at":"2025-09-25 14:33:25","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":72405836,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure8ForestPlotspQTL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7510513/v1/97b88cf36c74859446c68101.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Gene Regulatory Effects in Leukemia Based on Multi-omics Mendelian Randomization, Network Pharmacology, and Functional Validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLeukemia refers to a malignant hematological neoplasm characterized by abnormal clonal expansion of leukemia cells in the bone marrow\u003csup\u003e[1]\u003c/sup\u003e. The prevalence of leukemia varies significantly across different regions, with the highest age-standardized prevalence in North America and Western Europe exceeding 40 per 100,000 people, while in Southeast Asia it is approximately 19.33 per 100,000, and in East Asia it is about 20.57 per 100,000\u003csup\u003e[2]\u003c/sup\u003e. Clinically, leukemia can be classified into four categories: acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL)\u003csup\u003e[3]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe limitations of conventional treatment methods have prompted the development of new therapeutic strategies for leukemia. Although chemotherapy and radiotherapy have certain efficacy in killing cancer cells, these methods often cause severe damage to the patient's body and may not completely eradicate the lesions\u003csup\u003e[4]\u003c/sup\u003e. With the advancement of science and technology, gene therapy has gradually entered the field of leukemia treatment as an emerging therapeutic approach. This approach not only enhances treatment specificity but also reduces damage to normal cells and minimizes side effects\u003csup\u003e[5]\u003c/sup\u003e. For example, in many clinical trials, CAR-T cell therapy has demonstrated significant efficacy, particularly in patients with B-cell acute lymphoblastic leukemia (B-ALL), with approximately 90% achieving complete remission, and a subset maintaining long-term disease-free survival during follow-up\u003csup\u003e[6, 7]\u003c/sup\u003e. Numerous preclinical and clinical studies have evaluated the application of CRISPR/Cas9 in leukemia treatment. In animal models, studies have shown that knocking out the BCR-ABL gene using CRISPR technology can significantly reduce tumor burden and improve hematopoietic function\u003csup\u003e[8, 9]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, small interfering RNA (siRNA) and antisense oligonucleotides (ASO), as important tools in gene therapy, have shown promising prospects in the treatment of leukemia in recent years. ASOs are designed to target specific mutant genes, such as TP53, and achieve the desired gene silencing effect after delivery via lipid nanoparticles (LNP). Dose-response studies in clinical trials have indicated that appropriate doses of siRNA or ASO can significantly improve efficacy and reduce side effects, demonstrating the potential of these strategies in leukemia treatment\u003csup\u003e[10, 11]\u003c/sup\u003e. However, some targeted drugs developed for leukemia and other tumors may induce resistance to conventional treatments or even lead to disease recurrence\u003csup\u003e[12, 13]\u003c/sup\u003e. Therefore, to improve treatment outcomes and avoid resistance and recurrence, there is an urgent need to identify new therapeutic targets.\u003c/p\u003e\n\u003cp\u003eIn recent years, the multi-omics integration approach has gained increasing attention in leukemia research, as it can reveal the complex biological characteristics of the disease by integrating various omics data, including genomic, transcriptomic, and proteomic data. Expression quantitative trait loci (eQTL) refer to genetic loci in the genome associated with gene expression levels; by analyzing associations between genetic variants and gene expression, they reveal the regulatory effects of genetic variation on gene expression. mRNA expression of genes involved in the development and progression of complex diseases correlates with genetic variations, and eQTL analysis provides critical insights into the genetics of complex diseases\u003csup\u003e[14–16]\u003c/sup\u003e. Protein quantitative trait loci (pQTL) denote specific genomic regions associated with variations in protein expression levels; they provide genetic evidence for causal relationships between potential variants, affected genes, protein levels, and related physiological effects, serving as a valuable resource for drug development\u003csup\u003e[17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR) is a research methodology that employs genetic variants associated with specific exposures as instrumental variables to estimate causal relationships between target exposures and expected outcomes\u003csup\u003e[18]\u003c/sup\u003e. In drug-target MR analyses, eQTL associated with gene expression levels are frequently utilized as instrumental variables to investigate the effects of druggable genes. Specifically, cis-acting expression quantitative trait loci (cis-eQTL) located in proximal genomic regions of target genes are typically selected due to their strong association with gene expression\u003csup\u003e[19]\u003c/sup\u003e. Concurrently, MR analysis of pQTL and leukemia GWAS summary data detects the impact of protein levels on outcomes, further validating the significance of these genes. Furthermore, summary-data-based Mendelian randomization (SMR) integrates GWAS data with eQTL study data to effectively identify genes whose expression levels are associated with complex traits due to pleiotropy\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBuilding on this, in the present study, we combined eQTL and pQTL to perform genome-wide association MR analyses for four common types of leukemia; integrating SMR analysis, we identified genes closely associated with leukemia. Additionally, we validated these findings through in vitro cell experiments. Finally, we explored potential drugs targeting these genes using network pharmacology. These genes may play pivotal roles in leukemia pathogenesis and progression, and our research offers valuable insights for developing innovative therapeutic strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Research Design\u003c/h2\u003e\n\u003cp\u003eThis study was based on summary data from genome-wide association studies (GWAS). Ethical approval was waived since participant consent had been obtained during the original studies. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the detailed framework of the study design.\u003c/p\u003e\n\u003cp\u003eWe first obtained cis-eQTL for genes from the eQTLGen Consortium as exposures. Using a two-sample MR approach, we investigated causal relationships between these eQTLs and four leukemia outcomes. Concurrently, we performed pQTL-level causal analyses for four leukemias using large-scale plasma protein data from the deCODE Genetics Consortium. Results from eQTL and pQTL analyses were intersected.\u003c/p\u003e\n\u003cp\u003eSubsequently, SMR analysis was employed to validate associations between the intersected genes and four leukemias, aiming to identify genes significantly associated with leukemia.In parallel, we validated these findings using leukemia cell models through CCK-8 assay, flow cytometry, and Transwell experiments. Finally, potential targeted drugs for the identified genes were screened using HEB and ITCM databases, followed by molecular docking simulations of the selected compounds.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Data Sources\u003c/h2\u003e\n\u003cp\u003eThis study employed a two-sample MR design to investigate the causal relationships of both eQTL and pQTL with leukemia risk, utilizing publicly available GWAS summary statistics.\u003c/p\u003e\n\u003cp\u003eSummary statistics for eQTLs of 31,684 genes were obtained from the eQTLGen Consortium\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, which integrates data from 37 individual studies comprising predominantly European-ancestry participants.\u003c/p\u003e\n\u003cp\u003eGenetic instruments (cis-pQTLs) for plasma proteins were derived from a large-scale proteomics study by the deCODE Genetics Consortium. This study quantified plasma protein levels using the aptamer-based SomaScan platform in 35,559 individuals of European descent, identifying cis-pQTLs for 4,907 distinct proteins.\u003c/p\u003e\n\u003cp\u003eGWAS datasets for different leukemia subtypes were sourced from the FinnGen project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r11.finngen.fi/\u003c/span\u003e\u003c/span\u003e), a Finnish biobank initiative collecting and analyzing genomic and health data from 500,000 participants\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Each leukemia dataset included approximately 345,115 individuals, comprising 1,520 diagnosed cases.\u003c/p\u003e\n\u003cp\u003eIn brief, eQTLs and pQTLs served as the exposures, while leukemia status was the outcome. Leukemia diagnoses were defined according to the International Classification of Diseases (ICD-10) codes: C910 for ALL, C920 for AML, C911 for CLL, and C921 for CML. Detailed information on the datasets used for the MR analyses is presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDataset Information\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDatasets\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSources\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eICD-10 coding\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSample size\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecis-eQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTLGen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31684\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecis-pQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edeCODE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4907\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eOutcome\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcute lymphocytic leukaemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinnGen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC910\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345331\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcute myeloid leukaemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinnGen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC920\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic lymphocytic leukaemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinnGen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345961\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic myeloid leukaemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinnGen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEuropean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMales and Females\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345251\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 MR Analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003ch2\u003e2.3.1 Instrumental Variable Selection and Data Processing\u003c/h2\u003e\n\u003cp\u003eThis study employed single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which must satisfy three core assumptions for MR analysis: Assumption 1 (Relevance): IVs must be strongly associated with the exposure; Assumption 2 (Independence): IVs must not associate with confounders; Assumption 3 (Exclusion restriction): IVs affect the outcome solely through the exposure. We constructed a directed acyclic graph (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) incorporating IVs (SNPs), exposures (eQTL, pQTL) and outcomes (ALL, AML, CLL, CML) to illustrate these assumptions.\u003c/p\u003e\n\u003cp\u003eGWAS summary data underwent sequential SNP filtering to meet these assumptions: First, SNPs were extracted from cis-eQTL/cis-pQTL data using a stringent genome-wide significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5.0\u0026times;10⁻⁸). Subsequent clumping analysis based on 1000 Genomes European data ensured SNP independence with a linkage disequilibrium (LD) threshold of r\u0026sup2; \u0026lt; 0.001 within 10,000 kb windows\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Palindromic SNPs with incompatible alleles between exposures and outcomes (e.g., A/G vs. A/C) were removed. Forward-strand alleles were inferred using allele frequencies where available; otherwise, palindromic SNPs were excluded. Finally, SNPs with F-statistics\u0026thinsp;\u0026lt;\u0026thinsp;10 were excluded to avoid weak instrument bias, calculated as: (F\u0026thinsp;=\u0026thinsp;\u0026beta;\u0026sup2;/se\u0026sup2;)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e。\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n\u003ch2\u003e2.3.2 MR analysis study design\u003c/h2\u003e\n\u003cp\u003eWe employed the inverse-variance weighted (IVW) method as the primary analytical approach, synthesizing Wald ratio effect estimates from individual SNPs. Secondary MR methods included MR-Egger, weighted median, simple mode, and weighted mode.\u003c/p\u003e\n\u003cp\u003eWhen no heterogeneity or horizontal pleiotropy was detected, the IVW fixed-effects model served as the principal analytical method. In the presence of heterogeneity, results from both IVW random-effects models and the weighted median method were integrated for interpretation. Where horizontal pleiotropy among SNPs was identified, the MR-Egger method was adopted as the primary analytical approach. Non-primary MR methods served as sensitivity analyses to evaluate the robustness of core models.Final results were considered statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003e2.3.3 Sensitivity Analyses\u003c/h2\u003e\n\u003cp\u003eHeterogeneity testing: Cochran's Q test assessed between-instrument heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance). Pleiotropy evaluation: MR-Egger intercept analysis detected horizontal pleiotropy (intercept P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating no significant bias). Complementary validation: Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis identified and removed outlier SNPs with pleiotropic effects\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e2.3.4 Statistical Software and Data Visualization\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed using R software (version 4.1.2) with the \"TwoSampleMR\" package and core R libraries. Visualization included: 1) Scatter plots with regression curves illustrating individual SNP effects on exposures and outcomes, 2) Forest plots of final causal estimates, 3) MR analysis scatter plots with fitted curves.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 SMR Analysis\u003c/h2\u003e\n\u003cp\u003eWe intersected the aforementioned results to identify genes demonstrating causal associations with leukemia in both eQTL and pQTL analyses, followed by SMR analysis of these candidate genes. SMR is an innovative methodology that utilizes summary statistics from GWAS and eQTL studies to identify pleiotropic associations between specific gene expression levels and complex traits. SMR analyses were performed using the SMR software package (version 1.3.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/#Download\u003c/span\u003e\u003c/span\u003e). SNPs significantly associated with cis-eQTLs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were incorporated in the SMR analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5 Experimental Validation\u003c/h2\u003e\n\u003cp\u003eGiven the limited number of SNPs associated with cis-eQTLs and cis-pQTLs, potential bias in predicted outcomes necessitated experimental validation. Consequently, in vitro cell-based assays were conducted to validate candidate genes.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n\u003ch2\u003e2.5.1 Gene Silencing and Validation\u003c/h2\u003e\n\u003cp\u003eTarget gene silencing: Leukemia cells (MEC-1 and K562) were transiently transfected with gene-specific siRNAs (Engreen Biosystem Co) using Lipofectamine\u0026trade; 3000 (Thermo Fisher Scientific), with parallel negative control siRNA (si-NC) transfection groups. Cells were harvested 48 hours post-transfection for subsequent analyses.\u003c/p\u003e\n\u003cp\u003eSilencing efficiency validation: Gene expression levels were quantified via quantitative real-time PCR (qRT-PCR). Total RNA was extracted using TRIzol\u0026trade; reagent (Invitrogen) and reverse-transcribed to cDNA with the PrimeScript\u0026trade; RT reagent kit (Takara Bio). Amplification was performed on the QuantStudio\u0026trade; 5 system (Applied Biosystems) using SYBR Green qPCR Master Mix (Servicebio Technology, Wuhan) with primer sequences detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. \u0026beta;-actin served as the endogenous control, and silencing efficiency was calculated using the2-\u0026Delta;\u0026Delta;CTmethod.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePrimer sequences\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePrimer sequences(5'\u0026rarr;3')\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIGFLR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF: GGCCGCCTTGAATACTGGAA\u003c/p\u003e\n\u003cp\u003eR: GGTCATTGAGTCCGCAGTTTT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF: AGCGCAATTACCTACAAGGTG\u003c/p\u003e\n\u003cp\u003eR: CTTCCCATGTTAAGTTGTGCCT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026beta;-actin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF: CATGTACGTTGCTATCCAGGC\u003c/p\u003e\n\u003cp\u003eR: CTCCTTAATGTCACGCACGAT\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e2.5.2 Cell Proliferation and Apoptosis Assays\u003c/h2\u003e\n\u003cp\u003eCCK-8 proliferation/apoptosis analysis: Silenced groups (si-IGFLR1, si-CLEC1B), control and si-NC cells were seeded in 96-well plates at 5\u0026times;10\u0026sup3; cells/well (n\u0026thinsp;=\u0026thinsp;6). At 12, 24, 36 and 48 hours post-transfection, CCK-8 reagent (Biosharp, China) was added. After 2-hour incubation at 37\u0026deg;C, OD450 nm was measured using a microplate reader (BioTek Synergy H1). Inhibition rate was calculated as: % Inhibition = [(ODcontrol - ODsilenced) / (ODcontrol - ODblank)] \u0026times; 100%.\u003c/p\u003e\n\u003cp\u003eFlow cytometric apoptosis detection: After 48-hour transfection, cells were stained using Annexin V-FITC/PI Apoptosis Kit (Meilunbio, Dalian). Apoptotic cells were analyzed on Attune\u0026trade; NxT Flow Cytometer (Thermo Fisher), with FlowJo v10.8 quantifying early (Annexin V+/PI\u0026minus;) and late (Annexin V+/PI+) apoptotic populations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e2.5.3 Cell Invasion Assessment\u003c/h2\u003e\n\u003cp\u003eTranswell invasion assay: Transfected cells (1\u0026times;10⁵/insert) in serum-free medium were seeded into Transwell\u0026reg; chambers (8 \u0026micro;m pore, Corning), with 10% FBS medium in lower chamber. Following 24-hour incubation at 37\u0026deg;C, invaded cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Invading cells were counted in three random 20\u0026times; fields. Dye was solubilized for OD 570nm measurement. Experiments were triplicated.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e2.6 Potential Drug Identification\u003c/h2\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e2.6.1 Potential Drug Screening\u003c/h2\u003e\n\u003cp\u003eIn order to identify prospective therapeutic agents that specifically target CLEC1B and IGFLR1, we carried out an exhaustive compound screening using the HEB and ITCM databases\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n\u003ch2\u003e2.6.2 Molecular Docking Analysis\u003c/h2\u003e\n\u003cp\u003eTo assess the feasibility of CLEC1B as a therapeutic target for CLL, we conducted molecular docking simulations between associated drugs( Cetylic acid, Citric acid and Nicotine) and CLEC1B. Similarly, for IGFLR1, a potential target in CML treatment, we performed molecular docking simulations with Coumestrol. The three-dimensional spatial structures of CLEC1B and IGFLR1 were sourced from the Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www1.rcsb.org/\u003c/span\u003e\u003c/span\u003e) and the AlphaFold Protein Structure Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.com/\u003c/span\u003e\u003c/span\u003e). Concurrently, the molecular structures of the relevant drugs were extracted from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe molecular docking simulations were conducted using the CBDock2 platform, employing a structure-based blind docking approach as previously described\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The protein-ligand complexes generated from the docking simulations were saved in the .pdb file format. Visualization of the molecular docking results was achieved using the 3D visualization tools provided by CBDock2. Additionally, the interactions between the protein and the ligand were analyzed using LigPlot\u0026thinsp;+\u0026thinsp;v2.2.8.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses and graphical representations were performed using GraphPad Prism v10.1.2 (GraphPad Software, San Diego, CA). Continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Normality-assessed data used two-tailed t-test or ANOVA; non-parametric data employed Mann-Whitney U test. Statistical significance was defined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 MR Analysis Findings\u003c/h2\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n\u003ch2\u003e3.1.1 eQTL MR Results\u003c/h2\u003e\n\u003cp\u003eAs shown in Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e and S7, MR analysis using the IVW method revealed potential causal associations between eQTLs and four leukemia subtypes.\u003c/p\u003e\n\u003cp\u003eSpecifically, 1,693 genes showed significant associations with ALL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), driven by 34,666 significant SNPs; 1,615 genes were associated with AML (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), involving 34,688 SNPs; 1,920 genes demonstrated significant links with CLL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), corresponding to 40,575 SNPs; and 1,649 genes were related to CML (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), encompassing 34,704 SNPs. Detailed information on the selected instrumental variables is provided in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n\u003ch2\u003e3.1.2 pQTL MR Results\u003c/h2\u003e\n\u003cp\u003epQTL MR analysis further uncovered protein-level causal associations with leukemia subtypes (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e and S8).\u003c/p\u003e\n\u003cp\u003eResults demonstrated: in ALL, 16 plasma proteins exerted significant causal effects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), driven by 143 SNPs; in AML, 20 plasma proteins showed significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), involving 149 SNPs; in CLL, 30 plasma proteins exhibited significant links (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), containing 227 SNPs; and in CML, 43 plasma proteins demonstrated significant effects (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), mediated by 303 SNPs. Comprehensive details of selected instrumental variables are documented in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003e3.1.3 Intersection Analysis Identifies Co-Significant Genes\u003c/h2\u003e\n\u003cp\u003eBy integrating eQTL and pQTL results (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), we identified 12 co-significant genes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across four leukemia subtypes:\u003c/p\u003e\n\u003cp\u003e(1) ALL: MST1 - eQTL: OR\u0026thinsp;=\u0026thinsp;1.92 [95% CI\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;3.14], P\u0026thinsp;=\u0026thinsp;0.009; pQTL: OR\u0026thinsp;=\u0026thinsp;0.75 [95% CI\u0026thinsp;=\u0026thinsp;0.61\u0026ndash;0.92], P\u0026thinsp;=\u0026thinsp;0.006;\u003c/p\u003e\n\u003cp\u003e(2) AML: TCL1A - eQTL: OR\u0026thinsp;=\u0026thinsp;2.26 [95% CI\u0026thinsp;=\u0026thinsp;1.46\u0026ndash;3.52], P\u0026thinsp;=\u0026thinsp;0.0003; pQTL: OR\u0026thinsp;=\u0026thinsp;3.06 [95% CI\u0026thinsp;=\u0026thinsp;1.59\u0026ndash;5.86], P\u0026thinsp;=\u0026thinsp;0.0008;\u003c/p\u003e\n\u003cp\u003e(3) CLL: CLEC1B - eQTL: OR\u0026thinsp;=\u0026thinsp;0.84 [95% CI\u0026thinsp;=\u0026thinsp;0.74\u0026ndash;0.95], P\u0026thinsp;=\u0026thinsp;0.005; pQTL: OR\u0026thinsp;=\u0026thinsp;1.96 [95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;3.24], P\u0026thinsp;=\u0026thinsp;0.008; CREB3L4 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.61 [95% CI\u0026thinsp;=\u0026thinsp;0.41\u0026ndash;0.9], P\u0026thinsp;=\u0026thinsp;0.013; pQTL: OR\u0026thinsp;=\u0026thinsp;0.64 [95% CI\u0026thinsp;=\u0026thinsp;0.46\u0026ndash;0.9], P\u0026thinsp;=\u0026thinsp;0.009;DOCK9 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.61 [95% CI\u0026thinsp;=\u0026thinsp;0.44\u0026ndash;0.85], P\u0026thinsp;=\u0026thinsp;0.003; pQTL: OR\u0026thinsp;=\u0026thinsp;0.24 [95% CI\u0026thinsp;=\u0026thinsp;0.09\u0026ndash;0.6], P\u0026thinsp;=\u0026thinsp;0.003; THG1L - eQTL: OR\u0026thinsp;=\u0026thinsp;0.81 [95% CI\u0026thinsp;=\u0026thinsp;0.7\u0026ndash;0.93], P\u0026thinsp;=\u0026thinsp;0.003; pQTL: OR\u0026thinsp;=\u0026thinsp;0.38 [95% CI\u0026thinsp;=\u0026thinsp;0.19\u0026ndash;0.74], P\u0026thinsp;=\u0026thinsp;0.005;\u003c/p\u003e\n\u003cp\u003e(4) CML: CD48 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.45 [95% CI\u0026thinsp;=\u0026thinsp;0.26\u0026ndash;0.77], P\u0026thinsp;=\u0026thinsp;0.004; pQTL: OR\u0026thinsp;=\u0026thinsp;0.39 [95% CI\u0026thinsp;=\u0026thinsp;0.19\u0026ndash;0.77], P\u0026thinsp;=\u0026thinsp;0.007; IGFLR1 - eQTL: OR\u0026thinsp;=\u0026thinsp;1.65 [95% CI\u0026thinsp;=\u0026thinsp;1.1\u0026ndash;2.47], P\u0026thinsp;=\u0026thinsp;0.016; pQTL: OR\u0026thinsp;=\u0026thinsp;2.72 [95% CI\u0026thinsp;=\u0026thinsp;1.3\u0026ndash;5.69], P\u0026thinsp;=\u0026thinsp;0.008; ISOC1 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.55 [95% CI\u0026thinsp;=\u0026thinsp;0.37\u0026ndash;0.81], P\u0026thinsp;=\u0026thinsp;0.002; pQTL: OR\u0026thinsp;=\u0026thinsp;4.87 [95% CI\u0026thinsp;=\u0026thinsp;2.05\u0026ndash;11.58], P\u0026thinsp;=\u0026thinsp;0.0003; NMT1 - eQTL: OR\u0026thinsp;=\u0026thinsp;2.43 [95% CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;5.26], P\u0026thinsp;=\u0026thinsp;0.025; pQTL: OR\u0026thinsp;=\u0026thinsp;26.11 [95% CI\u0026thinsp;=\u0026thinsp;2.23\u0026ndash;305.87], P\u0026thinsp;=\u0026thinsp;0.009; RAF1 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.26 [95% CI\u0026thinsp;=\u0026thinsp;0.08\u0026ndash;0.8], P\u0026thinsp;=\u0026thinsp;0.02; pQTL: OR\u0026thinsp;=\u0026thinsp;53.78 [95% CI\u0026thinsp;=\u0026thinsp;4.61\u0026ndash;627.32], P\u0026thinsp;=\u0026thinsp;0.001; ST3GAL6 - eQTL: OR\u0026thinsp;=\u0026thinsp;0.53 [95% CI\u0026thinsp;=\u0026thinsp;0.31\u0026ndash;0.9], P\u0026thinsp;=\u0026thinsp;0.02; pQTL: OR\u0026thinsp;=\u0026thinsp;0.58 [95% CI\u0026thinsp;=\u0026thinsp;0.38\u0026ndash;0.87], P\u0026thinsp;=\u0026thinsp;0.01.\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate significant associations between gene expression and disease risk, further validating the crucial role of gene regulation in leukemogenesis. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e provides comprehensive OR values, 95% CIs, and P-values for all identified gene loci.\u003c/p\u003e\n\u003cp\u003eNotably, several genes (MST1, CLEC1B, ISOC1, RAF1) exhibited discordant effect directions between transcriptomic and proteomic levels. For example, MST1 showed increased disease risk with higher mRNA expression (eQTL: OR\u0026thinsp;=\u0026thinsp;1.92, 95% CI 1.17\u0026ndash;3.14, P\u0026thinsp;=\u0026thinsp;0.009), yet demonstrated reduced risk with elevated protein levels (pQTL: OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI 0.61\u0026ndash;0.92, P\u0026thinsp;=\u0026thinsp;0.006); CLEC1B exhibited protective effects at the transcript level (eQTL: OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI 0.74\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.005), but risk effects at the protein level (pQTL: OR\u0026thinsp;=\u0026thinsp;1.96, 95% CI 1.19\u0026ndash;3.24, P\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorresponding OR values, 95% CI, and P-values of co-significant genetic loci\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTypes of leukemia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eeQTL analysis results\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epQTL analysis results\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMST1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;1.92 [1.17\u0026ndash;3.14],\u0026nbsp;P\u0026nbsp;= 0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.75 [0.61\u0026ndash;0.92],\u0026nbsp;P\u0026nbsp;= 0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTCL1A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;2.26 [1.46\u0026ndash;3.52],\u0026nbsp;P\u0026nbsp;= 0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;3.06 [1.59\u0026ndash;5.86],\u0026nbsp;P\u0026nbsp;= 0.0008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eCLL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.84 [0.74\u0026ndash;0.95],\u0026nbsp;P\u0026nbsp;= 0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;1.96 [1.19\u0026ndash;3.24],\u0026nbsp;P\u0026nbsp;= 0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCREB3L4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.61 [0.41\u0026ndash;0.90],\u0026nbsp;P\u0026nbsp;= 0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.64 [0.46\u0026ndash;0.90],\u0026nbsp;P\u0026nbsp;= 0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDOCK9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.61 [0.44\u0026ndash;0.85],\u0026nbsp;P\u0026nbsp;= 0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.24 [0.09\u0026ndash;0.60],\u0026nbsp;P\u0026nbsp;= 0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTHG1L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.81 [0.70\u0026ndash;0.93],\u0026nbsp;P\u0026nbsp;= 0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.38 [0.19\u0026ndash;0.74],\u0026nbsp;P\u0026nbsp;= 0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.45 [0.26\u0026ndash;0.77],\u0026nbsp;P\u0026nbsp;= 0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.39 [0.19\u0026ndash;0.77],\u0026nbsp;P\u0026nbsp;= 0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIGFLR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;1.65 [1.10\u0026ndash;2.47],\u0026nbsp;P\u0026nbsp;= 0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;2.72 [1.30\u0026ndash;5.69],\u0026nbsp;P\u0026nbsp;= 0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eISOC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.55 [0.37\u0026ndash;0.81],\u0026nbsp;P\u0026nbsp;= 0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;4.87 [2.05\u0026ndash;11.58],\u0026nbsp;P\u0026nbsp;= 0.0003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNMT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;2.43 [1.12\u0026ndash;5.26],\u0026nbsp;P\u0026nbsp;= 0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;26.11 [2.23\u0026ndash;305.87],\u0026nbsp;P\u0026nbsp;= 0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRAF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.26 [0.08\u0026ndash;0.80],\u0026nbsp;P\u0026nbsp;= 0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;53.78 [4.61\u0026ndash;627.32],\u0026nbsp;P\u0026nbsp;= 0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eST3GAL6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.53 [0.31\u0026ndash;0.90],\u0026nbsp;P\u0026nbsp;= 0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;0.58 [0.38\u0026ndash;0.87],\u0026nbsp;P\u0026nbsp;= 0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n\u003ch2\u003e3.1.4 Sensitivity Analyses\u003c/h2\u003e\n\u003cp\u003eWe conducted comprehensive sensitivity analyses including heterogeneity testing, horizontal pleiotropy assessment, and MR-Steiger directionality analysis. Results for co-significant genes are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, with full data in Supplementary Tables S5, S6 and Figures \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eCochran's Q test indicated heterogeneity for CREB3L4-pQTL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The IVW random-effects model yielded OR\u0026thinsp;=\u0026thinsp;0.64 [95% CI\u0026thinsp;=\u0026thinsp;0.46\u0026ndash;0.90], P\u0026thinsp;=\u0026thinsp;0.009, while the weighted median method showed OR\u0026thinsp;=\u0026thinsp;0.75 [95% CI\u0026thinsp;=\u0026thinsp;0.52\u0026ndash;1.09], P\u0026thinsp;=\u0026thinsp;0.13. Given this lack of concordant statistical evidence, we concluded that the CREB3L4-pQTL association with CLL lacks significance and will exclude it from further analyses. No significant heterogeneity was detected in other results.\u003c/p\u003e\n\u003cp\u003eMR-Egger regression intercept analysis revealed no evidence of horizontal pleiotropy among selected SNPs. MR-PRESSO testing detected no outliers in significant gene instruments (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Furthermore, scatter plots demonstrated robust regression relationships (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e and S4). Finally, symmetrical funnel plots supported the reliability of MR analyses by confirming the integrity of statistical inferences (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e and S6).\u003c/p\u003e\n\u003cp\u003eAssociations between genes and leukemia estimated by multiple MR methods are documented in Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e, with heterogeneity and pleiotropy test results in Supplementary Tables S5 and S6 respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eQualitative and pleiotropy test results of co-significant genes in Parazacco spilurus subsp. spilurus, along with MR-Steiger causal direction test\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposures\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposed database\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ from IVW\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQ from MR-Egger\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePval_Q from IVW\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePval_Q from MR-Egger\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMR-Steiger\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePval of MR-Egger interception\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMST1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eALL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTCL1A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCLL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCREB3L4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCLL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDOCK9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCLL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTHG1L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCLL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCD48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIGFLR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eISOC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e51.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNMT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRAF1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eST3GAL6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCML\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eeQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epQTL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTRUE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003csup\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003erefer to existence of heterogeneity between SNPs\u003c/strong\u003e, \u003csup\u003e\u003cstrong\u003e#\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003erefer to existence of pleiotropy between SNPs.\u003c/strong\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n\u003ch2\u003e3.1.5 SMR verification results\u003c/h2\u003e\n\u003cp\u003eTo validate the functional relevance of MR-identified genes, we performed SMR analysis. As demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary Table \u003cspan class=\"InternalRef\"\u003eS7\u003c/span\u003e, cis-eQTLs of CLEC1B and IGFLR1 showed significant associations with CLL and CML occurrence, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating their potential functional impacts. Specifically, CLEC1B cis-eQTLs suggested protective effects against CLL, whereas IGFLR1 cis-eQTLs were significantly associated with increased CML risk.\u003c/p\u003e\n\u003cp\u003eFigure 6 visually summarizes the per-SNP analyses for these two genes through forest plots and scatter plots of eQTL and pQTL associations.\u003c/p\u003e\n\u003cp\u003eIntegrative analysis revealed: both CLEC1B gene expression (eQTL-MR) and SMR analysis demonstrated protective effects (OR\u0026thinsp;\u0026lt;\u0026thinsp;1), but protein-level associations (pQTL-MR) showed risk effects (OR\u0026thinsp;\u0026gt;\u0026thinsp;1), suggesting potential disruption by post-translational modifications(PTMs). In contrast, IGFLR1 exhibited consistent risk effects across all levels: gene expression (eQTL-MR, OR\u0026thinsp;\u0026gt;\u0026thinsp;1), SMR analysis (OR\u0026thinsp;\u0026gt;\u0026thinsp;1), and protein quantification (pQTL-MR, OR\u0026thinsp;\u0026gt;\u0026thinsp;1), indicating a concerted pathogenic mechanism.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Experimental Validation Results\u003c/h2\u003e\n\u003cp\u003eTo experimentally validate the causal roles of CLEC1B in CLL and IGFLR1 in CML identified by MR, comprehensive in vitro studies were conducted using human leukemia cell lines MEC-1 (CLL model) and K562 (CML model). Gene-specific silencing was achieved via siRNA transfection, followed by phenotypic assays for proliferation, apoptosis, and invasion.\u003c/p\u003e\n\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.1 Gene Silencing Efficiency Validation\u003c/h2\u003e\n\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-B, transient siRNA transfection significantly reduced target gene expression: si-CLEC1B decreased CLEC1B mRNA to 7.23% \u0026plusmn; 1.38% of control in MEC-1 cells (P\u0026thinsp;=\u0026thinsp;0.002); si-IGFLR1 reduced IGFLR1 expression to 20% \u0026plusmn; 6.13% in K562 cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.2 Cell Proliferation and Apoptosis Assays\u003c/h2\u003e\n\u003cp\u003eCCK-8 assays (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC-D) demonstrated that CLEC1B knockdown (Group si-CLEC1B) significantly inhibited MEC-1 proliferation in a time-dependent manner. Specifically, inhibition rates at 12h, 24h, 36h, and 48h were 55.27% \u0026plusmn; 2.44%, 73.70% \u0026plusmn; 4.22%, 81.85% \u0026plusmn; 2.19%, and 91.00% \u0026plusmn; 3.44%, respectively. Significant differences between adjacent time points were observed (12h vs 0h: P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; 24h vs 12h: P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; 36h vs 24h: P\u0026thinsp;=\u0026thinsp;0.004; 48h vs 36h: P\u0026thinsp;=\u0026thinsp;0.001). Similarly, IGFLR1 knockdown (Group si-IGFLR1) suppressed K562 proliferation with increasing inhibition rates: 21.84% \u0026plusmn; 1.49% at 12h, 33.11% \u0026plusmn; 4.47% at 24h, 43.78% \u0026plusmn; 4.54% at 36h, and 54.87% \u0026plusmn; 4.57% at 48h. Adjacent time point comparisons showed significant differences (12h vs 0h: P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; 24h vs 12h: P\u0026thinsp;=\u0026thinsp;0.001; 36h vs 24h: P\u0026thinsp;=\u0026thinsp;0.002; 48h vs 36h: P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003eThese findings indicate that both CLEC1B and IGFLR1 critically regulate leukemia cell proliferation in a time-dependent manner.\u003c/p\u003e\n\u003cp\u003eFlow cytometry analysis (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE-J) confirmed that silencing either CLEC1B or IGFLR1 significantly suppressed proliferation and induced apoptosis. Specifically, CLEC1B-silenced MEC-1 cells (Group si-CLEC1B) showed 4-fold higher apoptosis versus controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while IGFLR1-knockdown K562 cells (Group si-IGFLR1) exhibited 3-fold increased apoptosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), both with concomitant proliferation inhibition.\u003c/p\u003e\n\u003cp\u003eCollectively, these results demonstrate that CLEC1B and IGFLR1 play essential roles in leukemia cell survival, and their silencing effectively induces apoptosis while inhibiting proliferation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.3 Cell Invasion Capacity Assessment\u003c/h2\u003e\n\u003cp\u003eUsing Transwell invasion assays, we found that CLEC1B knockdown did not significantly affect the invasive capacity of MEC-1 cells, whereas IGFLR1 knockdown markedly inhibited the invasion of K562 cells. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, the number of invading MEC-1 cells in the si-CLEC1B group showed no statistical difference compared to controls; conversely, si-IGFLR1-treated K562 cells exhibited significantly reduced transmigration (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Absorbance measurements yielded results consistent with this trend.\u003c/p\u003e\n\u003cp\u003eThese results indicate that IGFLR1 plays a critical role in leukemia cell invasion, while CLEC1B may not be directly involved in this biological process.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Molecular Docking Analysis of CLEC1B and IGFLR1\u003c/h2\u003e\n\u003cp\u003eFor CLEC1B, the drug screening identified three effective compounds\u0026mdash;Cetylic acid, Citric acid, and Nicotine\u0026mdash;and molecular docking simulations were performed to evaluate CLEC1B's potential as a therapeutic target for CLL (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA, B, C).\u003c/p\u003e\n\u003cp\u003eThe potential drug screening targeting IGFLR1 identified two promising substances: Coumestrol and Nickel. Molecular docking analysis was performed to evaluate IGFLR1 as a viable therapeutic target for CML (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eD). The docking experiment uncovered a binding pocket (C1) with a Vina score of -7.2, indicating a strong affinity between Coumestrol and IGFLR1. The 3D molecular docking diagram provided insight into the binding orientation of Coumestrol within the IGFLR1 protein. The calculated volume of the binding pocket was 2058 \u0026Aring;\u0026sup3;, signifying a relatively large binding site. The coordinates of the binding pocket's center were (0, -4, -4), with the docking dimensions measuring (20, 28, 20) in the x, y, and z axes, respectively. Furthermore, the 2D molecular docking diagram elucidated several critical interactions, including hydrogen bonds and hydrophobic contacts. These findings emphasize the promising potential of Coumestrol as a candidate molecule for targeting IGFLR1 in future therapeutic investigations (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMolecular Docking Analysis of CLEC1B, IGFLR1and Drugs.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComplexes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVina score\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCavity volume (\u0026Aring;3)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCenter (x, y, z)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDocking size (x, y, z)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCetylic acid - CLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-4.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28, 0, 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23, 23, 23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCitric acid - CLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28, 0, 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17, 17, 17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNicotine - CLEC1B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e107\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28, 0, 12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17, 17, 17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoumestrol - IGFLR1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0, -4, -4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20, 28, 20\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGene therapies show considerable potential in leukemia treatment, particularly for targeted interventions addressing specific genetic mutations. Recent studies have established leukemia stem cells as pivotal drivers in leukemogenesis, progression, and relapse\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, rendering therapies targeting these cells a critical research focus. Additionally, the substantial heterogeneity of leukemia and paucity of biomarkers make precision medicine an urgent unmet need. Emerging genomic and immunotherapeutic approaches offer promising avenues to address this challenge\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study employed two-sample MR to investigate causal effects of gene expression and plasma proteins across four leukemias, elucidating biomarker-disease causal relationships. By integrating multi-omics data, these findings provide novel mechanistic insights and potential therapeutic directions for precision leukemia treatment. We identified CLEC1B and IGFLR1 as potential therapeutic targets, with experimental validation of their biological functions establishing foundations for targeted therapies. Our findings underscore complex gene-disease causal architectures, advancing leukemia research frontiers.\u003c/p\u003e\u003cp\u003eCLEC1B encodes a critical type II transmembrane receptor featuring a carbohydrate-recognition domain (CRD) that mediates intercellular interactions and signal transduction\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Its CRD exhibits specific binding to multiple ligands, enabling essential roles in immune responses and platelet activation\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. CLEC1B's cell-type-specific expression patterns across immune subsets have garnered substantial research interest. Expression in dendritic cells and macrophages critically regulates immune responses, modulating immune cell infiltration and activity within tumor microenvironments\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Mechanistically, CLEC1B may drive M2 polarization of TAMs\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e],\u003c/sup\u003e suppressing T-cell activity to promote tumor growth and metastasis. Furthermore, CLEC1B expression is regulated by epigenetic modifications\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e that alter transcriptional activity, potentially impacting tumor immune evasion and immunotherapy responses. Notably, CLEC1B expression correlates positively with PD-L1 levels\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, suggesting it may potentiate immune checkpoint activation within tumor microenvironments, thereby modulating therapeutic efficacy.\u003c/p\u003e\u003cp\u003eIntriguingly, we report the first evidence of eQTL-pQTL effect direction dissociation in leukemia for CLEC1B, with protective effects at the transcript level (OR\u0026thinsp;\u0026lt;\u0026thinsp;1) but risk effects at the protein level (OR\u0026thinsp;\u0026gt;\u0026thinsp;1).\u003c/p\u003e\u003cp\u003eWe posit potential mechanisms: First, PTMs\u0026mdash;chemical alterations occurring after protein synthesis\u0026mdash;critically regulate cellular signaling, gene expression, and metabolic homeostasis. Advances in mass spectrometry and bioinformatics have revealed diverse PTM types that contribute to both physiological processes and disease pathogenesis (e.g., cancer, neurodegeneration)\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Our findings indicate pathological hijacking of PTMs within the leukemia microenvironment. While CLEC1B expression enhances NK-cell cytotoxicity and antitumor immunity\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e(consistent with eQTL OR\u0026thinsp;\u0026lt;\u0026thinsp;1), leukemia-specific PTMs (phosphorylation/glycosylation/ubiquitination) may aberrantly modify protein conformation/activity, converting it into an oncogenic driver\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e (aligning with pQTL OR\u0026thinsp;\u0026gt;\u0026thinsp;1). Genomic instability in leukemia cells may disrupt PTM regulation. As an immunoregulatory factor in hematologic malignancies\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, CLEC1B PTMs could be dysregulated by aberrant B-cell receptor signaling, driving protein-level risk effects. Technical limitations in quantifying PTMs for pQTL studies may introduce bias, as plasma measurements may not capture intracellular dynamics\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Secondly, tissue-specific biological differences may be amplified since eQTLs derive from specific tissues (e.g., blood/bone marrow)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, whereas pQTLs reflect plasma proteins\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. In leukemia, plasma protein levels represent secretory phenotypes, while eQTLs capture intracellular gene regulation. As a soluble receptor, CLEC1B plasma concentrations may be confounded by tumor-secreted factors (e.g., exosomes)\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, potentially obscuring protective effects observed in eQTLs. Finally, genetic instrument robustness in MR may be compromised by population heterogeneity (ancestral origins/substructure), causing effect direction divergence\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn vitro experiments demonstrated that CLEC1B silencing promoted apoptosis and suppressed proliferation in MEC-1 cells, suggesting its high-expression state confers pro-survival/proliferative advantages increasing leukemia risk. This aligns with pQTL-MR risk effects (OR\u0026thinsp;\u0026gt;\u0026thinsp;1), indicating elevated protein levels may promote oncogenesis.\u003c/p\u003e\u003cp\u003eThese insights elucidate CLEC1B's dual roles in leukemia and provide rationale for targeted therapies (e.g., CLEC1B inhibitors), highlighting the necessity of addressing molecular-level discordance in precision medicine.\u003c/p\u003e\u003cp\u003eOn the other hand, we provide the first evidence establishing a causal relationship between IGFLR1 and CML, offering novel molecular insights into CML pathogenesis. The association of IGFLR1 expression with CML cell proliferation, apoptosis, and invasion underscores its potential as a therapeutic target and provides rationale for clinical translation.\u003c/p\u003e\u003cp\u003eSubstantial evidence indicates oncogenic roles for IGFLR1 across multiple cancers. For instance, elevated IGFLR1 mRNA in tumor versus matched adjacent tissues suggests involvement in tumorigenesis and progression. Moreover, IGFLR1 expression correlates with advanced clinical stage and pathological grade, predicting poor prognosis in clear cell renal cell carcinoma (ccRCC)\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The immunomodulatory function of IGFLR1 within tumor microenvironments is increasingly recognized. IGFLR1 upregulation associates with altered tumor-infiltrating immune profiles, particularly suppressing CD8\u0026thinsp;+\u0026thinsp;T cells and CXCL13\u0026thinsp;+\u0026thinsp;BHLHE40\u0026thinsp;+\u0026thinsp;TH1-like cells with potent anti-tumor activity\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. In stage II/III colorectal cancer, membrane/cytoplasmic IGFLR1 co-expresses with FOXP3 cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and IFN response pathway components, suggesting utility in tumor classification and prognostication\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWhile these findings align with our observations, current research primarily focuses on solid tumors. The specific functions and immunomodulatory potential of IGFLR1 in hematologic malignancies\u0026mdash;particularly within leukemia microenvironments\u0026mdash;remain largely unexplored and warrant dedicated investigation.\u003c/p\u003e\u003cp\u003eMolecular docking revealed strong binding potential between coumestrol and IGFLR1 (Vina score = -7.2).This finding identifies novel candidate molecules for drug development targeting IGFLR1, facilitating future screening efforts. Prior studies indicate promising therapeutic potential for targeted small molecules in leukemia, particularly for gene-specific interventions\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Future investigations should characterize compound-gene interactions to develop effective therapeutic strategies.\u003c/p\u003e\u003cp\u003eAlthough current studies have demonstrated the importance of CLEC1B and IGFLR1 in the tumor microenvironment, many key scientific questions remain to be answered. Their context-dependent regulation by intrinsic/extrinsic factors warrants investigation into microenvironmental dynamics.Mechanisms underlying their roles in immune evasion require elucidation. Future work should delineate their immune evasion pathways and interactions with other immunomodulators to define holistic impacts on leukemogenesis.\u003c/p\u003e\u003cp\u003eOur study has the following advantages: First, we establish the first integrated pipeline combining genome-wide MR (eQTL/pQTL), SMR, functional assays to identify leukemia-associated genes (CLEC1B and IGFLR1), providing more robust causal inference than observational studies. This offers a novel paradigm for candidate gene prioritization. Crucially, wet-lab validation significantly enhanced findings reliability. Second, comprehensive profiling across four major leukemia subtypes. This enabled assessment of pleiotropic causal genes. Third, utilization of publicly available GWAS summary statistics provided cost-efficient genetic insights. Finally, restriction to European ancestry minimized confounding from population stratification.\u003c/p\u003e\u003cp\u003eLimitations of our study: First, incomplete functional validation of all candidate genes limits biological interpretation. Second, sample size constraints may affect generalizability across subtypes. Third, absence of clinical validation precludes assessment of translational potential. Future studies should incorporate preclinical models and larger cohorts to enhance reliability.\u003c/p\u003e\u003cp\u003eIn conclusion, we establish causal roles for CLEC1B and IGFLR1 in leukemia and identify actionable drug candidates. Integration of genetics and experimental data provides foundations for precision leukemia therapy. Future investigations should dissect their roles in disease progression and treatment response to advance clinical translation.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study integrates multi-omics Mendelian randomization (cis-eQTL/cis-pQTL) with functional validation to establish, for the first time, causal driver effects of CLEC1B in CLL and IGFLR1 in CML pathogenesis: Genetic evidence demonstrates significant associations between their expression levels and leukemia risk; Functional assays confirm that silencing CLEC1B inhibited proliferation and induced apoptosis in CLL cells, while IGFLR1 knockdown suppressed proliferation, induced apoptosis, and significantly impaired invasion capacity in CML cells. Molecular docking identified lead compounds targeting these genes: coumestrol (IGFLR1, Vina score = -7.2), providing novel therapeutic candidates for precision leukemia therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eSupervision and writing (review \u0026amp; editing): GJ.C.; Conceptualization and writing (original draft): GJ.C. and ZY.S.; Revision: CJ.Z.; Funding acquisition: CJ.Z.; Investigation, Visualization, and Validation: ZY.S.; Formal analysis and Methodology: YL.L.and TJ.H.; Resources and Writing (review \u0026amp; editing):YL.L.and TJ.H..\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDeclaration of authors\u0026apos; contributions\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll authors made substantial contributions to the manuscript, including conception and design, preparing the manuscript, and approving the final version. Supervision and writing (review \u0026amp; editing): Guanjun Chen; Conceptualization and writing (original draft): Guanjun Chen and Zhenyu Song; Revision: Chunjiang Zhu; Funding acquisition: Chunjiang Zhu; Investigation, Visualization, and Validation: Zhenyu Song; Formal analysis and Methodology: Yulan Li and Tianjun Huang; Resources and Writing (review \u0026amp; editing):Yulan Li and Tianjun Huang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the article is provided in this article or supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors report any directly competing interests for the current study.\u003c/p\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBispo, J. 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Biochem. Biotechnol.\u003c/em\u003e \u003cb\u003e197\u003c/b\u003e (1), 427\u0026ndash;442 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"QTL, Mendelian randomization, Leukemia, IGFLR1, CLEC1B","lastPublishedDoi":"10.21203/rs.3.rs-7510513/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7510513/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Leukemia is a type of malignant hematological disease, for which conventional treatments have limited efficacy, and there is an urgent need for novel therapeutic targets.This study investigates the causal effects of gene expression and plasma proteins on four subtypes of leukemia using a multi-omics integration strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Based on publicly available GWAS data from European populations, two-sample Mendelian randomization (MR) was employed to analyze the causal relationships between cis-expression quantitative trait loci (cis-eQTL, eQTLGen consortium, n=31,684) and cis-plasma protein quantitative trait loci (cis-pQTL, deCODE consortium, n=4,907) with four types of leukemia (FinnGen database, n≈345,000). Co-significant genes were screened through intersection analysis, and their associations were validated using the SMR method. At the experimental level, functional validation of key genes (CLEC1B/IGFLR1) was conducted in MEC-1/K562 leukemia cell lines: after siRNA silencing, silencing efficiency was detected by quantitative real-time PCR (qRT-PCR), proliferation and apoptosis were assessed using Cell Counting Kit-8 (CCK-8) and flow cytometry, and invasion ability was examined by Transwell assay. Targeted drugs were screened based on HEB and ITCM databases, and molecular docking simulations were performed using the CBDock2 platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e MR analysis identified CLEC1B (chronic lymphocytic leukemia) and IGFLR1 (chronic myeloid leukemia) as significant causal genes (P\u0026lt;0.05). Cell experiments confirmed that silencing CLEC1B inhibited CLL cell proliferation and promoted apoptosis (P\u0026lt;0.05), and silencing IGFLR1 significantly inhibited CML cell proliferation, promoted apoptosis, and suppressed CML cell invasion (P\u0026lt;0.05). Molecular docking revealed that Coumestrol has strong binding potential with IGFLR1 (Vina score = -7.2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIGFLR1 and CLEC1B are potential targets for leukemia therapy, and their corresponding small molecule compounds provide new directions for drug development. This study integrates genetic and experimental evidence, laying the foundation for precision therapy in leukemia.\u003c/p\u003e","manuscriptTitle":"Study on the Gene Regulatory Effects in Leukemia Based on Multi-omics Mendelian Randomization, Network Pharmacology, and Functional Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 14:33:15","doi":"10.21203/rs.3.rs-7510513/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T06:11:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T03:22:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15155385102423886102528395539366309529","date":"2025-10-13T06:33:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T05:31:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103973924249066696292018745942524293238","date":"2025-09-17T06:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-17T01:53:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-05T07:42:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T17:27:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T04:44:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-01T16:39:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c735c433-122b-4e3e-bff3-c5b6190ab3ae","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54982774,"name":"Biological sciences/Cancer"},{"id":54982775,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":54982776,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-28T12:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 14:33:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7510513","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7510513","identity":"rs-7510513","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}