Identification of C4BPA as a genetically informed drug target in NSCLC: An integrative single-cell and multi-omics study based on the druggable genes

Identification of C4BPA as a genetically informed drug target in NSCLC: An integrative single-cell and multi-omics study based on the druggable genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of C4BPA as a genetically informed drug target in NSCLC: An integrative single-cell and multi-omics study based on the druggable genes Zhihan Xiao, Xinji Liu, Wei Tang, Yan Lv, Tongyu Zhang, Xu Zhan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6833129/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Human Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract Background Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Despite advancements in treatment, drug resistance and limited therapeutic efficacy persist, underscoring the urgent need for novel and mechanistically informed therapeutic strategies. Identifying genetically supported drug targets may accelerate the development of precision therapies in NSCLC. Methods We implemented an integrative multi-omics framework combining single-cell RNA sequencing (scRNA-seq), genome-wide association studies (GWAS), and molecular quantitative trait locus (QTL) datasets including expression (eQTL), protein (pQTL), and DNA methylation (mQTL) QTLs. Druggable candidates were systematically evaluated using a suite of Mendelian randomization (MR) approaches—including summary data-based MR (SMR), generalized SMR (GSMR), and genetic risk score (GRS) analysis. Epigenetic regulation and downstream signaling were further explored through mediation MR analysis. Results C4BPA, a complement-regulatory macromolecule, emerged as a causal risk factor for NSCLC across multiple MR models, with consistent findings validated at both transcriptomic and proteomic levels. Epigenetic activation of C4BPA via DNA methylation was observed, and C4BPA expression was shown to promote NSCLC progression through the inflammatory chemokine CCL8 signaling axis. Sensitivity analyses confirmed the robustness of causal inference with no evidence of horizontal pleiotropy. Conclusions Our findings identify C4BPA as a genetically validated and biologically plausible therapeutic target for NSCLC. This study demonstrates the power of integrating single-cell transcriptomics with population-scale omics and causal inference to uncover actionable targets, offering a scalable framework for advancing precision oncology in lung cancer. NSCLC scRNA-seq eQTL pQTL C4BPA Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung cancer is one of the most prevalent malignancies and remains the leading cause of cancer-related mortality worldwide [ 1 ]. Among its subtypes, non-small cell lung cancer (NSCLC) accounts for approximately 76% of cases and comprises a heterogeneous group of tumors, including adenocarcinoma and squamous cell carcinoma [ 2 ]. Approximately 75% of patients are currently diagnosed with advanced disease [ 3 ], and while existing therapeutic modalities, including chemotherapy, confer some clinical benefit, their overall efficacy remains limited, with response rates frequently falling below 50% [ 4 ]. Moreover, the emergence of resistance to targeted therapies—such as those addressing EGFR, RAS/RAF/PI3K, and mTOR pathway aberrations—poses a formidable challenge [ 5 ]. Consequently, the development of innovative approaches to surmount these obstacles and enhance patient outcomes is of paramount importance. Druggable genes are defined as those encoding proteins amenable to modulation by drug-like small molecules, identified through sequence and structural homology to known drug targets [ 6 ]. These genes represent critical focal points in the landscape of drug discovery and development. Furthermore, the convergence of genome-wide association studies (GWAS) with molecular biology heralds a transformative approach to uncovering and validating novel therapeutic targets for NSCLC. Within this framework, Mendelian randomization (MR) emerges as a robust methodological paradigm, leveraging genetic variants as instrumental variables (IVs) to elucidate causal relationships between prospective drug targets and cancer outcomes [ 7 , 8 ]. Such advances provide a compelling genetic rationale for prioritizing targets, thereby expediting the trajectory from discovery to clinical translation [ 9 ]. Recent advancements in genomics and MR have heralded transformative progress in oncology, facilitating the discovery of novel therapeutic targets across a spectrum of cancers, including prostate and breast malignancies [ 10 , 11 ]. However, the deployment of these cutting-edge technologies in NSCLC remains nascent, particularly with regard to the integration of single-cell RNA sequencing (scRNA-seq), expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and DNA methylation quantitative trait loci (mQTL) datasets with GWAS of druggable genes. This study seeks to address this critical gap by integrating data from druggable gene eQTLs, pQTLs, DNA mQTLs, and GWAS analyses of NSCLC patients to identify druggable genes that may serve as promising therapeutic targets for NSCLC. Through the application of a comprehensive analytical framework encompassing scRNA-seq analysis, eQTL, pQTL, and mQTL analysis, as well as advanced methodologies such as summary data-based MR (SMR), generalized summary-data-based MR (GSMR), two-sample MR, genetic risk scores (GRS), mediated MR, and diverse sensitivity analyses, this research aims to unravel the molecular intricacies of NSCLC and uncover novel pathways for therapeutic development. Materials and methods Study design The overarching framework of the study is visually summarized in Fig. 1 . Briefly, our investigation began with the analysis of scRNA-seq data derived from NSCLC to identify differentially expressed genes (DEGs) associated with the disease. From these DEGs, we prioritized druggable genes for subsequent examination. A two-sample MR analysis was then performed, leveraging cis-eQTLs of the selected druggable genes in blood as exposures and GWAS data for NSCLC as outcomes, to elucidate the causal relationship between gene expression and disease. Stringent inclusion and exclusion criteria were employed to select appropriate single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). To ensure robustness, a comprehensive suite of sensitivity analyses was conducted to validate the quality of the MR findings. Subsequently, we performed cis-pQTL analysis to provide an additional layer of validation by examining the influence of protein expression levels on NSCLC outcomes. Moreover, we employed SMR, GSMR and GRS methodologies to further interrogate the causal associations between the identified druggable genes and NSCLC. Finally, we delved into the upstream and downstream regulatory mechanisms governing these genes, offering deeper insights into their biological roles within the context of NSCLC. Data sources The 10x scRNA-seq dataset was obtained from GSE200972 ( https://www.ncbi.nlm.nih.gov/geo/ ) [ 12 ]. This dataset comprises 11 samples of NSCLC tissue and 8 samples of adjacent non-cancerous tissue. The selection of these specimens was strategically designed to illuminate the cellular heterogeneity inherent in NSCLC and its neighboring microenvironment, thereby enabling a comprehensive exploration of gene expression profiles at single-cell resolution. Finan et al . identified a comprehensive set of 4,479 druggable genes, encompassing 1,427 genes encoding targets of approved or clinical-phase therapeutics, 682 genes encoding proteins that interact with known drug molecules or share similarity with approved drug targets, and 2,370 genes belonging to pivotal druggable gene families or encoding proteins exhibiting distant homology to validated drug targets [ 6 ]. This diverse repertoire of druggable genes presents a broad array of potential avenues for therapeutic exploration. Of these, cis-eQTL data in blood were available for 2,525 genes, as determined through the eQTLGen Consortium [ 13 ]. This consortium integrates 37 datasets encompassing a combined cohort of 31,684 individuals, providing a robust resource for gene-expression analysis. The pQTL dataset, encompassing 4,907 plasma proteins measured in 35,559 participants, was derived from the Decode cohort [ 14 ]. Within this dataset, a total of 1,553 cis-pQTLs associated with these plasma proteins were identified. The mQTL data were obtained from the GoDMC database ( http://mqtldb.godmc.org.uk/downloads ) [ 15 ]. This comprehensive dataset encompasses information derived from 27,750 samples analyzed using the Illumina 450K platform. Further details regarding these datasets are available in the corresponding original publications. Circulating inflammatory proteins were predominantly derived from a GWAS encompassing 14,824 healthy individuals. This analysis identified numerous common genetic variants influencing circulating cytokine levels, shedding light on the genetic underpinnings of systemic inflammation [ 16 ]. The corresponding data, including GWAS summary statistics for 91 distinct circulating inflammatory proteins, are accessible through the GWAS Catalog ( https://www.ebi.ac.uk/gwas/summary-statistics ) under accession numbers GCST90274758 to GCST90274848. The GWAS data for non-small cell lung cancer (NSCLC) were obtained from the Finngen database ( https://www.finngen.fi/en ), comprising 6,446 cases and 378,749 controls. For comprehensive details regarding sample collection, analytical methodologies, and key findings, readers are encouraged to consult the original publication. scRNA-Seq Data Processing The quality control, analysis, and interrogation of the scRNA-seq data were conducted using the R package “Seurat” ( https://github.com/satijalab/seurat ) [ 17 ]. The analytical workflow unfolded as follows: (1) Cells failing to meet predefined quality thresholds were excluded based on specific cellular characteristics, including nFeature_RNA > 200, 200 < nCount_RNA < 30,000, percent.mt < 20, and percent.rb < 20. (2) Data normalization was performed using the “LogNormalize” method, which facilitated batch correction and data adjustment. (3) Cellular clustering was achieved through a shared nearest neighbor (SNN) modularity optimization-based algorithm implemented via the “FindClusters” function, employing a resolution parameter set at 0.6. Finally, the identification of DEGs was carried out using the “FindAllMarkers” function, with a log fold-change (logFC) threshold of 1 and a min.pct of 0.25. Cell annotation and differentially expressed genes (DEGs) based on cell types Cell annotation was primarily informed by established literature and the CellMarker database ( http://bio-bigdata.hrbmu.edu.cn/CellMarker/ ). Following cell annotation, differential gene expression analysis was performed for each cell type utilizing the “FindAllMarkers” function. Genes meeting the criteria of an adjusted P -value 1 were deemed significant DEGs and advanced to subsequent stages of the analysis. Instrumental variables (IVs) selection To ensure the robustness and validity of our findings, it is imperative that three fundamental assumptions of MR analysis are satisfied [ 18 – 20 ]: (1) the IVs must exhibit a strong association with the exposure; (2) the IVs must be independent of any confounding variables; and (3) the IVs must influence the outcome solely through the exposure, without any alternative pathways, thereby avoiding horizontal pleiotropy. In adherence to these assumptions, a meticulous selection protocol was employed for each druggable gene investigated in our study. Initially, we applied a stringent threshold, selecting SNPs from eQTL, pQTL, and mQTL datasets, restricting consideration to those with P -values surpassing the genome-wide significance threshold of 5.0 × 10⁻⁸. Subsequently, to ensure the independence of the selected SNPs, we implemented clumping based on linkage disequilibrium (LD) metrics derived from the 1,000 Genomes Project population reference panel. This procedure employed an LD threshold of r² < 0.01 with a clumping window of 10,000 kb, yielding a set of mutually independent SNPs for each druggable gene. The F-statistic served as a critical measure in the MR analysis, quantifying the strength of the association between the IVs and the exposure variable. This statistic also facilitated the identification and exclusion of weak instruments, which could otherwise introduce bias. Accordingly, SNPs with F-statistics below 10 were excluded from further analysis to mitigate the risk of weak instrument bias [ 21 ]. Mendelian randomization analysis of eQTLs, pQTLs and mQTLs MR analysis was conducted utilizing the R package “TwoSampleMR” [ 22 ]. For instances where only a single SNP served as the IV, the Wald ratio method was employed. In cases where the IV comprised two or more SNPs, five statistical approaches were applied: inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode [ 23 ]. Prior studies have demonstrated that while the IVW method is relatively conservative, it offers greater robustness compared to the alternative techniques [ 24 ]. Consequently, the primary results were derived from the IVW method, regardless of the presence of heterogeneity, with findings from the supplementary methods serving as secondary support. To ensure the reliability of the analysis, a series of sensitivity tests was performed. The potential heterogeneity among IVs was assessed using Cochran’s Q test, with a P -value below 0.05 indicating significant heterogeneity [ 25 ]. Additionally, MR-Egger regression was employed to detect potential pleiotropy in the relationship between exposures and outcomes [ 25 ]. A P -value below 0.05 for the MR-Egger regression intercept was indicative of pleiotropy, undermining the validity of the MR analysis results. Summary-data-based MR (SMR) analysis To evaluate the association between druggable genes and the risk of NSCLC, we employed SMR. This approach, leveraging the most significantly associated cis-eQTLs, achieves substantially greater statistical power compared to traditional MR analyses, particularly when the exposure and outcome datasets are derived from two independent cohorts with large sample sizes [ 26 ]. The selection of top cis-eQTLs was based on a genomic window spanning ± 1000 kb around the target gene, with a stringent significance threshold of P < 5.0 × 10⁻⁸. To differentiate true causal relationships from spurious associations due to linkage, the heterogeneity in dependent instruments (HEIDI) test was employed. Variants with P -HEIDI values < 0.05, indicative of likely pleiotropy, were excluded from subsequent analyses. Both SMR and HEIDI tests were conducted using the SMR software package (v1.3.1), ensuring methodological rigor and reproducibility. Generalized summary-data-based MR (GSMR) analysis In addition to using the SMR method, generalized summary-data-based MR (GSMR) was used to replicate the results. GSMR, an extension of SMR that uses weights from variance-covariance structures constructed from correlations between IVs, A generalized linear model was used to calculate the total causal effect size. GSMR can more accurately assess the influence of genetic factors on complex traits [ 27 , 28 ]. GSMR analyses were performed with “gsmr2” package. Genetic risk scores (GRSs) To corroborate the aforementioned findings, we performed a supplementary validation analysis using the GRS methodology. This analysis was implemented within the R programming environment, utilizing the "gtx" package (version 0.0.8 for Windows), which includes the grs.summary module and its integrated GRS function. The grs.summary module operates on SNP association summary statistics derived from GWAS, employing an approach akin to regressing an outcome on an additive GRS [ 29 , 30 ]. For uncorrelated SNPs, the causal effect estimate (𝛼) is derived as: $$\:\alpha\:\approx\:\frac{\sum\:{\omega\:}\beta\:{se}_{\beta\:}^{-2}}{{\sum\:}_{}^{{{\omega\:}}^{2}}{se}_{\beta\:}^{-2}}$$ where the standard error (se 𝛼 ) is computed as: $$\:{se}_{\alpha\:}\approx\:\frac{1}{{\sum\:}_{}^{{\omega\:}^{2}}{se}_{\beta\:}^{-2}}$$ Here, 𝜔 represents the estimated effects on the intermediate trait or biomarker, while 𝛽denotes the estimated effects on the response variable or outcome, accompanied by their respective standard errors (se 𝛽 ) [ 31 , 32 ]. Results Differentially expressed genes (DEGs) based on cell types We undertook a comprehensive analysis of the scRNA-seq dataset, meticulously filtered according to the criteria outlined in the preceding methods. This endeavor culminated in the identification of 36 discrete cell clusters through t-SNE clustering analysis (Fig. 2A-B). Across all samples, five principal cell types were annotated using the SingleR algorithm: Dendritic cells (DCs), Eosinophils, Macrophages, Monocytes, and Stromal cells (Fig. 2C-D). Furthermore, we examined differential gene expression at the single-cell level between NSCLC and control groups. The DEGs derived from each individual cell type were subsequently incorporated into next analyses (Fig. 2E, Supplementary Table 1–5 ). eQTL analysis for druggable genes From the DEGs identified through scRNA-seq analysis, we prioritized druggable genes for further investigation. Subsequently, we performed cis-eQTL analysis to assess the relationship between the expression of these druggable genes and NSCLC. In accordance with stringent criteria for IV selection, a total of 2,399 cis-eQTLs associated with druggable genes were identified as IVs. The F-statistics for all IVs exceeded the threshold of 10, indicating a robust instrument set devoid of weak instrument bias. Using IVW and the Wald ratio method, we identified significant associations between the expression of druggable genes and NSCLC across various cell types. In DC, the expression of 200 druggable genes was significantly correlated with NSCLC, of which 107 were identified as risk factors and 93 as protective factors. In Eosinophils, the expression of 20 druggable genes showed significant correlations, with 12 serving as risk factors and 8 as protective factors. Within Macrophages, 8 druggable genes exhibited significant associations, with 5 acting as risk factors and 3 as protective factors. In Monocytes, the expression of 91 druggable genes was significantly correlated with NSCLC, with 49 identified as risk factors and 42 as protective factors. Finally, in Stromal cells, 49 druggable genes were significantly associated with NSCLC, including 29 risk factors and 20 protective factors ( Supplementary Table 6 ). pQTL analysis for druggable genes To further substantiate the effect of druggable gene expression on NSCLC, we examined plasma protein levels using pQTL data. The pQTL analysis revealed that elevated plasma levels of eight druggable genes— C5 , C4BPA , GRN , CD33 , CXCL10 , KLRB1 , CBR1 , and CXCL5 —were associated with an increased risk of NSCLC. Conversely, higher plasma levels of three druggable genes— B4GALT1 , CACNA2D3 , and SECTM1 —were linked to a reduced risk of NSCLC, findings that align with the results of the eQTL analysis (Table 1 ). Moreover, the absence of heterogeneity and horizontal pleiotropy in IVs for these significant druggable genes, as confirmed by Cochran’s Q test and MR-Egger regression, further bolsters the robustness of these observations (Table 1 ). Table 1 Significant pQTLs MR results between the expression of druggable genes protein and NSCLC. Protein Nsnp OR 95%CI P _value Cochran’s Q P MR-Egger intercept P C5 15 1.1804 1.0275 1.3560 0.0191 0.8417 0.7187 C4BPA 29 1.1779 1.0853 1.2784 0.0001 0.5894 0.2642 GRN 14 1.1905 1.0595 1.3376 0.0034 0.9056 0.9826 CD33 191 1.0294 1.0072 1.0521 0.0091 0.6877 0.3696 B4GALT1 13 0.8793 0.7749 0.9978 0.0462 0.5049 0.1958 CXCL10 3 1.4591 1.0231 2.0809 0.0370 0.9158 0.9044 KLRB1 20 1.1413 1.0177 1.2800 0.0239 0.2616 0.1904 CACNA2D3 64 0.8975 0.8341 0.9658 0.0039 0.7137 0.5787 CBR1 19 1.1333 1.0035 1.2798 0.0437 0.6710 0.1046 CXCL5 6 1.3049 1.0476 1.6255 0.0176 0.6009 0.6084 SECTM1 13 0.8397 0.7499 0.9402 0.0025 0.7426 0.5972 pQTL, protein quantitative trait loci; NSCLC, non-small cell lung cancer; MR, Mendelian randomization. SMR and HEIDI tests verified C4BPA To substantiate the observed findings, we conducted SMR and HEIDI tests on 11 druggable genes for which comprehensive summary-level data were available. Among these, only C4BPA demonstrated statistical significance in the SMR test ( P = 0.003), while the HEIDI test revealed no evidence of significant heterogeneity ( P > 0.05; Table 2 ). Table 2 SMR results between the expression of druggable genes and NSCLC. Gene Nsnp Probe_bp topSNP Beta Se P _value P _HEIDI C4BPA 14 207277607 rs8942 0.1102 0.0366 0.003 0.46 C5 17 123714616 rs2416813 0.1397 0.0794 0.079 0.65 SECTM1 16 80278900 rs75837323 -0.0856 0.0702 0.223 0.16 CBR1 7 37442239 rs73372463 0.0630 0.1823 0.730 0.68 NSCLC, non-small cell lung cancer; SMR, summary-data-based mendelian randomization. GSMR analysis To make our findings more convincing, we also added advanced GSMR analysis method, and the results of GSMR analysis showed a positive causal relationship between C4BPA expression and NSCLC (OR = 1.012, P = 0.003; Fig. 3A ). GRS C4BPA and NSCLC Aligned with the findings from the preceding multi-omics analyses, the GRS C4BPA demonstrated a causal relationship between C4BPA expression and NSCLC risk (OR = 1.036, P = 0.002, Fig. 3A-B ). Notably, sensitivity analyses revealed no significant heterogeneity ( P > 0.05), underscoring the robustness of the results. More importantly, we found that C4BPA was specifically expressed in lung tissue as queried in the HPA database (Fig. 3C) ( https://www.proteinatlas.org/ ). DNA methylation epigenetically activated C4BPA To elucidate the mechanisms through which C4BPA facilitates the initiation and progression of NSCLC, we employed a mediated MR framework to investigate upstream regulatory factors influencing C4BPA expression. From an initial pool of ten C4BPA DNA methylation sites, five loci (cg14856606, cg17803430, cg22491058, cg26430305, cg26514552) were retained following rigorous IV screening based on stringent inclusion criteria. In the first stage, we conducted a two-sample MR analysis to examine the relationship between DNA methylation sites and NSCLC. The findings revealed significant associations for three methylation sites (cg14856606: OR = 0.869, P = 0.004; cg17803430: OR = 0.481, P = 0.029; cg22491058: OR = 1.586, P = 0.011; Table 3 ). In the second stage, these three NSCLC-associated methylation sites were analyzed as exposures in an MR framework, with cis-eQTLs of C4BPA serving as outcomes. This analysis identified a single methylation site significantly associated with C4BPA expression (cg14856606: OR = 0.123, P = 2.802 × 10⁻⁵; Table 3 ). In the final stage, we integrated the results from the MR analyses of C4BPA DNA methylation sites and NSCLC, the MR associations between methylation sites and C4BPA , and the prior MR findings linking C4BPA expression to NSCLC. Through mediated MR analysis, we demonstrated that DNA methylation (cg14856606) influences NSCLC development via its regulatory effect on C4BPA expression. Notably, the mediation proportion attributed to C4BPA was approximately 50% (Fig. 4A). Table 3 The results of upstream mediated MR analysis. Exposure Outcome Methods Nsnp Beta OR 95%CI P_ value cg14856606 NSCLC IVW 3 -0.1405 0.8689 0.7895 0.9563 0.0041 cg17803430 NSCLC Wald ratio 1 -0.7312 0.4813 0.2497 0.9278 0.0290 cg22491058 NSCLC Wald ratio 1 0.4613 1.5861 1.1109 2.2647 0.0111 cg14856606 C4BPA IVW 3 -2.0951 0.1231 0.0462 0.3280 2.80E-05 NSCLC, non-small cell lung cancer; MR, Mendelian randomization. C4BPA promotes the development of NSCLC through the CCL8 signaling pathway To further elucidate the downstream mechanisms by which C4BPA influences the onset and progression of NSCLC, a multi-step analytical approach was undertaken. In the initial phase, we employed a two-sample MR analysis, using 91 circulating inflammatory proteins as exposures and NSCLC as the outcome. This analysis identified seven circulating inflammatory proteins significantly associated with NSCLC (Table 4 ). In the subsequent phase, C4BPA cis-eQTLs were utilized as exposures, and the seven inflammatory proteins identified in the first step were examined as outcomes in a second two-sample MR analysis. Among these, only monocyte chemoattractant protein-1 levels (CCL8) demonstrated a significant association with C4BPA . In the final step, we conducted a mediation MR analysis, with C4BPA cis-eQTLs as exposures, CCL8 as the mediating factor, and NSCLC as the outcome. The results suggested that C4BPA may contribute to NSCLC pathogenesis through its regulation of CCL8, with CCL8 mediating approximately 5% of the effect (Fig. 4B). Table 4 The results of downstream mediated MR analysis. Exposure Outcome Nsnp Method Beta OR 95%CI P _value CCL11 NSCLC 27 IVW 0.1472 1.1586 1.0530 1.2747 0.0025 TRAIL NSCLC 36 IVW -0.0805 0.9226 0.8629 0.9865 0.0184 FIt3L NSCLC 45 IVW -0.0905 0.9134 0.8467 0.9854 0.0193 CCL8 NSCLC 30 IVW 0.0895 1.0936 1.0125 1.1812 0.0229 CXCL6 NSCLC 25 IVW 0.0698 1.0723 1.0037 1.1457 0.0386 TSLP NSCLC 22 IVW -0.1286 0.8793 0.7780 0.9938 0.0394 CCL19 NSCLC 36 IVW -0.0800 0.9231 0.8542 0.9977 0.0435 C4BPA CCL11 37 IVW 0.0099 1.0100 0.9957 1.0245 0.1717 C4BPA CCL19 37 IVW -0.0010 0.9990 0.9853 1.0128 0.8823 C4BPA CXCL6 37 IVW 0.0131 1.0132 0.9995 1.0272 0.0590 C4BPA FIt3L 37 IVW -0.0082 0.9918 0.9750 1.0089 0.3467 C4BPA CCL8 37 IVW 0.0197 1.0199 1.0051 1.0349 0.0083 C4BPA TRAIL 37 IVW 0.0131 1.0132 0.9980 1.0286 0.0890 C4BPA TSLP 37 IVW 0.0007 1.0007 0.9853 1.0164 0.9271 NSCLC, non-small cell lung cancer; MR, Mendelian randomization. Discussion Targeted therapy continues to transform the landscape of oncology, offering more selective and effective treatment strategies for various malignancies, including non-small cell lung cancer (NSCLC). Although several targeted therapies have demonstrated significant clinical efficacy and are approved as first-line treatments for advanced NSCLC, many patients either do not respond or develop acquired resistance. These challenges highlight the pressing need to identify novel, mechanistically distinct drug targets to improve patient outcomes and therapeutic durability [ 33 ] Drug target identification and validation remain crucial bottlenecks in the drug development pipeline. Advances in human genetics have enabled the prioritization of druggable genes supported by robust causal evidence, substantially increasing the likelihood of clinical success. Particularly, genes with strong genetic associations to disease outcomes via functional variants are more likely to yield effective therapeutic targets. In this study, we employed a comprehensive integrative approach using scRNA-seq, multi-layered omics data, and MR to identify and validate potential druggable genes in NSCLC. Among the 2,429 genes screened, C4BPA emerged as a promising therapeutic target. This gene, which encodes a regulatory component of the complement cascade, had not previously been characterized in the context of NSCLC. Our analysis demonstrated that C4BPA expression is positively associated with NSCLC progression, as supported by multiple analytical methods including eQTL, pQTL, GSMR, SMR, and GRS analyses. These findings were reinforced by the absence of pleiotropy and heterogeneity in sensitivity tests, adding robustness to the inferred causal relationships. Importantly, our study extended beyond association by elucidating upstream and downstream regulatory mechanisms involving C4BPA. DNA methylation was found to act as an epigenetic activator of C4BPA, and MR mediation analysis confirmed a significant regulatory link. Furthermore, we identified CCL8, an inflammatory chemokine, as a downstream effector of C4BPA. The mediation analysis suggested that C4BPA may influence NSCLC pathogenesis through the CCL8 pathway, with CCL8 accounting for approximately 5% of the total effect. These mechanistic insights not only provide biological plausibility but also point to potential combinatorial targets for therapeutic intervention. C4BPA belongs to the regulator of complement activation (RCA) gene cluster and encodes a multimeric protein composed of seven α-chains and a single β-chain. While its role in immune modulation through the classical complement pathway is well established, its oncogenic potential has been underexplored. The specific expression of C4BPA in lung tissue, confirmed by the Human Protein Atlas, further supports its relevance as a lung-specific therapeutic candidate [ 34 ]. Despite the strengths of our study, several limitations should be noted. First, MR simulates the long-term, genetically predicted modulation of target gene expression, which may not fully capture the pharmacodynamic complexity of drug action. Second, our findings are based primarily on blood-derived eQTL and pQTL data, which may not fully reflect tissue-specific gene regulation in the lung. Third, the independent effects of C4BPA may be influenced by gene-gene and gene-environment interactions that are not accounted for in our current models. Finally, the causal role of C4BPA, though strongly supported by genetic data, requires validation through functional assays and preclinical studies. In conclusion, our study identifies C4BPA as a novel and genetically validated macromolecular target in NSCLC. By integrating scRNA-seq with multi-omics and causal inference methods, we offer a scalable framework for uncovering actionable therapeutic targets in complex diseases. These findings open avenues for the development of precision medicine strategies that may improve the management and prognosis of NSCLC patients. Abbreviations NSCLC non-small cell lung cancer eQTL gene quantitative trait loci pQTL protein quantitative trait loci mQTL methylation quantitative trait loci. ‌scRNA-seq,single-cell RNA sequencing IVs instrumental variables GWAS genome-wide association studies MR Mendelian randomization IVW Inverse-variance weighted GRS Genetic risk score SNP Single nucleotide polymorphism HEIDI Heterogeneity in the dependent instrument DEGs differentially expressed genes. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials All data used in this study are publicly available and listed in body text. Competing interests The authors declare that there is no confict of interest. Funding No funding. Authors' contributions All authors contributed significantly to, and are in agreement with, the content of the manuscript. Z.N and X.Z.H.: conceptualization; X.Z.H. and P.W.: data curation and resources; X.Z.H. and T.W.: formal analysis and software; S.Q.H, L.X.J., and Z.X.: investigation; X.Z.H. and L.Y.: methodology; W.Q., Z.R.J. and Z.N.: supervision; Z.N. and P.W.: validation; X.Z.H. and O.M.D.: visualization; X.Z.H. and T.W.: writing – original draft. Acknowledgements The authors thank the FinnGen and database for sharing the data. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Luo L, Lin H, Huang J, Lin B, Huang F, Luo H. 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Nat Genet. 2021;53(9):1311–21. Zhao JH, Stacey D, Eriksson N, Macdonald-Dunlop E, Hedman ÅK, Kalnapenkis A, Enroth S, Cozzetto D, Digby-Bell J, Marten J, et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol. 2023;24(9):1540–51. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502. Chen L, Yang H, Li H, He C, Yang L, Lv G. Insights into modifiable risk factors of cholelithiasis: A Mendelian randomization study. Hepatology. 2022;75(4):785–96. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309–30. Zhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, Arnold SM, Bickeböller H, Bojesen SE, Brennan P, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer. 2021;148(5):1077–86. Palmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, Davey Smith G, Sterne JA. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223–42. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 2018, 7. Xiao Z, Qian Y, Liu Y, Huang L, Si M, Wang Z, Zhang T, Chen X, Cao J, Chen L. Investigation of the Causal Relationship Between Alcohol Consumption and COVID-19: A Two-Sample Mendelian Randomization Study. Int J Comput Intell Syst 2023, 16(1). Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36(11):1783–802. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–208. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R, Robinson MR, McGrath JJ, Visscher PM, Wray NR, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 2018;9(1):224. Wang K, Zhang Q, Zhang P, Yang Q, Pan F, Zha B. Use of bidirectional Mendelian randomization to unveil the association of Helicobacter pylori infection and autoimmune thyroid diseases. Sci Adv. 2024;10(31):eadi8646. Luo Q, Wen Z, Li Y, Chen Z, Long X, Bai Y, Huang S, Yan Y, Lin R, Mo Z. Assessment Causality in Associations Between Serum Uric Acid and Risk of Schizophrenia: A Two-Sample Bidirectional Mendelian Randomization Study. Clin Epidemiol. 2020;12:223–33. Liu Y, Xiao Z, Ye K, Xu L, Zhang Y. Smoking, alcohol consumption, diabetes, body mass index, and peptic ulcer risk: A two-sample Mendelian randomization study. Front Genet. 2022;13:992080. Xiao Z, Wang Z, Zhang T, Liu Y, Si M. Bidirectional Mendelian randomization analysis of the genetic association between primary lung cancer and colorectal cancer. J Transl Med. 2023;21(1):722. Liu Y, Si M, Qian Y, Liu Y, Wang Z, Zhang T, Wang Z, Ye K, Xiang C, Xu L, et al. Bidirectional Mendelian randomization analysis investigating the genetic association between primary breast cancer and colorectal cancer. Front Immunol. 2023;14:1260941. Wang K, Li R, Zhang Y, Qi W, Fang T, Yue W, Tian H. Prognostic Significance and Therapeutic Target of CXC Chemokines in the Microenvironment of Lung Adenocarcinoma. Int J Gen Med. 2022;15:2283–300. Blom AM, Bergström F, Edey M, Diaz-Torres M, Kavanagh D, Lampe A, Goodship JA, Strain L, Moghal N, McHugh M, et al. A novel non-synonymous polymorphism (p.Arg240His) in C4b-binding protein is associated with atypical hemolytic uremic syndrome and leads to impaired alternative pathway cofactor activity. J Immunol. 2008;180(9):6385–91. Additional Declarations No competing interests reported. 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08:15:16","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":721998,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6833129/v1/6eec8016eb04131a08b043cb.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of C4BPA as a genetically informed drug target in NSCLC: An integrative single-cell and multi-omics study based on the druggable genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is one of the most prevalent malignancies and remains the leading cause of cancer-related mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among its subtypes, non-small cell lung cancer (NSCLC) accounts for approximately 76% of cases and comprises a heterogeneous group of tumors, including adenocarcinoma and squamous cell carcinoma [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Approximately 75% of patients are currently diagnosed with advanced disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and while existing therapeutic modalities, including chemotherapy, confer some clinical benefit, their overall efficacy remains limited, with response rates frequently falling below 50% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the emergence of resistance to targeted therapies\u0026mdash;such as those addressing EGFR, RAS/RAF/PI3K, and mTOR pathway aberrations\u0026mdash;poses a formidable challenge [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, the development of innovative approaches to surmount these obstacles and enhance patient outcomes is of paramount importance.\u003c/p\u003e \u003cp\u003eDruggable genes are defined as those encoding proteins amenable to modulation by drug-like small molecules, identified through sequence and structural homology to known drug targets [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These genes represent critical focal points in the landscape of drug discovery and development. Furthermore, the convergence of genome-wide association studies (GWAS) with molecular biology heralds a transformative approach to uncovering and validating novel therapeutic targets for NSCLC. Within this framework, Mendelian randomization (MR) emerges as a robust methodological paradigm, leveraging genetic variants as instrumental variables (IVs) to elucidate causal relationships between prospective drug targets and cancer outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Such advances provide a compelling genetic rationale for prioritizing targets, thereby expediting the trajectory from discovery to clinical translation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent advancements in genomics and MR have heralded transformative progress in oncology, facilitating the discovery of novel therapeutic targets across a spectrum of cancers, including prostate and breast malignancies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the deployment of these cutting-edge technologies in NSCLC remains nascent, particularly with regard to the integration of single-cell RNA sequencing (scRNA-seq), expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and DNA methylation quantitative trait loci (mQTL) datasets with GWAS of druggable genes.\u003c/p\u003e \u003cp\u003eThis study seeks to address this critical gap by integrating data from druggable gene eQTLs, pQTLs, DNA mQTLs, and GWAS analyses of NSCLC patients to identify druggable genes that may serve as promising therapeutic targets for NSCLC. Through the application of a comprehensive analytical framework encompassing scRNA-seq analysis, eQTL, pQTL, and mQTL analysis, as well as advanced methodologies such as summary data-based MR (SMR), generalized summary-data-based MR (GSMR), two-sample MR, genetic risk scores (GRS), mediated MR, and diverse sensitivity analyses, this research aims to unravel the molecular intricacies of NSCLC and uncover novel pathways for therapeutic development.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe overarching framework of the study is visually summarized in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. Briefly, our investigation began with the analysis of scRNA-seq data derived from NSCLC to identify differentially expressed genes (DEGs) associated with the disease. From these DEGs, we prioritized druggable genes for subsequent examination. A two-sample MR analysis was then performed, leveraging cis-eQTLs of the selected druggable genes in blood as exposures and GWAS data for NSCLC as outcomes, to elucidate the causal relationship between gene expression and disease. Stringent inclusion and exclusion criteria were employed to select appropriate single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). To ensure robustness, a comprehensive suite of sensitivity analyses was conducted to validate the quality of the MR findings. Subsequently, we performed cis-pQTL analysis to provide an additional layer of validation by examining the influence of protein expression levels on NSCLC outcomes. Moreover, we employed SMR, GSMR and GRS methodologies to further interrogate the causal associations between the identified druggable genes and NSCLC. Finally, we delved into the upstream and downstream regulatory mechanisms governing these genes, offering deeper insights into their biological roles within the context of NSCLC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eThe 10x scRNA-seq dataset was obtained from GSE200972 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This dataset comprises 11 samples of NSCLC tissue and 8 samples of adjacent non-cancerous tissue. The selection of these specimens was strategically designed to illuminate the cellular heterogeneity inherent in NSCLC and its neighboring microenvironment, thereby enabling a comprehensive exploration of gene expression profiles at single-cell resolution.\u003c/p\u003e \u003cp\u003eFinan \u003cem\u003eet al\u003c/em\u003e. identified a comprehensive set of 4,479 druggable genes, encompassing 1,427 genes encoding targets of approved or clinical-phase therapeutics, 682 genes encoding proteins that interact with known drug molecules or share similarity with approved drug targets, and 2,370 genes belonging to pivotal druggable gene families or encoding proteins exhibiting distant homology to validated drug targets [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This diverse repertoire of druggable genes presents a broad array of potential avenues for therapeutic exploration. Of these, cis-eQTL data in blood were available for 2,525 genes, as determined through the eQTLGen Consortium [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This consortium integrates 37 datasets encompassing a combined cohort of 31,684 individuals, providing a robust resource for gene-expression analysis.\u003c/p\u003e \u003cp\u003eThe pQTL dataset, encompassing 4,907 plasma proteins measured in 35,559 participants, was derived from the Decode cohort [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within this dataset, a total of 1,553 cis-pQTLs associated with these plasma proteins were identified.\u003c/p\u003e \u003cp\u003eThe mQTL data were obtained from the GoDMC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mqtldb.godmc.org.uk/downloads\u003c/span\u003e\u003cspan address=\"http://mqtldb.godmc.org.uk/downloads\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This comprehensive dataset encompasses information derived from 27,750 samples analyzed using the Illumina 450K platform. Further details regarding these datasets are available in the corresponding original publications.\u003c/p\u003e \u003cp\u003eCirculating inflammatory proteins were predominantly derived from a GWAS encompassing 14,824 healthy individuals. This analysis identified numerous common genetic variants influencing circulating cytokine levels, shedding light on the genetic underpinnings of systemic inflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The corresponding data, including GWAS summary statistics for 91 distinct circulating inflammatory proteins, are accessible through the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/summary-statistics\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/summary-statistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under accession numbers GCST90274758 to GCST90274848.\u003c/p\u003e \u003cp\u003eThe GWAS data for non-small cell lung cancer (NSCLC) were obtained from the Finngen database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), comprising 6,446 cases and 378,749 controls. For comprehensive details regarding sample collection, analytical methodologies, and key findings, readers are encouraged to consult the original publication.\u003c/p\u003e\n\u003ch3\u003escRNA-Seq Data Processing\u003c/h3\u003e\n\u003cp\u003eThe quality control, analysis, and interrogation of the scRNA-seq data were conducted using the R package \u0026ldquo;Seurat\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/satijalab/seurat\u003c/span\u003e\u003cspan address=\"https://github.com/satijalab/seurat\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The analytical workflow unfolded as follows: (1) Cells failing to meet predefined quality thresholds were excluded based on specific cellular characteristics, including nFeature_RNA\u0026thinsp;\u0026gt;\u0026thinsp;200, 200\u0026thinsp;\u0026lt;\u0026thinsp;nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;30,000, percent.mt\u0026thinsp;\u0026lt;\u0026thinsp;20, and percent.rb\u0026thinsp;\u0026lt;\u0026thinsp;20. (2) Data normalization was performed using the \u0026ldquo;LogNormalize\u0026rdquo; method, which facilitated batch correction and data adjustment. (3) Cellular clustering was achieved through a shared nearest neighbor (SNN) modularity optimization-based algorithm implemented via the \u0026ldquo;FindClusters\u0026rdquo; function, employing a resolution parameter set at 0.6. Finally, the identification of DEGs was carried out using the \u0026ldquo;FindAllMarkers\u0026rdquo; function, with a log fold-change (logFC) threshold of 1 and a min.pct of 0.25.\u003c/p\u003e\n\u003ch3\u003eCell annotation and differentially expressed genes (DEGs) based on cell types\u003c/h3\u003e\n\u003cp\u003eCell annotation was primarily informed by established literature and the CellMarker database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.hrbmu.edu.cn/CellMarker/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.hrbmu.edu.cn/CellMarker/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Following cell annotation, differential gene expression analysis was performed for each cell type utilizing the \u0026ldquo;FindAllMarkers\u0026rdquo; function. Genes meeting the criteria of an adjusted \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an average log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 were deemed significant DEGs and advanced to subsequent stages of the analysis.\u003c/p\u003e\n\u003ch3\u003eInstrumental variables (IVs) selection\u003c/h3\u003e\n\u003cp\u003eTo ensure the robustness and validity of our findings, it is imperative that three fundamental assumptions of MR analysis are satisfied [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: (1) the IVs must exhibit a strong association with the exposure; (2) the IVs must be independent of any confounding variables; and (3) the IVs must influence the outcome solely through the exposure, without any alternative pathways, thereby avoiding horizontal pleiotropy. In adherence to these assumptions, a meticulous selection protocol was employed for each druggable gene investigated in our study. Initially, we applied a stringent threshold, selecting SNPs from eQTL, pQTL, and mQTL datasets, restricting consideration to those with \u003cem\u003eP\u003c/em\u003e-values surpassing the genome-wide significance threshold of 5.0 \u0026times; 10⁻⁸. Subsequently, to ensure the independence of the selected SNPs, we implemented clumping based on linkage disequilibrium (LD) metrics derived from the 1,000 Genomes Project population reference panel. This procedure employed an LD threshold of r\u0026sup2; \u0026lt; 0.01 with a clumping window of 10,000 kb, yielding a set of mutually independent SNPs for each druggable gene. The F-statistic served as a critical measure in the MR analysis, quantifying the strength of the association between the IVs and the exposure variable. This statistic also facilitated the identification and exclusion of weak instruments, which could otherwise introduce bias. Accordingly, SNPs with F-statistics below 10 were excluded from further analysis to mitigate the risk of weak instrument bias [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis of eQTLs, pQTLs and mQTLs\u003c/h2\u003e \u003cp\u003eMR analysis was conducted utilizing the R package \u0026ldquo;TwoSampleMR\u0026rdquo; [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For instances where only a single SNP served as the IV, the Wald ratio method was employed. In cases where the IV comprised two or more SNPs, five statistical approaches were applied: inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Prior studies have demonstrated that while the IVW method is relatively conservative, it offers greater robustness compared to the alternative techniques [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, the primary results were derived from the IVW method, regardless of the presence of heterogeneity, with findings from the supplementary methods serving as secondary support. To ensure the reliability of the analysis, a series of sensitivity tests was performed. The potential heterogeneity among IVs was assessed using Cochran\u0026rsquo;s Q test, with a \u003cem\u003eP\u003c/em\u003e-value below 0.05 indicating significant heterogeneity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, MR-Egger regression was employed to detect potential pleiotropy in the relationship between exposures and outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A \u003cem\u003eP\u003c/em\u003e-value below 0.05 for the MR-Egger regression intercept was indicative of pleiotropy, undermining the validity of the MR analysis results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSummary-data-based MR (SMR) analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the association between druggable genes and the risk of NSCLC, we employed SMR. This approach, leveraging the most significantly associated cis-eQTLs, achieves substantially greater statistical power compared to traditional MR analyses, particularly when the exposure and outcome datasets are derived from two independent cohorts with large sample sizes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The selection of top cis-eQTLs was based on a genomic window spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;1000 kb around the target gene, with a stringent significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.0 \u0026times; 10⁻⁸. To differentiate true causal relationships from spurious associations due to linkage, the heterogeneity in dependent instruments (HEIDI) test was employed. Variants with \u003cem\u003eP\u003c/em\u003e-HEIDI values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicative of likely pleiotropy, were excluded from subsequent analyses. Both SMR and HEIDI tests were conducted using the SMR software package (v1.3.1), ensuring methodological rigor and reproducibility.\u003c/p\u003e\n\u003ch3\u003eGeneralized summary-data-based MR (GSMR) analysis\u003c/h3\u003e\n\u003cp\u003eIn addition to using the SMR method, generalized summary-data-based MR (GSMR) was used to replicate the results. GSMR, an extension of SMR that uses weights from variance-covariance structures constructed from correlations between IVs, A generalized linear model was used to calculate the total causal effect size. GSMR can more accurately assess the influence of genetic factors on complex traits [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. GSMR analyses were performed with \u0026ldquo;gsmr2\u0026rdquo; package.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic risk scores (GRSs)\u003c/h2\u003e \u003cp\u003eTo corroborate the aforementioned findings, we performed a supplementary validation analysis using the GRS methodology. This analysis was implemented within the R programming environment, utilizing the \"gtx\" package (version 0.0.8 for Windows), which includes the grs.summary module and its integrated GRS function. The grs.summary module operates on SNP association summary statistics derived from GWAS, employing an approach akin to regressing an outcome on an additive GRS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For uncorrelated SNPs, the causal effect estimate (\u0026#120572;) is derived as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\alpha\\:\\approx\\:\\frac{\\sum\\:{\\omega\\:}\\beta\\:{se}_{\\beta\\:}^{-2}}{{\\sum\\:}_{}^{{{\\omega\\:}}^{2}}{se}_{\\beta\\:}^{-2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere the standard error (se\u003csub\u003e\u0026#120572;\u003c/sub\u003e) is computed as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{se}_{\\alpha\\:}\\approx\\:\\frac{1}{{\\sum\\:}_{}^{{\\omega\\:}^{2}}{se}_{\\beta\\:}^{-2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u0026#120596; represents the estimated effects on the intermediate trait or biomarker, while \u0026#120573;denotes the estimated effects on the response variable or outcome, accompanied by their respective standard errors (se\u003csub\u003e\u0026#120573;\u003c/sub\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes (DEGs) based on cell types\u003c/h2\u003e \u003cp\u003eWe undertook a comprehensive analysis of the scRNA-seq dataset, meticulously filtered according to the criteria outlined in the preceding methods. This endeavor culminated in the identification of 36 discrete cell clusters through t-SNE clustering analysis (Fig.\u0026nbsp;2A-B). Across all samples, five principal cell types were annotated using the SingleR algorithm: Dendritic cells (DCs), Eosinophils, Macrophages, Monocytes, and Stromal cells (Fig.\u0026nbsp;2C-D). Furthermore, we examined differential gene expression at the single-cell level between NSCLC and control groups. The DEGs derived from each individual cell type were subsequently incorporated into next analyses (Fig.\u0026nbsp;2E, \u003cb\u003eSupplementary Table\u0026nbsp;1\u0026ndash;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eeQTL analysis for druggable genes\u003c/h2\u003e \u003cp\u003eFrom the DEGs identified through scRNA-seq analysis, we prioritized druggable genes for further investigation. Subsequently, we performed cis-eQTL analysis to assess the relationship between the expression of these druggable genes and NSCLC. In accordance with stringent criteria for IV selection, a total of 2,399 cis-eQTLs associated with druggable genes were identified as IVs. The F-statistics for all IVs exceeded the threshold of 10, indicating a robust instrument set devoid of weak instrument bias. Using IVW and the Wald ratio method, we identified significant associations between the expression of druggable genes and NSCLC across various cell types. In DC, the expression of 200 druggable genes was significantly correlated with NSCLC, of which 107 were identified as risk factors and 93 as protective factors. In Eosinophils, the expression of 20 druggable genes showed significant correlations, with 12 serving as risk factors and 8 as protective factors. Within Macrophages, 8 druggable genes exhibited significant associations, with 5 acting as risk factors and 3 as protective factors. In Monocytes, the expression of 91 druggable genes was significantly correlated with NSCLC, with 49 identified as risk factors and 42 as protective factors. Finally, in Stromal cells, 49 druggable genes were significantly associated with NSCLC, including 29 risk factors and 20 protective factors (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003epQTL analysis for druggable genes\u003c/h2\u003e \u003cp\u003eTo further substantiate the effect of druggable gene expression on NSCLC, we examined plasma protein levels using pQTL data. The pQTL analysis revealed that elevated plasma levels of eight druggable genes\u0026mdash;\u003cem\u003eC5\u003c/em\u003e, \u003cem\u003eC4BPA\u003c/em\u003e, \u003cem\u003eGRN\u003c/em\u003e, \u003cem\u003eCD33\u003c/em\u003e, \u003cem\u003eCXCL10\u003c/em\u003e, \u003cem\u003eKLRB1\u003c/em\u003e, \u003cem\u003eCBR1\u003c/em\u003e, and \u003cem\u003eCXCL5\u003c/em\u003e\u0026mdash;were associated with an increased risk of NSCLC. Conversely, higher plasma levels of three druggable genes\u0026mdash;\u003cem\u003eB4GALT1\u003c/em\u003e, \u003cem\u003eCACNA2D3\u003c/em\u003e, and \u003cem\u003eSECTM1\u003c/em\u003e\u0026mdash;were linked to a reduced risk of NSCLC, findings that align with the results of the eQTL analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, the absence of heterogeneity and horizontal pleiotropy in IVs for these significant druggable genes, as confirmed by Cochran\u0026rsquo;s Q test and MR-Egger regression, further bolsters the robustness of these observations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificant pQTLs MR results between the expression of druggable genes protein and NSCLC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q \u003c/p\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003cp\u003eintercept \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB4GALT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.0809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLRB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCACNA2D3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSECTM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003epQTL, protein quantitative trait loci; NSCLC, non-small cell lung cancer; MR, Mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSMR and HEIDI tests verified C4BPA\u003c/h2\u003e \u003cp\u003eTo substantiate the observed findings, we conducted SMR and HEIDI tests on 11 druggable genes for which comprehensive summary-level data were available. Among these, only \u003cem\u003eC4BPA\u003c/em\u003e demonstrated statistical significance in the SMR test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), while the HEIDI test revealed no evidence of significant heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSMR results between the expression of druggable genes and NSCLC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbe_bp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etopSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e_HEIDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207277607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers8942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123714616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers2416813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSECTM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80278900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers75837323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37442239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers73372463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNSCLC, non-small cell lung cancer; SMR, summary-data-based mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGSMR analysis\u003c/h2\u003e \u003cp\u003eTo make our findings more convincing, we also added advanced GSMR analysis method, and the results of GSMR analysis showed a positive causal relationship between \u003cem\u003eC4BPA\u003c/em\u003e expression and NSCLC (OR\u0026thinsp;=\u0026thinsp;1.012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGRS\u003csub\u003eC4BPA\u003c/sub\u003e and NSCLC\u003c/h2\u003e \u003cp\u003eAligned with the findings from the preceding multi-omics analyses, the GRS\u003csub\u003e\u003cem\u003eC4BPA\u003c/em\u003e\u003c/sub\u003e demonstrated a causal relationship between \u003cem\u003eC4BPA\u003c/em\u003e expression and NSCLC risk (OR\u0026thinsp;=\u0026thinsp;1.036, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cb\u003eFig.\u0026nbsp;3A-B\u003c/b\u003e). Notably, sensitivity analyses revealed no significant heterogeneity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), underscoring the robustness of the results. More importantly, we found that C4BPA was specifically expressed in lung tissue as queried in the HPA database (Fig.\u0026nbsp;3C) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDNA methylation epigenetically activated C4BPA\u003c/h2\u003e \u003cp\u003eTo elucidate the mechanisms through which \u003cem\u003eC4BPA\u003c/em\u003e facilitates the initiation and progression of NSCLC, we employed a mediated MR framework to investigate upstream regulatory factors influencing \u003cem\u003eC4BPA\u003c/em\u003e expression. From an initial pool of ten \u003cem\u003eC4BPA\u003c/em\u003e DNA methylation sites, five loci (cg14856606, cg17803430, cg22491058, cg26430305, cg26514552) were retained following rigorous IV screening based on stringent inclusion criteria. In the first stage, we conducted a two-sample MR analysis to examine the relationship between DNA methylation sites and NSCLC. The findings revealed significant associations for three methylation sites (cg14856606: OR\u0026thinsp;=\u0026thinsp;0.869, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; cg17803430: OR\u0026thinsp;=\u0026thinsp;0.481, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029; cg22491058: OR\u0026thinsp;=\u0026thinsp;1.586, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the second stage, these three NSCLC-associated methylation sites were analyzed as exposures in an MR framework, with cis-eQTLs of \u003cem\u003eC4BPA\u003c/em\u003e serving as outcomes. This analysis identified a single methylation site significantly associated with \u003cem\u003eC4BPA\u003c/em\u003e expression (cg14856606: OR\u0026thinsp;=\u0026thinsp;0.123, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.802 \u0026times; 10⁻⁵; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the final stage, we integrated the results from the MR analyses of \u003cem\u003eC4BPA\u003c/em\u003e DNA methylation sites and NSCLC, the MR associations between methylation sites and \u003cem\u003eC4BPA\u003c/em\u003e, and the prior MR findings linking \u003cem\u003eC4BPA\u003c/em\u003e expression to NSCLC. Through mediated MR analysis, we demonstrated that DNA methylation (cg14856606) influences NSCLC development via its regulatory effect on \u003cem\u003eC4BPA\u003c/em\u003e expression. Notably, the mediation proportion attributed to \u003cem\u003eC4BPA\u003c/em\u003e was approximately 50% (Fig.\u0026nbsp;4A).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of upstream mediated MR analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP_\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14856606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg17803430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.7312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg22491058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.5861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.2647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14856606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.0951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.80E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNSCLC, non-small cell lung cancer; MR, Mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eC4BPA promotes the development of NSCLC through the CCL8 signaling pathway\u003c/h2\u003e \u003cp\u003eTo further elucidate the downstream mechanisms by which \u003cem\u003eC4BPA\u003c/em\u003e influences the onset and progression of NSCLC, a multi-step analytical approach was undertaken. In the initial phase, we employed a two-sample MR analysis, using 91 circulating inflammatory proteins as exposures and NSCLC as the outcome. This analysis identified seven circulating inflammatory proteins significantly associated with NSCLC (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the subsequent phase, \u003cem\u003eC4BPA\u003c/em\u003e cis-eQTLs were utilized as exposures, and the seven inflammatory proteins identified in the first step were examined as outcomes in a second two-sample MR analysis. Among these, only monocyte chemoattractant protein-1 levels (CCL8) demonstrated a significant association with \u003cem\u003eC4BPA\u003c/em\u003e. In the final step, we conducted a mediation MR analysis, with \u003cem\u003eC4BPA\u003c/em\u003e cis-eQTLs as exposures, CCL8 as the mediating factor, and NSCLC as the outcome. The results suggested that \u003cem\u003eC4BPA\u003c/em\u003e may contribute to NSCLC pathogenesis through its regulation of CCL8, with CCL8 mediating approximately 5% of the effect (Fig.\u0026nbsp;4B).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of downstream mediated MR analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIt3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.1812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCL19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.8823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIt3L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC4BPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNSCLC, non-small cell lung cancer; MR, Mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTargeted therapy continues to transform the landscape of oncology, offering more selective and effective treatment strategies for various malignancies, including non-small cell lung cancer (NSCLC). Although several targeted therapies have demonstrated significant clinical efficacy and are approved as first-line treatments for advanced NSCLC, many patients either do not respond or develop acquired resistance. These challenges highlight the pressing need to identify novel, mechanistically distinct drug targets to improve patient outcomes and therapeutic durability [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDrug target identification and validation remain crucial bottlenecks in the drug development pipeline. Advances in human genetics have enabled the prioritization of druggable genes supported by robust causal evidence, substantially increasing the likelihood of clinical success. Particularly, genes with strong genetic associations to disease outcomes via functional variants are more likely to yield effective therapeutic targets.\u003c/p\u003e \u003cp\u003eIn this study, we employed a comprehensive integrative approach using scRNA-seq, multi-layered omics data, and MR to identify and validate potential druggable genes in NSCLC. Among the 2,429 genes screened, C4BPA emerged as a promising therapeutic target. This gene, which encodes a regulatory component of the complement cascade, had not previously been characterized in the context of NSCLC. Our analysis demonstrated that C4BPA expression is positively associated with NSCLC progression, as supported by multiple analytical methods including eQTL, pQTL, GSMR, SMR, and GRS analyses. These findings were reinforced by the absence of pleiotropy and heterogeneity in sensitivity tests, adding robustness to the inferred causal relationships.\u003c/p\u003e \u003cp\u003eImportantly, our study extended beyond association by elucidating upstream and downstream regulatory mechanisms involving C4BPA. DNA methylation was found to act as an epigenetic activator of C4BPA, and MR mediation analysis confirmed a significant regulatory link. Furthermore, we identified CCL8, an inflammatory chemokine, as a downstream effector of C4BPA. The mediation analysis suggested that C4BPA may influence NSCLC pathogenesis through the CCL8 pathway, with CCL8 accounting for approximately 5% of the total effect. These mechanistic insights not only provide biological plausibility but also point to potential combinatorial targets for therapeutic intervention.\u003c/p\u003e \u003cp\u003e \u003cem\u003eC4BPA\u003c/em\u003e belongs to the regulator of complement activation (RCA) gene cluster and encodes a multimeric protein composed of seven α-chains and a single β-chain. While its role in immune modulation through the classical complement pathway is well established, its oncogenic potential has been underexplored. The specific expression of C4BPA in lung tissue, confirmed by the Human Protein Atlas, further supports its relevance as a lung-specific therapeutic candidate [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the strengths of our study, several limitations should be noted. First, MR simulates the long-term, genetically predicted modulation of target gene expression, which may not fully capture the pharmacodynamic complexity of drug action. Second, our findings are based primarily on blood-derived eQTL and pQTL data, which may not fully reflect tissue-specific gene regulation in the lung. Third, the independent effects of C4BPA may be influenced by gene-gene and gene-environment interactions that are not accounted for in our current models. Finally, the causal role of C4BPA, though strongly supported by genetic data, requires validation through functional assays and preclinical studies.\u003c/p\u003e \u003cp\u003eIn conclusion, our study identifies C4BPA as a novel and genetically validated macromolecular target in NSCLC. By integrating scRNA-seq with multi-omics and causal inference methods, we offer a scalable framework for uncovering actionable therapeutic targets in complex diseases. These findings open avenues for the development of precision medicine strategies that may improve the management and prognosis of NSCLC patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egene quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethylation quantitative trait loci. \u0026zwnj;scRNA-seq,single-cell RNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenetic risk score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeterogeneity in the dependent instrument\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll data used in this study are publicly available and listed in body text.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe authors declare that there is no confict of interest.\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\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAll authors contributed significantly to, and are in agreement with, the content of the manuscript. Z.N and X.Z.H.: conceptualization; X.Z.H. and P.W.: data curation and resources; X.Z.H. and T.W.: formal analysis and software; S.Q.H, L.X.J., and Z.X.: investigation; X.Z.H. and L.Y.: methodology; W.Q., Z.R.J. and Z.N.: supervision; Z.N. and P.W.: validation; X.Z.H. and O.M.D.: visualization; X.Z.H. and T.W.: writing – original draft.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe authors thank the FinnGen and database for sharing the data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo L, Lin H, Huang J, Lin B, Huang F, Luo H. Risk factors and prognostic nomogram for patients with second primary cancers after lung cancer using classical statistics and machine learning. Clin Exp Med. 2023;23(5):1609\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones GS, Baldwin DR. Recent advances in the management of lung cancer. Clin Med (Lond). 2018;18(Suppl 2):s41\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahiri A, Maji A, Potdar PD, Singh N, Parikh P, Bisht B, Mukherjee A, Paul MK. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer. 2023;22(1):40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu WJ, Du Y, Wen R, Yang M, Xu J. Drug resistance to targeted therapeutic strategies in non-small cell lung cancer. 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Nat Genet. 2021;53(12):1712\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMin JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, et al. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet. 2021;53(9):1311\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao JH, Stacey D, Eriksson N, Macdonald-Dunlop E, Hedman \u0026Aring;K, Kalnapenkis A, Enroth S, Cozzetto D, Digby-Bell J, Marten J, et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat Immunol. 2023;24(9):1540\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Yang H, Li H, He C, Yang L, Lv G. Insights into modifiable risk factors of cholelithiasis: A Mendelian randomization study. Hepatology. 2022;75(4):785\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, Arnold SM, Bickeb\u0026ouml;ller H, Bojesen SE, Brennan P, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer. 2021;148(5):1077\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer TM, Lawlor DA, Harbord RM, Sheehan NA, Tobias JH, Timpson NJ, Davey Smith G, Sterne JA. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al. The MR-Base platform supports systematic causal inference across the human phenome. \u003cem\u003eeLife\u003c/em\u003e 2018, 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Z, Qian Y, Liu Y, Huang L, Si M, Wang Z, Zhang T, Chen X, Cao J, Chen L. Investigation of the Causal Relationship Between Alcohol Consumption and COVID-19: A Two-Sample Mendelian Randomization Study. Int J Comput Intell Syst 2023, 16(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med. 2017;36(11):1783\u0026ndash;802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, Montgomery GW, Goddard ME, Wray NR, Visscher PM, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. 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Clin Epidemiol. 2020;12:223\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Xiao Z, Ye K, Xu L, Zhang Y. Smoking, alcohol consumption, diabetes, body mass index, and peptic ulcer risk: A two-sample Mendelian randomization study. Front Genet. 2022;13:992080.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Z, Wang Z, Zhang T, Liu Y, Si M. Bidirectional Mendelian randomization analysis of the genetic association between primary lung cancer and colorectal cancer. J Transl Med. 2023;21(1):722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Si M, Qian Y, Liu Y, Wang Z, Zhang T, Wang Z, Ye K, Xiang C, Xu L, et al. Bidirectional Mendelian randomization analysis investigating the genetic association between primary breast cancer and colorectal cancer. Front Immunol. 2023;14:1260941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Li R, Zhang Y, Qi W, Fang T, Yue W, Tian H. Prognostic Significance and Therapeutic Target of CXC Chemokines in the Microenvironment of Lung Adenocarcinoma. Int J Gen Med. 2022;15:2283\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlom AM, Bergstr\u0026ouml;m F, Edey M, Diaz-Torres M, Kavanagh D, Lampe A, Goodship JA, Strain L, Moghal N, McHugh M, et al. A novel non-synonymous polymorphism (p.Arg240His) in C4b-binding protein is associated with atypical hemolytic uremic syndrome and leads to impaired alternative pathway cofactor activity. J Immunol. 2008;180(9):6385\u0026ndash;91.\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":"human-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hugm","sideBox":"Learn more about [Human Genomics](http://humgenomics.biomedcentral.com/)","snPcode":"40246","submissionUrl":"https://submission.nature.com/new-submission/40246/3","title":"Human Genomics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, scRNA-seq, eQTL, pQTL, C4BPA","lastPublishedDoi":"10.21203/rs.3.rs-6833129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6833129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide. Despite advancements in treatment, drug resistance and limited therapeutic efficacy persist, underscoring the urgent need for novel and mechanistically informed therapeutic strategies. Identifying genetically supported drug targets may accelerate the development of precision therapies in NSCLC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe implemented an integrative multi-omics framework combining single-cell RNA sequencing (scRNA-seq), genome-wide association studies (GWAS), and molecular quantitative trait locus (QTL) datasets including expression (eQTL), protein (pQTL), and DNA methylation (mQTL) QTLs. Druggable candidates were systematically evaluated using a suite of Mendelian randomization (MR) approaches\u0026mdash;including summary data-based MR (SMR), generalized SMR (GSMR), and genetic risk score (GRS) analysis. Epigenetic regulation and downstream signaling were further explored through mediation MR analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eC4BPA, a complement-regulatory macromolecule, emerged as a causal risk factor for NSCLC across multiple MR models, with consistent findings validated at both transcriptomic and proteomic levels. Epigenetic activation of C4BPA via DNA methylation was observed, and C4BPA expression was shown to promote NSCLC progression through the inflammatory chemokine CCL8 signaling axis. Sensitivity analyses confirmed the robustness of causal inference with no evidence of horizontal pleiotropy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings identify C4BPA as a genetically validated and biologically plausible therapeutic target for NSCLC. This study demonstrates the power of integrating single-cell transcriptomics with population-scale omics and causal inference to uncover actionable targets, offering a scalable framework for advancing precision oncology in lung cancer.\u003c/p\u003e","manuscriptTitle":"Identification of C4BPA as a genetically informed drug target in NSCLC: An integrative single-cell and multi-omics study based on the druggable genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-14 08:15:10","doi":"10.21203/rs.3.rs-6833129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-16T02:49:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T16:28:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T23:31:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52758793382267646866248351995390590815","date":"2025-06-13T04:54:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311926197231821760565331257512879817861","date":"2025-06-13T04:32:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318440841073711261134943880500432556563","date":"2025-06-11T05:33:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T04:11:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T01:56:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T01:55:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Genomics","date":"2025-06-06T03:16:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hugm","sideBox":"Learn more about [Human Genomics](http://humgenomics.biomedcentral.com/)","snPcode":"40246","submissionUrl":"https://submission.nature.com/new-submission/40246/3","title":"Human Genomics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ac1313c-fe47-4ab7-9720-65b281188070","owner":[],"postedDate":"June 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:03:30+00:00","versionOfRecord":{"articleIdentity":"rs-6833129","link":"https://doi.org/10.1186/s40246-025-00829-3","journal":{"identity":"human-genomics","isVorOnly":false,"title":"Human Genomics"},"publishedOn":"2025-10-06 15:58:27","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2025-06-14 08:15:10","video":"","vorDoi":"10.1186/s40246-025-00829-3","vorDoiUrl":"https://doi.org/10.1186/s40246-025-00829-3","workflowStages":[]},"version":"v1","identity":"rs-6833129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6833129","identity":"rs-6833129","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}