The Association Between White Blood Cell Count and Relative Risk of Non-Small Cell Lung Cancer

The Association Between White Blood Cell Count and Relative Risk of Non-Small Cell Lung Cancer | 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 The Association Between White Blood Cell Count and Relative Risk of Non-Small Cell Lung Cancer Xiao Yang, Wenyi Liu, Jiaqi Wang, Weifeng Xia, Lanxiang Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5773221/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background High abundance of eosinophils has been proved to associated with favorable disease progression in non-small cell lung cancer (NSCLC) in the previous observational studies, but the causal relationship remains unclear. It is also unclear whether white blood cell (WBC) counts are essential for the risk of NSCLC. Methods Using multiple methods of Mendelian randomization (MR), we assessed the causality of WBC count, particularly basophil, eosinophil, monocyte, lymphocyte, and neutrophil counts on the risk of NSCLC, which includes squamous carcinoma and adenocarcinoma. Single cell RNA-sequencing and RNA-sequencing analysis illustrate the underline mechanism of the causality and its biological effects. Results Univariable MR analysis indicated the protective effect of elevated eosinophil counts on NSCLC and adenocarcinoma subtype. The protective effect of eosinophils persisted even after adjusting. The protective effect of functions mainly by immune activating, and it contribute to better survival and favorable response to immune therapy. Univariate MR analysis also states the risk role of neutrophil. Sequencing based analysis proved the immune inhibit functions of neutrophil, which lead to worse survival and immune treatment response. Conclusion Our study indicated a correlation between circulating eosinophil counts, neutrophil counts, and the development of NSCLC. And sequencing analysis confirm this relationship and illustrated the underline mechanism. NSCLC WBC mendelian randomization eosinophils single-cell RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to the 2021 Global Cancer Report, lung cancer (LC) continues to be one of the most prevalent and lethal malignancies globally, both in terms of incidence and mortality rate. Comprising 85% of lung cancer, none small cell lung cancer (NSCLC) is the most prevalent type [ 1 , 2 ]. Based on histological types, NSCLC is primarily categorized into squamous cell carcinoma and adenocarcinoma [ 3 ]. Due to the diverse pathological characteristic and pathogenesis, the development of NSCLC is individual, leading to divergent outcome. Given the current challenges, it is imperative to identify novel shared risk factors, and develop preventive and treatment strategies against NSCLC [ 4 ]. Standard white blood cell (WBC) tests categorize WBCs into five subtypes: neutrophils, lymphocytes, monocytes, eosinophils, and basophils [ 5 ]. Alterations in WBC count have been shown to contribute to cancer susceptibility, oncogenesis, progression, and also associate with cancer mortality. For example, increased neutrophil content indicates poor treatment response in NSCLC. And the neutrophil-to-lymphocyte ratio (NLR) also associates with survival in NSCLC, ovarian cancer and well-differentiated thyroid cancer [ 6 , 7 ]. However, there are limitations of understanding the correlation between NSCLC risk and comprehensive WBC subtypes, and the underlying mechanism remain unsolved. With the continuous development of genomics, more and more genetic factors have been involved in cancer development [ 8 ]. The Mendelian randomization (MR) technique has become popular because of its superior capacity to infer causality between features, a unique benefit compared to other techniques. It utilizes single-nucleotide polymorphisms (SNPs) formed through genetic inheritance and randomly distributed across different populations to assess the target effect. SNPs, as instrumental variables, are not affected by interference from the majority of confounding factors [ 9 ]. Bulk RNA-sequencing (RNA-seq) data can provide genetic alterations of functional mechanism to explain the novel phenomenon. Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of phenotypic heterogeneity at the cellular level, providing mechanistic insights unobtainable through bulk sequencing approaches. In this study, MR analyses were performed using both multiple MR methods to investigate potential genomic associations between WBC subtypes and NSCLC, the underlying mechanism was explored by multiple sequencing analysis. Methods Study Design We sought to explore the correlation between different subtypes of WBCs and risk of NSCLC, and also assess the correlation between WBCs subtypes and NSCLC subtypes using genetic epidemiology alongside supplementary techniques. Firstly, univariate MR analysis took place to evaluation the causative impact of WBC counts on NSCLC as well as its histological subtypes in both training court and validation court. Secondly, a multivariable MR (MVMR) analysis was conducted by incorporating the counts of all five WBC subtypes into the model to assess the direct effects of each subtype. Additionally, reverse MR analysis was conducted in search of reverse causal relationships between them. Then, we utilized mediation MR to evaluate whether eosinophils acted as a mediator in clinical features. Finally, through integrated analysis of sequencing datasets, we mechanistically linked identified risk factors to immunotherapy efficacy and survival outcomes, providing a molecular framework for patient stratification (Fig. 1 ). GWAS Sources The summary statistics data for SNPs related to WBCs (basophils, entire WBCs, eosinophils, monocytes, lymphocytes, and neutrophils) (N = 563946; year = 2020; study IDs "ieu-b-29, ieu-b-30, ieu-b-33, ieu-b-31, ieu-b-32, and ieu-b-34") and SNPs related to smoking (N = 44052; year = 2022; study ID "ieu-b-4857"), body mass (N = 681275; year = 2018; study ID "ieu-b-40"), financial difficulties (N = 459742; year = 2018; study ID "ukb-b-9776") and incoming (N = 397751; year = 2018; study ID "ukb-b-7408") were downloaded from the IEU GWAS public database ( https://gwas.mrcieu.ac.uk/datasets/ ). The IEU GWAS database has extensively documented the quality control procedures and pipelines utilized in generating GWAS data. The GWAS summary statistics for training and validation, including NSCLC and its subtype-associated SNPs, were sourced from the FinnGen collection ( https://www.finngen.fi/en/access_results , Version = r10) and the International Lung Cancer Consortium (ILCCO, https://ilcco.iarc.fr/ ). Owing to the restricted availability of NSCLC-specific data in ILCCO, we employed two general lung cancer datasets as alternatives. All GWAS data mentioned above were obtained from European populations. Instrumental Variable Selection In MR analysis and MVMR, instrumental variables (IVs) should satisfy the assumption of being “associated with the exposure”. Firstly, SNP selection was performed by setting a threshold of P < 5×10 − 8 , while also ensuring linkage disequilibrium conditions of R 2 10000. Subsequently, we used sample size (N), independent SNP number (K), SNP effect size (β), minor allele frequency (MAF) and standard error (se) to calculate the F statistic, indicative of reliability. The computation adopted the formula: \(\:F\:=\:\left[\frac{R2}{1-R2}\right]\times\:\frac{N-K-1}{K}\) , where \(\:R2\:=\:2\:\times\:\:MAF\:\times\:\:\left(1-MAF\right)\times\:\:\left(\frac{\beta\:}{se}\right)2\) . SNPs with F < 10 were considered weak IVs and were excluded [ 10 ]. Next, potential confounding factors related to other traits were eliminated by querying the SNP on the PhenoScanner website ( http://www.phenoscanner.medschl.cam.ac.uk/ , Supplementary Table 1). Outcome-related SNPs were extracted, and allele data linking exposure to outcome were obtained, with reciprocal SNPs being removed. The subsequent MR analysis was performed based on the remaining SNPs. In reverse MR analysis and mediation MR analysis, due to the limited selection of instruments, we employed relatively lenient selection criteria ( P < 5×10 − 6 or P < 5×10 − 5 , R 2 10000) to screen IVs. SNPs selected through this screening process were used for subsequent analysis. Mendelian randomization The Inverse variance weighted (IVW) approach in MR analysis integrates the causative effects of multiple genetic variants and is not affected by heterogeneity and pleiotropy. IVW provides the most precise results when the selected genetic variants are all valid IVs [ 11 ]. The Wald ratio method weights the impact of each variant on the risk of the target disease by its effect on the exposure. Subsequently, individual MR aggregated using random-effects inverse variance weighting analysis to generate a comprehensive summary estimate. Supplementary techniques, including weighted median and MR Egger, are used to assess the reliability and robustness of MR results [ 12 , 13 ]. The weighted median estimate is a statistical measure where individual estimates of MR are given weights corresponding to their precision, resulting in a median value. Conversely, MR-Egger regression provides a weighted linear regression, with SNP outcome associations being regressed on SNP exposure associations. In this study, MR analyses, encompassing IVW, weighted median, and MR Egger methods, were performed utilizing the R package "TwoSampleMR" [ 14 ]. Various perceptual analysis and statistical techniques have been used in assessing the effectiveness of IVs. The Cochran's Q test was employed to calculate p-values to measure heterogeneity, with P ≥ 0.05 suggesting the random-effects framework with IVW, and P < 0.05 necessitating the fixed-effects framework with IVW [ 15 ]. Horizontal pleiotropy was evaluated and outliers were identified using the MR-PRESSO global test and MR-Egger intercept. At a significance level of P 0.05, the average pleiotropic effect was evaluated using the MR Egger intercept. In cases where horizontal pleiotropy was significant, the MR-PRESSO outlier test was applied for correction by identifying and removing outliers, with a threshold of MR-PRESSO global test P < 0.05). Moreover, bias in causal estimates was identified both before and after outlier removal using the MR-PRESSO distortion test. Every analysis was carried out utilizing the "MR-PRESSO" package in R [ 16 ]. Leave-one-out analysis was conducted in order to enhance the trustworthiness of the results [ 17 ]. Multivariable MR represents a variant to conventional MR, employed to measure the causal effect of WBCs on NSCLC and its histological subtypes. For each exposure, we employed the "Mendelian Randomization" package to estimate the direct causal effects. Taking NSCLC and its subtypes as the exposure and WBCs as the outcome, reverse MR analysis was conducted to assess reverse causal relationships and demonstrate the absence of bidirectional causality between exposure and outcome. We utilized the same GWAS dataset previously referenced for reverse MR analysis. The Finnish Biobank provided cancer IVs. WBCs, as outcomes, were obtained from the IEU GWAS public source. A two-step mediation study utilizing two MR analyses linked by common variables was conducted to evaluate if these common variables moderate the relationship between clinical traits and cancer [ 18 ]. In particular, we assessed whether WBC count is one of the pathways from smoking, body mass, financial difficulties or incoming index to NSCLC. RNA sequencing data acquisition and preprocessing The eosinophil scRNA-seq court with 11 samples and NSCLC immune therapy court composed of scRNA-seq data with 15 anti-PD1 treatment patients and RNA-seq were obtained in Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ) with the identifiers GSEGSE182001 and GSE207422 respectively [ 19 , 20 ]. The other RNA-seq data containing survival information was downloaded from The Cancer Genome Atlas Program (TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga ) with identifiers TCGA-LUAD and TCGA-LUSC. RNA-seq data were standardized to Transcripts Per Million (TPM) and subjected to log2 transformation for subsequent analysis. The scRNA-seq data underwent preprocessing by feature gene filtering, normalization, and subsequent dimensionality reduction and clustering via Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) utilizing the R package "Seurat" (Version 4.2.0). The cells were labeled with the marker genes associated with the study source. Downstream analysis of RNA sequencing data The gene enrichment scores analysis in scRNA-seq data and RNA-seq data were calculated via the function "AddModuleScore" in "Seurat" and R package "GSVA". The developmental progression of cells was delineated by "Monocle2". We performed survival analysis using R package "survival" and "survminer". All statistical analyses were executed utilizing R software version 4.3.1. Results Screening the causal association between circulating WBC counts and NSCLC To assess the causal effects of WBC counts on the risk of NSCLC, we firstly conducted univariate MR analyses involving six circulating WBC subtypes (whole WBCs, basophils, eosinophils, monocytes, lymphocytes, and neutrophils). The results indicated that there were no significant correlations between whole WBC counts and NSCLC. But in the subtype analysis, the decreased level of eosinophils was closely correlated with increased risk of NSCLC (IVW OR = 0.893, 95%CI = 0.804–0.994, P = 0.038), which was more pronounced in adenocarcinoma (IVW OR = 0.803, 95%CI = 0.691–0.961, P = 0.017, Fig. 2 A). We examined the effectiveness of eosinophil counts in the ILCCO dataset, and effect of eosinophils in LC (IVW OR = 0.897, 95%CI = 0.791 − 1.017, P = 0.091) and adenocarcinoma (IVW OR = 0.833, 95%CI = 0.677 − 1.026, P = 0.086) remained protective (Fig. 2 B). Although the results were not statistically different in the weighted median and MR-Egger analyses, the direction of their OR values remained consistent (Supplementary Table 2). According to supplementary Table 3, MR-Egger intercepts and MR-PERSSO global tests failed to detect horizontal pleiotropy in this causal relationship. The IVW method, the other subtypes including monocytes (IVW OR = 1.104, 95%CI = 1.014–1.202, P = 0.022) and neutrophils (IVW OR = 1.430, 95%CI = 1.024–1.997, P = 0.038), were provide to be risk factors for NSCLC and squamous cell carcinoma subtype, respectively. Unfortunately, there were no same and significant effect in the ILCCO court, and the MR-PERSSO global tests revealed the existence of horizontal pleiotropy. Furthermore, the IVW method suggested no causal association between NSCLC subtype and other subtypes of circulating WBCs (Supplementary Table 2). Reverse MR analysis was performed to investigate reverse causality. There was no evidence of reverse causality between eosinophils and susceptibility to NSCLC or adenocarcinoma subtype (Supplementary Table 4). Because there was no causality between whole WBCs and NSCLC, we focused on the association between WBC subforms and NSCLC. Multivariable MR was then employed to determine whether genetic predisposition affected the correlation between circulating WBC subtypes and susceptibility of NSCLC as well as its subtypes. Our findings suggested that increased eosinophil counts persistently correlated with reduced risk of NSCLC (IVW OR = 0.865, 95% CI = 0.749–0.998, P = 0.047, Supplementary Table 5). Nevertheless, the protective effect of eosinophil on adenocarcinoma subtype was attenuated, and the relationship between other subtypes of WBCs and NSCLC was consistent with the results of MR analysis. The results of MR-Egger intercept indicated no evidence of horizontal pleiotropy (Supplementary Table 5). Assessing the protective role of eosinophils Considering the causal effect between eosinophil counts and NSCLC, we tempt to find the clinical evidence to prove the positive role of eosinophil. We firstly investigated whether eosinophil count served as a mediator between NSCLC and some clinical risk factors such as smoking, body mass, financial difficulties and income using two-step MR. Although we reduced the screening criteria, there showed no intersection SNPs between eosinophil count and body mass or incoming. The residual findings revealed no significant causal association between these clinical factors and eosinophil counts, and vice versa (Supplementary Table 6). And then we tempt to find the biological evidence of the protective role of eosinophil. We firstly mapped the mouse eosinophil scRNA-seq atlas (Fig. 3 A), and the cells were annotated according to markers including Siglecf, Il5ra, Ccr3, and Epx (Fig. 3 B). This atlas was composed of eosinophils separate from blood, bone marrow, and tissue (Fig. 3 C). And the eosinophils were divided into five clusters (Fig. 3 D). We speculated the developmental trajectories of the eosinophils (Fig. 3 E). The result revealed that the blood derived eosinophils were mainly mature cells, they were positioned at the penultimate stage of the developmental trajectory (Fig. 3 F). These cells can trigger immune activation through cellular response to chemokine and integrin-mediated signaling pathway (Fig. 3 G). These analyses indicated the protective anti-tumor role of eosinophils. We assessed tissue preference of the eosinophils via calculating the cell specific gene enrichment scores (Fig. 3 H). The lung adenocarcinoma (LUAD) showed the higher enrichment of eosinophils compared to lung squamous carcinoma (LUSC), which could explain the risk difference of Mendelian randomization analysis. And in the Anti-PD1 therapy RNA-seq court, better treatment response indicated higher eosinophil enrichment (Fig. 3 I). What’s more, elevated eosinophil infiltration was significantly associated with improved clinical outcomes in NSCLC and LUAD (Fig. 3 J, K). LUSC patients with high eosinophil infiltration showed worse prognosis (Fig. 3 L), which was also accordance with the Mendelian randomization risk disease preference. Taken together, eosinophil demonstrate significant anti-tumor activity in NSCLC, particularly in the LUAD subtype, through enhanced immunomodulatory functions. Its increased infiltration correlates with improved clinical outcomes, and its counts performed a protective role in NSCLC. Neutrophil defined an immunosuppressive in NSCLC We previously found that neutrophil count was a risk factor in the LUSC subtype (Fig. 2 A), but the underline mechanism was not revealed yet. We performed scRNA-seq analysis of the anti-PD1 treatment patients using the previously published dataset. According to the markers, the cells were clustered into T cell, B cell, mast cell, epithelial cell, neutrophil, plasmacytoid dendritic cell (pDC), myeloid cell and plasma cell (Fig. 4 A, Supplementary Fig. 1). With the treatment of anti-PD1, the patients had pathologic types included LUAD and LUSC, demonstrated objective responses to therapy, including partial response (PR) or stable disease (SD) (Fig. 4 B, C, D). We evaluated the cell specific pathologic preference of all cells, we found that although myeloid cells enriched higher in LUSC, but neutrophil had no significant enrichment difference between LUAD and LUSC (Fig. 4 E). Of all the immune cells, neutrophil demonstrated the most potent immunosuppressive capacity (Fig. 4 F). When responding to immune therapy, patients with stable disease showed profound neutrophil infiltration in both scRNA-seq data and RNA-seq data (Fig. 4 G, H). Among NSCLC, LUAD, and LUSC, higher percentages of neutrophil indicated worse survival rates. Consistent with the univariate MR result, this situation was most pronounced in LUSC (Fig. 4 I, J, K). In general, neutrophil was a risk factor in NSCLC, it inhibited the immune function and lead to poor immune response. Its higher infiltration level was negatively correlated with survival rate. These phenotypes were more significantly in LUSC subtype. Discussion Tumor progression involves many aspects, among which changes in circulating WBC count can help monitor tumor progression [ 21 ]. To investigate the causal relationship between circulating WBC and NSCLC, we conducted a large-scale gene cohort analysis using GWAS data. Univariable MR analysis, multivariate MR analysis and reverse MR analysis were also used to systematically indicate that elevated levels of eosinophils can protect against NSCLC. We found that only IVW showed significant protecting effect of eosinophils, the results of MR-Egger and weight median were not statistically significant. The situation remains stable in the ILCCO court. Although difference of algorithms and human populations will lead to result deviation. We still believe these results provide efficient evidence supporting the protective role of eosinophils in NSCLC. Our subsequent analysis demonstrates that peripheral blood eosinophils exhibit greater maturity than their bone marrow counterparts, along with enhanced chemokine-mediated immunoregulatory functions. These findings suggest that elevated eosinophil levels may serve as a favorable indicator for immune therapy and prognosis. Consistent with our findings, many reports have indicated a negative correlation between eosinophil count and the progression of other type of cancers, such as colorectal cancer and prostate cancer [ 22 , 23 ]. Blomberg et al . discovered that in breast cancer, eosinophils could collaborate with CD4 + T cells to inhibit immune checkpoints, thereby enhancing the response of immune cells against tumor cells [ 24 ]. Wong et al . documented an inverse relationship between quartiles of eosinophil count and the likelihood of lung adenocarcinoma [ 25 ]. It is well-known that eosinophils exert beneficial effects in allergic conditions and parasitic infections, including allergic rhinitis, asthma and schistosomiasis [ 26 , 27 ]. MR analyses also have indicated an association between eosinophil level and allergic diseases [ 28 , 29 ]. Furthermore, a summary review of studies on allergic diseases and cancer have reported that allergic diseases can decrease the incidence of NSCLC [ 30 ]. In contrast with eosinophil, neutrophil was indicated a risk role in univariate MR of training set, which mainly functioned in LUSC. But in the multivariable MR analysis and validation set, the impact of neutrophils on risk was not statistically significant. We first assume that the risk effect of neutrophils counts holds. And the further scRNA-seq analysis proved the immune inhibitory function. Patients in the anti-PD1-resistant group exhibited significantly elevated neutrophil counts compared to responsive counterparts. Notably, increased neutrophil infiltration correlated with poorer clinical outcomes, especially in LUSC. These findings align with established literature on neutrophil heterogeneity in cancer [ 31 ]. Importantly, tumor-infiltrating neutrophils predominantly exhibit a tumor-associated neutrophil (TAN) phenotype, demonstrating potent immunosuppressive activity. In contrast, peripheral blood neutrophils comprise both conventional neutrophils and TANs, potentially diluting their net prognostic impact. This compartment-specific differential effect may vary across patient populations [ 32 ]. Our study results demonstrated that eosinophils protect against the development of NSCLC, which may through modulating immune responses. In fact, eosinophil-derived neurotoxin (EDN) is an important molecule exerting anti-tumor effects [ 33 ]. Previous investigation has shown that IgE antibodies secreted by eosinophils can inhibit tumorigenesis [ 34 ]. Tanizaki et al . indicated that eosinophils play prognostic and/or predictive roles in patients with advanced NSCLC who previously did not respond to systemic therapy and were subsequently treated with nivolumab [ 35 ]. In a recent cancer and eosinophil review, it was determined that eosinophils could infiltrate into tumor tissue and, under specific conditions, produced and released numerous biologically active substances, including chemokines, enzymes, cytokines, and other molecules, and affected tumor progression [ 36 ]. Our results further demonstrate that neutrophils promote tumor progression through immunosuppressive mechanisms. Neutrophil inhibition significantly attenuates tumor growth and reduces malignant progression [ 37 ]. By characterizing the opposing roles of eosinophils (protective) and neutrophils (pro-tumorigenic), we can future identify more potential biomarkers for tumor progression and prognosis. These findings may enhance diagnostic precision in clinical settings. The distinct molecular targets of eosinophils and neutrophils offer promising avenues for personalized therapy. This dual-target strategy could improve treatment efficacy across varying disease stages and patient subtypes The potential causal effects of circulating white blood cells on NSCLC were monitored using MR methods. However, there are several limitations to this study. Firstly, public databases lack data on other types of circulating WBCs and NSCLC; therefore, we selected European ancestry for analysis. Thus, the causal effects in other populations remain unknown. Due to the limited number of obtained SNPs, the threshold was adjusted to P < 1 × 10 − 6 / P < 1 × 10 − 5 in variable screening for reverse MR and mediation MR, expanding the scope of SNP inclusion. This approach may lead to confounding effects. What’s more, due to limited study data, subgroup analyses based on gender, age, and other variables were not conducted. And the used IVs represent the final variation in WBC count; thus, short-term changes in eosinophils cannot be used for MR analysis of NSCLC progression. In the validation court of ILCCO, given the absence of NSCLC-specific datasets, we incorporated two additional lung cancer (LC) cohorts as surrogate populations for our analysis. While scRNA-seq and bulk RNA-seq analyses have revealed the pro-tumorigenic properties of neutrophils and tumor-suppressive effects of eosinophils, due to the deficiency of sample, some statistics may be not significantly enough, and further mechanistic studies are also required to validate these findings at the molecular level. In summary, our integrated genetic and biological analyses demonstrate that elevated eosinophil levels confer protection against NSCLC development with particularly pronounced effects in LUAD through enhanced immunostimulatory mechanisms. Neutrophil infiltration promotes NSCLC progression, showing stronger immunosuppressive activity in LUSC. The underlying biological mechanisms deserve further studies. Declarations Supplementary information Supplementary materials are included in Supplementary Table 1-6 and Supplementary Figure 1. Acknowledgements We are grateful to all individuals who participated in this research project. Author contributions Xiao Yang: Methodology, Formal analysis and Writing - review & editing; Wenyi Liu: Methodology, Data curation and Writing - original draft; Jiaqi Wang: Validation; Weifeng Xia: Methodology; Lanxiang Wu: Conceptualization and Funding acquisition. Data Availability The original contributions presented in the study are included in the article Supplementary Material, further inquiries can be directed to the corresponding author. Funding This work was supported by the Natural Science Foundation of China (No. 82073938, 82274023, 82373135), Youth Innovation in Future Medicine, Chongqing Medical University (No. W0093), and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (No. KJQN202200432). Competing interests The authors declare that they have no competing interests. References Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature. 2018;553(7689):446–54. Yu H, Li SB. Role of LINC00152 in non-small cell lung cancer. J Zhejiang Univ Sci B. 2020;21(3):179–91. Reck M, et al. Management of non-small-cell lung cancer: recent developments. Lancet. 2013;382(9893):709–19. Chen S, Wu S. Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data. J Med Internet Res. 2020;22(3):e17695. Nicholson LB. The immune system. Essays Biochem. 2016;60(3):275–301. 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Supplementary Files SupplementaryTable1.xlsx SupplementaryFigure1andTable26.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2025 Editor assigned by journal 07 May, 2025 Reviews received at journal 02 May, 2025 Reviews received at journal 01 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5773221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443752689,"identity":"ef8e44f7-07df-49b8-919f-cd6eeeb192c7","order_by":0,"name":"Xiao Yang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Yang","suffix":""},{"id":443752690,"identity":"92a9282d-b680-49ef-aca9-d9207378bd91","order_by":1,"name":"Wenyi Liu","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Liu","suffix":""},{"id":443752691,"identity":"c5ad7691-de6b-4353-901b-d3267836b6c8","order_by":2,"name":"Jiaqi Wang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Wang","suffix":""},{"id":443752692,"identity":"eaf5f53e-5265-4364-91f7-e3254c7120e6","order_by":3,"name":"Weifeng Xia","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Xia","suffix":""},{"id":443752693,"identity":"972e4f1a-94e9-470a-afa7-ce7ea0b72a14","order_by":4,"name":"Lanxiang Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACPhDxAcqRIEoLGxAzziBZCzMPaVokcsykbWqsow0OMB+8zcNgl0dYC88ZM+mcY+m5Gw6wJVvzMCQXE9bC3rtNOrfhMFALj5k0D8OBxAaCWph5t0lbgrXwfyNSC8gWRogtbERq4Tn/2bIH6JeZh9mMLecYJBPWwi+RlnjjR411bt/x5oc33lTYEdYCBcxgxMBgQKR6Bqj6UTAKRsEoGAXYAQBiujRL9mOR9QAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lanxiang","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-01-06 11:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5773221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5773221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81001211,"identity":"a9cca516-d3d7-42af-8928-ea5259e6f405","added_by":"auto","created_at":"2025-04-21 06:26:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218837,"visible":true,"origin":"","legend":"\u003cp\u003eDesign flowchart for this study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/a32fa7a4a4898df3a861823c.png"},{"id":81001215,"identity":"569b9837-f35b-457a-bc25-647e0fc69f22","added_by":"auto","created_at":"2025-04-21 06:26:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":446250,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate Mendelian randomization analysis for Circulating WBCs and NSCLC risk. Forest plot showing univariate MR results in the training FinnGen court (A) and validation ILCCO court (B).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/0cbeab8aaab85233a4110033.png"},{"id":81002431,"identity":"fedcae49-c299-410f-9448-a382443d92d1","added_by":"auto","created_at":"2025-04-21 06:34:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":608081,"visible":true,"origin":"","legend":"\u003cp\u003eSequencing analysis reveal the protect function of eosinophils. A. The UMAP plot of eosinophils distribution. B. The feature plot of eosinophil marker genes. C. The UMAP plot of eosinophil tissue origins. D. The UMAP plot of five eosinophil subclusters. E. The cell development locus of eosinophil. F. Distribution of developmental trajectories of eosinophils from different sources. G. Functional enrichment based on pseudotime. H. Eosinophil enrichment difference between LUAD and LUSC. I. Differences in eosinophilic infiltration in various treatment responses. Comparative survival analysis revealed eosinophil infiltration-dependent prognostic disparities across NSCLC (J), LUAD (K) and LUSC (L).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/d32af2b87d0fef0f8e6a2de1.png"},{"id":81001221,"identity":"8698630f-5692-4b0d-ad2f-6fa9d28cb6e5","added_by":"auto","created_at":"2025-04-21 06:26:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":292762,"visible":true,"origin":"","legend":"\u003cp\u003eBulk RNA-seq and scRNA-seq analysis of neutrophil. The UMAP plot of different cell types (A), pathological classification (B), treatment (C) and treatment response (D). E. The immune cell infiltration differences between LUAD and LUSC. F. The immune inhibitory functions of immune cells. Infiltration of neutrophils with different immunotherapy effects in scRNA-seq (G) and RNA-seq (H). Survival analysis based on neutrophil infiltration in NSCLC (I), LUAD (J) and LUSC (K).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/fa0218122513e916dabdcfe9.png"},{"id":81004373,"identity":"09e6deaa-7746-4630-b4e9-a8c768d3ac50","added_by":"auto","created_at":"2025-04-21 06:50:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1836303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/9b366432-e11f-495a-968f-fd8d5a18fb7a.pdf"},{"id":81004005,"identity":"d0e806f1-f952-4f74-8cbd-e64f6d518798","added_by":"auto","created_at":"2025-04-21 06:42:27","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":121156,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/c05f75ef99f72792e4ae0df2.xlsx"},{"id":81001210,"identity":"10c4a4f0-7f44-42f3-9d3b-cce73550f33e","added_by":"auto","created_at":"2025-04-21 06:26:27","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1293312,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1andTable26.doc","url":"https://assets-eu.researchsquare.com/files/rs-5773221/v1/4bf5e4bcf9bf175a916ec36a.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Association Between White Blood Cell Count and Relative Risk of Non-Small Cell Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the 2021 Global Cancer Report, lung cancer (LC) continues to be one of the most prevalent and lethal malignancies globally, both in terms of incidence and mortality rate. Comprising 85% of lung cancer, none small cell lung cancer (NSCLC) is the most prevalent type [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Based on histological types, NSCLC is primarily categorized into squamous cell carcinoma and adenocarcinoma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Due to the diverse pathological characteristic and pathogenesis, the development of NSCLC is individual, leading to divergent outcome. Given the current challenges, it is imperative to identify novel shared risk factors, and develop preventive and treatment strategies against NSCLC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStandard white blood cell (WBC) tests categorize WBCs into five subtypes: neutrophils, lymphocytes, monocytes, eosinophils, and basophils [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Alterations in WBC count have been shown to contribute to cancer susceptibility, oncogenesis, progression, and also associate with cancer mortality. For example, increased neutrophil content indicates poor treatment response in NSCLC. And the neutrophil-to-lymphocyte ratio (NLR) also associates with survival in NSCLC, ovarian cancer and well-differentiated thyroid cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are limitations of understanding the correlation between NSCLC risk and comprehensive WBC subtypes, and the underlying mechanism remain unsolved.\u003c/p\u003e \u003cp\u003eWith the continuous development of genomics, more and more genetic factors have been involved in cancer development [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Mendelian randomization (MR) technique has become popular because of its superior capacity to infer causality between features, a unique benefit compared to other techniques. It utilizes single-nucleotide polymorphisms (SNPs) formed through genetic inheritance and randomly distributed across different populations to assess the target effect. SNPs, as instrumental variables, are not affected by interference from the majority of confounding factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Bulk RNA-sequencing (RNA-seq) data can provide genetic alterations of functional mechanism to explain the novel phenomenon. Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of phenotypic heterogeneity at the cellular level, providing mechanistic insights unobtainable through bulk sequencing approaches. In this study, MR analyses were performed using both multiple MR methods to investigate potential genomic associations between WBC subtypes and NSCLC, the underlying mechanism was explored by multiple sequencing analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe sought to explore the correlation between different subtypes of WBCs and risk of NSCLC, and also assess the correlation between WBCs subtypes and NSCLC subtypes using genetic epidemiology alongside supplementary techniques. Firstly, univariate MR analysis took place to evaluation the causative impact of WBC counts on NSCLC as well as its histological subtypes in both training court and validation court. Secondly, a multivariable MR (MVMR) analysis was conducted by incorporating the counts of all five WBC subtypes into the model to assess the direct effects of each subtype. Additionally, reverse MR analysis was conducted in search of reverse causal relationships between them. Then, we utilized mediation MR to evaluate whether eosinophils acted as a mediator in clinical features. Finally, through integrated analysis of sequencing datasets, we mechanistically linked identified risk factors to immunotherapy efficacy and survival outcomes, providing a molecular framework for patient stratification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGWAS Sources\u003c/h3\u003e\n\u003cp\u003eThe summary statistics data for SNPs related to WBCs (basophils, entire WBCs, eosinophils, monocytes, lymphocytes, and neutrophils) (N\u0026thinsp;=\u0026thinsp;563946; year\u0026thinsp;=\u0026thinsp;2020; study IDs \"ieu-b-29, ieu-b-30, ieu-b-33, ieu-b-31, ieu-b-32, and ieu-b-34\") and SNPs related to smoking (N\u0026thinsp;=\u0026thinsp;44052; year\u0026thinsp;=\u0026thinsp;2022; study ID \"ieu-b-4857\"), body mass (N\u0026thinsp;=\u0026thinsp;681275; year\u0026thinsp;=\u0026thinsp;2018; study ID \"ieu-b-40\"), financial difficulties (N\u0026thinsp;=\u0026thinsp;459742; year\u0026thinsp;=\u0026thinsp;2018; study ID \"ukb-b-9776\") and incoming (N\u0026thinsp;=\u0026thinsp;397751; year\u0026thinsp;=\u0026thinsp;2018; study ID \"ukb-b-7408\") were downloaded from the IEU GWAS public database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The IEU GWAS database has extensively documented the quality control procedures and pipelines utilized in generating GWAS data. The GWAS summary statistics for training and validation, including NSCLC and its subtype-associated SNPs, were sourced from the FinnGen collection (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Version\u0026thinsp;=\u0026thinsp;r10) and the International Lung Cancer Consortium (ILCCO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ilcco.iarc.fr/\u003c/span\u003e\u003cspan address=\"https://ilcco.iarc.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Owing to the restricted availability of NSCLC-specific data in ILCCO, we employed two general lung cancer datasets as alternatives. All GWAS data mentioned above were obtained from European populations.\u003c/p\u003e\n\u003ch3\u003eInstrumental Variable Selection\u003c/h3\u003e\n\u003cp\u003eIn MR analysis and MVMR, instrumental variables (IVs) should satisfy the assumption of being \u0026ldquo;associated with the exposure\u0026rdquo;. Firstly, SNP selection was performed by setting a threshold of \u003cem\u003eP\u003c/em\u003e \u0026lt; 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, while also ensuring linkage disequilibrium conditions of R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001 and kb \u0026gt; 10000. Subsequently, we used sample size (N), independent SNP number (K), SNP effect size (β), minor allele frequency (MAF) and standard error (se) to calculate the F statistic, indicative of reliability. The computation adopted the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F\\:=\\:\\left[\\frac{R2}{1-R2}\\right]\\times\\:\\frac{N-K-1}{K}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R2\\:=\\:2\\:\\times\\:\\:MAF\\:\\times\\:\\:\\left(1-MAF\\right)\\times\\:\\:\\left(\\frac{\\beta\\:}{se}\\right)2\\)\u003c/span\u003e\u003c/span\u003e. SNPs with F \u0026lt; 10 were considered weak IVs and were excluded [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Next, potential confounding factors related to other traits were eliminated by querying the SNP on the PhenoScanner website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Supplementary Table\u0026nbsp;1). Outcome-related SNPs were extracted, and allele data linking exposure to outcome were obtained, with reciprocal SNPs being removed. The subsequent MR analysis was performed based on the remaining SNPs.\u003c/p\u003e \u003cp\u003eIn reverse MR analysis and mediation MR analysis, due to the limited selection of instruments, we employed relatively lenient selection criteria (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e or \u003cem\u003eP\u003c/em\u003e \u0026lt; 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.01, and kb \u0026gt;10000) to screen IVs. SNPs selected through this screening process were used for subsequent analysis.\u003c/p\u003e\n\u003ch3\u003eMendelian randomization\u003c/h3\u003e\n\u003cp\u003eThe Inverse variance weighted (IVW) approach in MR analysis integrates the causative effects of multiple genetic variants and is not affected by heterogeneity and pleiotropy. IVW provides the most precise results when the selected genetic variants are all valid IVs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Wald ratio method weights the impact of each variant on the risk of the target disease by its effect on the exposure. Subsequently, individual MR aggregated using random-effects inverse variance weighting analysis to generate a comprehensive summary estimate. Supplementary techniques, including weighted median and MR Egger, are used to assess the reliability and robustness of MR results [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The weighted median estimate is a statistical measure where individual estimates of MR are given weights corresponding to their precision, resulting in a median value. Conversely, MR-Egger regression provides a weighted linear regression, with SNP outcome associations being regressed on SNP exposure associations. In this study, MR analyses, encompassing IVW, weighted median, and MR Egger methods, were performed utilizing the R package \"TwoSampleMR\" [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious perceptual analysis and statistical techniques have been used in assessing the effectiveness of IVs. The Cochran's Q test was employed to calculate p-values to measure heterogeneity, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05 suggesting the random-effects framework with IVW, and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 necessitating the fixed-effects framework with IVW [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Horizontal pleiotropy was evaluated and outliers were identified using the MR-PRESSO global test and MR-Egger intercept. At a significance level of P 0.05, the average pleiotropic effect was evaluated using the MR Egger intercept. In cases where horizontal pleiotropy was significant, the MR-PRESSO outlier test was applied for correction by identifying and removing outliers, with a threshold of MR-PRESSO global test \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Moreover, bias in causal estimates was identified both before and after outlier removal using the MR-PRESSO distortion test. Every analysis was carried out utilizing the \"MR-PRESSO\" package in R [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Leave-one-out analysis was conducted in order to enhance the trustworthiness of the results [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultivariable MR represents a variant to conventional MR, employed to measure the causal effect of WBCs on NSCLC and its histological subtypes. For each exposure, we employed the \"Mendelian Randomization\" package to estimate the direct causal effects. Taking NSCLC and its subtypes as the exposure and WBCs as the outcome, reverse MR analysis was conducted to assess reverse causal relationships and demonstrate the absence of bidirectional causality between exposure and outcome. We utilized the same GWAS dataset previously referenced for reverse MR analysis. The Finnish Biobank provided cancer IVs. WBCs, as outcomes, were obtained from the IEU GWAS public source. A two-step mediation study utilizing two MR analyses linked by common variables was conducted to evaluate if these common variables moderate the relationship between clinical traits and cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In particular, we assessed whether WBC count is one of the pathways from smoking, body mass, financial difficulties or incoming index to NSCLC.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing data acquisition and preprocessing\u003c/h3\u003e\n\u003cp\u003eThe eosinophil scRNA-seq court with 11 samples and NSCLC immune therapy court composed of scRNA-seq data with 15 anti-PD1 treatment patients and RNA-seq were obtained in Gene Expression Omnibus (\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) with the identifiers GSEGSE182001 and GSE207422 respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The other RNA-seq data containing survival information was downloaded from The Cancer Genome Atlas Program (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with identifiers TCGA-LUAD and TCGA-LUSC. RNA-seq data were standardized to Transcripts Per Million (TPM) and subjected to log2 transformation for subsequent analysis. The scRNA-seq data underwent preprocessing by feature gene filtering, normalization, and subsequent dimensionality reduction and clustering via Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) utilizing the R package \"Seurat\" (Version 4.2.0). The cells were labeled with the marker genes associated with the study source.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDownstream analysis of RNA sequencing data\u003c/h2\u003e \u003cp\u003eThe gene enrichment scores analysis in scRNA-seq data and RNA-seq data were calculated via the function \"AddModuleScore\" in \"Seurat\" and R package \"GSVA\". The developmental progression of cells was delineated by \"Monocle2\". We performed survival analysis using R package \"survival\" and \"survminer\". All statistical analyses were executed utilizing R software version 4.3.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eScreening the causal association between circulating WBC counts and NSCLC\u003c/h2\u003e \u003cp\u003eTo assess the causal effects of WBC counts on the risk of NSCLC, we firstly conducted univariate MR analyses involving six circulating WBC subtypes (whole WBCs, basophils, eosinophils, monocytes, lymphocytes, and neutrophils). The results indicated that there were no significant correlations between whole WBC counts and NSCLC. But in the subtype analysis, the decreased level of eosinophils was closely correlated with increased risk of NSCLC (IVW OR\u0026thinsp;=\u0026thinsp;0.893, 95%CI\u0026thinsp;=\u0026thinsp;0.804\u0026ndash;0.994, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), which was more pronounced in adenocarcinoma (IVW OR\u0026thinsp;=\u0026thinsp;0.803, 95%CI\u0026thinsp;=\u0026thinsp;0.691\u0026ndash;0.961, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We examined the effectiveness of eosinophil counts in the ILCCO dataset, and effect of eosinophils in LC (IVW OR\u0026thinsp;=\u0026thinsp;0.897, 95%CI\u0026thinsp;=\u0026thinsp;0.791\u0026thinsp;\u0026minus;\u0026thinsp;1.017, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.091) and adenocarcinoma (IVW OR\u0026thinsp;=\u0026thinsp;0.833, 95%CI\u0026thinsp;=\u0026thinsp;0.677\u0026thinsp;\u0026minus;\u0026thinsp;1.026, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086) remained protective (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Although the results were not statistically different in the weighted median and MR-Egger analyses, the direction of their OR values remained consistent (Supplementary Table\u0026nbsp;2). According to supplementary Table\u0026nbsp;3, MR-Egger intercepts and MR-PERSSO global tests failed to detect horizontal pleiotropy in this causal relationship. The IVW method, the other subtypes including monocytes (IVW OR\u0026thinsp;=\u0026thinsp;1.104, 95%CI\u0026thinsp;=\u0026thinsp;1.014\u0026ndash;1.202, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) and neutrophils (IVW OR\u0026thinsp;=\u0026thinsp;1.430, 95%CI\u0026thinsp;=\u0026thinsp;1.024\u0026ndash;1.997, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), were provide to be risk factors for NSCLC and squamous cell carcinoma subtype, respectively. Unfortunately, there were no same and significant effect in the ILCCO court, and the MR-PERSSO global tests revealed the existence of horizontal pleiotropy. Furthermore, the IVW method suggested no causal association between NSCLC subtype and other subtypes of circulating WBCs (Supplementary Table\u0026nbsp;2). Reverse MR analysis was performed to investigate reverse causality. There was no evidence of reverse causality between eosinophils and susceptibility to NSCLC or adenocarcinoma subtype (Supplementary Table\u0026nbsp;4). Because there was no causality between whole WBCs and NSCLC, we focused on the association between WBC subforms and NSCLC. Multivariable MR was then employed to determine whether genetic predisposition affected the correlation between circulating WBC subtypes and susceptibility of NSCLC as well as its subtypes. Our findings suggested that increased eosinophil counts persistently correlated with reduced risk of NSCLC (IVW OR\u0026thinsp;=\u0026thinsp;0.865, 95% CI\u0026thinsp;=\u0026thinsp;0.749\u0026ndash;0.998, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047, Supplementary Table\u0026nbsp;5). Nevertheless, the protective effect of eosinophil on adenocarcinoma subtype was attenuated, and the relationship between other subtypes of WBCs and NSCLC was consistent with the results of MR analysis. The results of MR-Egger intercept indicated no evidence of horizontal pleiotropy (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessing the protective role of eosinophils\u003c/h2\u003e \u003cp\u003eConsidering the causal effect between eosinophil counts and NSCLC, we tempt to find the clinical evidence to prove the positive role of eosinophil. We firstly investigated whether eosinophil count served as a mediator between NSCLC and some clinical risk factors such as smoking, body mass, financial difficulties and income using two-step MR. Although we reduced the screening criteria, there showed no intersection SNPs between eosinophil count and body mass or incoming. The residual findings revealed no significant causal association between these clinical factors and eosinophil counts, and vice versa (Supplementary Table\u0026nbsp;6). And then we tempt to find the biological evidence of the protective role of eosinophil. We firstly mapped the mouse eosinophil scRNA-seq atlas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and the cells were annotated according to markers including Siglecf, Il5ra, Ccr3, and Epx (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This atlas was composed of eosinophils separate from blood, bone marrow, and tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). And the eosinophils were divided into five clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We speculated the developmental trajectories of the eosinophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The result revealed that the blood derived eosinophils were mainly mature cells, they were positioned at the penultimate stage of the developmental trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These cells can trigger immune activation through cellular response to chemokine and integrin-mediated signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These analyses indicated the protective anti-tumor role of eosinophils. We assessed tissue preference of the eosinophils via calculating the cell specific gene enrichment scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). The lung adenocarcinoma (LUAD) showed the higher enrichment of eosinophils compared to lung squamous carcinoma (LUSC), which could explain the risk difference of Mendelian randomization analysis. And in the Anti-PD1 therapy RNA-seq court, better treatment response indicated higher eosinophil enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). What\u0026rsquo;s more, elevated eosinophil infiltration was significantly associated with improved clinical outcomes in NSCLC and LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ, K). LUSC patients with high eosinophil infiltration showed worse prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL), which was also accordance with the Mendelian randomization risk disease preference. Taken together, eosinophil demonstrate significant anti-tumor activity in NSCLC, particularly in the LUAD subtype, through enhanced immunomodulatory functions. Its increased infiltration correlates with improved clinical outcomes, and its counts performed a protective role in NSCLC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNeutrophil defined an immunosuppressive in NSCLC\u003c/h2\u003e \u003cp\u003eWe previously found that neutrophil count was a risk factor in the LUSC subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), but the underline mechanism was not revealed yet. We performed scRNA-seq analysis of the anti-PD1 treatment patients using the previously published dataset. According to the markers, the cells were clustered into T cell, B cell, mast cell, epithelial cell, neutrophil, plasmacytoid dendritic cell (pDC), myeloid cell and plasma cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Supplementary Fig.\u0026nbsp;1). With the treatment of anti-PD1, the patients had pathologic types included LUAD and LUSC, demonstrated objective responses to therapy, including partial response (PR) or stable disease (SD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C, D). We evaluated the cell specific pathologic preference of all cells, we found that although myeloid cells enriched higher in LUSC, but neutrophil had no significant enrichment difference between LUAD and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Of all the immune cells, neutrophil demonstrated the most potent immunosuppressive capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). When responding to immune therapy, patients with stable disease showed profound neutrophil infiltration in both scRNA-seq data and RNA-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H). Among NSCLC, LUAD, and LUSC, higher percentages of neutrophil indicated worse survival rates. Consistent with the univariate MR result, this situation was most pronounced in LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI, J, K). In general, neutrophil was a risk factor in NSCLC, it inhibited the immune function and lead to poor immune response. Its higher infiltration level was negatively correlated with survival rate. These phenotypes were more significantly in LUSC subtype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTumor progression involves many aspects, among which changes in circulating WBC count can help monitor tumor progression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To investigate the causal relationship between circulating WBC and NSCLC, we conducted a large-scale gene cohort analysis using GWAS data. Univariable MR analysis, multivariate MR analysis and reverse MR analysis were also used to systematically indicate that elevated levels of eosinophils can protect against NSCLC. We found that only IVW showed significant protecting effect of eosinophils, the results of MR-Egger and weight median were not statistically significant. The situation remains stable in the ILCCO court. Although difference of algorithms and human populations will lead to result deviation. We still believe these results provide efficient evidence supporting the protective role of eosinophils in NSCLC. Our subsequent analysis demonstrates that peripheral blood eosinophils exhibit greater maturity than their bone marrow counterparts, along with enhanced chemokine-mediated immunoregulatory functions. These findings suggest that elevated eosinophil levels may serve as a favorable indicator for immune therapy and prognosis.\u003c/p\u003e \u003cp\u003eConsistent with our findings, many reports have indicated a negative correlation between eosinophil count and the progression of other type of cancers, such as colorectal cancer and prostate cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Blomberg \u003cem\u003eet al\u003c/em\u003e. discovered that in breast cancer, eosinophils could collaborate with CD4\u003csup\u003e+\u003c/sup\u003e T cells to inhibit immune checkpoints, thereby enhancing the response of immune cells against tumor cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Wong \u003cem\u003eet al\u003c/em\u003e. documented an inverse relationship between quartiles of eosinophil count and the likelihood of lung adenocarcinoma [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is well-known that eosinophils exert beneficial effects in allergic conditions and parasitic infections, including allergic rhinitis, asthma and schistosomiasis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. MR analyses also have indicated an association between eosinophil level and allergic diseases [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, a summary review of studies on allergic diseases and cancer have reported that allergic diseases can decrease the incidence of NSCLC [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In contrast with eosinophil, neutrophil was indicated a risk role in univariate MR of training set, which mainly functioned in LUSC. But in the multivariable MR analysis and validation set, the impact of neutrophils on risk was not statistically significant. We first assume that the risk effect of neutrophils counts holds. And the further scRNA-seq analysis proved the immune inhibitory function. Patients in the anti-PD1-resistant group exhibited significantly elevated neutrophil counts compared to responsive counterparts. Notably, increased neutrophil infiltration correlated with poorer clinical outcomes, especially in LUSC. These findings align with established literature on neutrophil heterogeneity in cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Importantly, tumor-infiltrating neutrophils predominantly exhibit a tumor-associated neutrophil (TAN) phenotype, demonstrating potent immunosuppressive activity. In contrast, peripheral blood neutrophils comprise both conventional neutrophils and TANs, potentially diluting their net prognostic impact. This compartment-specific differential effect may vary across patient populations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study results demonstrated that eosinophils protect against the development of NSCLC, which may through modulating immune responses. In fact, eosinophil-derived neurotoxin (EDN) is an important molecule exerting anti-tumor effects [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous investigation has shown that IgE antibodies secreted by eosinophils can inhibit tumorigenesis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Tanizaki \u003cem\u003eet al\u003c/em\u003e. indicated that eosinophils play prognostic and/or predictive roles in patients with advanced NSCLC who previously did not respond to systemic therapy and were subsequently treated with nivolumab [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In a recent cancer and eosinophil review, it was determined that eosinophils could infiltrate into tumor tissue and, under specific conditions, produced and released numerous biologically active substances, including chemokines, enzymes, cytokines, and other molecules, and affected tumor progression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our results further demonstrate that neutrophils promote tumor progression through immunosuppressive mechanisms. Neutrophil inhibition significantly attenuates tumor growth and reduces malignant progression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. By characterizing the opposing roles of eosinophils (protective) and neutrophils (pro-tumorigenic), we can future identify more potential biomarkers for tumor progression and prognosis. These findings may enhance diagnostic precision in clinical settings. The distinct molecular targets of eosinophils and neutrophils offer promising avenues for personalized therapy. This dual-target strategy could improve treatment efficacy across varying disease stages and patient subtypes\u003c/p\u003e \u003cp\u003eThe potential causal effects of circulating white blood cells on NSCLC were monitored using MR methods. However, there are several limitations to this study. Firstly, public databases lack data on other types of circulating WBCs and NSCLC; therefore, we selected European ancestry for analysis. Thus, the causal effects in other populations remain unknown. Due to the limited number of obtained SNPs, the threshold was adjusted to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e / \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e in variable screening for reverse MR and mediation MR, expanding the scope of SNP inclusion. This approach may lead to confounding effects. What\u0026rsquo;s more, due to limited study data, subgroup analyses based on gender, age, and other variables were not conducted. And the used IVs represent the final variation in WBC count; thus, short-term changes in eosinophils cannot be used for MR analysis of NSCLC progression. In the validation court of ILCCO, given the absence of NSCLC-specific datasets, we incorporated two additional lung cancer (LC) cohorts as surrogate populations for our analysis. While scRNA-seq and bulk RNA-seq analyses have revealed the pro-tumorigenic properties of neutrophils and tumor-suppressive effects of eosinophils, due to the deficiency of sample, some statistics may be not significantly enough, and further mechanistic studies are also required to validate these findings at the molecular level.\u003c/p\u003e \u003cp\u003eIn summary, our integrated genetic and biological analyses demonstrate that elevated eosinophil levels confer protection against NSCLC development with particularly pronounced effects in LUAD through enhanced immunostimulatory mechanisms. Neutrophil infiltration promotes NSCLC progression, showing stronger immunosuppressive activity in LUSC. The underlying biological mechanisms deserve further studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eSupplementary information\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSupplementary materials are included in Supplementary Table 1-6 and Supplementary Figure 1.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe are grateful to all individuals who participated in this research project.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eXiao Yang: Methodology, Formal analysis and Writing - review \u0026amp; editing; Wenyi Liu: Methodology, Data curation and Writing - original draft; Jiaqi Wang: Validation; Weifeng Xia: Methodology; Lanxiang Wu: Conceptualization and Funding acquisition.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China\u0026nbsp;(No. 82073938, 82274023, 82373135), Youth Innovation in Future Medicine, Chongqing Medical University (No. W0093), and the Scientific and Technological Research Program of Chongqing Municipal Education Commission (No. KJQN202200432).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHerbst RS, Morgensztern D, Boshoff C. 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[Research progress on eosinophils in lung cancer]. Zhonghua Yu Fang Yi Xue Za Zhi. 2023;57(11):1895\u0026ndash;900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeglia F et al. Analysis of classical neutrophils and polymorphonuclear myeloid-derived suppressor cells in cancer patients and tumor-bearing mice. J Exp Med, 2021. 218(4).\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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, WBC, mendelian randomization, eosinophils, single-cell RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-5773221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5773221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh abundance of eosinophils has been proved to associated with favorable disease progression in non-small cell lung cancer (NSCLC) in the previous observational studies, but the causal relationship remains unclear. It is also unclear whether white blood cell (WBC) counts are essential for the risk of NSCLC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing multiple methods of Mendelian randomization (MR), we assessed the causality of WBC count, particularly basophil, eosinophil, monocyte, lymphocyte, and neutrophil counts on the risk of NSCLC, which includes squamous carcinoma and adenocarcinoma. Single cell RNA-sequencing and RNA-sequencing analysis illustrate the underline mechanism of the causality and its biological effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariable MR analysis indicated the protective effect of elevated eosinophil counts on NSCLC and adenocarcinoma subtype. The protective effect of eosinophils persisted even after adjusting. The protective effect of functions mainly by immune activating, and it contribute to better survival and favorable response to immune therapy. Univariate MR analysis also states the risk role of neutrophil. Sequencing based analysis proved the immune inhibit functions of neutrophil, which lead to worse survival and immune treatment response.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study indicated a correlation between circulating eosinophil counts, neutrophil counts, and the development of NSCLC. And sequencing analysis confirm this relationship and illustrated the underline mechanism.\u003c/p\u003e","manuscriptTitle":"The Association Between White Blood Cell Count and Relative Risk of Non-Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 06:26:22","doi":"10.21203/rs.3.rs-5773221/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T10:19:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T10:18:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T10:26:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T17:01:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T14:10:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164082035995500460290328769715158136496","date":"2025-04-22T09:38:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96813079313224359124243637945397828827","date":"2025-04-18T07:03:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T09:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162629535973446748714593912640137722064","date":"2025-04-16T09:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121375748784437901773375161923027976292","date":"2025-04-16T09:38:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-16T09:09:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-16T06:12:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-03-31T04:01:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b33cbfa-ff24-48ca-bfa9-c7b89a88082a","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T10:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 06:26:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5773221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5773221","identity":"rs-5773221","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}