Genetic-Based Tools for Investigating Causal Associations between Immune Cells, Blood Metabolites, and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study

doi:10.21203/rs.3.rs-4790333/v1

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Research Article

Genetic-Based Tools for Investigating Causal Associations between Immune Cells, Blood Metabolites, and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-4790333/v1

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Background

Previous observational studies have suggested a potential link between immune cell and blood metabolite levels and lung cancer risk, but the causality remains unclear. We aimed to investigate this relationship using a two-sample Mendelian randomization (MR) study and to explore the potential mediation by blood metabolites.

Methods

Genome-wide association study (GWAS) exposure data were extracted from immune cell levels in 3757 Europeans and blood metabolite levels in 8192 Europeans and ultimately analyzed in integration with the GWAS dataset of European lung cancer cases containing 492,803 samples. The inverse variance weighting (IVW) method was mainly applied for MR analysis, and MR-Egger regression with MR residuals was used to assess the potential level pleiotropy. Heterogeneity was detected using Cochran's Q test. Reverse MR analyses were also performed to assess reverse causality.

Results

MR analysis conclusively identified 5 immune cell and 20 metabolite profiles as strongly causally associated with lung cancer risk (p < 0.01). In addition, reverse MR analysis and mediated Mendelian analysis revealed that one type of immune cell may mitigate the risk of developing lung cancer by influencing a specific blood metabolite-related metric: CD39 + secreting Tregs (OR: 0.958, 95% CI: 0.931–0.985, p = 0.002) and sphingomyelin (d18:2/14:0, d18:1/14:1) levels (OR: 1.176, 95% CI: 1.041–1.329, p = 0.009).

Conclusions

Our study confirmed a causal relationship between immune cells and lung cancer risk, which may be mediated by blood metabolites. These findings provide a basis for future investigations into targeted prevention strategies.

Mendelian randomization

Immune cells

Blood metabolites

Lung cancer

Risk factors

Lung cancer ranks among malignancies, with the highest incidence and mortality rates worldwide [1]. The five-year survival rate for lung cancer remains below 20% [1, 2]. Despite advancements in therapeutic approaches such as targeted therapy and immunotherapy, treatment resistance persists and poses a significant challenge [3]. Lung cancer symptoms are often nonspecific, and over 70% of patients are diagnosed at an advanced stage (International Association for the Study of Lung Cancer (IASLC) stage III or IV) [4]. Consequently, there is an urgent need to comprehend the mechanisms underlying lung cancer development and progression and to devise novel, safe, and highly effective treatment or prevention strategies.

The immune system plays a pivotal role in the development, infiltration, and metastasis of lung cancers. Immune cells serve to identify and eliminate malignant cells, thereby fulfilling an immunosurveillance role [5]. However, malignant cells can evade immune surveillance through various mechanisms [6]. Moreover, immune cells constitute a vital component of the immune microenvironment of lung cancer, encompassing diverse T and B cells [7]. Notably, the multifaceted roles of lung cancer-associated T-cells have been extensively studied. CD4 + Th1 cells, activated CD8 + T cells, and γδ-T cells typically participate in type I immune responses and are correlated with favorable prognosis in patients with lung cancer [8, 9]. As for B cells, proliferating B cells are observable in 35% of lung cancers and play a significant role in tumor development [10]. The interplay between immune cells and lung cancer is a complex and dynamic research topic. Nonetheless, the influence of immune cells on the risk of lung cancer remains unclear.

Metabolomics serves as a crucial complement to genomics, transcriptomics, and proteomics. Qualitative and quantitative analyses of metabolites provide a more direct and comprehensive understanding of the organismal functional states and pathophysiological changes. Numerous studies have underscored the close association between immune cells, metabolites, and cancer [11, 12]. Certain blood metabolites exhibit superior sensitivity and specificity in discriminatory capacity compared to classical biomarkers such as CEA, NSE, CYFRA21-1, and SCC [13], aiding in lung cancer screening and differential diagnosis [14]. Furthermore, some metabolites have been utilized as biomarkers to construct diagnostic models for lung cancer [15]. Hence, our study aimed to explore the potential role of blood metabolites in lung cancer risk and their mediating effects on lung cancer risk.

In epidemiology, Mendelian randomization (MR) serves as an analytical tool to investigate etiological inferences and infer relationships between risk factors (exposure) and outcomes [16]. However, whether immune cells can influence lung cancer risk by modulating blood metabolite levels remains unclear. Immune cells were selected as the exposure variable, lung cancer as the outcome variable, and blood metabolites as the mediator variables for Mendelian randomization (MR) analysis. Our objective was to explore potential causal relationships and lay a theoretical foundation for further investigation of the intricate mechanisms and risk factors associated with lung cancer.

2.1 Ethical Approval Statement

The summary data used in this study are available for downloads. All GWAS participating in this study obtained ethical approval from the respective institutions.

2.2 Study Planning

A two-step Mendelian randomization (MR) approach was employed to investigate the association between immune cells and genetically predicted lung cancer risk and to assess whether blood metabolites could mediate this association. All statistical analyses were conducted using publicly available genome-wide association studies (GWAS) data. Single nucleotide polymorphisms (SNPs) associated with immune cells and blood metabolites were used as independent variables (IVs). The flowchart of the study is shown in Fig. 1.

2.3 Immune Cells Data, Blood Metabolite Data, and Lung Cancer Outcomes Data Sources

Data for 731 immune cell traits (Ebi-a-GCST0001391 to Ebi-a-GCST0002121) were obtained from the GWAS catalogue (Genome-Wide Association Studies (GWAS) catalog (https://gwas.mrcieu.ac.uk/) [17]. These immune cell traits encompass seven groups: B cells, CDC, mature stage T cells, monocytes, myeloid cells, TBNK (B cells, natural killer cells, T cells), and Treg groups. The 731 immune cell traits included absolute cell (AC) counts (n = 118), relative cell (RC) counts (n = 192), median fluorescence intensity (MFI) reflecting the level of surface antigens (n = 389), and morphological parameters (MP) (n = 32). Blood metabolite data were obtained from independent GWAS data for 1091 blood metabolites and 309 metabolite ratios from 8192 individuals of European ancestry [18]. GWAS statistics for these 1400 metabolites were retrieved from the GWAS catalogue under accession number GCST90199621-GCST90201020. The lung cancer dataset analyzed in this study is available in GWAS Abstracts Data (https://gwas.mrcieu.ac.uk/). Lung Cancer: ebi-a- GCST90018875, containing a dataset of 492,803 sample sizes. All the participants in this study were of European descent.

2.4 Genetic Instrumental Variable (IV) Selection and Harmonization

Quality control of SNPs was conducted to obtain valid instrumental variables (IVs), quality control of single nucleotide polymorphisms (SNPs). SNPs meeting the following criteria were considered qualified IVs: (A) significant association with exposure factors, (B) not associated with factors potentially introducing confounding, and (C) statistically correlated with the outcome phenotype influenced by exposure [19]. SNPs that reached significance at the p < 1 × 10^-5 level were included as IVs. To address possible linkage disequilibrium (LD) between adjacent SNPs, a clustering method with r^2 = 0.001 and kb = 10,000 was employed. Subsequently, the F statistic was calculated to evaluate the strength of the selected IVs using the formula: F = R^2 × (N - k − 1) / [(1 - R^2) × k], where R^2 represents the proportion of exposed variation explained by IV, N is the sample size for genomic association analysis, and k is the number of IVs. Empirically, an F-statistic exceeding 10 indicates that results derived from valid instrumental variables are not significantly affected by weak instrumental bias, although it does not ensure adequate statistical power to test a specific hypothesis [20]. Following stringent screening, a set of high-quality IVs was obtained to underpin the subsequent causal inference analyses.

2.5 Statistical Analyses

Five methods were employed in MR analysis ("MR Egger" [21], "Weighted median" [22], "Inverse variance weighted (IVW)" [23], "Simple mode" [24], and "Weighted mode" [22]), with IVW as the primary method for causal estimation due to its superior accuracy and robustness. A significance threshold of p < 0.05 denoted a significant correlation between exposure and outcome. Cochran's Q statistic and the corresponding p-values were used to assess heterogeneity among the selected IVs. To address potential horizontal pleiotropy, the commonly used MR-Egger method was applied, which indicated the presence of horizontal pleiotropy if the intercept term was statistically significant. Additionally, the MR-PRESSO method from the MR-PRESSO package [25] was used to identify and exclude any potential outliers with horizontal pleiotropic effects that could significantly impact the estimation results. Scatterplots and funnel plots were used for data evaluation. The scatterplot demonstrated that outliers had no significant effect on the results, whereas the funnel plot indicated the absence of heterogeneity and confirmed correlation robustness. Furthermore, reverse MR analyses were conducted to assess the potential reverse causality. All analyses were performed using R version 4.3.1 (http://www.Rproject.org), along with the "TwoSampleMR," "ieugwasr," and "gwasglue" gwasglue software packages.

3.1 Total Effect of Immune Cells on Lung Cancer

Initially, 18,621 SNPs (immune cells; genome-wide locus significance level, p < 1 × 10^-5) were identified as instrumental variables (IVs) from the large-scale GWAS. The F-statistics of these IVs consistently exceeded the threshold of 10, indicating no indication of weak instrument bias. Subsequently, the IVW method was used to discern causal relationships between the five immune cell traits and traits associated with lung cancer from a multitude of immune cells (Table S1). The results of the IVW analyses indicated that genetic susceptibility to lung cancer increased for certain immune cell types: IgD- CD38dim B cell (OR = 1.050, 95% CI = 1.016–1.086, P = 0.0039) and CD25 on CD39 + CD4 + T cells (OR = 1.034, 95% CI = 1.009–1.059, P = 0.0062). Conversely, a decreased risk of lung cancer was observed for other immune cell fractions associated with genetically predicted abundance: CD39 + Treg cells (OR: 0.958, 95% CI: 0.931–0.985, p = 0.0023), IgD + B cells (OR: 0.900, 95% CI: 0.835–0.970, p = 0.0061), and CD45 on CD33br HLA DR + CD14dim cells (OR: 0.962, 95% CI: 0.937–0.988, p = 0.0039). The scatterplot and forest plot of the effect of CD39 + Treg cell-related SNPs on lung cancer are presented, demonstrating the strength of the association and effect value between these SNPs and lung cancer risk, respectively (Figs. 2 and S1). Cochran's Q-statistic and MR-Egger's intercept test indicated no heterogeneity or horizontal pleiotropy in the MR analysis (Table S2). Moreover, none of the SNPs severely violated the overall effect of immune cells on lung cancer in leave-one-out sensitivity analysis (Figure S2). In the funnel plot, the SNP dots were balanced in the results, suggesting no significant heterogeneity in the data (Figure S3).

3.2 Reverse MR Analysis

Previously, we identified five immune cell characteristics that were strongly associated with the risk of lung cancer. Subsequently, we conducted reverse MR analysis using lung cancer as the exposure factor and immune cells as the outcome. The results revealed that, except for IgD + B cells (OR = 0.850, 95% CI [0.725, 0.996], P = 0.045), the remaining four immune cell types did not show significant results. Therefore, we retained these four negative results for further analyses (Table S3).

3.3 Causal Effects of Blood Metabolites on Lung Cancer

As previously described, we screened 34,843 SNPs (blood metabolites; genome-wide significance level, p < 1 × 10 − 5). Subsequently, using the IVW method, we identified 20 causal relationships between the blood metabolite characteristics and lung cancer risk.

These include 13 blood metabolite characteristics associated with a relatively high risk of lung cancer: Adrenate (22:4n6) levels, Sphingomyelin (d18:1/18:1, d18:2/18:0) levels, Pantoate levels, Sphingomyelin (d18:2/14:0, d18:1/14:1) levels, Ethylparaben sulfate levels, Oleate/vaccenate (18:1) levels, 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) levels, 1-myristoyl-2-arachidonoyl-GPC (14:0/20:4) levels, Eicosapentaenoate (EPA; 20:5n3) levels, Two unknown metabolites (X-12407 levels, X-24556 levels), Arachidonate (20:4n6) to pyruvate ratio, Arachidonate (20:4n6) to linoleate (18:2n6) ratio, and 7 blood metabolite characteristics associated with a relatively low risk of lung cancer: 2-methoxyresorcinol sulfate levels, P-cresol glucuronide levels, Delta-CEHC levels, Creatine levels, Oleoyl-linoleoyl-glycerol (18:1 to 18:2) to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) ratio, Adenosine 5'-monophosphate (AMP) to glutamate ratio, and Retinol (Vitamin A) to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) ratio. The p-values for these blood metabolites were all less than 0.01, and the summary calculation results are shown in Table S4. In this section, we focus on showcasing scatter and forest plots that highlight the influence of SNPs associated with sphingomyelin (d18:2/14:0, d18:1/14:1) levels on lung cancer. These plots illustrate the strength of association and effect values between blood metabolite-related SNPs and the risk of lung cancer (Figs. 3 and S4).

3.4 Effects of Immune Cells on Blood Metabolites

Following the identification of four immune cells and 20 blood metabolite profiles critical for lung cancer, we investigated their causal roles. Using the IVW method in the MR analysis, we found significant correlations between immune cells and blood metabolites. Specifically, CD39 + Treg cells were highly correlated with sphingomyelin (d18:2/14:0, d18:1/14:1) levels (OR = 0.977, 95% CI [0.957–0.998], P = 0.035) and the adenosine 5'-monophosphate (AMP) to glutamate ratio (OR = 0.974, 95% CI [0.950–0.998], P = 0.033). Additionally, IgD - CD38dim B cells were significantly correlated with sphingomyelin (d18:1/18:1, d18:2/18:0) levels (OR = 1.026, 95% CI [1.000–1.053], P = 0.049). No heterogeneity or horizontal pleiotropy was observed in any of these results and there was no dominant effect of a particular SNP on the causal estimates (Table S5 and Figure S5). The funnel plot indicated balanced SNPs across results, suggesting no significant heterogeneity in the data (Figure S6). The scatterplot and forest plot illustrate the effect of CD39 + Treg cell-associated SNPs on Sphingomyelin (d18:2/14:0, d18:1/14:1) levels, demonstrating the association strength and effect value between immune cell-associated SNPs and blood metabolites (Figs. 4 and S7).

3.5 Immune cells mediate the impact of blood metabolites on the risk of lung cancer occurrence.

After analyzing the causal effects of immune cells on lung cancer and blood metabolites in our dataset, we conducted mediation analyses to characterize the mediating effects of two sphingomyelin levels and the adenosine 5'-monophosphate (AMP) to glutamate ratio on the association between immune cells and lung cancer. The mediation effect of sphingomyelin (d18:2/14:0, d18:1/14:1) levels in the causal pathway from CD39 + Treg cells to lung cancer was − -0.00369 (95% CI [-0.00716, -0.000218]; P = 0.037, Table S6), accounting for 8.52% of the total effect. Additionally, the mediation effect of the adenosine 5'-monophosphate (AMP) to glutamate ratio in the causal pathway from CD39 + Treg cells to lung cancer was 0.00466 (95% CI [0.000321, 0.00899]; P = 0.035, Table S6), accounting for − 10.8% of the total effect. However, Sphingomyelin (d18:1/18:1, d18:2/18:0) levels mediated 0.00368 (95% CI [0.0000423, 0.0074]; p = 0.052, Table S6) of IgD- CD38dim B cells, with a mediating effect validation resulting in a p-value greater than 0.05. Therefore, this was not further analyzed in this study. Here, we focus on showcasing a forest plot delineating the impact of CD39 + Treg cells on lung cancer occurrence via the modulation of sphingomyelin (d18:2/14:0, d18:1/14:1) levels. (Fig. 5).

Previous studies have firmly established the pivotal role of immune cells in lung cancer development [26, 27]. Additionally, blood metabolites have been shown to modulate tumor growth by altering the metabolic milieu of the tumor microenvironment, potentially influencing cancer cell metabolism [28]. Some studies have indicated that lung tumors can be sustained by metabolites such as lactate [29]. However, whether immune cells exert a direct or indirect influence on lung cancer risk through blood metabolites remains an intriguing question.

In this study, we conducted a two-sample Mendelian randomization (MR) analysis and revealed a significant causal relationship between the five types of immune cells and lung cancer (IgD- CD38dim B cell, CD39 + Treg cells, IgD + B cells, CD25 on CD39 + CD4 + T cells, and CD45 on CD33br HLA DR + CD14dim cells). Subsequently, the causal effects on 20 blood metabolite characteristics were analyzed using the IVW method. Notably, only CD39 + Treg cells exhibited a robust correlation with sphingomyelin (d18:2/14:0, d18:1/14:1) levels, and a significant impact on lung cancer risk.

A previous Mendelian study highlighted immune-mediated genetic pathways that lead to impaired lung function and heightened susceptibility to lung cancer [30], focusing primarily on lung respiratory markers. However, our investigation specifically focused on the direct effect of immune cells on lung cancer risk, revealing that elevated levels of CD39 + secretory Treg cells are associated with a reduced risk of lung cancer. Previous research has demonstrated significantly higher CD39 expression in tumor-infiltrating tissues, such as ovarian, pancreatic, and testicular tumors [31], with CD39 exerting profound regulatory effects on tumor cell growth, differentiation, invasion, and migration [32–34]. Nevertheless, research on the role of CD39+-secreting Treg cells in lung cancer development is currently limited, and conclusive findings are yet to be reached. Simultaneously, some studies indicate that cancer manifests only when Treg function is weakened, failing to effectively suppress inflammation and restore epithelial health [35]. This suggests that Tregs are capable of preventing inflammation-related carcinogenesis, which may support the interpretation of this study. CD39 + secretory Tregs play a role in mitigating inflammation. Considering that tumor development frequently coincides with local or systemic inflammation, CD39 + secretory Treg cells may hinder the development of precancerous lesions by suppressing inflammatory responses in the lungs, thereby reducing the conducive microenvironment for lung cancer development.

Sphingomyelin (SM) is a key component of the hydrophobic matrix in mammalian membranes and is predominantly located within the plasma membrane, the trans-Golgi network, and lysosomes [36]. Studies have underscored both the enrichment of SM and its pro-tumorigenic effects in various cancers, with SM biosynthesis emerging as a downstream target of oncogenes and modulator of oncogenic activity. For instance, the oncogene Bcr-abl, implicated in chronic myeloid leukemia (CML) relapse, has been shown to upregulates SMS1 expression and activity in CML cell lines [37]. Moreover, SM enrichment has been documented in hepatocellular carcinomas and brain tumours [38, 39]. However, the role of SM biosynthesis is multifaceted, with reports highlighting its antitumorigenic effects [40]. In our study, we observed that elevated sphingomyelin levels (d18:2/14:0, d18:1/14:1) were associated with an increased risk of lung cancer, supporting its pro-tumorigenic perspective. Additionally, our findings revealed that sphingomyelin (d18:2/14:0, d18:1/14:1) levels partially mediated the association between CD39 + Treg cells and lung cancer risk (mediated proportion, approximately 8.52%). These results suggest that CD39 + Tregs may modulate the risk of lung cancer development by influencing sphingomyelin (d18:2/14:0, d18:1/14:1) levels, shedding light on a novel tumorigenesis mechanism.

Nevertheless, we acknowledge the wide confidence interval for the mediating effect of sphingomyelin (16.5% and 0.503%), indicating the involvement of other unknown mediators in the relationship between CD39 + Treg cells and lung cancer risk. Future investigations should explore additional metabolic, signal transduction, or immunoregulatory pathways through which CD39 + Treg cells may affect lung cancer development and progression.

In this study, we investigated the causal effects using a Mendelian randomization (MR) design. This approach mimics a randomized controlled trial in an observational setting, offering cost-effectiveness and mitigating the risk of reverse causal effects. However, this study has several limitations. First, potential heterogeneity and horizontal pleiotropy were challenging to assess adequately, which may have affected the robustness of our findings. Second, the generalizability of our conclusions was constrained by the exclusive use of data from a European population, warranting validation in other populations to enhance the external validity of our results. Third, the mediated proportion of adenosine 5'-monophosphate (AMP) to glutamate ratio was found to be negative. Considering the possibility that this proportion may independently influence the outcome rather than serve as a mediating factor, this result was not extensively discussed in the article. Additionally, the absence of demographic information, such as gender and ethnicity, in the original dataset hindered our ability to perform subgroup analyses, limiting the comprehensive understanding of potential effect modifiers.

In conclusion, our findings contribute to a deeper understanding of the mechanism underlying the role of CD39 + Treg cells in lung carcinogenesis and highlight sphingomyelin as a potentially important mediator. This discovery unveils a novel target for intervention in lung cancer by targeting the Treg or Sphingomyelin metabolism-related pathways. Further research is warranted to elucidate the molecular mechanisms involved and to facilitate the translation of our findings into clinical practice.

SNPs

Single Nucleotide Polymorphisms

IVs

Instrumental Variables

Mendelian Randomization

GWAS

Genome-Wide Association Study

IVW

Inverse Variance Weighting

Odds Ratio

Confidence Interval

Absolute Cell

Relative Cell

MFI

Median Fluorescence Intensity

Morphological Parameters

TBNK

T cells, B cells, Natural Killer cells

Treg

Regulatory T cells

Linkage Disequilibrium

CML

Chronic Myeloid Leukemia

SMS1

Sphingomyelin Synthase 1

Sphingomyelin

AMP

Adenosine 5'-Monophosphate

CEA

Carcinoembryonic Antigen

NSE

Neuron-Specific Enolase

SMS

Sphingomyelin Synthase

CYFRA21

1-Cytokeratin 19 Fragment

SCC

Squamous Cell Carcinoma Antigen

IASLC

International Association for the Study of Lung Cancer

Conflict of Interest

The authors disclose no conflicts of interest.

Funding

This work was supported by grants from the China Scholarship Council and University of Manchester.

Author Contribution

Conceptualization, Huang Yangyu; Data curation, Huang Yangyu and Guan Xu Chen; Formal analysis, Guan Xu Chen; Funding acquisition, Adam Hurlstone; Investigation, Huang Yangyu; Methodology, Huang Yangyu; Project administration, Adam Hurlstone; Resources, Huang Yangyu and Guan Xu Chen; Software, Huang Yangyu and Guan Xu Chen; Supervision, Adam Hurlstone; Validation, Huang Yangyu and Guan Xu Chen; Writing – original draft, Huang Yangyu; Writing – review & editing, Huang Yangyu, Guan Xu Chen and Adam Hurlstone.

Acknowledgement

I express my sincere gratitude to my supervisor Prof. Adam Hurlstone for his guidance, the University of Manchester for resources, and the China Scholarship Council for financial support. We also thank colleagues and friends who contributed through collaboration and discussions.

Availability of data and materials

The datasets analyzed in the present study are available in GWAS summary data (https://gwas.mrcieu.ac.uk/; lung cancer: ebi-a- GCST90018875; 731 immune cell traits: ebi-a-GCST0001391 to ebi-a-GCST0002121). 1400 blood metabolites traits: GCST90199621-GCST90201020).

Sung HF, J.Siegel RL, Soerjomataram I, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. https://doi.org/10.3322/caac.21660.
Global Burden of Disease, Cancer C, Fitzmaurice C, Abate D, Abbasi N, Abbastabar H, Abd-Allah F, Abdel-Rahman O, et al. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2019;5:1749–68. https://doi.org/10.1001/jamaoncol.2019.2996.
Yin X, Liao H, Yun H, Lin N, Li S, et al. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol. 2022;86:146–59. https://doi.org/10.1016/j.semcancer.2022.08.002.
Rotow J, Bivona TG. Understanding and targeting resistance mechanisms in NSCLC. Nat Rev Cancer. 2017;17:637–58. https://doi.org/10.1038/nrc.2017.84.
Tartour E, Zitvogel L. Lung cancer: potential targets for immunotherapy. Lancet Respir Med. 2013;1:551–63. .https://doi.org/10.1016/S2213-2600(13)70159-0.
Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol. 2002;3(11):991–8. https://doi.org/10.1038/ni1102-991.
Remark R, Becker C, Gomez JE, Damotte D, Dieu-Nosjean MC, Sautes-Fridman C, et al. The non-small cell lung cancer immune contexture. A major determinant of tumor characteristics and patient outcome. Am J Respir Crit Care Med. 2015;191:377–90. https://doi.org/10.1164/rccm.201409-1671PP.
Bremnes RM, Busund LT, Kilvaer TL, Andersen S, Richardsen E, Paulsen EE, et al. The Role of Tumor-Infiltrating Lymphocytes in Development, Progression, and Prognosis of Non-Small Cell Lung Cancer. J Thorac Oncol. 2016;11:789–800. https://doi.org/10.1016/j.jtho.2016.01.015.
Schalper KA, Brown J, Carvajal-Hausdorf D, McLaughlin J, Velcheti V, Syrigos KN, et al. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J Natl Cancer Inst. 2015. 107.https://doi.org/10.1093/jnci/dju435.
Wang SS, Liu W, Ly D, Xu H, Qu L, Zhang L. Tumor-infiltrating B cells: their role and application in anti-tumor immunity in lung cancer. Cell Mol Immunol. 2019;16:6–18. https://doi.org/10.1038/s41423-018-0027-x.
Guerra L, Bonetti L, Brenner D. Metabolic Modulation of Immunity: A New Concept in Cancer Immunotherapy. Cell Rep. 2020;32:107848. https://doi.org/10.1016/j.celrep.2020.107848.
Hurley HJ, Dewald H, Rothkopf ZS, Singh S, Jenkins F, Deb P, et al. Frontline Science: AMPK regulates metabolic reprogramming necessary for interferon production in human plasmacytoid dendritic cells. J Leukoc Biol. 2021;109:299–308. https://doi.org/10.1002/JLB.3HI0220-130.
Liu Z, Wang L, Gao S, Xue Q, Tan F, Li Z, Gao Y. Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer. Int J Surg. 2023;109:297–312. https://doi.org/10.1097/JS9.0000000000000006.
Xu R, Wang J, Zhu Q, Zou C, Wei Z, Wang H, et al. Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer. Biomark Res. 2023;11:71. https://doi.org/10.1186/s40364-023-00497-2.
Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin. 2021;71:333–58. https://doi.org/10.3322/caac.21670.
Sekula P, Del Greco MF, Pattaro C, Kottgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27:3253–65. https://doi.org/10.1681/ASN.2016010098.
Orru V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, Sole G, et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet. 2020;52:1036–45. https://doi.org/10.1038/s41588-020-0684-4.
Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet. 2023;55:44–53. https://doi.org/10.1038/s41588-022-01270-1.
Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2019;4:186. https://doi.org/10.12688/wellcomeopenres.15555.3.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. .https://doi.org/10.1136/bmj.k601.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. https://doi.org/10.1093/ije/dyv080.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14. https://doi.org/10.1002/gepi.21965.
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. https://doi.org/10.1002/gepi.21758.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46:1985–98. https://doi.org/10.1093/ije/dyx102.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. https://doi.org/10.1038/s41588-018-0099-7.
Weeden CE, Gayevskiy V, Marceaux C, Batey D, Tan T, Yokote K, et al. Early immune pressure initiated by tissue-resident memory T cells sculpts tumor evolution in non-small cell lung cancer. Cancer Cell. 2023;41:837–52. e836.https://doi.org/10.1016/j.ccell.2023.03.019.
O'Callaghan DS, O'Donnell D, O'Connell F, O'Byrne KJ. The role of inflammation in the pathogenesis of non-small cell lung cancer. J Thorac oncology: official publication Int Association Study Lung Cancer. 2010;5(12):2024–36. https://doi.org/10.1097/jto.0b013e3181f387e4.
Lien EC, Westermark AM, Zhang Y, Yuan C, Li Z, Lau AN, et al. Low glycaemic diets alter lipid metabolism to influence tumour growth. Nature. 2021;599:302–7. https://doi.org/10.1038/s41586-021-04049-2.
Faubert B, Li KY, Cai L, Hensley CT, Kim J, Zacharias LG, et al. Lactate Metabolism Hum Lung Tumors Cell. 2017;171:358–71. e359.https://doi.org/10.1016/j.cell.2017.09.019.
Kachuri L, Johansson M, Rashkin SR, Graff RE, Bosse Y, Manem V, et al. Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility. Nat Commun. 2020;11:27. https://doi.org/10.1038/s41467-019-13855-2.
Bastid J, Regairaz A, Bonnefoy N, Dejou C, Giustiniani J, Laheurte C, et al. Inhibition of CD39 enzymatic function at the surface of tumor cells alleviates their immunosuppressive activity. Cancer Immunol Res. 2015;3:254–65. https://doi.org/10.1158/2326-6066.CIR-14-0018.
Cai XY, Wang XF, Li J, Dong JN, Liu JQ, Li NP, et al. High expression of CD39 in gastric cancer reduces patient outcome following radical resection. Oncol Lett. 2016;12:4080–6. https://doi.org/10.3892/ol.2016.5189.
Kunzli BM, Berberat PO, Giese T, Csizmadia E, Kaczmarek E, Baker C, et al. Upregulation of CD39/NTPDases and P2 receptors in human pancreatic disease. Am J Physiol Gastrointest Liver Physiol. 2007;292:G223–230. https://doi.org/10.1152/ajpgi.00259.2006.
Sun X, Wu Y, Gao W, Enjyoji K, Csizmadia E, Muller CE, et al. CD39/ENTPD1 expression by CD4 + Foxp3 + regulatory T cells promotes hepatic metastatic tumor growth in mice. Gastroenterology. 2010;139:1030–40. https://doi.org/10.1053/j.gastro.2010.05.007.
Erdman SE, Poutahidis T. Cancer inflammation and regulatory T cells. Int J Cancer. 2010;127(4):768–79. https://doi.org/10.1002/ijc.25430.
Goni FM, Sphingomyelin. What is it good for? Biochem Biophys Res Commun. 2022;633:23–5. .https://doi.org/10.1016/j.bbrc.2022.08.074.
Marien E, Meister M, Muley T, Fieuws S, Bordel S, Derua R, et al. Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles. Int J Cancer. 2015;137:1539–48. https://doi.org/10.1002/ijc.29517.
Li Z, Guan M, Lin Y, Cui X, Zhang Y, Zhao Z, Zhu J. Aberrant Lipid Metabolism in Hepatocellular Carcinoma Revealed by Liver Lipidomics. Int J Mol Sci. 2017;18. https://doi.org/10.3390/ijms18122550.
Sun GY, Leung BS. Phospholipids and acyl groups of subcellular membrane fractions from human intracranial tumors. J Lipid Res. 1974;15:423–31. https://doi.org/10.1016/s0022-2275(20)36791-2.
Brandan YR, Guaytima EDV, Favale NO, et al. The inhibition of sphingomyelin synthase 1 activity induces collecting duct cells to lose their epithelial phenotype. Biochim Biophys Acta Mol Cell Res. 2018;1865:309–22. https://doi.org/10.1016/j.bbamcr.2017.11.004.

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Genetic-Based Tools for Investigating Causal Associations between Immune Cells, Blood Metabolites, and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study

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