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The causal relationship between white blood cell counts and hepatocellular carcinoma: a Mendelian randomization study



Most of hepatocellular carcinoma (HCC) arises on the background of chronic inflammation. The presence of infiltrating inflammatory cells is associated with tumour initiation, progression and clinical response to treatment. The influence of white blood cell (WBC) subtype counts on HCC progression remains unclear.


In this study, we performed a Mendelian randomization (MR) study with the validation of two datasets. The summary data for WBC counts were extracted from a recent large GWAS of individuals of European ancestry. The GWAS data related to HCC were obtained from the UK Biobank (UKB). Univariable and multivariable MR analyses were used to identify risk factors genetically associated with HCC risks.


In the discovery dataset, multivariable MR analysis revealed that sum basophil neutrophil counts had an independent causal effect on the occurrence of HCC, with the sum basophil neutrophil counts as follows: (OR = 0.437, P = 0.003, CI 0.252–0.757). Similarly, in the validation dataset, total basophil neutrophil counts were also been identified as an independent risk factor for HCC. The sum basophil neutrophil counts were as follows: (OR = 0.574, P = 0.021, CI 0.358–0.920).


In the European population, genetically predicted lower total basophil neutrophil counts might be an independent risk factor for HCC.


Liver cancer is the fourth leading cause of cancer-related deaths worldwide [1]. Hepatocellular carcinoma (HCC), which accounts for more than 80% of primary liver cancer, is an aggressive tumour that frequently occurs in the setting of cirrhosis and chronic liver disease [2, 3]. For HCC patients, surgical resection is one of the most effective treatments. Despite improvements in treatment strategies, the 5-year survival rate of HCC patients remains unsatisfying compared with that of other cancers patients [4].

Various inflammatory factors play an important role in tumour growth, progression, angiogenesis, and metastases. Notably, white blood cell (WBC) counts are widely accepted biomarkers of systematic inflammation. WBCs mainly comprised five subtypes, including neutrophils, lymphocytes, monocytes, basophils and eosinophils. Inflammatory markers derived from blood samples, such as the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR) and platelet-to-lymphocyte ratio (PLR), have been identified as potentially valuable prognostic markers in patients with various types of cancer, including HCC [5,6,7,8]. For example, pretreatment peripheral neutrophils, lymphocytes, and monocytes are reported to be independently associated with the outcomes of HCC patients [9]. Growing evidence shows that neutrophils play an important role in the HCC pathogenesis such as tumorigenesis, local tumour progression and metastasis [10]. Several clinical studies demonstrated that elevated neutrophil count and increased NLR are markers of advanced disease, poor prognosis and poor response to therapy in HCC [11,12,13,14]. In regard to causal inference, observational studies may be biased by confounding factors and reverse causality. Since the causal associations between WBC subtypes and HCC risk have not been thoroughly investigated, identifying host factors predisposing individuals to HCC is urgently needed to improve primary prevention and develop treatment strategies.

Mendelian randomization (MR) studies, which use genes as instrumental variables (IVs) to research disease associations, can effectively solve the confounding and reverse causation associated with traditional observational studies [15, 16]. Because genetic variations are randomly inherited from parents to offspring during pregnancy, these genetic variations are unlikely to be affected by potential confounding factors and reverse causality. Genome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with HCC-related traits and WBC subtypes [17,18,19], which creates an opportunity to use the MR approach to test genetic and potential causal relationships between WBC subtype count and HCC risk.

Our present study attempted to use MR analysis to identify SNPs strongly related to blood WBC subtype counts to evaluate the causal association between WBC subtypes and HCC risk.


Summarized statistics of WBC counts from a genome-wide association study (GWAS)

The GWAS summary statistics of the WBC counts in our study included 8 phenotypes: eosinophil counts, basophil counts, neutrophil counts, lymphocyte counts, monocyte counts, sum eosinophil basophil counts, sum neutrophil eosinophil counts and sum basophil neutrophil counts. The WBC count-related SNPs were obtained from a recent large GWAS of European ancestry patients. The sum of white blood cell counts was defined as follows: first, the corresponding white blood cell counts were added, and then the data were transformed into SD [18]. The large GWAS included a total of 173,480 European ancestry individuals from three large-scale UK studies—INTERVAL (n = 40,521), approved by the Cambridge (East) Research Ethics Committee, UK Biobank (n = 87,265) and UK BiLEVE (a selected subset of the UK Biobank cohort, n = 45,694). These GWASs tested univariate associations of 36 indices with 29.5 million imputed variants that passed quality control filters (MAF > 0.01%) and used stepwise multiple regression to identify a parsimonious subset of genetic variants explaining the genome-wide significant associations for each trait. Our study expands the repertoire of genes and regulatory mechanisms governing haematopoietic development in humans and opens potential avenues for targeting key pathways involved in abnormal or dysregulated haematopoiesis (Table 1).

Table 1 Summary of white cell counts

Extraction of SNPs associated with HCC

We extracted the 2 summary GWAS statistics of HCC from the UK Biobank, which included 456,348 and 456,276 individuals of European ancestry, and the study adjusted for age, age squared, and study-specific covariates (UKB. The disease codes for hepatocellular carcinoma in the UK Biobank are “ICD 10, C22.0” and “Data-Field 20001_1024”) [20]. When assessing the causality between exposures and HCC, the summary statistics of HCC with “ICD 10, C22.0” were initially used as the discovery set, and the “Data-Field 20001_1024” GWAS was used for validation. In univariable MR analysis, we simply estimated the relationship between each risk factor and HCC. In the multivariable MR analysis, we tried to identify the independent risk factors of HCC.

Mendelian randomization design and instrumental variables selection

In MR, genetic variant(s) are used as IVs for assessing the causal effect of the exposure on the outcome. The fundamental conditions for a genetic variant to satisfy an IV are summarized as follows: (1) the variant is associated with the exposure; (2) the variant is not associated with any confounder of the exposure–outcome association; (3) the variant does not affect the outcome, except possibly via its association with the exposure. We selected the significant genetic variants associated with the exposures of interest from the GWAS (significance level p < 5 × 10−8). The minor allele frequency of the SNP was  > 0.01. Then, the SNPs used in our study satisfied linkage disequilibrium (LD, r2 < 0.001, kb > 10,000) in the given genome region, and the SNPs with palindromic structures were removed. F statistics (F = beta2/se2) were used to evaluate the remaining SNP power, so we calculated F statistics for each SNP. The SNPs with F statistics  < 10 were identified as weak instruments, and then we excluded them (Fig. 1).

Fig. 1
figure 1

The main design of this MR study

Mendelian randomization analysis and sensitivity test

For the univariable MR, the inverse variance weighted (IVW) method, the MR-Egger method and the weighted median (WM) were used to estimate the effect of the exposures on outcome. For multivariable MR, we used regression-based IVW, a method of weighted averaging of random variables. In this study, IVW was the main method adopted in the statistical analysis. MR-Egger and weighted-median (WM) methods were used as supplements to the IVW method.

We performed the MR-PRESSO global test, outlier test, and distortion test to identify and remove SNPs with horizontal pleiotropy. If any outliers existed, we repeated the evaluation of causal relationships. The intercept test of MR-Egger and Cochran’s Q test in IVW and the MR-Egger model were used to assess pleiotropy and heterogeneity [16]. In the case of pleiotropy, we prefer to use the MR-Egger. If the P value in Cochran’s Q test was significant (P < 0.05), the WM model was used to analyse the statistics. Otherwise, a fixed-effects model was used. Furthermore, we conducted a leave-one-out analysis. The statistical power were tested by online approval (

Genetic variants associated with exposures at genome-wide significance (P < 5 × 10−8) were then LD-pruned (distance threshold = 10,000 kb, r2 = 0.001) using the clump_data command in the “TwoSampleMR” package in R to identify an independent set of variants to serve as a genetic instrument for exposures. Univariable MR analysis was performed by the R packages “Two Sample MR” and “Mendelian randomization”. Multivariable MR was performed by the R packages “MVMR” and “Mendelian randomization”. MR-PRESSO was conducted using the R package “MRPRESSO”. Data visualization was conducted using R software 4.1.1 (


Among the exposures of WBC counts, the genetically predicted counts of most WBCs may not be associated with the risk of HCC except the sum basophil neutrophil counts, based on both datasets. Lower total basophil neutrophil counts may be an independent risk factor for HCC. Univariable MR analysis based on one dataset suggested that eosinophil counts had a significant association with HCC, but the association became nonsignificant after adjustments for other traits.

Univariable MR analysis of exposures on HCC risks in the discovery stage

To characterize the relationship between WBC counts and the risk of HCC, we constructed a genetic instrument for WBC counts using 73–173 independent SNPs associated with the above 8 traits at a genome-wide level of significance (P < 5 × 10−8), which accounted for 1.24–33.86% of the variability in the WBC counts. The mean F-statistic ranged from 68.63 to 115.78, suggesting that the risk of weak instrument bias was low. Univariable MR analysis identified a lower sum of basophil neutrophil counts as a risk factor for HCC. Briefly, each 1-SD increase in the sum basophil neutrophil count could help reduce the risk of HCC (OR = 0.400, P = 0.0001, CI 0.233–0.684). We found no strong evidence to support causal associations between HCC risk and the other WBC subtypes including eosinophil counts (OR = 0.560, P = 0.059, CI 0.307–1.022), basophil counts (OR = 1.039, P = 0.939, CI 0.384–2.811), neutrophil counts (OR = 1.144, P = 0.832, CI 0.328–3.989), lymphocyte counts (OR = 0.636, P = 0.198, CI 0.320–1.266), monocyte counts (OR = 1.213, P = 0.472, CI 0.717–2.051), sum eosinophil basophil counts (OR = 0.691, P = 0.236, CI 0.375–1.273), and sum neutrophil eosinophil counts (OR = 0.806, P = 0.544, CI 0.403–1.614) (Table 2).

Table 2 The effect estimates, test of heterogeneity and test of pleiotropy of white cell counts on HCC (discovery)

Multivariable MR analysis of exposures on HCC risks in the discovery stage

Then, we explored the causal relationship between WBC counts and HCC by conducting a multivariable MR analysis. We observed that sum basophil neutrophil counts had an independent causal effect on the occurrence of HCC: eosinophil counts (OR = 0.502, P = 0.159, CI 0.192–1.309), basophil counts (OR = 1.088, P = 0.873, CI 0.386–3.065), neutrophil counts (OR = 0.605, P = 0.459, CI 0.160–2.288), lymphocyte counts (OR = 0.666, P = 0.266, CI 0.326–1.363), monocyte counts (OR = 1.201, P = 0.511, CI 0.696–2.072), sum eosinophil basophil counts (OR = 1.145, P = 0.786, CI 0.431–3.044), and sum neutrophil eosinophil counts (OR = 1.245, P = 0.746, CI 0.330–4.705). The OR of HCC decreased per 1-SD increase in the sum basophil neutrophil counts (OR = 0.437, P = 0.003, CI 0.252–0.757) (Fig. 2).

Fig. 2
figure 2

The forest plot of the multivariable Mendelian randomization results based on the discovery dataset. Odds ratio (OR) is per 1-SD increase; 95% LCI is the lower limit of the 95% confidence interval; 95% UCI is the upper limit of the 95% confidence interval

Univariable MR analysis of exposures on HCC risks in validation stage

In the validation stage, we successfully replicated the MR results of WBC counts. We validated these risk factors for HCC by MR analysis. Univariable MR results demonstrated that sum basophil neutrophil counts had a significant causal effect on HCC. The OR of HCC decreased per 1-SD increase in the sum basophil neutrophil counts (OR = 0.573, P = 0.018, CI 0.361–0.909). Eosinophil counts had a weak protective effect on HCC: eosinophil counts (OR = 0.568, P = 0.033, CI 0.338–0.954). Other WBC subtypes have no causal relationship with HCC risk: basophil counts (OR = 0.509, P = 0.123, CI 0.215–1.202), neutrophil counts (OR = 0.817, P = 0.509, CI 0.449–1.488), lymphocyte counts (OR = 0.944, P = 0.851, CI 0.521–1.711), monocyte counts (OR = 0.671, P = 0.085, CI 0.426–1.056), sum eosinophil basophil counts (OR = 0.596, P = 0.054, CI 0.352–1.009), and sum neutrophil eosinophil counts (OR = 0.693, P = 0.230, CI 0.381–1.262) (Table 3).

Table 3 The effect estimates, test of heterogeneity and test of pleiotropy of white cell counts on HCC (validation)

Multivariable MR analysis of exposures on HCC risks in validation stage

Multivariable MR analysis in validation stage also revealed that the sum of the basophil neutrophil counts was an independent HCC risk factor. It is worth noting that after adjustments for other traits, the association between eosinophil counts and HCC became nonsignificant: eosinophil counts (OR = 0.596, P = 0.214, CI 0.264–1.348), basophil counts (OR = 0.547, P = 0.180, CI 0.226–1.320), neutrophil counts (OR = 1.806, P = 0.308, CI 0.580–5.623), lymphocyte counts (OR = 0.982, P = 0.954, CI 0.535–1.805), monocyte counts (OR = 0.723, P = 0.175, CI 0.453–1.155), sum eosinophil basophil counts (OR = 0.996, P = 0.992, CI 0.434–2.285), sum neutrophil eosinophil counts (OR = 0.462, P = 0.183, CI 0.148–1.440), and sum basophil neutrophil counts (OR = 0.574, P = 0.021, CI 0.358–0.920) (Fig. 3).

Fig. 3
figure 3

The forest plot of the multivariable Mendelian randomization results based on the validation dataset. Odds ratio (OR) is per 1-SD increase; 95% LCI is the lower limit of the 95% confidence interval; 95% UCI is the upper limit of the 95% confidence interval

Sensitivity analysis

We observed that the confidence interval of the exposures was relatively wide, which was considered to be caused by low sample size. It cannot be ruled out that there would be weak connections between the other WBC count traits and HCC. Another possibility of the null findings observed in our MR analyses could be explained by the low proportion of variances in some of the exposures (F statistics  < 100). On the other hand, we deemed that sum basophil neutrophil counts had a causal relationship with HCC.

There was heterogeneity of neutrophil counts (P < 0.05) in the discovery stage. All the results of these risk factors were the MR-PRESSO corrected results if outliers were detected. No significant horizontal pleiotropic effects were detected in the MR-Egger test (for the intercept of MR-Egger, all P values were more than 0.05). The statistical power of these exposures was 100%.


To our knowledge, this is the first MR study evaluating the potential causal association between WBC subtype counts and the risk of HCC. Our results suggest potential causal effects of lower sum basophil neutrophil counts on HCC occurrence in the European population. No evidence was found for causal effects of neutrophil counts, lymphocyte counts, monocyte counts, eosinophil counts, basophil counts, sum eosinophil basophil counts and sum eosinophil neutrophils on the risk of HCC.

It has been reported that most of HCC cases arise on the background of chronic inflammation. Infiltrating inflammatory cells is associated with tumour initiation, progression and clinical treatment response. Neutrophils are the most common leukocytes in circulation [21]. The increase in neutrophil counts changes the formation of the tumour microenvironment and inflammatory microenvironment, promoting HCC growth and metastasis, while neutrophils are reported to have antitumour potential [22]. Neutrophil-derived reactive oxygen species (ROS), such as nitric oxide and hydrogen peroxide, are cytotoxic towards cancer cells [23, 24]. Basophils play an important role in regulating innate immune response to infection and tissue repair. Gastric cancer-infiltrating basophils were identified as an independent adverse prognosticator [25]. However, a low percentage of basophils was associated with an increased number of pulmonary metastases in a breast cancer mouse model, which suggested that basophils had a protective role in breast cancer [26]. In ovarian cancer, higher basophil counts were positively correlated with improved prognosis [27]. Therefore, the role of basophils in the immune responses of cancers remains controversial.

Our univariable and multivariable analysis revealed that no causal association was observed between neutrophil counts, basophil counts and the occurrence of HCC based on the discovery and validation datasets. Our MR analysis suggested a seemingly increased risk of higher neutrophils in the multivariable models, while the sum (predominantly based on neutrophils) was associated with reduced HCC risk. The reason for this result may be that the SNPs associated with the sum (predominantly based on neutrophils) and those associated with neutrophils are largely different. For example, the top 5 SNPs with the largest effect value for neutrophils are rs56378716, rs12600856, rs56388170, rs11725704, and rs41313381. On the other hand, the top 5 SNPs with the largest effect value for sum were rs3812049, rs150425398, rs8028409, rs183034862, and rs17462448 (Sum basophil neutrophil counts) and rs34599082, rs114050631, rs445, rs4760, and rs1982094 (Sum neutrophil eosinophil counts).

Eosinophils play a prominent role in responses to allergic, inflammatory and immunoregulatory situations. A previous study reported that 0.5% of more than 2000 patients with malignant tumours of all histological types exhibited eosinophilia [28]. Eosinophils could promote the adverse effect of tumour metastasis [29]. Besides, eosinophilia may be a potential causal risk factor in squamous cell lung cancer progression in the East Asian population, while the association between eosinophil counts and lung cancer was not significant in the European population [30]. To date, there is no related literature to report the relationship between eosinophils and HCC. In the validation dataset, our univariable MR analysis showed that eosinophil counts had a significant association with HCC, suggesting a protective role of eosinophils for the risk of HCC. However, after adjustments for other traits, the association became nonsignificant. Several studies have reported that eosinophilic infiltration in HCC samples may be correlated with hepatocarcinogenesis and HCC progression [31,32,33]. The eosinophilic infiltration in HCC samples is different from the eosinophil counts in the blood used in our study.

Lymphocytes, which are usually recruited to the TME and engage in cell-mediated tumour responses, are associated with the prognosis of patients with various types of cancer [34, 35]. Peripheral lymphopenia can impair the host’s antitumour response and is conducive to tumour progression and dissemination [36]. Monocyte counts have been reported as prognostic markers for various cancers patients. Monocytes-derived tumour-associated macrophages are reported to be associated with cancer progression [37]. In HCC, low lymphocyte counts and high monocyte counts were significantly associated with reduced overall survival [9]. Our MR analysis based on two datasets found that neither lymphocyte counts nor monocyte counts were associated with the risk of HCC, which suggested that genetically predicted counts of most WBC subtypes might not be associated with HCC risk except the sum basophil neutrophil counts.

Our study attempted to reduce confounding bias through MR methods such as strict IV selection procedures, sensitivity tests and obtaining consistent findings in the two datasets. However, some limitations should not be ignored. First, because our datasets were both from European populations, the results should be further validated in other non-European ethnicities. Second, due to the limitation of the datasets, the association between genetically predicted NLR, LMR and HCC risk was not evaluated. Third, our MR analysis revealed no correlation between HCC risk and several exposures, such as the sum of basophils and eosinophils and the sum of neutrophils and eosinophils, which does not mean that these exposures have no effect on HCC development. These results may be related to the bias caused by the insufficient HCC sample size. Last, future studies are needed to clarify the mechanism of WBC subtypes in HCC occurrence.


Our MR analysis suggests that genetically predicted sum basophil neutrophil counts are associated with the risk of HCC. This study provides a novel finding that European ancestry individuals who had lower genetic level counts of sum basophil neutrophils are likely at risk of HCC. Clinicians should raise awareness of total basophil neutrophil counts in clinical practice.

Availability of data and materials

The summary statistics of exposures were available on (; the summary statistics of outcome were available on (GWAS Catalog (


  1. Llovet JM, Zucman-Rossi J, Pikarsky E, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2016;2:16018.

    Article  Google Scholar 

  2. Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1–61.

    Article  Google Scholar 

  3. Ferlay J, Colombet M, Soerjomataram I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer. 2019;144(8):1941–53.

    Article  CAS  Google Scholar 

  4. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33.

    Article  Google Scholar 

  5. Chen Y, Liao Y, Lam LM, et al. Pretreatment biomarkers as prognostic predictors of survival in patients with pancreatic cancer treated with gemcitabine-based therapy and 5-fluorouracil: neutrophil-to-lymphocyte ratio vs platelet-to-lymphocyte ratio. Int J Med Sci. 2020;17(10):1449–57.

    Article  CAS  Google Scholar 

  6. Okugawa Y, Toiyama Y, Yamamoto A, et al. Lymphocyte-C-reactive protein ratio as promising new marker for predicting surgical and oncological outcomes in colorectal cancer. Ann Surg. 2020;272(2):342–51.

    Article  Google Scholar 

  7. Dharmapuri S, Ozbek U, Lin JY, et al. Predictive value of neutrophil to lymphocyte ratio and platelet to lymphocyte ratio in advanced hepatocellular carcinoma patients treated with anti-PD-1 therapy. Cancer Med. 2020;9(14):4962–70.

    Article  CAS  Google Scholar 

  8. Margetts J, Ogle LF, Chan SL, et al. Neutrophils: driving progression and poor prognosis in hepatocellular carcinoma? Br J Cancer. 2018;118(2):248–57.

    Article  CAS  Google Scholar 

  9. Hong YM, Yoon KT, Hwang TH, Cho M. Pretreatment peripheral neutrophils, lymphocytes and monocytes predict long-term survival in hepatocellular carcinoma. BMC Cancer. 2020;20(1):937.

    Article  CAS  Google Scholar 

  10. Geh D, Leslie J, Rumney R, Reeves HL, Bird TG, Mann DA. Neutrophils as potential therapeutic targets in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2022;19(4):257–73.

    Article  CAS  Google Scholar 

  11. Guo L, Ren H, Pu L, Zhu X, Liu Y, Ma X. The prognostic value of inflammation factors in hepatocellular carcinoma patients with hepatic artery interventional treatments: a retrospective study. Cancer Manag Res. 2020;12:7173–88.

    Article  CAS  Google Scholar 

  12. Kong W, Xu H, Cheng J, et al. The prognostic role of a combined fibrinogen and neutrophil-to-lymphocyte ratio score in patients with resectable hepatocellular carcinoma: a retrospective study. Med Sci Monit. 2020;26:e918824.

    Article  CAS  Google Scholar 

  13. Okamura Y, Sugiura T, Ito T, et al. Neutrophil to lymphocyte ratio as an indicator of the malignant behaviour of hepatocellular carcinoma. Br J Surg. 2016;103(7):891–8.

    Article  CAS  Google Scholar 

  14. Schobert IT, Savic LJ, Chapiro J, et al. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE. Eur Radiol. 2020;30(10):5663–73.

    Article  CAS  Google Scholar 

  15. Davies NM, Holmes MV, Davey SG. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.

    Article  Google Scholar 

  16. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey SG. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.

    Article  Google Scholar 

  17. Kanai M, Akiyama M, Takahashi A, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018;50(3):390–400.

    Article  CAS  Google Scholar 

  18. Astle WJ, Elding H, Jiang T, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167(5):1415–29.

    Article  CAS  Google Scholar 

  19. Reiner AP, Lettre G, Nalls MA, et al. Genome-wide association study of white blood cell count in 16,388 African Americans: the continental origins and genetic epidemiology network (COGENT). PLoS Genet. 2011;7(6):e1002108.

    Article  CAS  Google Scholar 

  20. Jiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet. 2021;53(11):1616–21.

    Article  CAS  Google Scholar 

  21. Kruger P, Saffarzadeh M, Weber AN, et al. Neutrophils: between host defence, immune modulation, and tissue injury. PLoS Pathog. 2015;11(3):e1004651.

    Article  CAS  Google Scholar 

  22. Kalafati L, Kourtzelis I, Schulte-Schrepping J, et al. Innate immune training of granulopoiesis promotes anti-tumor activity. Cell. 2020;183(3):771-85 e12.

    Article  CAS  Google Scholar 

  23. Gershkovitz M, Caspi Y, Fainsod-Levi T, et al. TRPM2 mediates neutrophil killing of disseminated tumor cells. Cancer Res. 2018;78(10):2680–90.

    Article  CAS  Google Scholar 

  24. Rice CM, Davies LC, Subleski JJ, et al. Tumour-elicited neutrophils engage mitochondrial metabolism to circumvent nutrient limitations and maintain immune suppression. Nat Commun. 2018;9(1):5099.

    Article  CAS  Google Scholar 

  25. He X, Cao Y, Gu Y, et al. Clinical outcomes and immune metrics in intratumoral basophil-enriched gastric cancer patients. Ann Surg Oncol. 2021;28(11):6439–50.

    Article  Google Scholar 

  26. Wang C, Chen YG, Gao JL, et al. Low local blood perfusion, high white blood cell and high platelet count are associated with primary tumor growth and lung metastasis in a 4T1 mouse breast cancer metastasis model. Oncol Lett. 2015;10(2):754–60.

    Article  CAS  Google Scholar 

  27. Bax HJ, Chauhan J, Stavraka C, et al. Basophils from cancer patients respond to immune stimuli and predict clinical outcome. Cells. 2020.

    Article  Google Scholar 

  28. Isaacson NH, Rapoport P. Eosinophilia in malignant tumors; its significance. Ann Intern Med. 1946;25(6):893–902.

    Article  CAS  Google Scholar 

  29. Li F, Du X, Lan F, et al. Eosinophilic inflammation promotes CCL6-dependent metastatic tumor growth. Sci Adv. 2021.

    Article  Google Scholar 

  30. Wang Z, Chen B, Fu Y, et al. Eosinophilia and lung cancer: analysis from real-world data and Mendelian randomization study. Front Med (Lausanne). 2022;9:830754.

    Article  Google Scholar 

  31. Kung IT, Chan SK, Fung KH. Fine-needle aspiration in hepatocellular carcinoma combined cytologic and histologic approach. Cancer. 1991;67(3):673–80.;2-h.

    Article  CAS  Google Scholar 

  32. Kwak HS, Han YM, Lee JM. Focal eosinophilic infiltration of the liver mimicking hepatocellular carcinoma: case reports. Clin Imaging. 2004;28(1):36–9.

    Article  Google Scholar 

  33. Watanabe T, Aikawa K, Kanefuji T, et al. Pelioid-type hepatocellular carcinoma with numerous eosinophilic infiltrations in a patient with primary biliary cirrhosis. Hepatol Res. 2008;38(4):421–7.

    Article  Google Scholar 

  34. Fu Q, Chen N, Ge C, et al. Prognostic value of tumor-infiltrating lymphocytes in melanoma: a systematic review and meta-analysis. Oncoimmunology. 2019;8(7):1593806.

    Article  Google Scholar 

  35. Zhang D, He W, Wu C, et al. Scoring system for tumor-infiltrating lymphocytes and its prognostic value for gastric cancer. Front Immunol. 2019;10:71.

    Article  CAS  Google Scholar 

  36. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14(10):1014–22.

    Article  CAS  Google Scholar 

  37. Sasaki A, Iwashita Y, Shibata K, Matsumoto T, Ohta M, Kitano S. Prognostic value of preoperative peripheral blood monocyte count in patients with hepatocellular carcinoma. Surgery. 2006;139(6):755–64.

    Article  Google Scholar 

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The authors thank all investigators and participants from UK Biobank for sharing genetic association estimates for HCC. Besides, we would like to thank all investigators contributing to GWAS of WBC counts.


This work was supported by the Taishan Scholars Program for Experts of Shandong Province, National Natural Science Foundation of China (82172647, 82073200, 81874178), Funds for Independent Cultivation of Innovative Team from Universities in Jinan (2020GXRC023), Shandong Provincial Natural Science Foundation (ZR2021ZD26, ZR2021MH194) and China Postdoctoral Science Foundation (2020M682192, 2022T150385).

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Study concept and design: TL, ZRD; analyses the data: GQP, CCY, XLS; quality assessment: TL, ZRD; draft the manuscript: ZRD, GQP. All authors read and approved the final manuscript.

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Correspondence to Zhao-Ru Dong or Tao Li.

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All participating studies involved in the GWASs obtained informed consent from the study population and gained approval from the ethics committees of the UK Biobank, UK BiLEVE and INTERVAL. As we utilized publicly available data sets to perform MR, no additional ethics approval was required.

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Pan, GQ., Yang, CC., Shang, Xl. et al. The causal relationship between white blood cell counts and hepatocellular carcinoma: a Mendelian randomization study. Eur J Med Res 27, 278 (2022).

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