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. 2025 Sep 29;16:1752. doi: 10.1007/s12672-025-03536-6

The causal relationship between platelet indices and liver cancer: a bidirectional two-sample Mendelian randomization study

Xin He 1,#, Yuzhe Zhang 1,#, Zhengsheng Yi 1,#, Xiaohui Zhao 1, Yusheng Shi 1,✉, Shengni Hua 1,✉
PMCID: PMC12480237  PMID: 41021163

Abstract

Background

Platelet indices are non-invasive biomarkers used to assess disease status. Observational studies have shown that platelet indices are related to liver cancer. However, the specific relationship between the two is still inconclusive. We conducted a Mendelian randomization (MR) study to investigate the causal relationship between platelet indices and liver cancer.

Methods

We identified 2 cancer outcomes (liver cell carcinoma, malignant neoplasm of liver and intrahepatic bile ducts) from genome-wide association studies (GWAS). We used inverse variance weighted (IVW) method as the main method. In order to evaluate the potential horizontal pleiotropy or heterogeneity, sensitivity analyses were examined via MR-Egger regression, heterogeneity test, pleiotropy test.

Results

Two-Sample MR analysis showed there were causal relationships between platelet indices and liver cancer. The mean platelet volume was positively associated with hepatocellular carcinoma (HCC) risk, while platelet count was inversely correlated with HCC risk, platelet endothelial cell adhesion molecule levels were significantly related to an increased malignant neoplasm of liver and intrahepatic bile ducts risk. In addition, we conducted reverse MR analyses and found that malignant neoplasm of liver and intrahepatic bile ducts was positively associated with platelet count and platelet crit, but negatively correlated with platelet distribution width. Following multivariable mendelian randomization adjustments for potential confounding variables, the independent effects of platelet indices on HCC were not statistically significant.

Conclusions

The bidirectional causal relationship between platelet indices and liver cancer may be mediated by confounding factors such as liver cirrhosis. Further validation through mediation MR analyses or larger-scale replication studies is required to elucidate these causal pathways.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03536-6.

Keywords: Platelet indices, Liver cancer, Mendelian randomization

Introduction

Liver cancer has emerged as a significant global health burden, ranking as the sixth most prevalent malignancy and the fourth leading cause of cancer-related mortality worldwide [1]. The primary pathogenic factors contributing to hepatocellular carcinoma (HCC) include chronic HBV/HCV infection, cirrhosis, alcohol abuse, non-alcoholic steatohepatitis (NASH), and genetic predisposition [2]. Epidemiological studies have identified several predictive markers for HCC development within a 5-year timeframe, including age, sex, liver cirrhosis status, HBeAg seropositivity, quantitative HBsAg levels, liver stiffness measurement (LSM), albumin-bilirubin (ALBI) grade, and platelet count. Notably, the age-Male-ALBI-Platelets (aMAP) risk stratification system—a composite scoring model incorporating age, male sex, ALBI grade, and platelet count—has demonstrated robust predictive accuracy in identifying high-risk populations for HCC [3]. Owing to its clinical validity, the aMAP score was formally integrated into the 2022 Chinese guidelines for the diagnosis and management of primary liver cancer.

Platelets, anucleate cytoplasmic fragments derived from bone marrow megakaryocytes [4], play critical roles in hemostatic regulation, tumor progression, and vascular permeability modulation [5]. Platelets also facilitate hepatocyte proliferation through the secretion of bioactive mediators, including vascular endothelial growth factor (VEGF), transforming growth factor-beta (TGF-β), and hepatocyte growth factor (HGF), which activate downstream proliferative signaling cascades [6, 7]. Clinically, platelet counts have emerged as prognostic biomarkers for HCC, the elevated levels are correlated to adverse outcomes. Pang et al. reported a significantly higher recurrence rate in non-cirrhotic HCC patients exhibiting platelet counts ≥ 148 × 10⁹/L [8], implicating thrombocytosis as a potential risk factor for post-treatment HCC recurrence. Consistently, pretreatment thrombocytosis has been associated with increased risks of HCC recurrence following liver transplantation [9] and extrahepatic metastasis after curative resection [10]. These findings underscore the imperative to elucidate the mechanistic interplay between platelets and hepatocarcinogenesis.

To address confounding biases inherent in observational studies, Mendelian randomization (MR) employs genetic variants—typically single nucleotide polymorphisms (SNPs) strongly associated with the exposure—as instrumental variables (IVs) to infer causal relationships between exposures and outcomes. We used bidirectional MR analysis to detect the causal relationship between platelet indices and liver cancer risk.

Methods

Study design and data sources

We conducted a bidirectional two-sample MR analysis to investigate the causal relationship between platelet indices and liver cancer. For the primary MR analysis, we evaluated the causal effects of five platelet parameters on liver cancer risk. Concurrently, a reverse-direction MR analysis was performed to examine the causal effects of liver cancer on these platelet indices. The process of MR is shown in Fig. 1. We also performed multivariable mendelian randomization (MVMR) to mitigate potential pleiotropy by adjusting for confounding factors such as liver cirrhosis and diabetes mellitus. The genome-wide association study (GWAS) summary statistics for platelet indices were obtained from European populations in the UK Biobank (UKB) and European Bioinformatics Institute (EBI) databases. For primary liver cancer, cases were defined as “Malignant neoplasm of liver and intrahepatic bile ducts” based on FinnGen data, comprising 218,792 Europeans (304 cases and 218,488 healthy controls) with 16,380,466 SNPs. Additionally, HCC data sourced from the Integrative Epidemiology Unit (IEU) Open GWAS database included 372,184 Europeans (168 cases and 372,016 healthy controls) and 6,304,034 SNPs. In the MVMR analysis, the GWAS data for primary biliary cirrhosis (PBC) comprised 11,375 Europeans with 119,756 SNPs, while the GWAS for type 2 diabetes mellitus (T2DM) included 215,654 samples and 16,380,440 SNPs. The information of summary statistics was shown in the Table S1. Since all data utilized in this study were obtained from publicly accessible databases, this research does not involve any individual privacy concerns or ethical risks associated with human subject participation.

Fig. 1.

Fig. 1

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The main design of MR in this study

Genetic instrument selection

In MR, genetic variants are used as IVs to assess the causal effect of exposure on outcome. The basic conditions for genetic variants satisfying IVs are summarized as follows: (1) variants are associated with exposure; (2) variants are independent of outcome given the exposure and all the confounders; (3) variants can only affect outcomes through exposure. To satisfy the three core assumptions, we implemented rigorous IV selection criteria as follows: (1) SNPS with strong associations with exposure factors were selected, that is, the extracted SNPS should be below the genome-wide significance threshold (P < 5 × 10− 8 ). (2) SNPs with linkage disequilibrium (LD) were excluded through a clumping procedure, which removed SNPs with an LD threshold of r2 ≥ 0.001 within a genomic window of 10,000 kb. (3) SNPs with weak associations to the exposure (F-statistic < 10) were removed to mitigate weak instrument bias. All retained SNPs exhibited F > 10, indicating minimal risk of weak instrument bias in our analyses.

MR statistical analysis

Two-sample MR was performed using genetic data extracted from GWAS. We used the two-sample MR package in R (version 4.2.3) to consolidate and analyze the data. To determine whether platelet indices might be a risk factor for liver cancer, we first treated platelet indices as the exposure and liver cancer as the outcome. As exploring the effect of liver cancer on platelet indices, in the reverse MR analysis process, liver cancer was used as the exposure and platelet indices as the outcome. In this study, IVW was the main method adopted in the statistical analysis and used to calculate the combined effect of all SNPs. For traits containing only one IV, the Wald ratio test was used to estimate the association between platelet indices and liver cancer. MR-Egger and weighted-median methods were used as supplements to the IVW method. Horizontal pleiotropy was examined using MR-Egger. Cochran’s Q test was conducted to evaluate the heterogeneity, if the P-value was greater than 0.05, it was considered that there was no heterogeneity in the included instrumental variables and that heterogeneity had a negligible effect on the causal effect estimates. In the visualization section, funnel plots were used to evaluate possible directional pleiotropy, leave-one-out analysis was utilized to investigate the heterogeneity of the MR, and forest plots were used to show the results for each SNP and the final MR.

Results

IVs selection

Five indices including mean platelet volume (MPV), platelet count, PECAM levels, platelet crit (PCT), and platelet distribution width (PDW), were analyzed by dual-sample bidirectional MR. According to the following criteria: significance threshold of P < 5 × 10− 8, independence test with r2 ≤ 0.001 at a 10,000-kb window, a total of 674 IVs were selected. The F-statistics of all IVs were > 10, indicating no evidence of weak-tool bias. Extensive information on the cohorts used is available on the IEU Open GWAS webpage (https://gwas.mrcieu.ac.uk/) [11].

MR analysis of platelet indices on liver cancer risks

As shown in Table 1, MPV, platelet count, and levels of PECAM were statistically associated with the risk of liver cancer. We found a positive association between MPV and the risk of HCC (OR = 1.00017, 95% CI = [1.00003, 1.00031]; P = 0.016), between PECAM levels and the risk of liver and intrahepatic bile duct malignancies (OR = 1.53520, 95% CI = [1.00823, 2.33761]; P = 0.016). While platelet count was inversely associated with risk for HCC (OR = 0.99977, 95% CI = [0.99956, 0.99997]; P = 0.025) (Fig. 2). the scatter plots showed the causal relationship between platelet indices and liver cancer (Fig. 3). To assess the robustness of the above findings, we performed Cochran’s Q test for heterogeneity and evaluated horizontal pleiotropy using the MR-Egger intercept test. It showed no horizontal pleiotropy and heterogeneity. Detailed supplementary information is provided in the Table S2. The funnel plot did not show significant heterogeneity (Figure S1). The leave-one out analysis revealed that no SNP affected overall results (Figure S2).

Table 1.

MR results of causal effects between platelet indices and liver cancer

Exposure Outcome Method N-snp β SE OR (95%CI) P
MPV Liver cell carcinoma IVW 310 1.74 × 10− 4 7.28 × 10− 5 1.00017 (1.00003, 1.00031) 0.016
Platelet count IVW 360 -2.29 × 10− 4 1.02 × 10− 4 0.99977 (0.99956, 0.99997) 0.025
PECAM Malignant neoplasm of liver and intrahepatic bile ducts IVW 4 4.28 × 10− 1 2.14 × 10− 1 1.53520 (1.00823, 2.33761) 0.045
Malignant neoplasm of liver and intrahepatic bile ducts Platelet count Wald ratio 1 1.18 × 10− 2 4.25 × 10− 3 1.01191 (1.00350, 1.02038) 0.005
PCT Wald ratio 1 1.53 × 10− 2 4.18 × 10− 3 1.01545 (1.00715, 1.02381) 0.000
PDW Wald ratio 1 -2.13 × 10− 2 4.35 × 10− 3 0.97882 (0.97051, 0.98721) 0.000
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N-snp, the number of SNPs used in the analysis; OR, odds ratio; CI, confidence interval; SE, standard error; IVW, inverse variance weighted. PECAM, platelet endothelial cell adhesion molecules; PDW, platelet distribution width; MPV, mean platelet volume; PCT, platelet crit

Fig. 2.

Fig. 2

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Forest plot to visualize effects of variation in platelet indices on liver cancer. nSNP, the number of SNP; OR, odds ratio; CI, confidence interval

Fig. 3.

Fig. 3

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Scatter plots of causal relationship between platelet indices and liver cancer. (A) MPV on HCC; (B) platelet count on HCC; (C) PECAM on malignant neoplasm of liver and intrahepatic ducts. HCC, hepatocellular carcinoma; MPV, mean platelet volume; PECAM, platelet endothelial cell adhesion molecules

Reverse MR analysis

The results of an inverse MR analysis showed that liver and intrahepatic bile duct malignancy also had a causal effect on platelet count, PCT, and PDW (Table 1). Statistically significant results were obtained when we performed reverse MR analysis (Wald ratio, P < 0.05). With liver cancer as the exposure factor and platelet indices as the outcome variable, liver and intrahepatic bile duct malignancies were positively correlated with platelet counts (OR = 1.01191, 95% CI = [1.00350, 1.02038]; P = 0.005) and PCT (OR = 1.01545, 95% CI = [1.00715, 1.02381]; P = 0.000), but negatively related to PDW (OR = 0.97882, 95% CI = [0.97051, 0.98721]; P = 0.000) (Fig. 4).

Fig. 4.

Fig. 4

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Forest plot to visualize effects of genetically predicted liver cancer on platelet indices

Multivariable Mendelian randomization analysis

Considering the results may be influenced by the confounding factors, MVMR analysis adjusted for PBC and T2DM mellitus were conducted, and results were presented in Table 2. The IVW method demonstrated that the association between platelet indices (MPV, platelet count) and HCC may be confounded by PBC or T2DM. After adjusting for PBC and T2DM, MPV and platelet count showed no significant causal effect on HCC.

Table 2.

MVMR analysis of platelet indices and HCC

Exposure Outcome Methods β OR (95%CI) P
MPV Liver cell carcinoma PBC -1.09E-05 0.99998(0.99984, 1.00013) 0.882
T2DM -0.00018 0.99981(0.99948, 1.00014) 0.269
Platelet count PBC -4.15E-05 0.99995(0.99982, 1.00009) 0.540
T2DM -0.00011 0.99988(0.99958, 11.00018) 0.455
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MVMR, multivariable mendelian randomization; PBC, primary biliary cirrhosis; T2DM, type 2 diabetes mellitus

Discussion

Our study represents the first comprehensive investigation into the bidirectional causal relationship between platelet indices and hepatocarcinogenesis. It demonstrated that genetically predicted MPV, platelet count, and levels of PECAM are associated with liver cancer risk, providing novel research direction for the clinical prevention and treatment of liver cancer.

Platelet endothelial cell adhesion molecule-1 (PECAM-1), a transmembrane glycoprotein of the immunoglobulin superfamily, is ubiquitously expressed on platelets, endothelial cells, monocytes, neutrophils, and lymphocytes. This multifunctional molecule regulates diverse cellular processes, including proliferation, apoptosis, migration, and immunological reaction [12]. PECAM-1 serves as a biomarker of platelet activation, its diminished expression is correlated with antiplatelet effects [13]. PECAM-1 also facilitates endothelial cell motility during angiogenesis through its dual roles in intercellular adhesion and signal transduction [14]. Emerging evidence implicates platelets can promote tumor metastasis though the underlying mechanisms which remain incompletely elucidated [15, 16]. The adhesion between platelets and tumor cells mediated by PECAM-1 has been proposed to enhance tumor-endothelial interactions, thereby facilitating tumor metastatic dissemination [17]. Delisser et al. demonstrated that PECAM-1 accelerates metastatic progression in advanced malignancies by modulating tumor proliferative dynamics [18]. There are no clear studies on the relationship between PECAM-1 level and the risk of liver cancer development so far. Tissue inhibitor of metalloproteinase 1 (TIMP-1) is a member of the broad-spectrum endogenous inhibitor family of matrix metallopeptidases (MMPs) and participates in the remodeling of extracellular matrix by regulating the activity of MMPs [19]. Elevated TIMP-1 expression in liver cancer tissues compared to non-tumorous liver parenchyma underscores its role in hepatocarcinogenesis [20]. Mechanistically, PECAM-1 facilitates TIMP-1 secretion from endothelial cells into the tumor microenvironment (TME), which leads to the proliferation of tumor cells [21]. Experimental studies reveal that PECAM-1 promotes epithelial-mesenchymal transition (EMT) of HCC through upregulated integrinβ1 and activated FAK-Akt pathway, thereby promoting tumor metastasis, suggesting that PECAM-1 can be served as a potentially poor prognostic marker for HCC [22]. Malik et al. demonstrated that deletion of the PECAM-1 gene partially against intrahepatic cholangiocarcinoma (ICC) progression in murine models [23]. Our data are consistent with the findings of these researches, revealing a significant positive association between PECAM-1 levels and malignancy risk of hepatic and intrahepatic bile duct tissues.

As a biomarker of platelet activation [24], MPV has been shown to be associated with diseases such as acute myocardial infarction [25], acute ischemic cerebrovascular events [26], Crohn’s disease [27], diabetes mellitus [28], and juvenile systemic lupus erythematosus [29]. Furthermore, increased MPV has been observed in breast cancer [30], thyroid cancer [31], endometrial cancer [32], and lung cancer [33]. Kurt et al. [34] found that patients with chronic liver disease exhibited significantly elevated MPV compared to healthy individuals, while even higher MPV were observed in HCC patients than those with chronic liver disease. Consistent with these findings, our MR analysis demonstrated a positive causal association between MPV and HCC. It was revealed that larger platelets are younger, more aggregation-prone, and exhibit higher enzymatic activity compared to smaller counterparts [35]. Activated platelets play pivotal roles in inflammatory responses ( a crucial component of tumorigenesis and progression ) while simultaneously facilitating tumor angiogenesis and neoplastic growth [36–38]. These are also agreement with our results.

To date, numerous studies have investigated the relationship between platelet count and HCC, yet the conclusions remain divergent. One study reported that low platelet count serves as an independent risk factor for HCC development [39]. In line with this, Everson et al. [40] found that thrombocytopenia was associated with HCC occurrence in patients with hepatitis C-related cirrhosis. Furthermore, reduced platelet levels have also been correlated to diminished postoperative survival rates in HCC patients undergoing hepatectomy [41]. Conversely, emerging studies have documented that thrombocytosis is linked to greater tumor burden and potentially more aggressive tumor behavior in HCC patients [42, 43]. Furthermore, elevated platelet count has been identified as a predictor of poor prognosis for non-cirrhotic HCC cases [44]. Our MR analysis demonstrated a positive causal association between MPV and HCC risk, while revealing an inverse causal relationship for platelet count with HCC development. After addressing weak instrument bias and validating through sensitivity analyses, we observed that the MPV had an OR of 1.00017, and the platelet count had an OR of 0.99977. Given their minimal impact on the risk of HCC development, subsequent MVMR analyses, adjusted for confounding factors including primary biliary cirrhosis, revealed that the effects of platelet indices on HCC were not statistically significant. It is necessary to expand the sample size to further validate the causal relationship between MPV, platelet count, and HCC.

Our data analysis also showed that liver and intrahepatic bile duct malignancies were positively correlated with platelet count and PCT, but negatively correlated with PDW. HCC is frequently associated with paraneoplastic syndromes, including hypercholesterolemia, hypoglycemia, hypercalcemia, and erythrocytosis. Notably, thrombocytosis is observed in pediatric hepatoblastoma, though its prevalence in HCC remains less defined [45, 46]. Thrombopoietin (TPO) is a megakaryocyte growth factor and critical regulator of megakaryocyte differentiation and thrombopoiesis [47]. Retrospective analyses indicate that approximately 2.7% of HCC patients exhibit thrombocytosis, with mechanistic parallels to other paraneoplastic syndromes. These findings support the classification of thrombocytosis as a paraneoplastic manifestation in HCC, likely attributable to ectopic TPO overproduction by neoplastic tissues [48]. This pathophysiological mechanism aligns with the observed associations in our analyses. However, the relationship between intrahepatic bile duct malignancy and platelet count has not been reported so far, and further studies are needed to verify it. PCT represents the volume fraction of platelets in the blood [49] and reflects the condition of platelet particles and active substances. According to the calculation formula of PCT, PCT is directly proportional to platelet count [50], with elevated values indicative of thrombocythemia. As mentioned above, we hypothesize that hepatobiliary malignancies (including intrahepatic bile duct cancers) may similarly drive thrombocytosis, thereby elevating PCT levels. In contrast to PCT, PDW, a parameter quantifying platelet size heterogeneity, demonstrated a negative correlation with liver and intrahepatic bile duct malignancies in our study. PDW can be used in the differential diagnosis of thrombocytopenia [51]. Paradoxically, retrospective analyses report elevated PDW in HCC cohorts, suggesting its potential utility as a surrogate marker of disease progression [52]. The reverse MR results indicate that malignant neoplasm of liver and intrahepatic bile ducts exerts a causal influence on platelet indices, suggesting that the observed association between platelet indices and liver cancer may not be directly driven by platelet indices themselves. In fact, there is a complex bidirectional interaction between platelets and tumors: Most HCC patients are complicated by cirrhosis, which leads to portal hypertension and splenomegaly, accelerating platelet destruction in the spleen. Additionally, HCC stimulates platelet maturation and activation through the synthesis and release of thrombopoietin. Activated platelets promote tumor growth directly by releasing various growth factors (e.g., VEGF, PDGF) and indirectly by facilitating tumor angiogenesis, thereby enhancing the proliferation and invasive potential of HCC cells [53]. More intensive researches are urgently needed.

This study has several limitations. Firstly, as our data were exclusively derived from European populations, the findings cannot be generalized to other ethnic groups, which restricts the external validity of the results. Secondly, although MVMR adjustment was performed for confounders such as PBC and T2DM, the possibility of residual confounding by potential unmeasured factors (e.g., viral hepatitis, non-alcoholic fatty liver disease) impacting causal inferences cannot be ruled out. Finally, the reverse MR analysis utilized a limited number of SNPs, making it prone to pleiotropy and other biases, thereby compromising the reliability of the causal inferences. While we observed a genetic-level association between platelet indices and liver cancer, future MR studies with larger sample sizes and randomized controlled trials are needed to elucidate the complex bidirectional interactions between platelets and liver cancer pathogenesis.

Conclusions

In summary, these MR analyses suggest a bidirectional causal relationship between platelet indices and liver cancer, but the presence of confounding factors or intermediary effects cannot be excluded. The role of platelet indices as prevention strategy and therapeutic biomarkers for liver cancer requires further research validation.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (155.8KB, pdf)

Acknowledgements

We would like to thank the IEU Open GWAS database publicly available.

Abbreviations

MR

Mendelian randomization

GWAS

Genome-wide association studies

IVW

Inverse variance weighted

HCC

Hepatocellular carcinoma

OR

Odds ratio

CI

Confidence interval

LSM

Liver stiffness measurement

ALBI

Albumin-bilirubin

VEGF

Vascular endothelial growth factor

TGF

Transforming growth factor

HGF

Hepatocyte growth factor

SNPs

Single nucleotide polymorphisms

IVs

Instrumental variables

MPV

Mean platelet volume

PECAM

Platelet endothelial cell adhesion molecule

PCT

Platelet crit

PDW

Platelet distribution width

TIMP-1

Tissue inhibitor of metalloproteinase 1

MMPs

Matrix metallopeptidases

TME

Tumor microenvironment

ICC

Intrahepatic cholangiocarcinoma

TPO

Thrombopoietin

PBC

Primary biliary cirrhosis

T2DM

Type 2 diabetes mellitus

Author contributions

ZYZ: Conceived and designed the study; Performed the MR analysis. XH, ZSY: Analysed and interpreted the data; Wrote the paper. XHZ: Wrote the paper. YSS and SNH: Supervised the study. All authors read and approved the final version of the manuscript.

Funding

This work was supported by the National Nature Science Foundation of China (Grant No. 81902460), Scientific Research Startup Foundation of Zhuhai People’s Hospital (Grant No. 2020KYQD-01), Xiangshan Talent Foundation of Zhuhai People’s Hospital (Grant No. 2021XSYC-09).

Data availability

The original contributions presented in the study are included in the article/Supplementary Material. The datasets can be obtained from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk/).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xin He, Yuzhe Zhang and Zhengsheng Yi contributed equally to this work.

Contributor Information

Yusheng Shi, Email: syszxm@hotmail.com.

Shengni Hua, Email: huashengni123@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (155.8KB, pdf)

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material. The datasets can be obtained from the IEU Open GWAS database (https://gwas.mrcieu.ac.uk/).

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