In this study, univariate MR analyses indicated an inverse association between MCV or MCH and PCa, whereas eosinophil and basophil counts were positively associated with PCa risk. Nonetheless, no statistically significant causal relationships were identified between PCa and the other twelve hematological parameters examined. Subsequent multivariable MR analysis highlighted a pronounced association between elevated basophil count and an increased incidence of PCa. Within this analytical framework, no significant associations were observed for MCV, MCH, or eosinophil count with PCa risk. Further investigations into reverse causality through MR analysis suggested that PCa may lead to an increase in both neutrophil and red blood cell counts. These findings underscore the complexity of the relationships between hematological indicators and PCa, warranting additional research to clarify the mechanisms underpinning these associations.
Our study's findings partially resonate with those from prior observational research, exploring the nexus between hematologic parameters and PCa risk and outcomes. The prospective analysis leveraging data from the UK Biobank by Watts et al. reported elevated risks of PCa associated with increased red blood cell and platelet counts, while lower risks were observed with higher MCV, MCH, and mean corpuscular hemoglobin concentration. Notably, they identified a link between higher white blood cell or neutrophil counts and PCa mortality(Watts et al. 2020). Our results corroborate these observations, especially our reverse Mendelian Randomization analysis indicating a neutrophil count increase in PCa, potentially elucidating the connection with PCa mortality. Porcaro AB et al. observed that white blood cell count could be an independent predictor for PCa risk in patients undergoing transurethral resection of the prostate for lower urinary tract symptoms(Porcaro et al. 2021). Although our study did not mirror this specific finding, the insight adds valuable context to the broader discussion on hematologic markers as potential PCa risk indicators. Research by Bahig H et al. on the association between neutrophil count and overall survival in localized PCa supports the notion that neutrophil count may serve as an independent predictor of patient outcomes(Bahig et al. 2015), Our findings, indicating an increase in neutrophil count associated with PCa, align with their results, suggesting a need for further investigations to elucidate the relationship and its implications for PCa prognosis. Furthermore, Hadadi A et al.'s retrospective multicenter study found a correlation between basophil count and poorer outcomes in metastatic hormone-sensitive PCa(Hadadi et al. 2022), This aligns with our experimental data, indicating a genetic association between elevated basophil count and an increased risk of developing PCa. Such congruence with existing literature underscores the potential of basophil count as a biomarker for PCa risk and outcomes, highlighting the relevance of our findings in the broader context of PCa research. Together, these studies and our own work contribute to a growing body of evidence that underscores the complex interplay between hematologic parameters and PCa, warranting further exploration to fully understand their clinical and biological significance.
The existing body of research has established various associations between hematological parameters and PCa, highlighting the potential of these indicators in understanding disease progression and prognosis. Wang F et al. demonstrated a significant correlation between preoperative red cell distribution width (RDW) and lymphovascular invasion in PCa, suggesting RDW's importance in the disease's advancement(Wang et al. 2022). Similarly, Albayrak et al. found RDW to be predictive of an increased risk of PCa, further underscoring its role in disease progression(Albayrak et al. 2014). Furthermore, Yu Z et al. identified a link between elevated platelet counts and poorer prognoses in PCa, adding another layer to the complex relationship between hematological markers and cancer outcomes(Yu et al. 2023). Complementing these findings, Song W et al. reported that integrating platelet distribution width with total or free prostate-specific antigen levels could refine the diagnostic precision for PCa, enhancing the clinical value of PSA testing(Song et al. 2022). Despite these insightful correlations, our study did not replicate these associations, potentially highlighting the influence of confounding variables and the intricate dynamics governing the interaction between hematological markers and PCa. This discrepancy underscores the necessity for cautious interpretation of hematological indicators in PCa research and diagnosis, recognizing the multifaceted nature of their relationship with the disease. Our findings, diverging from the established literature, emphasize the critical need for further investigation to delineate the specific roles these markers play in PCa's pathophysiology and their utility in clinical practice.
Unraveling the intricate association between hematological markers and PCa presents significant challenges, chiefly due to the paucity of definitive factors directly correlated with PCa's initiation and progression. This MR analysis brings to light the potential protective roles of MCV and MCH against PCa. These parameters, indicative of red blood cell size and hemoglobin content respectively, may reflect cellular metabolic activities that inhibit the emergence and growth of malignant cells in PCa. Additionally, our findings suggest that an increase in eosinophil and basophil counts may elevate PCa risk, possibly signaling changes in the body's inflammatory state. Chronic inflammation is closely associated with heightened cancer risk, involving complex interactions that may engage with the tumor microenvironment, inflammation, immune surveillance, and cancer progression pathways mediated by chemokines and cytokines(Nagarsheth et al. 2017, Hughes and Nibbs 2018). Therefore, the observed elevation in eosinophil and basophil counts could augment PCa risk through inflammation-related pathways(Melo et al. 2013, Yamaguchi et al. 2009). Moreover, our study indicates that PCa may lead to increased neutrophil and red blood cell counts, with the former potentially linked to inflammation-induced metabolic changes and the latter associated with PCa's tendency for bone marrow metastasis. Bone marrow stromal cells, capable of enhancing PCa cell survival by inhibiting the action of tumor necrosis factor-related apoptosis-inducing ligand, may contribute to increased blood cell production in the bone marrow(Cross et al. 2007). Therefore, sustaining the activity of PCa cells may lead to an elevation in the production of blood cells within the bone marrow. Additionally, as tumor cells advance, their oxygen consumption tends to increase(Cook et al. 2012), potentially accounting for the heightened presence of oxygen-carrying red blood cells.
Our investigation elucidates causal relationships between PCa and various hematological markers, including MCV, MCH, eosinophil count, basophil count, neutrophil count, and red blood cell count, aligning with insights from prior observational studies. While these associations suggest potential underlying mechanisms, the precise biological principles and mechanisms demand further detailed exploration to be definitively established. A particularly noteworthy finding from our multivariable Mendelian randomization analysis is the distinct correlation observed between basophil count and both the incidence and progression of PCa. This correlation, hitherto underexplored in the breadth of observational literature, signals a promising new direction for scientific inquiry, inviting researchers to delve into previously uncharted territories of PCa pathophysiology.
This study explores the causal relationships between various hematological markers and PCa utilizing state-of-the-art genetic tools from the latest and largest GWAS datasets for these diseases. Implementing a bidirectional and multivariate MR approach, we rigorously assessed the causal links while employing a comprehensive set of sensitivity analyses to address potential pleiotropic biases, thereby ensuring the robustness of our MR findings. The investigation was specifically focused on participants of European ancestry within the GWAS datasets to minimize the influence of population stratification bias on the results. The observed consistency of genetic susceptibility across different data sources and MR models for the 12 cancers in relation to PCa reinforces the credibility of our findings, suggesting a minimal likelihood of influence by horizontal pleiotropy.
However, the interpretation of these results must be approached with caution due to several limitations. Notably, Cochran's Q test indicated the presence of heterogeneity among the IVs, leading to the adoption of the IVW random-effects method as the primary MR approach for its acknowledged robustness(12). Additionally, the MR-PRESSO test identified outliers and potential horizontal pleiotropy, which were effectively adjusted for after outlier correction, thereby reducing the impact of influential values on the overall results. The use of the MR-Egger Intercept P further assessed the likelihood of horizontal pleiotropy, indicating a low probability and thus providing further confidence in the causal inferences drawn. The study also addressed the issue of potential overfitting due to sample overlap between GWAS datasets for the same trait by carefully selecting instrumental variables from large-scale GWAS and integrating findings from multiple data sources. Despite these measures, the generalizability of the findings to populations beyond those of European ancestry remains a limitation. Variabilities in results obtained through different MR methods for the same outcome highlighted the complexity of the analysis, while the call for further biological research underscores the need for a deeper understanding of the mechanisms underlying these findings.