Melanoma ranks as the third most common type of skin cancer and has witnessed the fastest increase in incidence among all malignant tumors in recent years (1). Moreover, it remains the only type of cancer for which the objective of reducing mortality in the healthy population has not been met in recent years, imposing a significant economic burden on both individuals and society. In 2020, there were 325,000 new melanoma cases worldwide, with the highest incidence rates reported in Australia and New Zealand (2). The risk of developing melanoma is linked to factors such as ultraviolet exposure, skin pigmentation, and genetic predispositions (3). Research indicates that individuals with lighter skin are at a notably higher risk of melanoma compared to those with darker skin tones (4). Early-stage melanoma primarily undergoes surgical removal, boasting a five-year survival rate of 91% (5). However, the majority of diagnoses occur at intermediate or advanced stages, where options are limited and prognoses are poor (6). In terms of prevention, widespread screening is not recommended due to melanoma’s characteristics, with emphasis instead placed on educating and screening those at high risk (3). Identifying melanoma’s pathogenic factors and discovering new treatments are therefore critically important.

Lipid-lowering medications, such as statins, PCSK9 inhibitors, and NPC1L1 inhibitors, play a crucial role in preventing cardiovascular diseases. Beyond their primary use in cardiovascular prevention, these drugs also exhibit anti-inflammatory, antioxidant, immune-regulatory, neuroprotective, and anti-diabetic properties (7). Some studies have suggested that lipid-lowering drugs may be effective in cancer treatment (8). However, evidence indicates that statin use could increase melanoma risk (9). Current retrospective studies and meta-analyses have yet to achieve consensus on this issue. This may be attributed to the inherent methodological biases and small sample sizes of observational studies, as well as variability in regions, populations, baseline characteristics, and follow-up durations across different cohorts, which can obscure the relationship between study findings and conclusions. Therefore, it is essential to conduct comprehensive and rigorous research to definitively determine the causal link between lipid-lowering drugs and melanoma risk.

Mendelian randomization (MR) is a robust method that employs genetic variation as an instrumental variable (IV) to explore the association between exposure factors and diseases (10). MR analysis reduces confounding factors and reverse causality by utilizing the segregation and independent assortment of genes transmitted from parents to offspring. In scenarios devoid of pleiotropy (i.e., genetic variations influencing the disease via alternative pathways) and population stratification, MR can accurately determine disease risk (11). Furthermore, genetic variations can forecast and mirror the impact of medications on individuals. This is attributed to the fact that genetic variations, whether adjacent to or within the gene encoding a drug’s target, can regulate its expression, thereby influencing the activity of the pharmacological target. Our objective is to perform a two-sample MR analysis to assess the causal impact of genetic surrogates for lipid-lowering drugs on melanoma risk in a thorough and dependable manner, aiming also to offer novel insights into the prevention and treatment of melanoma.

To our knowledge, this is the inaugural utilization of drug target MR analysis for examining the causal link between lipid-lowering drug exposure and melanoma risk. Employing Mendelian randomization, we investigated the impact of three prevalent LDL-C reduction drug targets (HMGCR, PCSK9, and NPC1L1 genes) on melanoma risk. Our findings revealed that HMGCR was associated with a reduced risk of melanoma, PCSK9 with an elevated risk, and NPC1L1 expression bore no significant relationship with melanoma risk.

The genetic proxy for HMGCR expression is notably linked to a reduced melanoma risk, suggesting that HMG-CoA reductase inhibitors may elevate melanoma incidence. Statins, emblematic of HMG-CoA reductase inhibitors, decrease LDL-C synthesis by suppressing HMGCR expression and are the primary choice for cardiovascular disease prevention in clinical settings. A meta-analysis encompassing 11 studies observed an increased melanoma risk with statin use (9), aligning with our results. We propose that the immunoregulatory effects of statins, particularly immunosuppression, play a crucial role in this association. Research indicates increased melanoma risk in individuals experiencing immunosuppression, such as those undergoing solid organ transplants or receiving immunosuppressive therapy, compared to their non-immunosuppressed counterparts (24). Currently, it is understood that statins increase Foxp3 mRNA transcription and Foxp3 protein production by stimulating Foxp3 expression. This activation increases the proportion of regulatory T cells, leading to immunosuppression and potentially facilitating melanoma development (25). Therefore, clinical practitioners should exercise caution when prescribing statins to patients with melanoma. Moreover, vigilance against melanoma development may be warranted in patients taking Atorvastatin.

Our investigation revealed an association between PCSK9 gene expression and an increased risk of melanoma, suggesting that PCSK9 inhibitors, which reduce LDL-C levels, may offer a specific protective effect against melanoma. Despite the long-term use of PCSK9 inhibitors for managing LDL-C levels, their relationship with melanoma has been sparsely researched. Existing studies highlight the significant role of PCSK9 expression in melanoma’s pathogenesis, particularly noting the critical contribution of cholesterol accumulation in tumors facilitated by PCSK9. Cholesterol’s vital roles in cell proliferation (26) and its influence on cancer progression and the tumor microenvironment (27) are well documented. According to Yan Gu et al., PCSK9 potentially promotes melanoma partly through its regulation of lipid/cholesterol metabolism (28). Moreover, PCSK9 expression appears to have a profound impact on the systemic immune system, aiding in the immune evasion of melanoma cells. It disrupts the recirculation of major histocompatibility protein class I (MHCI) to the cell surface by binding to MHCI and facilitating its lysosomal degradation, thereby hampering the infiltration of cytotoxic T cells within tumors (29). Another study suggests the systemic influence of PCSK9 on immune mechanisms might surpass its effects on MHCI, with observed changes in the immune system including modifications to CD8 molecules, crucial for the functions of CD8+ T cells such as granzymes and perforin 1, CD3 subunits of the T-cell receptor complex (CD3E, CD3D, CD3G), complements, PAMPs like TLR8/toll-like receptor 8, cytokines (TGFβ1 and INFγ), chemokines (CCL5, CCR7, CCL4, CXCR6, among others), PI3KCG [signaling that regulates immune checkpoint PD-L1 expression in cancer cells (30)], and various immune checkpoints (PD1, PD-L1, PD-L2, CTLA4, TIGIT, CD96, LAG3, among others) (28). Additionally, Ahnak’s role in regulating melanoma metastasis to lung epithelial cells via PCSK9 expression has been identified, where PCSK9 acts as an inhibitor of TNF α-mediated apoptosis, thus promoting melanoma cell metastasis to the lungs (31). Huimin Sun et al. discovered that PCSK9 inhibitors could decrease liver metastasis of melanoma cells by reducing circulating cholesterol levels (32).

This study presents several significant advantages. Firstly, to our knowledge, this is the inaugural MR study to investigate the causal impact of lipid-lowering drugs on melanoma. Secondly, by utilizing inherited mutations in genes encoding drug targets, the study mirrors the potential effects of common LDL-C reduction therapies, thereby reflecting the influence of lifetime drug exposure on melanoma while circumventing the limitations associated with exposure and follow-up in clinical trials or observational studies. Thirdly, the employment of aggregate genetic association data from large-scale GWAS allows for causal inferences to be made with high statistical power. Fourthly, utilizing the genetic and socio-cultural homogeneity of the European population minimizes false associations due to demographic stratification and other confounding factors.

However, there are limitations to this study. The MR estimated values represent long-term regulation of disease risk by drug targets, which may not directly correlate with the effects observed during shorter periods of drug administration. Thus, the risk reduction through PCSK9 inhibitors for treating melanoma might not align with the magnitudes estimated in this MR study. Secondly, while MR analysis facilitates causal inferences, it does not explore specific biological mechanisms. Thirdly, the reliance on data predominantly from European populations limits the generalizability of the findings to other ethnic groups, necessitating future research to include diverse populations, such as Asians. Lastly, the absence of stratified analysis in the GWAS data, based on gender, age, disease progression, and other factors, restricts the ability to examine specific details, highlighting the need for future studies with larger sample sizes and more comprehensive analyses.

In conclusion, through Mendelian randomization, our study explored the potential causal link between lipid-lowering drugs and melanoma. The findings indicate that HMGCR inhibitors(statins) might contribute to melanoma development, whereas PCSK9 inhibitors (iromazumab and aliciumab) could offer protective effects against melanoma. This highlights the need for cautious use of statins in the clinical management of melanoma. PCSK9 inhibitors emerge as potential protective agents against melanoma, highlighting their promise as therapeutic options. Nonetheless, the intricate relationship between lipid-lowering drugs and melanoma necessitates further clinical investigations to explore the underlying mechanisms and confirm their clinical relevance comprehensively. Thus, our study establishes a groundwork for future research in this critical area of study.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

BY: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. HW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing. WS: Formal analysis, Investigation, Writing – original draft. JF: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Visualization, Writing – review & editing. SH: Formal analysis, Funding acquisition, Resources, Supervision, Validation, Writing – review & editing.