Cancer is widely recognized as a leading contributor to human mortality and a significant barrier to global efforts aimed at enhancing life expectancy. The incidence and fatality of cancer are on the rise worldwide, with a staggering 19 million individuals being newly diagnosed and over 10 million lives lost in the year 2020 [1]. The escalating worldwide health emergency highlights the pressing requirement for progress in cancer research, specifically in comprehending and battling the complex characteristics of the disease. In the ever-changing realm of oncology research, exploring the tumor microenvironment (TME) emerges as a pivotal area of study [2]. It encompasses not only the structural and metabolic characteristics of the tissue harboring the tumor but also the intricate cellular milieu, inclusive of the tumor cells' nucleus and cytoplasm. This holistic view of the TME underscores its significance during the processes of tumorigenesis and metastasis, delineating a complex interplay between the tumor and its surrounding environment.

Central to this interplay are the immune cells embedded within the TME, manifesting a dichotomous role [3], [4], [5]. On the one hand, they function as sentinels, capable of recognizing and eradicating nascent tumor cells, a process integral to cancer immunosurveillance. Conversely, these cells can paradoxically foster tumor growth, modulating their survival and development through mechanisms like autocrine and paracrine secretion. This ambivalence positions immune cells as a ‘double-edged sword’ in the TME, necessitating a detailed exploration of their multifaceted roles. The TME is populated with a diverse array of immune cells, including various leukocytes such as granulocytes, monocytes, lymphocytes, along with erythrocytes, platelets, mast cells, dendritic cells, and innate lymphoid cells, each contributing uniquely to the cancer-immune interplay [6].

In this study, we propose a novel strategy for cancer investigation by combining Mendelian randomization (MR) and MAGMA analysis. MR is an approach that utilizes genetic variations as instrumental variables (IVs) to investigate the possible causal relationship between lifelong exposure and outcome. In the realm of MR, employing the conceptual random assignment of alleles serves to navigate biases resulting from unobserved confounding variables, including lifestyle and environmental factors, and also addresses the problem of reverse causation [7]. The TSMR permits the evaluation of the relationship between IVs and exposure, as well as the relationship between IVs and outcomes in diverse populations [8]. GWAS explores genetic associations between traits based on SNPs and combines GWAS data with gene expression and methylation. GWAS has enabled the discovery of loci associated with gene expression or DNA methylation levels (eQTL or mQTL) [9], [10]. The concept of MR has been expanded and refined with the development of SMR, which allows for the utilization of independent GWAS summary statistics data and QTL data to prioritize potential causal genes from significant findings in GWAS [9]. By utilizing this approach in conjunction with a heterogeneity independent instruments (HEIDI) analysis, we could discriminate between potential causal connections and the presence of widespread linkage disequilibrium (LD) in the genome. In addition, MAGMA employs multiple regression models to uncover associations between genes and traits in GWAS datasets, presenting a robust approach for integrating gene expression and GWAS to identify significant expression-trait relationships [11]. Enhancing the capability to detect new traits linked to genes effectively mitigates the occurrence of unreliable outcomes in GWAS. In recent times, a growing number of scholars have effectively employed MAGMA to pinpoint genes linked to intricate diseases and characteristics. Furthermore, they have showcased enhanced statistical efficacy and accelerated speed when comparing this tool with other gene analysis software such as VEGAS, PLINK, ALIGATOR, INRICH, and MAGENTA [11]. This innovative methodology enables us to navigate the intricate genetic terrain of cancer with unprecedented accuracy.

This study aims to clarify the causality between immune cells and different cancer types through a comprehensive TSMR analysis. This is complemented by MAGMA analyses targeting immune cells, aiming to identify key immune cell-related genes. Our methodology involves a systematic approach where the potential roles of these genes in either promoting or inhibiting cancer progression are scrutinized through SMR. The uniqueness of our study resides in its extensive range, surpassing genetic examination to encompass pharmacological dimensions. This is accomplished by employing predictive drug modeling and conducting molecular docking investigations, thereby validating the pharmacological significance of the identified targets and presenting potential therapeutic opportunities. By incorporating genetic and pharmacological techniques, this research offers a comprehensive viewpoint on the involvement of immune cells in cancer, with the possibility of introducing innovative therapeutic approaches.