This study explores the causal relationship between plasma lipidome and 6 common cancers using Mendelian randomization. Genome-wide association study data on 179 lipid species were used as exposures, with genome-wide association study datasets for 6 cancers as outcomes. The primary method was inverse variance weighted analysis, supplemented by Mendelian randomization-Egger regression and weighted median approaches. Sensitivity analyses, including the Steiger test, were conducted to evaluate the directionality and robustness of the causal relationships. Key findings identified phosphatidylethanolamine (16:0_20:4) as a risk factor for hepatic cancer, while sphingomyelin (d40:1) was protective. For lung cancer, sterol ester (27:1/20:4) increased risk, whereas sterol ester (27:1/18:2) reduced it. Sterol ester (27:1/20:3) was a risk factor for colorectal cancer, while phosphatidylcholine (18:2_0:0) was protective. Phosphatidylcholine (16:0_20:4) increased esophageal cancer risk, while phosphatidylcholine (16:0_18:3) offered protection. Phosphatidylinositol (18:0_20:4) increased thyroid cancer risk, while phosphatidylinositol (16:0_18:2) was protective. Diacylglycerol (18:1_18:2) was protective against breast cancer. These findings shed light on the causal link between lipid metabolism and cancer risk, contributing to the understanding of lipid involvement in cancer pathogenesis and offering potential strategies for early detection and treatment.

Background: The plasma lipidome is intricately associated with cancer. However, the causal relationship between them remains uncertain. Therefore, this study employs Mendelian randomization (MR) based on genetic principles to investigate the potential causal relationship between plasma lipidome and six common types of cancer. Methods: MR analysis utilizes publicly available genetic data, employing a genome-wide association study (GWAS) of 179 lipid species as exposure and GWAS datasets of six different cancers as outcomes. The inverse variance weighted (IVW) method serves as the primary approach, with MR-Egger regression and weighted median (WM) method employed as supplementary methods for analysis. Additionally, sensitivity analyses including Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis are conducted to assess the reliability and stability of causal relationships. The Steiger test is also utilized to determine the directionality of causal relationships. Results: The IVW analysis reveals that phosphatidylethanolamine (16:0_20:4) levels and others are implicated as risk factors for hepatic cancer, while sphingomyelin (d40:1) levels and others are identified as protective factors against hepatic cancer. Sterol ester (27:1/20:4) levels and others are associated with increased risk of lung cancer, whereas sterol ester (27:1/18:2) levels and others are associated with decreased risk of lung cancer. Sterol ester (27:1/20:3) levels and others are identified as risk factors for colorectal cancer, whereas phosphatidylcholine (18:2_0:0) levels and others are protective against colorectal cancer. Phosphatidylcholine (16:0_20:4) levels and others are linked to increased risk of esophageal cancer, while phosphatidylcholine (16:0_18:3) levels and others are associated with protection against esophageal cancer. Phosphatidylinositol (18:0_20:4) levels and others are identified as risk factors for thyroid cancer, whereas phosphatidylinositol (16:0_18:2) levels and others are protective against thyroid cancer. Diacylglycerol (18:1_18:2) levels and others are identified as protective factors against breast cancer. Conclusions: There exists a clear causal relationship between plasma lipidome and six types of cancer. Additionally, it has been observed that the same single-nucleotide polymorphisms (SNPs) serve as instrumental variables (IVs), influencing cancer through the plasma lipidome. This provides further avenues and methodologies for early screening and effective treatment of cancer.

Understanding the role of the tumor microenvironment in colorectal cancer (CRC) progression remains a challenge due to its complexity. Investigating the interplay between immune cell characteristics, serum metabolites, inflammatory protein factors, and CRC could unveil novel therapeutic avenues. We used 2-sample Mendelian randomization (MR) on Genome-Wide Association Studies (GWAS) data to explore causal links between 731 immune cell characteristics, 1400 serum metabolites, 91 inflammatory proteins, and CRC. Various MR methods, including inverse variance weighted (IVW) and MR-Egger, were applied to ensure robust analysis. Sensitivity analyses, such as the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis, were performed to check for pleiotropy, heterogeneity, and influential outliers. Following rigorous genetic variation screening, we identified 43 immune cell characteristics associated with CRC. Notably, 7 immunophenotypes, including CD39⁺ CD4⁺ T cell Absolute Count, exhibited significant associations as protective factors. Additionally, 36 other immunophenotypes showed significant causal relationships with CRC. Among serum metabolites, 37 were correlated with CRC, with 1-arachidonoyl-gpc (20: 4n6) being the most closely linked as a risk factor. Similarly, 36 serum metabolites displayed significant causal relationships with CRC. Seven inflammatory protein factors exhibited causal relationships with CRC, with 4 posing as risk factors and 3 as protective factors. Our study scrutinized 731 immune cell characteristics, 1400 serum metabolites, and 91 inflammatory protein factors within the tumor microenvironment. We confirmed causal relationships between 43 immune cell characteristics, 37 serum metabolites, and 7 inflammatory protein factors with CRC. These findings offer novel insights into the potential etiology, prevention, and treatment strategies for CRC.