A comprehensive analysis through Mendelian randomization of immunological markers and sphingolipid metabolism.

BACKGROUND

Lung cancer pathogenesis involves complex interactions between immune system components and metabolic pathways. However, the causal relationships between these factors remain unclear. This study aimed to employ Mendelian randomization (MR) analysis to establish causal links between immunological markers, metabolic factors, and lung cancer development, while integrating multi-omics data for comprehensive molecular characterization.

METHODS

We conducted a two-sample MR analysis of 731 immunological features and 1,400 metabolites using inverse-variance weighted methodology. The analysis was supplemented with single-cell expression profiling, functional enrichment analysis, and machine learning approaches. Multiple MR methodologies, including MR-Egger and heterogeneity testing via Cochran's Q statistic, were employed to validate findings. We specifically investigated the mediating role of sphingomyelin in the relationship between T cell %lymphocyte levels and lung cancer risk.

RESULTS

MR analysis identified 25 blood cell phenotypes significantly linked to lung cancer susceptibility, with memory antigen-presenting cells showing notable risk association (OR = 1.0763, 95%CI: 1.0147-1.1417, P = 0.0145). Seventy-six metabolites demonstrated causal influences on lung cancer pathogenesis, particularly within sphingolipid metabolism pathways. Single-cell profiling revealed significant differential expression of six genes (APCDD1L, CNTNAP4, GNG13, KIRREL2, LINC00628, and LIPK) between normal and tumor tissues. Machine learning models constructed from these findings demonstrated robust predictive performance in both TCGA and GEO datasets.

CONCLUSIONS

Through rigorous MR analysis, this study establishes causal relationships between specific immune markers, metabolic pathways, and lung cancer development.