The pathogenesis of pancreatic cancer is complex. While immune dysregulation and gut microbiota disturbances are considered key factors, the causal pathway between them remains unclear. This study employs a 2-step Mendelian randomization (MR) approach to empirically investigate the “immune cells - gut microbiota - pancreatic cancer” mediation pathway from a genetic perspective. Relevant data on 731 immune cell phenotypes (N = 3757), 473 gut microbiota (N=8293), and pancreatic cancer (N = 3,87,888) were extracted from large-scale genome-wide association study (GWAS) and FinnGen summary statistics. MR was employed to assess causal relationships. Inverse-variance weighted (IVW) regression served as the primary MR method, supplemented by sensitivity analyses to ensure robustness. Mediation analysis was performed to assess potential pathways from immune cells to pancreatic cancer mediated by gut microbiota. Two-step MR analyses revealed that 8 gut microbial taxa mediate the causal relationship between 14 immune cell phenotypes and pancreatic cancer. For example, Klebsiella mediated 10.2% of the protective effect of CD39+ monocytes on pancreatic cancer risk. Ruminococcus E sp003521625 accounted for 7.09% of the risk effect associated with HLA DR on B cells. Conversely, UBA7177 sp002491225 attenuated the protective effect of CD11b on CD33dim HLA DR− cells, with a mediation proportion of–10.5%. Notably, Brevibacillales exhibited the highest mediation proportion (15.5%) between Activated & resting Treg % CD4 Treg and pancreatic cancer. Our findings support a genetically predicted causal pathway linking immune cell phenotypes to pancreatic cancer via specific gut microbiota. These results underscore the mediating role of certain microbial taxa and offer novel insights for future strategies involving immune modulation or microbiota-based interventions in pancreatic cancer.
The profound phenotypic heterogeneity of circulating tumor cells (CTCs) presents a major analytical challenge, demanding technologies capable of high-plex, quantitative single-cell profiling. Here, we introduce PRISM (Phenotypic Resolution via Immuno-SERS Mapping), a biosensing platform engineered to meet this challenge. The platform integrates a high-efficiency dual-antibody (anti-EpCAM/anti-CSV) capture substrate with a suite of seven spectrally orthogonal SERS nanoprobes, enabling crosstalk-free, multiplexed quantification of epithelial, mesenchymal, and stem-like (E-M-S) markers. We demonstrate the platform's robust analytical performance, including high capture efficiency for heterogeneous cell lines and excellent linearity. Its superior phenotypic resolving power was validated by quantitatively distinguishing canonical cell line archetypes and tracking dynamic protein expression shifts during induced epithelial-mesenchymal transition. Applying PRISM to CTCs from pancreatic cancer patients, we introduce a novel data analysis framework, including a 'Metastasis Potential Score' (MPS), to translate high-dimensional spectral data into a clinically relevant metric for risk stratification. Furthermore, longitudinal analysis of patient samples demonstrates the platform's utility as a dynamic monitoring tool, capable of tracking therapy-induced phenotypic shifts. PRISM establishes a powerful analytical methodology for high-dimensional single-cell analysis, providing a robust tool for both fundamental cancer biology research and translational clinical applications.
Yanrong Wen, Mengxiang Liu, Chun-Hui Liu et al.· ACS Sensors· 0 citations
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