Tumor tissue-associated Phascolarctobacterium is associated with lymph node metastasis, prognosis, and immune-contexture features in colorectal cancer.

Lymph node metastasis (LNM) critically influences prognosis in colorectal cancer (CRC), yet the mechanisms driving this process, particularly the contribution of the intratumoral microbiota, remain insufficiently defined.

We performed 16S rRNA sequencing on tumor tissue from a discovery cohort of 122 CRC patients, followed by validation in one internal and one external validation cohort. Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and transcriptomic deconvolution were used to explore tumor immune-contexture features and tissue-associated Phascolarctobacterium-like signals. Bulk RNA-seq data were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA) and pathway enrichment to explore host transcriptomic modules and molecular pathways associated with microbial abundance.

Higher tumor tissue-associated Phascolarctobacterium abundance showed a modest positive association with lymph node metastasis and was associated with worse overall survival in the discovery cohort (HR = 3.892, 95% CI = 1.441-10.513, P = 0.007). These tumors showed exploratory immune-contexture differences, including lower CD8+ T-cell-related signals and higher macrophage/M2 macrophage-related signals. WGCNA identified exploratory abundance-associated modules enriched in keratinocyte differentiation, epithelial development, and MAPK signaling, whereas low-abundance-associated modules were linked to lipid metabolism and redox regulation.

Tumor tissue-associated Phascolarctobacterium abundance showed exploratory associations with lymph node metastasis, poorer overall survival in the discovery cohort, and immune-contexture features in CRC. These findings are exploratory and require validation in larger independent cohorts and functional studies.
Cancer
Care/Management
Policy

Authors

Li Li, Hu Hu, Liu Liu, Ma Ma, Ren Ren, Guo Guo, Zhang Zhang, Meng Meng, Liu Liu, Zhao Zhao, Zan Zan, Guan Guan, Bai Bai
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