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Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC–MS) metabolite analyses in 10 CRC (T(CRC)) and norma...

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Detalles Bibliográficos
Autores principales: Feng, Jiahui, Gong, Zhizhong, Sun, Zhangran, Li, Juan, Xu, Na, Thorne, Rick F., Zhang, Xu Dong, Liu, Xiaoying, Liu, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880203/
https://www.ncbi.nlm.nih.gov/pubmed/36712187
http://dx.doi.org/10.3389/fmicb.2023.1034325
Descripción
Sumario:Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC–MS) metabolite analyses in 10 CRC (T(CRC)) and normal paired tissues (T(HC)) along with 10 matched fecal samples (F(CRC)) and 10 healthy controls (F(HC)). The highest microbial phyla abundance from T(HC) and T(CRC) were Firmicutes, while the dominant phyla from F(HC) and F(CRC) were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in T(HC) than in T(CRC), while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in F(HC) than in F(CRC). Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between T(HC) and T(CRC) groups and 700 metabolites different between F(HC) and F(CRC) groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.