Feng Jiahui, Gong Zhizhong, Sun Zhangran, Li Juan, Xu Na, Thorne Rick F, Zhang Xu Dong, Liu Xiaoying, Liu Gang
School of Life Sciences, Anhui Medical University, Hefei, China.
Henan International Joint Laboratory of Non-coding RNA and Metabolism in Cancer, Henan Provincial Key Laboratory of Long Non-coding RNA and Cancer Metabolism, Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Front Microbiol. 2023 Jan 13;14:1034325. doi: 10.3389/fmicb.2023.1034325. eCollection 2023.
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) and normal paired tissues (T) along with 10 matched fecal samples (F) and 10 healthy controls (F). The highest microbial phyla abundance from T and T were Firmicutes, while the dominant phyla from F and F 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 than in T, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in F than in F. 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 and T groups and 700 metabolites different between F and F 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.
微生物群及其代谢产物在结直肠癌（CRC）致癌过程中的作用日益受到认可。为了揭示新的CRC生物标志物，我们对10对CRC组织（T）和正常配对组织（T）、10份匹配的粪便样本（F）以及10名健康对照者的粪便样本（F）进行了16S rRNA基因测序和液相色谱-质谱（LC-MS）代谢物分析。来自T和T的微生物门丰度最高的是厚壁菌门，而来自F和F的优势门是拟杆菌门，在四组中鉴定出72个不同的微生物属。在组织之间或粪便样本之间未检测到Chao1指数的变化，而非度量多维尺度分析（NMDS）显示粪便样本之间有明显的聚类，但组织之间没有。线性判别分析效应大小（LEfSe）分析表明，柄杆菌目和短波单胞菌属在T中比在T中更高，而伯克霍尔德菌科、萨特菌科、坦纳菌科和拟杆菌科在F中比在F中更高。微生物关联网络表明一些属具有显著不同的相关性。组织和粪便分析表明，脂质和类脂质分子是在粪便样本中检测到的最丰富的代谢物。此外，基于代谢谱的偏最小二乘判别分析（PLS-DA）显示CRC样本和正常样本有明显的聚类，T和T组之间共有102种差异代谢物，F和F组之间有700种代谢物不同。然而，在所有四组中仅检测到肉豆蔻酸。在组织和粪便中，属水平的微生物组和代谢组学数据之间记录到高度显著的正相关。并且几种代谢物与配对的微生物相关，表明微生物群-代谢组有很强的耦合，这也表明CRC代谢组特征的一部分可归因于微生物。大多数此类微生物组和代谢物在CRC患者中显示出方向一致的变化，表明其作为潜在生物标志物的效用。然而，需要进一步研究以增加样本量来验证这些发现。
