Department of Gastroenterology, the Shanghai Tenth People's Hospital, School of Medicine, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China.
Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-Sen University, Guangzhou, P. R. China.
Gut Microbes. 2023 Dec;15(2):2245562. doi: 10.1080/19490976.2023.2245562.
Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.
微生物特征在预测结直肠癌（CRC）方面具有显著潜力。本研究旨在评估多模态微生物特征、多王国物种、基因和单核苷酸变体（SNVs）在检测癌前腺瘤方面的诊断能力。我们通过 xMarkerFinder 对 750 个样本的全宏基因组测序数据进行了跨队列分析，以识别与腺瘤相关的微生物多模态特征。我们的数据显示，真菌物种在区分腺瘤与对照组方面优于其他王国的物种，ROC 曲线下面积（AUC）为 0.71。包括同义突变暗 SNVs 在内的微生物 SNVs 具有最强的诊断能力，AUC 值为 0.89，灵敏度为 0.79，特异性为 0.85，马修斯相关系数（MCC）为 0.74。SNV 生物标志物在三个独立验证队列中也表现出出色的性能（AUCs=0.83、0.82、0.76；灵敏度=1.0、0.72、0.93；特异性=0.67、0.81、0.67，MCCs=0.69、0.83、0.72），对腺瘤具有较高的疾病特异性。进一步支持上述结果的功能分析显示，细菌和真菌之间更频繁的跨王国关联，以及在腺瘤中群体感应、嘌呤和丁酸盐代谢的异常，在一个新招募的队列中通过 qRT-PCR 得到了验证。因此，这些数据扩展了我们对肠道微生物组中与腺瘤相关的多模态改变的理解，并为微生物 SNVs 用于 CRC 的早期检测提供了依据。
