背景。癌症患者的局部复发和远处转移是致死的主要原因。仅仅考虑物种丰度变化来鉴定癌症复发和转移标志物，妨碍了寻找解决方案的发现。假设。考虑微生物丰度变化和微生物相互作用有助于识别肿瘤复发和转移的微生物标志物。目的。本研究旨在同时考虑微生物丰度变化和微生物相互作用，以识别多种癌症类型中的复发和转移的微生物标志物。方法。从癌症基因组图谱项目（TCGA）中收集了1106例非复发和转移（随初次手术后3年内无复发和转移）的组织样本和912例复发或转移（随初次手术后3年内发生复发或转移）的组织样本，代表了11种癌症类型。结果。11种癌症的肿瘤组织细菌组成明显不同。其中，头颈部鳞状细胞癌（HNSC）、肺鳞状细胞癌（LUSC）、胃腺癌（STAD）和子宫内膜癌（UCEC）的组织微生物群落在预测患者复发和转移方面表现相对良好，接受者操作特征曲线下面积（AUC）分别为0.78、0.74、0.91和0.93。对于这四种癌症，同时考虑物种丰度变化和微生物相互作用，以九个属（Niastella、Schlesneria、Thioalkalivibrio、Phaeobacter、Sphaerotilus、Thiomonas、Lawsonia、Actinobacillus和Spiroplasma）的组合在预测患者生存方面表现最佳。结论。综上所述，我们的结果暗示全面考虑微生物丰度变化和微生物相互作用有助于挖掘具有预后信息的细菌标志物。

Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.