{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T15:09:14Z","timestamp":1783091354020,"version":"3.54.6"},"reference-count":51,"publisher":"Microbiology Society","issue":"8","funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant Nos. 32071465, 31871334, and 31671374"],"award-info":[{"award-number":["Grant Nos. 32071465, 31871334, and 31671374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the National Key R&D Program of China","award":["Grant No. 2018YFC0910502"],"award-info":[{"award-number":["Grant No. 2018YFC0910502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,25]]},"abstract":"\n Background.<\/jats:bold> 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.<\/jats:p>\n \n Hypothesis.<\/jats:bold> Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.<\/jats:p>\n \n Aim.<\/jats:bold> This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.<\/jats:p>\n \n Method.<\/jats:bold> One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3\u2009years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3\u2009years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).<\/jats:p>\n \n Results.<\/jats:bold> 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 (\n \n Niastella<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Schlesneria<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Thioalkalivibrio<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Phaeobacter<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Sphaerotilus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Thiomonas<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Lawsonia<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>, \n \n Actinobacillus<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic> and \n \n Spiroplasma<\/jats:ext-link>\n <\/jats:named-content>\n <\/jats:italic>) performed best in predicting patient survival.<\/jats:p>\n \n Conclusion.<\/jats:bold> 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.<\/jats:p>","DOI":"10.1099\/jmm.0.001744","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T14:16:07Z","timestamp":1692972967000},"source":"Crossref","is-referenced-by-count":8,"title":["A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types"],"prefix":"10.1099","volume":"72","author":[{"given":"Xuebo","family":"Li","sequence":"first","affiliation":[{"name":"Department of Radiotherapy, Weifang Yidu Central Hospital, Weifang, 262500, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5905-4896","authenticated-orcid":false,"given":"Xuelian","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiumin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Daqing Oilfield General Hospital, Daqing, 163001, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changjun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, PR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Ji","sequence":"additional","affiliation":[{"name":"Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, PR China"},{"name":"Geneis Beijing Co. 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