Table_1_Comprehensive analysis of microbiota signature across 32 cancer types.xlsx

Microbial communities significantly inhabit the human body. Evidence shows the interaction between the human microbiome and host cells plays a central role in multiple physiological processes and organ microenvironments. However, the majority of related studies focus on gut microbiota or specific tissues/organs, and the component signature of intratumor microbiota across various cancer types remains unclear. Here, we systematically analyzed the correlation between intratumor microbial signature with survival outcomes, genomic features, and immune profiles across 32 cancer types based on the public databases of Bacteria in Cancer (BIC) and The Cancer Genome Atlas (TCGA). Results showed the relative abundance of microbial taxa in tumors compared to normal tissues was observed as particularly noticeable. Survival analysis found that specific candidate microbial taxa were correlated with prognosis across various cancers. Then, a microbial-based scoring system (MS), which was composed of 64 candidate prognostic microbes, was established. Further analyses showed significant differences in survival status, genomic function, and immune profiles among the distinct MS subgroups. Taken together, this study reveals the diversity and complexity of microbiomes in tumors. Classifying cancer into different subtypes based on intratumor microbial signatures might reasonably reflect genomic characteristics, immune features, and survival status.

微生物群落（microbial communities）广泛定植于人体。已有研究表明，人类微生物组（human microbiome）与宿主细胞的互作在多种生理过程及器官微环境中发挥核心作用。然而，绝大多数相关研究聚焦于肠道菌群或特定组织/器官，而跨多种癌症类型的肿瘤内微生物群（intratumor microbiota）组成特征仍不明晰。本研究基于癌症细菌数据库（Bacteria in Cancer, BIC）与癌症基因组图谱（The Cancer Genome Atlas, TCGA）的公共数据集，系统分析了32种癌症中肿瘤内微生物特征与患者生存结局、基因组特征及免疫图谱的相关性。结果显示，相较于正常组织，肿瘤内微生物类群的相对丰度差异尤为显著。生存分析发现，特定候选微生物类群与多种癌症的患者预后存在显著相关性。随后，本研究构建了一套由64个候选预后微生物组成的微生物评分系统（microbial-based scoring system, MS）。进一步分析显示，不同MS亚组间的患者生存状态、基因组功能及免疫图谱均存在显著差异。综上，本研究揭示了肿瘤内微生物组的多样性与复杂性。基于肿瘤内微生物特征对癌症进行分型，可合理反映肿瘤的基因组特征、免疫特性及患者生存状态。