多年来，微生物组在肿瘤进展中的作用，特别是肿瘤微生物组，一直被忽视。肿瘤微生物组与肿瘤基因组之间的联系仍需要进一步研究。TCGA微生物组和基因组数据分别来自Haziza等人的文章和UCSC Xena数据库。在筛选数据集后，对肿瘤微生物组和基因组数据进行了单独的WGCNA网络构建。通过微生物和mRNA模块之间的相关性分析，鉴定了肿瘤基因组相关的微生物组模块，并选取了每种肿瘤类型的三个微生物模块。使用Reactome分析进行生物学过程的富集。实施机器学习技术来探索OAM的肿瘤类型特异性富集和预后价值，以及肿瘤微生物组区分TP53突变的能力。我们构建了总共182个肿瘤微生物组和570个mRNA WGCNA模块。我们的结果表明，肿瘤微生物组与肿瘤基因组之间存在相关性。基因富集分析结果表明，与肿瘤微生物组组别最相关的mRNA模块中的基因主要富集在感染、TP53转录调控及抗原呈递等方面。OAM与CD8+ T细胞或TAM1细胞的相关性分析表明，存在许多可能参与肿瘤免疫抑制或促进的微生物群落，如Williamsia在乳腺癌中、Biostraticola在胃癌中、Megasphaera在宫颈癌中和Lottiidibacillus在卵巢癌中。此外，结果表明，微生物组-基因组预后模型对短期预后具有良好的预测价值。对肿瘤TP53突变的分析表明，肿瘤微生物群落具有一定的区分TP53突变的能力，AUROC值为0.755。重要性分数较高的肿瘤微生物群落是Corallococcus、Bacillus和Saezia。最后，我们发现了一种潜在的抗癌微生物群落Tissierella，已经显示与包括乳腺癌、肺腺癌和胃癌在内的肿瘤的改善预后有关。肿瘤微生物组和肿瘤基因组之间存在联系，这种联系的存在并非偶然，可能改变肿瘤研究的格局。© 2023. BioMed Central Ltd., part of Springer Nature.

For many years, the role of the microbiome in tumor progression, particularly the tumor microbiome, was largely overlooked. The connection between the tumor microbiome and the tumor genome still requires further investigation.The TCGA microbiome and genome data were obtained from Haziza et al.'s article and UCSC Xena database, respectively. Separate WGCNA networks were constructed for the tumor microbiome and genomic data after filtering the datasets. Correlation analysis between the microbial and mRNA modules was conducted to identify oncogenome associated microbiome module (OAM) modules, with three microbial modules selected for each tumor type. Reactome analysis was used to enrich biological processes. Machine learning techniques were implemented to explore the tumor type-specific enrichment and prognostic value of OAM, as well as the ability of the tumor microbiome to differentiate TP53 mutations.We constructed a total of 182 tumor microbiome and 570 mRNA WGCNA modules. Our results show that there is a correlation between tumor microbiome and tumor genome. Gene enrichment analysis results suggest that the genes in the mRNA module with the highest correlation with the tumor microbiome group are mainly enriched in infection, transcriptional regulation by TP53 and antigen presentation. The correlation analysis of OAM with CD8+ T cells or TAM1 cells suggests the existence of many microbiota that may be involved in tumor immune suppression or promotion, such as Williamsia in breast cancer, Biostraticola in stomach cancer, Megasphaera in cervical cancer and Lottiidibacillus in ovarian cancer. In addition, the results show that the microbiome-genome prognostic model has good predictive value for short-term prognosis. The analysis of tumor TP53 mutations shows that tumor microbiota has a certain ability to distinguish TP53 mutations, with an AUROC value of 0.755. The tumor microbiota with high importance scores are Corallococcus, Bacillus and Saezia. Finally, we identified a potential anti-cancer microbiota, Tissierella, which has been shown to be associated with improved prognosis in tumors including breast cancer, lung adenocarcinoma and gastric cancer.There is an association between the tumor microbiome and the tumor genome, and the existence of this association is not accidental and could change the landscape of tumor research.© 2023. BioMed Central Ltd., part of Springer Nature.