Sheng Dashuang, Yue Kaile, Li Hongfeng, Zhao Lanlan, Zhao Guoping, Jin Chuandi, Zhang Lei
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Microbiome-X, National Institute of Health Data Science of China & Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
Microbiol Spectr. 2023 Mar 28;11(2):e0354922. doi: 10.1128/spectrum.03549-22.
Microbiota can influence the occurrence, development, and therapeutic response of a wide variety of cancer types by modulating immune responses to tumors. Recent studies have demonstrated the existence of intratumor bacteria inside ovarian cancer (OV). However, whether intratumor microbes are associated with tumor microenvironment (TME) and prognosis of OV still remains unknown. The RNA-sequencing data and clinical and survival data of 373 patients with OV in The Cancer Genome Atlas (TCGA) were collected and downloaded. According to the knowledge-based functional gene expression signatures (Fges), OV was classified into two subtypes, termed immune-enriched and immune-deficient subtypes. The immune-enriched subtype, which had higher immune infiltration enriched with CD8 T cells and the M1 type of macrophages (M1) and higher tumor mutational burden, exhibited a better prognosis. Based on the Kraken2 pipeline, the microbiome profiles were explored and found to be significantly different between the two subtypes. A prediction model consisting of 32 microbial signatures was constructed using the Cox proportional-hazard model and showed great prognostic value for OV patients. The prognostic microbial signatures were strongly associated with the hosts' immune factors. Especially, M1 was strongly associated with five species (Achromobacter deleyi and Microcella alkaliphila, sp. strain LEGU1, Ancylobacter pratisalsi, and Acinetobacter seifertii). Cell experiments demonstrated that Acinetobacter seifertii can inhibit macrophage migration. Our study demonstrated that OV could be classified into immune-enriched and immune-deficient subtypes and that the intratumoral microbiota profiles were different between the two subtypes. Furthermore, the intratumoral microbiome was closely associated with the tumor immune microenvironment and OV prognosis. Recent studies have demonstrated the existence of intratumoral microorganisms. However, the role of intratumoral microbes in the development of ovarian cancer and their interaction with the tumor microenvironment are largely unknown. Our study demonstrated that OV could be classified into immune-enriched and -deficient subtypes and that the immune enrichment subtype had a better prognosis. Microbiome analysis showed that intratumor microbiota profiles were different between the two subtypes. Furthermore, the intratumor microbiome was an independent predictor of OV prognosis that could interact with immune gene expression. Especially, M1 was closely associated with intratumoral microbes, and Acinetobacter seifertii could inhibit macrophage migration. Together, the findings of our study highlight the important roles of intratumoral microbes in the TME and prognosis of OV, paving the way for further investigation into its underlying mechanisms.
微生物群可通过调节对肿瘤的免疫反应,影响多种癌症类型的发生、发展及治疗反应。最近的研究已证实在卵巢癌(OV)内部存在肿瘤内细菌。然而,肿瘤内微生物是否与OV的肿瘤微环境(TME)及预后相关仍不清楚。收集并下载了癌症基因组图谱(TCGA)中373例OV患者的RNA测序数据以及临床和生存数据。根据基于知识的功能基因表达特征(Fges),OV被分为两种亚型,即免疫富集型和免疫缺陷型。免疫富集型具有更高的免疫浸润,富含CD8 T细胞和M1型巨噬细胞(M1),且肿瘤突变负担更高,其预后更好。基于Kraken2流程,对微生物组图谱进行了探索,发现两种亚型之间存在显著差异。使用Cox比例风险模型构建了一个由32个微生物特征组成的预测模型,该模型对OV患者显示出很大的预后价值。预后微生物特征与宿主免疫因子密切相关。特别是,M1与五种细菌(德莱伊无色杆菌、嗜碱微球菌菌株LEGU1、草地弯曲杆菌和赛弗蒂不动杆菌)密切相关。细胞实验表明,赛弗蒂不动杆菌可抑制巨噬细胞迁移。我们的研究表明,OV可分为免疫富集型和免疫缺陷型,且两种亚型之间肿瘤内微生物群图谱不同。此外,肿瘤内微生物群与肿瘤免疫微环境及OV预后密切相关。最近的研究已证实在肿瘤内部存在微生物。然而,肿瘤内微生物在卵巢癌发展中的作用及其与肿瘤微环境的相互作用在很大程度上尚不清楚。我们的研究表明,OV可分为免疫富集型和免疫缺陷型,且免疫富集型预后更好。微生物组分析表明,两种亚型之间肿瘤内微生物群图谱不同。此外,肿瘤内微生物群是OV预后的独立预测因子,可与免疫基因表达相互作用。特别是,M1与肿瘤内微生物密切相关,赛弗蒂不动杆菌可抑制巨噬细胞迁移。总之,我们的研究结果突出了肿瘤内微生物在OV的TME和预后中的重要作用,为进一步研究其潜在机制铺平了道路。