Rodriguez Rebecca M, Hernandez Brenda Y, Menor Mark, Deng Youping, Khadka Vedbar S
Bioinformatics Core, Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii Mānoa, Honolulu, HI, United States.
Population Sciences in the Pacific Program-Cancer Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States.
Comput Struct Biotechnol J. 2020 Mar 13;18:631-641. doi: 10.1016/j.csbj.2020.03.003. eCollection 2020.
Identification of microbial composition directly from tumor tissue permits studying the relationship between microbial changes and cancer pathogenesis. We interrogated bacterial presence in tumor and adjacent normal tissue strictly in pairs utilizing human whole exome sequencing to generate microbial profiles. Profiles were generated for 813 cases from stomach, liver, colon, rectal, lung, head & neck, cervical and bladder TCGA cohorts. Core microbiota examination revealed twelve taxa to be common across the nine cancer types at all classification levels. Paired analyses demonstrated significant differences in bacterial shifts between tumor and adjacent normal tissue across stomach, colon, lung squamous cell, and head & neck cohorts, whereas little or no differences were evident in liver, rectal, lung adenocarcinoma, cervical and bladder cancer cohorts in adjusted models. in stomach and in colon were found to be significantly higher in adjacent normal compared to tumor tissue after false discovery rate correction. Computational results were validated with tissue from an independent population by species-specific qPCR showing similar patterns of co-occurrence among and in gastric samples. This study demonstrates the ability to identify bacteria differential composition derived from human tissue whole exome sequences. Taken together our results suggest the microbial profiles shift with advanced disease and that the microbial composition of the adjacent tissue can be indicative of cancer stage disease progression.
直接从肿瘤组织中鉴定微生物组成有助于研究微生物变化与癌症发病机制之间的关系。我们利用人类全外显子测序严格成对检测肿瘤组织和相邻正常组织中的细菌存在情况,以生成微生物图谱。为来自胃、肝、结肠、直肠、肺、头颈、宫颈和膀胱的TCGA队列中的813例病例生成了图谱。核心微生物群检查显示,在所有分类水平上,有12个分类群在9种癌症类型中都很常见。配对分析表明,在胃、结肠、肺鳞状细胞和头颈队列中,肿瘤组织和相邻正常组织之间的细菌变化存在显著差异,而在调整模型中,肝、直肠、肺腺癌、宫颈癌和膀胱癌队列中几乎没有差异。在错误发现率校正后,发现胃和结肠中相邻正常组织中的[具体细菌名称1]和[具体细菌名称2]显著高于肿瘤组织。通过物种特异性qPCR对来自独立人群的组织进行验证,显示胃样本中[具体细菌名称1]和[具体细菌名称2]之间的共现模式相似。这项研究证明了从人类组织全外显子序列中识别细菌差异组成的能力。综合我们的结果表明,微生物图谱会随着疾病进展而变化,并且相邻组织的微生物组成可以指示癌症阶段的疾病进展。