Zheng Yajuan, Fang Zhaoyuan, Xue Yun, Zhang Jian, Zhu Junjie, Gao Renyuan, Yao Shun, Ye Yi, Wang Shihui, Lin Changdong, Chen Shiyang, Huang Hsinyi, Hu Liang, Jiang Ge-Ning, Qin Huanlong, Zhang Peng, Chen Jianfeng, Ji Hongbin
State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences , Shanghai, China.
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine , Shanghai, China.
Gut Microbes. 2020 Jul 3;11(4):1030-1042. doi: 10.1080/19490976.2020.1737487. Epub 2020 Apr 2.
Alterations of gut microbiota have been implicated in multiple diseases including cancer. However, the gut microbiota spectrum in lung cancer remains largely unknown. Here we profiled the gut microbiota composition in a discovery cohort containing 42 early-stage lung cancer patients and 65 healthy individuals through the 16S ribosomal RNA (rRNA) gene sequencing analysis. We found that lung cancer patients displayed a significant shift of microbiota composition in contrast to the healthy populations. To identify the optimal microbiota signature for noninvasive diagnosis purpose, we took advantage of Support-Vector Machine (SVM) and found that the predictive model with 13 operational taxonomic unit (OTU)-based biomarkers achieved a high accuracy in lung cancer prediction (area under curve, AUC = 97.6%). This signature performed reasonably well in the validation cohort (AUC = 76.4%), which contained 34 lung cancer patients and 40 healthy individuals. To facilitate potential clinical practice, we further constructed a 'patient discrimination index' (PDI), which largely retained the prediction efficiency in both the discovery cohort (AUC = 92.4%) and the validation cohort (AUC = 67.7%). Together, our study uncovered the microbiota spectrum of lung cancer patients and established the specific gut microbial signature for the potential prediction of the early-stage lung cancer.
肠道微生物群的改变与包括癌症在内的多种疾病有关。然而，肺癌患者的肠道微生物群谱在很大程度上仍不清楚。在此，我们通过16S核糖体RNA（rRNA）基因测序分析，对一个包含42例早期肺癌患者和65名健康个体的发现队列中的肠道微生物群组成进行了分析。我们发现，与健康人群相比，肺癌患者的微生物群组成发生了显著变化。为了确定用于无创诊断的最佳微生物群特征，我们利用支持向量机（SVM），发现基于13个操作分类单元（OTU）的生物标志物的预测模型在肺癌预测中具有较高的准确性（曲线下面积，AUC = 97.6%）。该特征在包含34例肺癌患者和40名健康个体的验证队列中表现良好（AUC = 76.4%）。为了促进潜在的临床应用，我们进一步构建了一个“患者鉴别指数”（PDI），该指数在发现队列（AUC = 92.4%）和验证队列（AUC = 67.7%）中都基本保留了预测效率。总之，我们的研究揭示了肺癌患者的微生物群谱，并建立了用于早期肺癌潜在预测的特定肠道微生物特征。
