Rezasoltani Sama, Sharafkhah Maryam, Asadzadeh Aghdaei Hamid, Nazemalhosseini Mojarad Ehsan, Dabiri Hossein, Akhavan Sepahi Abbas, Modarressi Mohammad Hossein, Feizabadi Mohammad Mehdi, Zali Mohammad Reza
Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Liver and Pancreatobiliary Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
J Microbiol Methods. 2018 Dec;155:82-88. doi: 10.1016/j.mimet.2018.11.007. Epub 2018 Nov 12.
Colorectal cancer (CRC) is the third leading cause of cancer, and presents a considerable disease burden, worldwide. Recently, the gut microbiota has been proposed as a potential risk factor for CRC, and even adenomatous polyps (AP). Here, the aim of this study was to investigate the role of selected gut bacteria as fecal bacterial biomarkers, in early detection of CRC and AP.
Fecal samples (n = 93) were collected from Taleghani Hospital, Tehran, Iran, between 2015 and 2017, from normal controls (NC), AP cases and CRC stage I patients, who were undergoing screening for colonoscopy. Absolute quantitative real time PCR (qPCR) assays were established for the quantification of bacterial marker candidates, in all cases and control groups. In order to evaluate the diagnostic value of bacterial candidates in distinguishing CRC from a polyp, receiver operating characteristic curve (ROC) was performed. Multiple logistic regressions were used to find the best combinations of the bacterial candidates, then, combinations were analyzed based on three methods, including linear combination, multiple logistic and factor analysis models.
According to the logistic model, combination of Fusobacterium nucleatum, Enterococcus feacalis, Streptococcus bovis, Enterotoxigenic Bacteroides fragilis (ETBF) and Porphyromonas spp. showed improved diagnostic performance, compared to each bacterium alone, as area under the receiver operating characteristic (AUROC) increases to 0.97, with 95% confidence interval. It was found that a simple linear combination was an appropriate model for discriminating AP and CRC cases, compared to the NC, with a sensitivity of 91.4% and specificity of 93.5%.
Our results indicated that based on fecal bacterial candidates, statistical simple linear combination model and ROC curve analysis, early detection of AP and CRC might be possible.
结直肠癌（CRC）是全球第三大癌症死因，带来了相当大的疾病负担。最近，肠道微生物群被认为是CRC甚至腺瘤性息肉（AP）的潜在危险因素。本研究旨在探讨特定肠道细菌作为粪便细菌生物标志物在CRC和AP早期检测中的作用。
2015年至2017年间，从伊朗德黑兰塔莱哈尼医院收集了93份粪便样本，样本来自接受结肠镜筛查的正常对照（NC）、AP病例和CRC I期患者。对所有病例组和对照组建立了绝对定量实时PCR（qPCR）检测方法，用于定量细菌标志物候选物。为了评估细菌候选物在区分CRC和息肉方面的诊断价值，绘制了受试者工作特征曲线（ROC）。采用多元逻辑回归寻找细菌候选物的最佳组合，然后基于线性组合、多元逻辑和因子分析模型三种方法对组合进行分析。
根据逻辑模型，具核梭杆菌、粪肠球菌、牛链球菌、产肠毒素脆弱拟杆菌（ETBF）和卟啉单胞菌属的组合显示出比单一细菌更好的诊断性能，受试者工作特征曲线下面积（AUROC）增加到0.97，95%置信区间。结果发现，与NC相比，简单线性组合是区分AP和CRC病例的合适模型，灵敏度为91.4%，特异性为93.5%。
我们的结果表明，基于粪便细菌候选物、统计简单线性组合模型和ROC曲线分析，可能实现AP和CRC的早期检测。
