Applying simple linear combination, multiple logistic and factor analysis methods for candidate fecal bacteria as novel biomarkers for early detection of adenomatous polyps and colon cancer

Abstract

BackgroundColorectal 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. Material and methodsFecal 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. ResultsAccording 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%. ConclusionOur 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.