Fecal microbial biomarkers combined with multi-target stool DNA test improve diagnostic accuracy for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a major global health burden. The current diagnostic tests have shortcomings of being invasive and low accuracy. AIM: To explore the combination of intestinal microbiome composition and multi-target stool DNA (MT-sDNA) test in the diagnosis of CRC. METHODS: We assessed the performance of the MT-sDNA test based on a hospital clinical trial. The intestinal microbiota was tested using 16S rRNA gene sequencing. This case-control study enrolled 54 CRC patients and 51 healthy controls. We identified biomarkers of bacterial structure, analyzed the relationship between different tumor markers and the relative abundance of related flora components, and distinguished CRC patients from healthy subjects by the linear discriminant analysis effect size, redundancy analysis, and random forest analysis. RESULTS: MT-sDNA was associated with Bacteroides. MT-sDNA and carcinoembryonic antigen (CEA) were positively correlated with the existence of Parabacteroides, and alpha-fetoprotein (AFP) was positively associated with Faecalibacterium and Megamonas. In the random forest model, the existence of Streptococcus, Escherichia, Chitinophaga, Parasutterella, Lachnospira, and Romboutsia can distinguish CRC from health controls. The diagnostic accuracy of MT-sDNA combined with the six genera and CEA in the diagnosis of CRC was 97.1%, with a sensitivity and specificity of 98.1% and 92.3%, respectively. CONCLUSION: There is a positive correlation of MT-sDNA, CEA, and AFP with intestinal microbiome. Eight biomarkers including six genera of gut microbiota, MT-sDNA, and CEA showed a prominent sensitivity and specificity for CRC prediction, which could be used as a non-invasive method for improving the diagnostic accuracy for this malignancy.

Diagnostic accuracy of the multi-target stool DNA test in detecting colorectal cancer: A hospital-based study.

BACKGROUND: The multi-target stool DNA test (MT-sDNA) has potential utility in the detection of colorectal cancer (CRC), but validation of its clinical accuracy has been limited in China. AIM: To evaluate the diagnostic performance of MT-sDNA and investigate the combined diagnostic value of alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) with MT-sDNA in CRC and adenomas. METHODS: We evaluated the performance of the MT-sDNA kit based on a hospital clinical trial. In this case-control study, 135 participants from the Affiliated Hospital of Medical School of Ningbo University, including 51 CRC patients, 23 patients with adenomas, and 61 healthy controls were enrolled. We used a risk scoring system to determine the positivity of tests with histological diagnosis or colonoscopy as the reference standard. RESULTS: The main indices of sensitivity, specificity and accuracy were evaluated. The sensitivity and specificity for CRC detection were 90.2% and 83.3%, respectively, with an accuracy of 89.8%. For adenoma, the sensitivity and specificity were 56.5% and 68.9%, respectively, with an accuracy of 73.1%. The sensitivity and specificity of MT-sDNA combined with CEA in the diagnosis of adenoma were 78.3% and 60.7%, respectively. CONCLUSION: The MT-sDNA test showed better performance in the detection of CRC, which was superior to AFP, CEA, and CA199 separately, but not for predicting adenomas. The combination of MT-sDNA with CEA further improved the sensitivity for adenoma diagnosis.

Methylation Status of Transcriptional Modulatory Genes Associated with Colorectal Cancer in Northeast China.

BACKGROUND/AIMS: Methylation status plays a causal role in carcinogenesis in targeted tissues. However, the relationship between the DNA methylation status of multiple genes in blood leukocytes and colorectal cancer (CRC) susceptibility as well as interactions between dietary factors and CRC risks are unclear. METHODS: We performed a case-control study with 466 CRC patients and 507 cancer-free controls to investigate the association among the methylation status of individual genes, multiple CpG site methylation (MCSM), multiple CpG site heterogeneous methylation and CRC susceptibility. Peripheral blood DNA methylation levels were detected by performing methylation-sensitive high-resolution melting. RESULTS: Total heterogeneous methylation of CA10 and WT1 conferred a significantly higher risk of CRC (adjusted odds ratio [ORadjusted], 5.445; 95% confidence interval [CI], 3.075 to 9.643; ORadjusted, 1.831; 95% CI, 1.100 to 3.047; respectively). Subjects with high-level MCSM (MCSM-H) status demonstrated a higher risk of CRC (ORadjusted, 4.318; 95% CI, 1.529 to 12.197). Additionally, interactions between the high-level intake of fruit and CRH, WT1, and MCSM on CRC were statistically significant. CONCLUSIONS: The gene methylation status of blood leukocytes may be associated with CRC risk. MCSM-H of blood leukocytes was associated with CRC, especially in younger people. Some dietary factors may affect hypermethylation status and influence susceptibility to CRC.

[Pre-diabetes mellitus influenced by hyperuricemia].

OBJECTIVE: To investigate the relationship and the influence between pre-diabetes mellitus (PDM) and hyperuricemia (HUA). METHODS: 157 PDM patients, aged 20 to 75 years old were selected from the Second Clinical Medical College of Harbin Medical University, from 2009 February to 2010 February and were divided into HUA group (76 cases) and NUA group (81 cases). All the patients had not been on thiazide drugs. T-test and Pearson correlation analysis were used to calculate the differences and correlation between uric acid and biochemical indicators. RESULTS: In the HUA group,BMI was (27.74±2.88 ) kg/m(2), waist to height ratio (WSR) was(0.55 ± 0.41), TC was (6.61 ± 0.73) mmol/L, TG was(3.94 ± 1.97) mmol/L, LDL-C was (3.60 ± 0.45) mmol/L and homeostasis model assessment-insulin resistance index (HOMA-IR) was (3.09 ± 1.20). There were significant differences noticed in BMI, TG, TC, LDL-C, HOMA-IR at higher level in the HUA group than those in the NUA group. Pre-diabetes uric acid levels were positively correlated with TG, TC, LDL-C while HOMA-IR(TG:r = 0.29, TC:r = 0.33,LDL-C:r = 0.49, HOMA-IR:r = 0.51, P < 0.05) was negatively correlated (r = -0.30, P < 0.05)with the HbA1c. CONCLUSION: The levels of PDM uric acid might both be related with TC, TG, LDL-C and HOMA-IR. The High level of uric acid status in vivo appeared closely related to HOMA-IR, which could further promote the progress of pre-diabetic patients to diabetes and causing dyslipidemia. Our findings suggested that the levels of pre-diabetes uric acid levels should be under concern.

[A preliminary study on the effects of meteorological factors on intracerebral hemorrhage death using the BP neural network model].

OBJECTIVE: Using the Back Propagation (BP) Neural Network Model to discover the relationship between meteorological factors and mortality of intracerebral hemorrhage, to provide evidence for developing an intracerebral hemorrhage prevention and control program, in Harbin. METHODS: Based on the characteristics of BP neural network, a neural network Toolbox of MATLAB 7.0 software was used to build Meteorological data of 2007 - 2009 with intracerebral hemorrhage mortality to predict the effect of BP neural network model, and to compare with the traditional multivariate linear regression model. RESULTS: Datas from the multivariate linear regression indicated that the cerebral hemorrhage death mortality had a negative correlation with maximum temperature and minimum humidity while having a positive correlation with the average relative humidity and the hours of sunshine. The linear correlation coefficient of intracerebral hemorrhage mortality was 0.7854, with mean absolute percentage (MAPE) as 0.21, mean square error (MSE) as 0.22, mean absolute error (MAE) as 0.19. The accuracy of forecasting was 81.31% with an average error rate as 0.19. The Fitting results of BP neural network model showed that non-linear correlation coefficient of intracerebral hemorrhage mortality was 0.7967, with MAPE as 0.19, MSE as 0.21, MAE as 0.18. The forecasting accuracy was 82.53% with the average error rate as 0.17. CONCLUSION: The BP neural network model showed a higher forecasting accuracy when compared to the multiple linear regression model on intracerebral hemorrhage mortality, using the data of 2010's.