Dataset Information

Fecal Bacteria as Non-Invasive Biomarkers for Colorectal Adenocarcinoma.

ABSTRACT: Colorectal adenocarcinoma (CRC) ranks one of the five most lethal malignant tumors both in China and worldwide. Early diagnosis and treatment of CRC could substantially increase the survival rate. Emerging evidence has revealed the importance of gut microbiome on CRC, thus fecal microbial community could be termed as a potential screen for non-invasive diagnosis. Importantly, few numbers of bacteria genus as non-invasive biomarkers with high sensitivity and specificity causing less cost would be benefitted more in clinical compared with the whole microbial community analysis. Here we analyzed the gut microbiome between CRC patients and healthy people using 16s rRNA sequencing showing the divergence of microbial composition between case and control. Furthermore, ExtraTrees classifier was performed for the classification of CRC gut microbiome and heathy control, and 13 bacteria were screened as biomarkers for CRC. In addition, 13 biomarkers including 12 bacteria genera and FOBT showed an outstanding sensitivity and specificity for discrimination of CRC patients from healthy controls. This method could be used as a non-invasive method for CRC early diagnosis.

SUBMITTER: Yuan B

PROVIDER: S-EPMC8383742 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Fecal Bacteria as Non-Invasive Biomarkers for Colorectal Adenocarcinoma.

Yuan Biao B Ma Bin B Yu Jing J Meng Qingkai Q Du Tao T Li Hongyi H Zhu Yueyan Y Sun Zikui Z Ma Siping S Song Chun C

Frontiers in oncology 20210810

Colorectal adenocarcinoma (CRC) ranks one of the five most lethal malignant tumors both in China and worldwide. Early diagnosis and treatment of CRC could substantially increase the survival rate. Emerging evidence has revealed the importance of gut microbiome on CRC, thus fecal microbial community could be termed as a potential screen for non-invasive diagnosis. Importantly, few numbers of bacteria genus as non-invasive biomarkers with high sensitivity and specificity causing less cost would be ...[more]

PMID: 34447694

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Project description:BackgroundThe intestinal flora is correlated with the occurrence of colorectal cancer. We evaluate a new predictive model for the non-invasive diagnosis of colorectal cancer based on intestinal flora to verify the clinical application prospects of the intestinal flora as a new biomarker in non-invasive screening of colorectal cancer.MethodsSubjects from two independent Asian cohorts (cohort I, consisting of 206 colorectal cancer and 112 healthy subjects; cohort II, consisting of 67 colorectal cancer and 54 healthy subjects) were included. A probe-based duplex quantitative PCR (qPCR) determination was established for the quantitative determination of candidate bacterial markers.ResultsWe screened through the gutMEGA database to identify potential non-invasive biomarkers for colorectal cancer, including Prevotella copri (Pc), Gemella morbillorum (Gm), Parvimonas micra (Pm), Cetobacterium somerae (Cs), and Pasteurella stomatis (Ps). A predictive model with good sensitivity and specificity was established as a new diagnostic tool for colorectal cancer. Under the best cutoff value that maximizes the sum of sensitivity and specificity, Gm and Pm had better specificity and sensitivity than other target bacteria. The combined detection model of five kinds of bacteria showed better diagnostic ability than Gm or Pm alone (AUC = 0.861, P < 0.001). These findings were further confirmed in the independent cohort II. Particularly, the combination of bacterial markers and fecal immunochemical test (FIT) improved the diagnostic ability of the five bacteria (sensitivity 67.96%, specificity 89.29%) for patients with colorectal cancer.ConclusionFecal-based colorectal cancer-related bacteria can be used as new non-invasive diagnostic biomarkers of colorectal cancer. Simultaneously, the molecular biomarkers in fecal samples are similar to FIT, have the applicability in combination with other detection methods, which is expected to improve the sensitivity of diagnosis for colorectal cancer, and have a promising prospect of clinical application.

Project description:BackgroundFecal immunochemical test (FIT), DNA mutation, DNA methylation, and microbial dysbiosis all showed promising in colorectal cancer (CRC) non-invasive detection. We assessed CRC detection with an assay combining all these strategies and investigated the effect of clinical features on the performance of this comprehensive test.MethodsWe performed a multidimensional analysis study using stool samples collected from 108 patients with CRC, 18 patients with colorectal adenoma, and 36 individuals with no evidence of colorectal disease. The multidimensional analysis of stool samples including FIT, stool DNA (sDNA) tests for three methylated genes (Septin9, NDRG4, BMP3) and three mutated genes (KRAS, BRAF, PI3KCA) using next generation sequencing as well as detection of stool bacteria level of Fusobacterium nucleatum and Parvimonas micra using qPCR method. We used a linear support vector classification model to analyze the data.ResultsThe sensitivity of FIT alone was 69.4% for CRC and 11.1% for adenoma. Separately, the sensitivity of the detection of intestinal bacteria, DNA mutation, and DNA methylation for CRC was 58.3, 50.0, and 51.9%, respectively. The combination of FIT and sDNA tests had a sensitivity of 81.5% for CRC (AUC: 0.93, better than FIT alone, P = 0.017) and 27.8% for adenoma with 94.4% specificity. Sensitivity of the multidimensional test to detect CRC with stage II (84.6%) and III (91.9%) CRC was relatively higher (88.2%) than that of patients with stage I (60.0%) and stage IV (75.0%) (P = 0.024). The rate of CRC detection increased with tumor size (P = 0.008) and age (P = 0.04). Interestingly, the rate of CRC detection was higher in smoking persons than non-smokers with marginal significance (P = 0.08).ConclusionsThe multidimensional assay of stool samples combining FIT and stool DNA tests further improved the diagnostic sensitivity for CRC. This could provide new approach for improvement of CRC screening and further demonstrations are warranted.

Project description:Intense exercise causes to organisms to have oxidative stress and inflammation at the gastrointestinal (GI) level. The reduction in intestinal blood flow and the exercise-linked thermal damage to the intestinal mucosa can cause intestinal barrier disruption, followed by an inflammatory response. Furthermore, the adaptation to exercise may affect the gut microbiota and the metabolome of the biofluids. The aim of the present research was to evaluate the presence of a GI derangement in hunting dogs through a non-invasive sampling as a consequence of a period of intense exercise in comparison with samples collected at rest. The study included nine dogs that underwent the same training regime for hunting wild boar. In order to counterbalance physiological variations, multiple-day replicates were collected and pooled at each experimental point for each dog. The samples were collected immediately at rest before the training (T0), after 60 days of training (T1), after 60 days of hunting wild boar (T2), and finally, at 60 days of rest after hunting (T3). A number of potential stress markers were evaluated: fecal cortisol metabolites (FCMs) as a major indicator of altered physiological states, immunoglobulin A (IgA) as an indicator of intestinal immune protection, and total antioxidant activity [total antioxidant capacity (TAC)]. Since stool samples contain exfoliated cells, we investigated also the presence of some transcripts involved in GI permeability [occludin (OCLN), protease-activated receptor-2 (PAR-2)] and in the inflammatory mechanism [interleukin (IL)-8, IL-6, IL-1b, tumor necrosis factor alpha (TNFα), calprotectin (CALP), heme oxygenase-1 (HO-1)]. Finally, the metabolome and the microbiota profiles were analyzed. No variation in FCM and IgA content and no differences in OCLN and CALP gene expression between rest and training were observed. On the contrary, an increase in PAR-2 and HO-1 transcripts, a reduction in total antioxidant activity, and a different profile of microbiota and metabolomics data were observed. Collectively, the data in the present study indicated that physical exercise in our model could be considered a mild stressor stimulus.

Project description:BackgroundMicrosatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort.MethodsPreoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC).ResultsWe identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions.ConclusionDifferences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models.Relevance statementNoninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies.Key pointsNoninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.

Dataset Information

Fecal Bacteria as Non-Invasive Biomarkers for Colorectal Adenocarcinoma.

Publications

Fecal Bacteria as Non-Invasive Biomarkers for Colorectal Adenocarcinoma.

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