ArticleCancer medicine2023
Improved diagnosis of colorectal cancer using combined biomarkers including Fusobacterium nucleatum, fecal occult blood, transferrin, CEA, CA19-9, gender, and age.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- The presence and relative abundance of salivary Fusobacterium nucleatum are not associated with colorectal cancer: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Serological signatures and pathological features for the management of CEA-negative colorectal cancer: development of combined diagnostic and prognostic nomograms.World journal of surgical oncology · 2026Article
- Pathogenic Roles ofPathogens (Basel, Switzerland) · 2026Review
- Fusobacterium nucleatum: ecology, pathogenesis and clinical implications.Nature reviews. Microbiology · 2026Review
- Review
- Review
- The multifaceted role of microRNAs in colorectal cancer: pathogenesis and therapeutic implications.Non-coding RNA research · 2025Review
- Gut microbiota as a new target for anticancer therapy: from mechanism to means of regulation.NPJ biofilms and microbiomes · 2025Review
- Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model.Frontiers in oncology · 2025Article
- A predictive nomogram for assessing the likelihood of retrieving 12 lymph nodes after rectal cancer surgery: a single-center study.Frontiers in oncology · 2025Article
- Metabolic Targets in CRC: The Emerging Role of Cytochrome P450 Inhibitors.Current pharmaceutical design · 2025Review
- Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.Apoptosis : an international journal on programmed cell death · 2024Article
- The multispecies microbial cluster of Fusobacterium, Parvimonas, Bacteroides and Faecalibacterium as a precision biomarker for colorectal cancer diagnosis.Molecular oncology · 2024Article
- Role of the gut microbiota in tumorigenesis and treatment.Theranostics · 2024Review
- Improved diagnosis of colorectal cancer using combined biomarkers including Fusobacterium nucleatum, fecal occult blood, transferrin, CEA, CA19-9, gender, and age.Cancer medicine · 2023Article
- Reducing the need for colonoscopy with adjusted FIT and FOBT for stool weight and serum Hb levels.Frontiers in medicine · 2023Article
- Explainable Machine Learning Models for Colorectal Cancer Prediction Using Clinical Laboratory Data.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConventional blood and stool tests are normally used for early screening of colorectal cancer (CRC) but the accuracy and efficiency remain to be improved. Recent findings suggest Fusobacterium nucleatum to be a biomarker for CRC. This study evaluated the role of F. nucleatum and developed CRC diagnostic models by combining F. nucleatum with fecal occult blood (FOB), transferrin (TRF), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), gender, and age. MATERIALS AND
methodsCandidates including 71 healthy individuals and 59 CRC patients were recruited. Abundance of F. nucleatum in stool or tissue samples was measured by quantitative real-time PCR. CEA, CA19-9, TRF, and FOB were measured in parallel. These biomarkers together with genders and ages were the seven parameters used to develop CRC diagnostic models. Ten different machine learning algorithms were tested to achieve the best performance.
resultsFecal F. nucleatum abundance was found significantly higher in CRC group compared to healthy group (p = 0.0005). Among the CRC patients, F. nucleatum abundance in tumor tissue was significantly higher than that in paracancerous tissue (p = 0.0087). CRC diagnostic models using different parameters were generated based on Logistic Regression algorithm, which showed good performance. The area under the curve (AUC) score of fecal F. nucleatum as the single diagnostic biomarker was 0.68 while the accuracy was 0.56. The diagnostic performance was obviously improved with the highest AUC (0.93) and accuracy (0.87) achieved when using all the 7 clinical parameters. The combination F. nucleatum + FOB + gender + age had the second highest AUC (0.92) and accuracy (0.85). A more utilitarian model using F. nucleatum + FOB showed relatively high AUC at 0.86 and accuracy at 0.81.
conclusionsF. nucleatum is valuable for CRC diagnosis. Combination of different clinical parameters could significantly improve CRC diagnostic performance. The combination F. nucleatum + FOB + gender + age may be an effective and noninvasive method for clinical application.
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