Targeted UPLC-MS Metabolic Analysis of Human Faeces Reveals Novel Low-Invasive Candidate Markers for Colorectal Cancer

Low invasive tests with high sensitivity for colorectal cancer and advanced precancerous lesions will increase adherence rates, and improve clinical outcomes. We have performed an ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-(TOF) MS)-based metabolomics study to identify faecal biomarkers for the detection of patients with advanced neoplasia. A cohort of 80 patients with advanced neoplasia (40 advanced adenomas and 40 colorectal cancers) and 49 healthy subjects were analysed in the study. We evaluated the faecal levels of 105 metabolites including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. We found 18 metabolites that were significantly altered in patients with advanced neoplasia compared to controls. The combinations of seven metabolites including ChoE(18:1), ChoE(18:2), ChoE(20:4), PE(16:0/18:1), SM(d18:1/23:0), SM(42:3) and TG(54:1), discriminated advanced neoplasia patients from healthy controls. These seven metabolites were employed to construct a predictive model that provides an area under the curve (AUC) median value of 0.821. The inclusion of faecal haemoglobin concentration in the metabolomics signature improved the predictive model to an AUC of 0.885. In silico gene expression analysis of tumour tissue supports our results and puts the differentially expressed metabolites into biological context, showing that glycerolipids and sphingolipids metabolism and GPI-anchor biosynthesis pathways may play a role in tumour progression.

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Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

Cancer Cell International ◽

10.1186/s12935-021-02424-7 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Tenghui Han ◽

Jun Zhu ◽

Xiaoping Chen ◽

Rujie Chen ◽

Yu Jiang ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Liver Metastasis ◽

Predictive Model ◽

Area Under The Curve ◽

Predictive Ability ◽

Subsequent Treatment ◽

Training Dataset ◽

Discriminative Ability ◽

Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

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Faecal haemoglobin can define risk of colorectal neoplasia at surveillance colonoscopy in patients at increased risk of colorectal cancer

United European Gastroenterology Journal ◽

10.1177/2050640620913674 ◽

2020 ◽

Vol 8 (5) ◽

pp. 559-566

Author(s):

Jayne Digby ◽

Shirley Cleary ◽

Lynne Gray ◽

Pooja Datt ◽

David R Goudie ◽

...

Keyword(s):

Colorectal Cancer ◽

Diagnostic Accuracy ◽

Limit Of Detection ◽

Colorectal Neoplasia ◽

Haemoglobin Concentration ◽

Surveillance Colonoscopy ◽

National Guidelines ◽

Predictive Values ◽

Advanced Neoplasia ◽

Increased Risk

Background Quantitative faecal immunochemical tests measure faecal haemoglobin concentration (f-Hb), which increases in the presence of colorectal neoplasia. Objective We examined the diagnostic accuracy of faecal immunochemical test (FIT)in patients at increased risk of colorectal cancer (CRC) attending for surveillance colonoscopy as per national guidelines. Methods A total of 1103 consecutive patients were prospectively invited to complete a FIT before their scheduled colonoscopy in two university hospitals in 2014– 2016. F-Hb was analysed on an OC-Sensor io automated analyser (Eiken Chemical Co., Ltd, Tokyo, Japan) with a limit of detection of 2 µg Hb/g faeces. The diagnostic accuracy of f-Hb for CRC and higher-risk adenoma was examined. Results A total of 643 patients returned a faecal test. After excluding 4 patients with known inflammatory bowel disease, 639 (57.9%) remained in the study: age range: 25–90 years (median: 64 years, interquartile range (IQR): 55–71): 54.6% male. Of 593 patients who also completed colonoscopy, 41 (6.9%) had advanced neoplasia (4 CRC, 37 higher-risk adenoma). Of the 238 patients (40.1%) who had detectable f-Hb, 31 (13.0%) had advanced neoplasia (2 CRC, 29 higher-risk adenoma) compared with 10 (2.8%) in those with undetectable f-Hb (2 CRC, 8 higher-risk adenoma). Detectable f-Hb gave negative predictive values of 99.4% for CRC and 97.2% for CRC plus higher-risk adenoma. Conclusion In patients at increased risk of CRC under colonoscopy surveillance, a test measuring faecal haemoglobin can provide an objective estimate of the risk of advanced neoplasia, and could enable tailored scheduling of colonoscopy.

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Faecal haemoglobin concentration is related to detection of advanced colorectal neoplasia in the next screening round

Journal of Medical Screening ◽

10.1177/0969141316653983 ◽

2016 ◽

Vol 24 (2) ◽

pp. 62-68 ◽

Cited By ~ 5

Author(s):

Jayne Digby ◽

Callum G Fraser ◽

Francis A Carey ◽

Robert H Diament ◽

Margaret Balsitis ◽

...

Keyword(s):

Colorectal Cancer ◽

Screening Programme ◽

Colorectal Neoplasia ◽

Haemoglobin Concentration ◽

Advanced Neoplasia ◽

Negative Results ◽

Adenoma Detection ◽

Advanced Colorectal Neoplasia ◽

Positive Results ◽

Diagnostic Outcomes

Objective To examine associations between faecal haemoglobin concentrations below the cut-off used in colorectal cancer screening and outcomes in the next screening round. Methods In the Scottish Bowel Screening Programme, faecal haemoglobin concentrations and diagnostic outcomes were investigated for participants with a negative result (faecal haemoglobin concentrations < 80.0 µg Hb/g faeces), followed by a positive result within two years. Results Of 37,780 participants with negative results, at the next screening round, 556 (1.5%) screened positive and 30,293 (80.2%) negative. Initial median faecal haemoglobin concentrations (2.1 µg Hb/g faeces, IQR: 0.0–13.2) were higher in those with subsequent positive results than those with subsequent negative results (0.0 µg Hb/g faeces, IQR: 0.0–1.4; p < 0.0001). Using faecal haemoglobin concentrations 0.0–19.9 µg Hb/g faeces as reference, logistic regression analysis showed high adjusted odds ratios for advanced neoplasia (advanced neoplasia: colorectal cancer or higher risk adenoma) detection at the next round of 14.3 (95% CI: 8.9–23.1) in those with initial faecal haemoglobin concentrations 20.0–39.9 µg Hb/g faeces, and 38.0 (95% CI: 20.2–71.2) with 60.0–79.9 µg Hb/g faeces. Conclusions A higher proportion of participants with faecal haemoglobin concentrations of ≥ 20 µg Hb/g faeces had advanced neoplasia detected at the next round than participants with lower faecal haemoglobin concentrations. Although most relevant when using high faecal haemoglobin concentrations cut-offs, studies of faecal haemoglobin concentrations and outcomes over screening rounds may provide strategies to direct available colonoscopy towards those at highest risk.

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Application of Artificial Intelligence in a Real-World Research for Predicting the Risk of Liver Metastasis in T1 Colorectal Cancer

10.21203/rs.3.rs-746689/v1 ◽

2021 ◽

Author(s):

Tenghui Han ◽

Jun Zhu ◽

Dong Xu ◽

Rujie Chen ◽

Shuai Wang ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Liver Metastasis ◽

Predictive Model ◽

Tumor Size ◽

Area Under The Curve ◽

Subsequent Treatment ◽

Tumor Type ◽

Discriminative Ability ◽

N Stage

Abstract Background: The liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. However, there is still no effective model to predict the risk of LM in T1 CRC patients and we aim to develop a novel and accurate predictive model.Methods: We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER) and Xijing hospital. Artificial intelligence (AI) and machine learning methods were adopted to establish the predictive model.Results: A total of 16785 and 326 T1 CRC patients from SEER database and our hospital were incorporated respectively in the study. We found that age, gender, married status, primary site, tumor size, carcinoembryonic antigen (CEA), tumor type, grade, N stage and perineural invasion were significant independent factors for predicting the presence of LM, among which tumor size is the most important. The stacking bagging model showed the best predictive capability, achieving a sensitivity of 0.8452, a specificity of 0.9566, and an area under the curve of 0.9631. In addition, the stacking model had an excellent discriminative ability and accurately screened out eight LM cases from 326 T1 patients in the outer validation cohort. Ultimately, we authenticated the prognostic value of the stacking model, which is consistent with the predictive result of LM.Conclusion: We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in our dataset.

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Plasma Lipid Profile Reveals Plasmalogens as Potential Biomarkers for Colon Cancer Screening

Metabolites ◽

10.3390/metabo10060262 ◽

2020 ◽

Vol 10 (6) ◽

pp. 262 ◽

Cited By ~ 1

Author(s):

Anna Maria A.P. Fernandes ◽

Marcia C.F. Messias ◽

Gustavo H.B. Duarte ◽

Gabrielle K.D. de Santis ◽

Giovana C. Mecatti ◽

...

Keyword(s):

Mass Spectrometry ◽

Colon Cancer ◽

Plasma Lipid ◽

Area Under The Curve ◽

Ultra Performance Liquid Chromatography ◽

High Energy ◽

Special Role ◽

Flight Mass Spectrometry ◽

Highly Sensitive ◽

And Control

In this era of precision medicine, there is an increasingly urgent need for highly sensitive tests for detecting tumors such as colon cancer (CC), a silent disease where the first symptoms may take 10–15 years to appear. Mass spectrometry-based lipidomics is an emerging tool for such clinical diagnosis. We used ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry operating in high energy collision spectral acquisition mode (MSE) mode (UPLC-QTOF-MSE) and gas chromatography (GC) to investigate differences between the plasmatic lipidic composition of CC patients and control (CTR) subjects. Key enzymes in lipidic metabolism were investigated using immuno-based detection assays. Our partial least squares discriminant analysis (PLS-DA) resulted in a suitable discrimination between CTR and CC plasma samples. Forty-two statistically significant discriminating lipids were putatively identified. Ether lipids showed a prominent presence and accordingly, a decrease in glyceronephosphate O-acyltransferase (GNPAT) enzyme activity was found. A receiver operating characteristic (ROC) curve built for three plasmalogens of phosphatidylserine (PS), named PS(P-36:1), PS(P-38:3) and PS(P-40:5), presented an area under the curve (AUC) of 0.998, and sensitivity and specificity of 100 and 85.7% respectively. These results show significant differences in CC patients’ plasma lipid composition that may be useful in discriminating them from CTR individuals with a special role for plasmalogens.

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