A new strategy for searching determinants in colorectal cancer progression through whole-part relationship combined with multi-omics

Xing, Fan; Zheng, Ran; Liu, Botong; Huang, Keke; Wang, Daguang; Su, Rui; Feng, Shouhua

doi:10.1016/j.talanta.2023.124543

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Abnormal metabolism in cancer has been considered an important signature of tumors that could elucidate the pathogenesis and provide potential therapeutic targets for clinical treatment, so altered metabolism has been a focus of cancer research. − Tiny differences in the complexity of biological systems are a challenge in the search for metabolic disorders . Furthermore, a key issue in data processing is to ensure that weak but significant metabolites are not masked by technical differences and instrumental noise .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

Zheng,

Su,

Fan

et al. 2024

Anal. Chem.

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The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

Zheng,

Su,

Fan

et al. 2024

Anal. Chem.

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Data-driven decision-making for precision diagnosis of digestive diseases

Jiang,

Wang,

Zhang

2023

BioMed Eng OnLine

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Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

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A new strategy for searching determinants in colorectal cancer progression through whole-part relationship combined with multi-omics

Cited by 2 publications

References 38 publications

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

Data-driven decision-making for precision diagnosis of digestive diseases

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