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.