Qi Luo, Xiaolei Chen, Linlin Zhao, Qinghua Hu, Juan Du, Shuang Shao
Association between social capital and utilization of essential public health services among elderly migrants: a multilevel logistic study based on the 2017 China migrant dynamic survey (CMDS)
BMC public health
metabolic signature identification colorectal cancer critical early diagnosis therapeutic approaches significantly block cancer progression improve patient survival combined untargeted metabolic analysis strategy based internal extractive electrospray ionization mass spectrometry machine learning approach analyze metabolites 173 pairs cancer samples matched normal tissue samples build robust metabolic signature models diagnostic purposes screening independent validation metabolic signatures colorectal cancers via machine learning methods logistic regressionl1 feature selection extreme gradient boosting classification performed generate panel seven signatures good diagnostic performance accuracy 8774 sensitivity 8582 specificity 8966 moreover seven signatures evaluated according ability distinguish cancer normal tissues metabolic molecule pc 300 showing good diagnostic performance addition genes associated pc 300 identified multiomics analysis combining metabolic data transcriptomic data analysis results showed pc 300 could promote proliferation colorectal cancer cell sw480 revealing correlation genetic changes metabolic dysregulation cancer overall results reveal potential determinants affecting metabolite dysregulation paving way mechanistic understanding altered tissue metabolites colorectal cancer design interventions manipulating levels circulating metabolites