Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Colon Cance r is the subject of a report. According to news reporting out of Changchun, Peop le's Republic of China, by NewsRx editors, research stated, "The metabolic signa ture identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and imp rove patient survival. Here, we combined an untargeted metabolic analysis strate gy based on internal extractive electrospray ionization mass spectrometry and th e machine learning approach to analyze metabolites in 173 pairs of cancer sample s and matched normal tissue samples to build robust metabolic signature models f or diagnostic purposes." Our news journalists obtained a quote from the research from Jilin University, " Screening and independent validation of metabolic signatures from colorectal can cers via machine learning methods (Logistic Regression_L1 for featu re selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accur acy 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 metabol ic molecule PC (30:0) showing good diagnostic performance. In addition, genes as sociated with PC (30:0) were identified by multiomics analysis (combining metabo lic 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."
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