Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.

ABSTRACT: Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental valida

SUBMITTER: Ahmadieh-Yazdi A 

PROVIDER: S-EPMC10632378 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature