Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta.

ABSTRACT: Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. T

SUBMITTER: Maurya NS 

PROVIDER: S-EPMC10115869 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature