A novel proteomic-based model for predicting colorectal cancer with Schistosoma japonicum co-infection by integrated bioinformatics analysis and machine learning.

ABSTRACT: Schistosoma japonicum infection is an important public health problem and the S. japonicum infection is associated with a variety of diseases, including colorectal cancer. We collected the paraffin samples of CRC patients with or without S. japonicum infection according to standard procedures. Data-Independent Acquisition was used to identify differentially expressed proteins (DEPs), protein-protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression) were used to identify candidate genes for diagnosing CRC with S. japonicum infection. To assess the diagnostic value, the nomogram and receiver operating c

SUBMITTER: Li S 

PROVIDER: S-EPMC10614356 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature