biostudies-literatureWang DShenzhen Science and Technology Innovation ProgramNational Natural Science Foundation of China29https://www.ebi.ac.uk/biostudies/studies/S-EPMC9628086biostudies-literatureUnknown17(1)<h4>Background</h4>Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Although marker genes associated with CRC have been identified previously, only a few have fulfilled the therapeutic demand. Therefore, based on differentially expressed genes (DEGs), this study aimed to establish a promising and valuable signature model to diagnose CRC and predict patient's prognosis.<h4>Methods</h4>The key genes were screened from DEGs to establish a multiscale embedded gene co-expression network, protein-protein interaction network, and survival analysis. A support vector machine (SVM) diagnostic model was constructed by a supervised classification algorithm. Univariate Cox analysis was performed to construct two prognostic signatures for overall survival and disease-freeBiology directIdentification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer.PMC9628086RCBS2021060910382304482100586Dai SLiufu JWang DYang QWang JXie BfalseIdentification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer.<h4>Background</h4>Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Although marker genes associated with CRC have been identified previously, only a few have fulfilled the therapeutic demand. Therefore, based on differentially expressed genes (DEGs), this study aimed to establish a promising and valuable signature model to diagnose CRC and predict patient's prognosis.<h4>Methods</h4>The key genes were screened from DEGs to establish a multiscale embedded gene co-expression network, protein-protein interaction network, and survival analysis. A support vector machine (SVM) diagnostic model was constructed by a supervised classification algorithm. Univariate Cox analysis was performed to construct two prognostic signatures for overall survival and disease-free2022-01-01T00:00:00Z2022 Nov2025-04-05T11:57:13.9Z2025-02-19T00:31:50.011ZS-EPMC96280863631997610.1186/s13062-022-00342-w