{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang D"],"funding":["Shenzhen Science and Technology Innovation Program","National Natural Science Foundation of China"],"pagination":["29"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9628086"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["<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-free"],"journal":["Biology direct"],"pubmed_title":["Identification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer."],"pmcid":["PMC9628086"],"funding_grant_id":["RCBS20210609103823044","82100586"],"pubmed_authors":["Dai S","Liufu J","Wang D","Yang Q","Wang J","Xie B"],"additional_accession":[]},"is_claimable":false,"name":"Identification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer.","description":"<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-free","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T11:57:13.9Z","creation":"2025-02-19T00:31:50.011Z"},"accession":"S-EPMC9628086","cross_references":{"pubmed":["36319976"],"doi":["10.1186/s13062-022-00342-w"]}}