A network-based predictive gene expression signature for recurrence risks in stage II colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31724326", "kind": "science-record", "revision": 8, "content_hash": "sha256:057434ac12270ac3a4b6da6a0ee29c08fc174525169661aed4fd739787740268", "created_at": "2026-11-01T15:40:51Z", "updated_at": "2026-11-01T22:44:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A network-based predictive gene expression signature for recurrence risks in stage II colorectal cancer.", "doi": "10.1002/cam4.2642", "pmid": "31724326", "authors": ["Wen-Jing Yang", "Hai-Bo Wang", "Wen-Da Wang", "Peng-Yu Bai", "Hong-Xia Lu", "Chang-He Sun", "Zi-Shen Liu", "Ding-Kun Guan", "Guo-Wang Yang", "Gan-Lin Zhang"], "journal": "Cancer medicine", "year": 2020, "abstract": "The current criteria for defining the recurrence risks of stage II colorectal cancer (CRC) are not robust; therefore, we aimed to explore novel gene signatures to predict recurrence risks and to reveal the underlying mechanisms of stage II CRC. First, the gene expression profiles of 124 patients with stage II CRC from The Cancer Genome Atlas (TCGA) database were obtained to screen differentially expressed genes (DEGs). A total of 202 DEGs, including 128 upregulated and 74 downregulated, were identified in the recurrence group (n = 24) compared to the nonrecurrence group (n = 100). Furthermore, the top 5 DEGs (ZNF561, WFS1, SLC2A1, MFI2, and PTGR1) were identified by random forest variable hunting, and four (ZNF561, WFS1, SLC2A1, and PTGR1) were selected to create a four-gene recurrent model (GRM), with an area under the curve (AUC) of 0.882 according to the receiver operating characteristic curve, and the robust diagnostic effectiveness of the GRM was further validated with another gene expression profiling dataset (GSE12032), with an AUC of 0.943. The diagnostic effectiveness of the GRM regarding recurrence was associated with poor disease-free survival in all stages of CRC. In addition, gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed 18 enriched functions and 6 enriched pathways. Four genes, ABCG2, CACNA1F, CYP19A1, and TF, were identified as hub genes by the protein-protein interaction network, which further validated that these genes were correlated with a poor pathologic stage and overall survival in all stages of CRC. In conclusion, the GRM can effectively classify stage II CRC into groups of high and low risks of recurrence, thereby making up for the prognostic value of the traditional clinicopathological risk factors defined by the National Comprehensive Cancer Network guidelines. The hub genes may be useful therapeutic targets for recurrence. Thus, the GRM and hub genes could offer clinical value in directing individualized and precision therapeutic regimens for stage II CRC patients.", "claims": [{"id": "claim-method", "statement": "edgeR is an R package used for the analysis of DEGs10 and was used in our study to screen the DEGs between the recurrence and nonrecurrence groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31724326", "locator": "sections[4] \"Data preprocessing and the identification of DEGs\" paragraph 2", "quote": "edgeR is an R package used for the analysis of DEGs10 and was used in our study to screen the DEGs between the recurrence and nonrecurrence groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed in current study can be obtained from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31724326", "locator": "sections[27] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The datasets used and/or analyzed in current study can be obtained from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31724326"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE12032"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE12032"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31724326", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cam4.2642", "sha256": 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