Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39382813", "kind": "science-record", "revision": 3, "content_hash": "sha256:e87881bd605c5fc63c410095e21ce8a8c0c9ea530592131c69c752eaa49811f8", "created_at": "2026-11-01T20:29:36Z", "updated_at": "2026-11-01T22:12:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis.", "doi": "10.1007/s12672-024-01421-2", "pmid": "39382813", "authors": ["Changhao Gu", "Lulu Jin", "Xiaoyan Lv", "Cheng Wang", "Congle Wen", "Xiuxiu Su"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Mitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.\nMETHODS: RNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.\nRESULTS: A predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.\nCONCLUSION: The mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.", "claims": [{"id": "claim-method", "statement": "The Lasso Cox regression method was used to identify mitosis-associated genes crucial for COAD prognosis, leading to the development of an evaluative risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39382813", "locator": "sections[3] \"Development and assessment of a mitotic-associated gene prognostic model\" paragraph 2", "quote": "The Lasso Cox regression method was used to identify mitosis-associated genes crucial for COAD prognosis, leading to the development of an evaluative risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This study analyzed publicly available datasets. These datasets are available from the TCGA (https://portal.gdc.cancer.gov/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39382813", "locator": "sections[23] \"notes\" paragraph 1", "quote": "This study analyzed publicly available datasets. These datasets are available from the TCGA (https://portal.gdc.cancer.gov/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Multivariate analysis showed a Hazard Ratio (HR) of 4.09 (95% CI 2.69—6.2) with a P-value < 0.001 (Fig. 4A).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Multivariate analysis showed a Hazard Ratio (HR) of 4.09 (95% CI 2.69—6.2) with a P-value < 0.001 (Fig. 4A)."}, "evidence": [{"asset": "source:pmid:39382813", "locator": "sections[12] \"Evaluation of the risk model as an independent prognostic factor\" paragraph 2", "quote": "Multivariate analysis showed a Hazard Ratio (HR) of 4.09 (95% CI 2.69—6.2) with a P-value < 0.001 (Fig. 4A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Multivariate analysis showed a Hazard Ratio (HR) of 4.09 (95% CI 2.69—6.2) with a P-value < 0.001 (Fig. 4A).", "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:39382813"], "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:GSE17536"], "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:GSE17536"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39382813", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01421-2", "sha256": "sha256:5927224f9ad970b518ef708c7d78d55996cdce13e160505a79b261a5e5c563b0", "bytes": 36978, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01421-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11464972", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17536", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17536", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17536", "version": null, "note": "This study analyzed publicly available datasets. 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