Construction of a novel copper-induced-cell-death-related gene signature for prognosis in colon cancer, with focus on KIF7.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39695482", "kind": "science-record", "revision": 3, "content_hash": "sha256:f6937c6e3fb40c0b9b7ecde88e0eaebe82ded9fad0841c50115ab4d8adadd886", "created_at": "2026-11-01T20:29:59Z", "updated_at": "2026-11-01T22:13:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a novel copper-induced-cell-death-related gene signature for prognosis in colon cancer, with focus on KIF7.", "doi": "10.1186/s12885-024-13315-1", "pmid": "39695482", "authors": ["Hua Li", "Jingying Chen", "Zhengxian Liu", "Lujuan Pan", "Xiaoling Lan", "Lihe Jiang", "Fuda Huang"], "journal": "BMC cancer", "year": 2024, "abstract": "BACKGROUND: Colon cancer (CC) is a leading cause of cancer-related mortality worldwide. Accurate prognostic markers are essential for patient risk stratification and personalized treatment. Copper-induced cell-death-related genes (CRG) have emerged as potential players in cancer prognosis, yet their role in CC remains unclear.\nMETHODS: This study aimed to comprehensively evaluate the expression of CRG and their roles in CC using gene expression and clinical data from TCGA and GEO databases. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses identified prognostic genes, leading to the construction of a CRG prognostic signature. The signature's predictive accuracy was validated using Kaplan-Meier survival curves, Receiver Operating Characteristic (ROC) curves, and a nomogram model. Additionally, we conducted experiments including immunofluorescence staining and cellular assays to validate the key genes' biological functions.\nRESULTS: A 12-gene CRG signature was significantly associated with overall survival in CC patients. The high-risk group, as classified by median risk score, exhibited significantly shorter survival times compared to the low-risk group. The signature's predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts. Notably, the risk score was significantly correlated with immune checkpoints, chemotherapy sensitivity, and tumor microenvironment. Furthermore, the risk score showed a strong association with immunotherapy response in patients from GSE78220 and GSE39688 cohorts. Bioinformatics analysis of KIF7, a key gene within the signature, revealed its upregulation in CC and significant associations with tumor mutation burden, microsatellite instability, and immune cell infiltration across various cancers. Experiments confirmed that KIF7 was upregulated in CC and its knockdown reduced cell proliferation, migration, and invasion.\nCONCLUSION: The CRG prognostic signature can effectively predict overall survival, immune microenvironment and chemotherapy response in CC. KIF7, as a potential prognostic marker, has significant potential for the prediction and treatment of CC.", "claims": [{"id": "claim-method", "statement": "We performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to select optimum prognostic genes with P < 0.01 using “glmnet” and “survival” packages.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39695482", "locator": "sections[4] \"Construction and validation of the prognostic CRG signature in CC\" paragraph 2", "quote": "We performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to select optimum prognostic genes with P < 0.01 using “glmnet” and “survival” packages."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the bioinformatics analysis are available in the [GEO] database [http://www.ncbi.nlm.nih.gov/geo, accession numbers: GSE39582, GSE17536, GSE78220 and GSE39688], [TCGA] database [https://genome-cancer.ucsc.edu/]. The data used during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39695482", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The datasets analyzed during the bioinformatics analysis are available in the [GEO] database [http://www.ncbi.nlm.nih.gov/geo, accession numbers: GSE39582, GSE17536, GSE78220 and GSE39688], [TCGA] database [https://genome-cancer.ucsc.edu/]. The data used during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The signature’s predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39695482", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The signature’s predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The signature’s predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts.", "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:39695482"], "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:39695482", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-024-13315-1", "sha256": "sha256:f691e07f0b61c91ce37d92d0f6ff7ff55d18b4b021465ebc85ff5b174ddfb11b", "bytes": 55498, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-024-13315-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11656869", "source": "europe_pmc_fulltext", "coverage": "whole_body", "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": "The datasets analyzed during the bioinformatics analysis are available in the [GEO] database [http://www.ncbi.nlm.nih.gov/geo, accession numbers: GSE39582, GSE17536, GSE78220 and GSE39688], [TCGA] database [https://genome-cancer.ucsc.edu/]. 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