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pmid:38737696
An innovative prognostic auxiliary for colon adenocarcinoma based on zinc finger protein genes.
Fan Xu · Jiahui Sun · Xinyue Gu · Qingxin Zhou
Translational cancer research · 2024 doi:10.21037/tcr-23-2158
Paper
complete
Experiment
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Data
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Analysis
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Execution
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Complete file
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We aimed to employ these genes to provide a reliable prognosis and treatment stratification tool for COAD patients.\nMethods: Cox and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied, utilizing The Cancer Genome Atlas (TCGA) metadata, to build a ZNF protein gene-based prognostic model. Using this model, patients in the training cohort and testing cohort (GSE17537) were labelled as either high or low risk. Kaplan-Meier (KM) survival analysis and time-dependent receiver operating characteristic (ROC) curve analysis were performed in the patients with opposite risk status to assess the predictive ability in each cohort. The potentiality of the mechanism was explored by the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the degrees of expression of model genes were validated by immunohistochemistry (IHC).\nResults: The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses. Patients in the high-risk subset had a considerably lower chance of surviving compared to those in the low-risk subset. There is a high probability that these model genes were attached to immune-related biological processes, which can be confirmed by the results of the above mechanistic methods. Moreover, patients in the low-risk subset also significantly outperformed the patients in the high-risk subset when calculating immune cells and function scores. Drug sensitivity and tumor immune dysfunction and exclusion (TIDE) analyses showed a clear difference in the immunological and chemotherapeutic efficacy predictions within the two risk groups. Additionally, the degrees of expression of model genes in high-risk and low-risk subsets presented great discrepancies.\nConclusions: The signature may be applied as a predictive classifier to shepherd special medication for COAD patients.", "claims": [{"id": "claim-method", "statement": "Cox and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied, utilizing The Cancer Genome Atlas (TCGA) metadata, to build a ZNF protein gene-based prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38737696", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Cox and the least absolute shrinkage and selection operator (LASSO) regression analysis were applied, utilizing The Cancer Genome Atlas (TCGA) metadata, to build a ZNF protein gene-based prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2158/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38737696", "locator": "sections[27] \"Footnotes\" paragraph 2", "quote": "Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2158/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38737696", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic model consisting of INSM1, PHF21B, RNF138, SYTL4, WRNIP1, ZNF585B, and ZNF514, classified patients into opposite risk statuses.", "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:38737696"], "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:GSE17537"], "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:GSE17537"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38737696", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-23-2158", "sha256": "sha256:7ce7f1d9cb53e092cae60630bcc21b2f847669695f40ced2a5a2f61c3fea05b0", "bytes": 52999, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-23-2158", "version": null, "note": "Full article fetched from PMC; 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