A Prognostic Model of Angiogenesis and Neutrophil Extracellular Traps Related Genes Manipulating Tumor Microenvironment in Colon Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37497410", "kind": "science-record", "revision": 3, "content_hash": "sha256:94bacd82d631f04a77dc3bd28e3a5c00dee0e38ad340dc93e859f53e898fc98e", "created_at": "2026-11-01T19:02:16Z", "updated_at": "2026-11-01T22:09:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Prognostic Model of Angiogenesis and Neutrophil Extracellular Traps Related Genes Manipulating Tumor Microenvironment in Colon Cancer.", "doi": "10.7150/jca.85778", "pmid": "37497410", "authors": ["Dongsheng Zhang", "Yan Zhao", "Shirui Wang", "Xiaowei Wang", "Yueming Sun"], "journal": "Journal of Cancer", "year": 2023, "abstract": "Colon adenocarcinoma (COAD) is one of the most common carcinomas worldwide. The main causes of cancer-related mortality of COAD are metastases. The fundamental processes for how angiogenesis and neutrophil extracellular traps (NETs) contributing to tumor progression and metastasis are still uncertain. In our study, The Cancer Genome Atlas (TCGA)-COAD dataset (train set) and GSE17536 (test set) were analyzed. Angiogenesis potential index (API) and NETs potential index (NPI) based on angiogenesis and NETs-related genes were respectively built using bioinformatic methods and machine learning algorithms. Subjects were split into groups with low API/NPI or high API/NPI. Survival analysis showed the high API and high NPI patients with the worst survival compared with the others. Between the high API/NPI group and the other groups, differentially expressed genes (DEGs) were found. A four-gene signature (TIMP1, FSL3, CALB2, and FABP4) was included in a risk model based on least absolute shrinkage and selection operator (LASSO) analysis. Additionally, the model displayed a significant association with many immune microenvironment characteristics. Finally, we verified the clinical significance of CALB2 expression and its role to promote the invasion and migration of colon cancer cells in vitro.", "claims": [{"id": "claim-method", "statement": "The glmnet package (version 4.1-3) was applied to incorporate the best candidate genes into the LASSO regression approach, which was then employed to create the API/NPI-related prognostic model24.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37497410", "locator": "sections[6] \"Development of API/NPI-related Prognostic Model\" paragraph 2", "quote": "The glmnet package (version 4.1-3) was applied to incorporate the best candidate genes into the LASSO regression approach, which was then employed to create the API/NPI-related prognostic model24."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37497410", "locator": "sections[28] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene signature (TIMP1, FSL3, CALB2, and FABP4) was included in a risk model based on least absolute shrinkage and selection operator (LASSO) analysis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37497410", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A four-gene signature (TIMP1, FSL3, CALB2, and FABP4) was included in a risk model based on least absolute shrinkage and selection operator (LASSO) analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A four-gene signature (TIMP1, FSL3, CALB2, and FABP4) was included in a risk model based on least absolute shrinkage and selection operator (LASSO) analysis.", "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:37497410"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37497410", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/jca.85778", "sha256": "sha256:4fea1125dbb35c538423d2c84be7e3d689b9d4f655271cb8aff8fbd7fd0c001b", "bytes": 54186, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7150/jca.85778", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10367930", "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 presented in this study can be found in online repositories. 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