Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30271205", "kind": "science-record", "revision": 9, "content_hash": "sha256:3c803b64ae43716d028052a6916963ee44143f48e2f84950320bb0267d86614e", "created_at": "2026-11-01T15:40:37Z", "updated_at": "2026-11-01T22:43:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer.", "doi": "10.2147/CMAR.S171855", "pmid": "30271205", "authors": ["Yihao Mao", "Qingyang Feng", "Peng Zheng", "Liangliang Yang", "Tianyu Liu", "Yuqiu Xu", "Dexiang Zhu", "Wenju Chang", "Meiling Ji", "Li Ren", "Ye Wei", "Guodong He", "Jianmin Xu"], "journal": "Cancer management and research", "year": 2018, "abstract": "PURPOSE: Tumor purity is defined as the proportion of cancer cells in the tumor tissue. The impact of tumor purity on colon cancer (CC) prognosis, genetic profile, and microenvironment has not been thoroughly accessed.\nMATERIALS AND METHODS: Clinical and transcriptomic data from three public datasets, GSE17536/17537, GSE39582, and TCGA, were retrospectively collected (n=1,248). Tumor purity of each sample was inferred by a computational method based on transcriptomic data. Survival-related analyses were performed on microarray dataset containing GSE17536/17537 and GSE39582 (n=794), whereas TCGA dataset was utilized for subsequent genomic analysis (n=454).\nRESULTS: Right-sided CC patients showed a significantly lower tumor purity. Low purity CC conferred worse survival, and tumor purity was identified as an independent prognostic factor. Moreover, high tumor purity CC patients benefited more from adjuvant chemotherapy. Subsequent genomic analysis found that the mutation burden was negatively associated with tumor purity, with only APC and KRAS significantly more mutated in high purity CC. However, no somatic copy number alteration event was correlated with tumor purity. Furthermore, immune-related pathways and immunotherapy-associated markers (programmed cell death protein 1 [PD-1], programmed death-ligand 1 [PD-L1], cytotoxic T-lymphocyte-associated protein 4 [CTLA-4], Lymphocyte-activation gene 3 [LAG-3] and T-cell immunoglobulin and mucin-domain containing-3 [TIM-3]) were highly enriched in low purity samples. Notably, the relative proportion of M2 macrophages and neutrophils, which indicated worse survival in CC, was negatively associated with tumor purity.\nCONCLUSION: Tumor purity exhibited potential value for CC prognostic stratification as well as adjuvant chemotherapy benefit prediction. The relative worse survival in low purity CC may attribute to higher mutation frequency in key pathways and purity-related microenvironmental changing.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier analyses and logrank tests were used to evaluate the relationship between groups and overall survival (OS). Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors; factors with P<0.1 in univariate Cox regression analyses were further evaluated in the multivariate Cox regression models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30271205", "locator": "sections[6] \"Statistical analysis\" paragraph 2", "quote": "Kaplan–Meier analyses and logrank tests were used to evaluate the relationship between groups and overall survival (OS). Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors; factors with P<0.1 in univariate Cox regression analyses were further evaluated in the multivariate Cox regression models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The MINiML formatted family files containing meta-data and Affymetrix HG133plus2 microarray data of GSE17536/17537 and GSE39582 were downloaded from GEO repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30271205", "locator": "sections[4] \"Tumor purity calculation\" paragraph 2", "quote": "The MINiML formatted family files containing meta-data and Affymetrix HG133plus2 microarray data of GSE17536/17537 and GSE39582 were downloaded from GEO repository."}], "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:30271205"], "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:GSE39582"], "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:GSE39582"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30271205", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/CMAR.S171855", "sha256": "sha256:67166bc6a81df15207f87b74bc037c52fb136423ae1f9775ba86fd01b1a5c91e", "bytes": 21860, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/CMAR.S171855", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC6149864", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39582", "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=GSE39582", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39582", "version": null, "note": "The MINiML formatted family files containing meta-data and Affymetrix HG133plus2 microarray data of GSE17536/17537 and 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