Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32099532", "kind": "science-record", "revision": 8, "content_hash": "sha256:5d400b2a442e4670a5fdca864f3c29dfd32c962a154d6b5305f71ba95063551e", "created_at": "2026-11-01T15:40:56Z", "updated_at": "2026-11-01T22:01:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.", "doi": "10.1186/s12935-020-1140-3", "pmid": "32099532", "authors": ["JinHui Liu", "Mingming Feng", "SiYue Li", "Sipei Nie", "Hui Wang", "Shan Wu", "Jiangnan Qiu", "Jie Zhang", "WenJun Cheng"], "journal": "Cancer cell international", "year": 2020, "abstract": "BACKGROUND: Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized.\nMETHODS: GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan-Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed.\nRESULTS: Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle.\nCONCLUSION: BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32099532", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We obtained gene expression profiles form GEO database (http://www.ncbi.nlm.nih.gov/geo/). GSE17025 dataset [14] covered 91 tumor and 12 normal tissue samples. 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Affymetrix Human Genome U133 Plus 2.0 Array [15] processed raw data."}], "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:32099532"], "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:GSE17025"], "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:GSE17025"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32099532", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-020-1140-3", "sha256": "sha256:c45435d0289b88da5e4069524c076cfc2672f728ef9694d5a96998371e012733", "bytes": 34087, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12935-020-1140-3", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7031962", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17025", "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=GSE17025", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17025", "version": null, "note": "We obtained gene expression profiles form GEO database (http://www.ncbi.nlm.nih.gov/geo/). 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"sha256:884cc4375e2453bf4a6acb410b2b2a9c76138413122537162b1e1e4a39e90fde", "bytes": 996, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[19]/table-wrap[1]#Tab1", "version": null, "note": "Univariate analysis and multivariate analysis of the correlation of six-gene expression with OS among endometrial cancer patients", "structure": {"pmcid": "PMC7031962", "section_index": 19, "section_title": "Identification of prognostic signature", "row_count": 8, "column_count": 7}, "table": {"label": "Table 1", "caption": "Univariate analysis and multivariate analysis of the correlation of six-gene expression with OS among endometrial cancer patients", "rows": [["Parameter", "Univariate analysis", "Multivariate analysis"], ["HR", "95% CI", "p value", "HR", "95% CI", "p value"], ["Age (≤ 60 vs > 60)", "1.788", "1.118–2.859", "0.015", "1.566", "0.956–2.566", "0.075"], ["Stage (stage I & stage II vs stage III & stage IV)", "4.070", 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