A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34595101", "kind": "science-record", "revision": 9, "content_hash": "sha256:45703a74e39a804e6076cc981ca4068121431282c4b6b3c2d19a080272091803", "created_at": "2026-11-01T15:41:54Z", "updated_at": "2026-11-01T22:47:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer.", "doi": "10.3389/fonc.2021.605810", "pmid": "34595101", "authors": ["Kaixuan Guo", "Cong Lai", "Juanyi Shi", "Zhuang Tang", "Cheng Liu", "Kuiqing Li", "Kewei Xu"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.\nMETHODS: Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the \"edgeR\" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.\nRESULTS: Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.\nCONCLUSION: The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.", "claims": [{"id": "claim-data-access", "statement": "PCa gene expression data (RNA-Seq) and the corresponding clinical data were downloaded from the TCGA database (https://portal.gdc.cancer.gov) and Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/). Transcriptome sequencing data and detailed clinical follow-up information derived from 498 PCa patients were downloaded from the TCGA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34595101", "locator": "sections[3] \"Collection of Prostate Cancer Data\" paragraph 2", "quote": "PCa gene expression data (RNA-Seq) and the corresponding clinical data were downloaded from the TCGA database (https://portal.gdc.cancer.gov) and Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/)."}, {"asset": "source:pmid:34595101", "locator": "sections[13] \"Initial Screening and Identification of Differentially Expressed Glycolysis-Related Genes\" paragraph 2", "quote": "Transcriptome sequencing data and detailed clinical follow-up information derived from 498 PCa patients were downloaded from the TCGA."}], "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:34595101"], "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:GSE70770"], "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:GSE70770"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34595101", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2021.605810", "sha256": "sha256:0a7997f23faa23017ff674fbd4ccc2f6b861b97461da2968debe5fb033ec77a3", "bytes": 35027, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2021.605810", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8476926", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE70770", "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=GSE70770", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70770", "version": null, "note": "PCa gene expression data (RNA-Seq) and the corresponding clinical data were downloaded from the TCGA database (https://portal.gdc.cancer.gov) and Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/). Transcriptome sequencing data and detailed clinical follow-up information derived from 498 PCa patients were downloaded from the TCGA.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "PCa gene expression data (RNA-Seq) and the corresponding clinical data were downloaded from the TCGA database (https://portal.gdc.cancer.gov) and Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/). 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