Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38515689", "kind": "science-record", "revision": 3, "content_hash": "sha256:1d24e149282f2ec2b80b1eb7ae740368435d38a41b7afbacb04a19997a37664d", "created_at": "2026-11-01T19:20:27Z", "updated_at": "2026-11-01T22:11:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer.", "doi": "10.1016/j.heliyon.2024.e27710", "pmid": "38515689", "authors": ["Cheng Shen", "Bing Zheng", "Zhan Chen", "Wei Zhang", "Xinfeng Chen", "Siyang Xu", "Jianfeng Ji", "Xingxing Fang", "Chunmei Shi"], "journal": "Heliyon", "year": 2024, "abstract": "Background: One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC.\nMethods: In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The \"ConsensusClusterPlus\" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors.\nResults: We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.", "claims": [{"id": "claim-method", "statement": "In this research, we used differential expression analysis, univariate and multivariate Cox regression, and 16 glycosylation-related genes (GRGs) to independently assess the prognosis of ccRCC using the Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38515689", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "In this research, we used differential expression analysis, univariate and multivariate Cox regression, and 16 glycosylation-related genes (GRGs) to independently assess the prognosis of ccRCC using the Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data used to support the conclusions of this investigation, including expression and clinical information, have been placed in the TCGA repository (https://portal.gdc.cancer.gov/), and 10.13039/100000085GEO repository (https://www.ncbi.nlm.nih.gov/geo/) All of the aforementioned databases allow users to obtain their data for free, and our research complied with their terms of service.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38515689", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "Data used to support the conclusions of this investigation, including expression and clinical information, have been placed in the TCGA repository (https://portal.gdc.cancer.gov/), and 10.13039/100000085GEO repository (https://www.ncbi.nlm.nih.gov/geo/) All of the aforementioned databases allow users to obtain their data for free, and our research complied with their terms of service."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper built and verified a 16-gene prognostic signature for ccRCC using differential expression analysis and univariate and multivariate Cox regression, with TCGA data used for development and GSE29609 used for external validation.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38515689", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research."}, {"asset": "source:pmid:38515689", "locator": "sections[1] \"Introduction\" paragraph 4", "quote": "In this research, we used differential expression analysis, univariate and multivariate Cox regression, and 16 glycosylation-related genes (GRGs) to independently assess the prognosis of ccRCC using the Cancer Genome Atlas (TCGA) database."}, {"asset": "source:pmid:38515689", "locator": "sections[5] \"Establishment and validation of glycosylation related prognostic models\" paragraph 2", "quote": "Results of survival analysis were validated in GSE29609 dataset (Fig. 3E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper built and verified a 16-gene prognostic signature for ccRCC using differential expression analysis and univariate and multivariate Cox regression, with TCGA data used for development and GSE29609 used for external validation.", "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:38515689"], "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. 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