Functions of RNF Family in the Tumor Microenvironment and Drugs Prediction in Grade II/III Gliomas.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35223862", "kind": "science-record", "revision": 5, "content_hash": "sha256:a6d5bb505dfb9564faa50d27ae7a290b3fe906b230744b04012101d61340259a", "created_at": "2026-11-01T17:36:59Z", "updated_at": "2026-11-01T22:48:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Functions of RNF Family in the Tumor Microenvironment and Drugs Prediction in Grade II/III Gliomas.", "doi": "10.3389/fcell.2021.754873", "pmid": "35223862", "authors": ["Jingwei Zhang", "Zeyu Wang", "Hao Zhang", "Ziyu Dai", "Xisong Liang", "Shuwang Li", "Xun Zhang", "Fangkun Liu", "Zhixiong Liu", "Kui Yang", "Quan Cheng"], "journal": "Frontiers in cell and developmental biology", "year": 2021, "abstract": "Increasing evidence has demonstrated that RING finger (RNF) proteins played a vital role in cellular and physiological processes and various diseases. However, the function of RNF proteins in low-grade glioma (LGG) remains unknown. In this study, 138 RNF family members revealed their role in LGG. The TCGA database was used as the training cohort; two CGGA databases and GSE108474 were selected as external validation cohorts. Patients were grouped into cluster 1 and cluster 2, both in the training and validation cohorts, using consensus clustering analysis. The prognosis of patients in cluster 1 is significantly better than that in cluster 2. Meanwhile, biofunction prediction was further introduced to explore the potential mechanisms that led to differences in survival outcomes. Patients in Cluster 2 showed more complicated immunocytes infiltration and highly immunosuppressive features than cluster 1. Enrichment pathways such as negative regulation of mast cell activation, DNA replication, mismatch repair, Th17 cell differentiation, antigen processing and presentation, dendritic cell antigen processing and presentation, dendritic cell differentiation were also enriched in cluster 2 patients. For the last, the main contributors were distinguished by employing a machine learning algorithm. A lot of targeted and small molecule drugs that are sensitive to patients in cluster 2 were predicted. Importantly, we discovered TRIM8, DTX2, and TRAF5 as the most vital contributors from the RNF family, which were related to immune infiltration in LGG tumor immune landscape. In this study, we demonstrated the predicted role of RNF proteins in LGG. In addition, we found out three markers among RNF proteins that are closely related to the immune aspects of LGG, which might serve as novel therapeutic targets for immunotherapy in the future.", "claims": [{"id": "claim-data-access", "statement": "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:35223862", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "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": "Kaplan-Meier analysis in the TCGA database between two clusters showed that patients in cluster 1 had a better prognosis than cluster 2 during the overall survival time (p < 0.0001; Figure 2C).", "kind": "quantitative", "target": {"value": 0.0001, "unit": "p-value", "tolerance": null, "source": "Kaplan-Meier analysis in the TCGA database between two clusters showed that patients in cluster 1 had a better prognosis than cluster 2 during the overall survival time (p < 0.0001; Figure 2C)."}, "evidence": [{"asset": "source:pmid:35223862", "locator": "sections[11] \"Clustering Model Based on RNF Genes Identified Two Clusters with Distinct Outcomes and Clinicopathological Features\" paragraph 2", "quote": "Kaplan-Meier analysis in the TCGA database between two clusters showed that patients in cluster 1 had a better prognosis than cluster 2 during the overall survival time (p < 0.0001; Figure 2C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Kaplan-Meier analysis in the TCGA database between two clusters showed that patients in cluster 1 had a better prognosis than cluster 2 during the overall survival time (p < 0.0001; Figure 2C).", "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:35223862"], "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:GSE108474"], "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:GSE108474"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35223862", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2021.754873", "sha256": 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A run that reproduces the effect is judged against this threshold."}], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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