Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33575212", "kind": "science-record", "revision": 6, "content_hash": "sha256:bfa964ec795c524d280925588d35805525368bc00a319b156d326880aedc183d", "created_at": "2026-11-01T15:41:25Z", "updated_at": "2026-11-01T21:24:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma.", "doi": "10.3389/fonc.2020.597996", "pmid": "33575212", "authors": ["Siyuan Tian", "Jingyi Liu", "Keshuai Sun", "Yansheng Liu", "Jiahao Yu", "Shuoyi Ma", "Miao Zhang", "Gui Jia", "Xia Zhou", "Yulong Shang", "Ying Han"], "journal": "Frontiers in oncology", "year": 2020, "abstract": "BACKGROUND: Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients.\nMETHODS: Differently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability.\nRESULTS: The prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis.\nCONCLUSIONS: The RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed using the “survival” package in R software to identify prognostic factors from above prognosis-related RBPs. Variables with P < 0.05 in univariate analysis were further included in the stepwise multivariate analysis. A predictive model was then designed based on the independent prognostic factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33575212", "locator": "sections[8] \"Construction and Validation of an RNA-Binding Protein-Associated Prognostic Model\" paragraph 2", "quote": "Univariate Cox regression analysis was performed using the “survival” package in R software to identify prognostic factors from above prognosis-related RBPs. Variables with P < 0.05 in univariate analysis were further included in the stepwise multivariate analysis. A predictive model was then designed based on the independent prognostic factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE54236 https://cancergenome.nih.gov/, TCGA-LIHC https://dcc.icgc.org/, ICGC-JP.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33575212", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, GSE54236 https://cancergenome.nih.gov/, TCGA-LIHC https://dcc.icgc.org/, ICGC-JP."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:33575212"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33575212", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2020.597996", "sha256": "sha256:5f8f2d240de138f1e2e0f99c805fd92b4fd70aec038923f09fa6766cd6715bc5", "bytes": 41605, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2020.597996", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7870868", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:33575212:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e498fbc36fa4102478c7935d61712df85ff5ca7127999436849c84ffbbb88e7c", "bytes": 846, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#T1", "version": null, "note": "The top 10 hub genes with the highest degree of connectivity in the PPI network.", "structure": {"pmcid": "PMC7870868", "section_index": 13, "section_title": "Protein–Protein Interaction Construction and Hub Genes Identification", "row_count": 11, "column_count": 3}, "table": {"label": "Table 1", "caption": "The top 10 hub genes with the highest degree of connectivity in the PPI network.", "rows": [["Gene symbol", "Gene description", "Degree"], ["POLR2K", "RNA Polymerase II Subunit K", "16"], ["HNRNPM", "Heterogeneous Nuclear Ribonucleoprotein M", "15"], ["SNRPE", "Small Nuclear Ribonucleoprotein Polypeptide E", "15"], ["DDX39A", "DExD-Box Helicase 39A", "14"], ["RRS1", "Ribosome Biogenesis Regulator 1 Homolog", "14"], ["HNRNPU", "Heterogeneous Nuclear Ribonucleoprotein U", "14"], ["BOP1", "BOP1 Ribosomal Biogenesis Factor", "13"], ["SNRPB", "Small Nuclear Ribonucleoprotein Polypeptides B And B1", "13"], ["EZH2", "Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit", "11"], ["ILF3", "Interleukin Enhancer Binding Factor 3", "11"]], "row_count": 11, "column_count": 3, "numeric_cells": 15, "header_rows": 1, "locator": "section[13]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:33575212:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:785654580ae5a4a59958aee04232eb12ae61dc2732adf4f5cae865b488f953fd", "bytes": 651, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[16]/table-wrap[1]#T2", "version": null, "note": "Association between genes expression and overall survival in HCC patients.", "structure": {"pmcid": "PMC7870868", "section_index": 16, "section_title": "Construction and Validation of the Prognostic Model", "row_count": 12, "column_count": 3}, "table": {"label": "Table 2", "caption": "Association between genes expression and overall survival in HCC patients.", "rows": [["Gene symbol", "HR(95%CI)", "P-Value"], ["A.", "", ""], ["HNRNPM", "2.025(1.401–2.925)", "0.000"], ["SNRPE", "1.287(1.026–1.615)", "0.029"], ["RRS1", "1.242(1.014–1.521)", "0.036"], ["BOP1", "1.321(1.121–1.558)", "0.000"], ["SNRPB", "1.498(1.201–1.868)", "0.000"], ["EZH2", "1.864(1.465–2.373)", "0.000"], ["ILF3", "1.575(1.157–2.144)", "0.004"], ["B.", "", ""], ["BOP1", "1.259(1.064–1.489)", "0.007"], ["EZH2", "1.801(1.409–2.303)", "0.000"]], "row_count": 12, "column_count": 3, "numeric_cells": 19, "header_rows": 1, "locator": "section[16]/table-wrap[1]#T2"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was performed using the “survival” package in R software to identify prognostic factors from above prognosis-related RBPs. 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A predictive model was then designed based on the independent prognostic factors."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}], "targets": [], "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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