Construction and validation of a joint diagnosis model based on random forest and artificial intelligence network for hepatitis B-related hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38482416", "kind": "science-record", "revision": 5, "content_hash": "sha256:73739d7976c61daf17c9f6605bde30cca243848024cf5ae0dee9973a0e11b25c", "created_at": "2026-11-01T19:20:25Z", "updated_at": "2026-11-01T22:53:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a joint diagnosis model based on random forest and artificial intelligence network for hepatitis B-related hepatocellular carcinoma.", "doi": "10.21037/tcr-23-1197", "pmid": "38482416", "authors": ["Xili Jiang", "Jiyun Hu", "Shucai Xie"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Hepatitis B virus (HBV) is the dominant pathogenic factor of hepatocellular carcinoma (HCC) in Asia and Africa. Early identification and clinical diagnosis are crucial for HBV-related HCC. Random forest (RF) and artificial neural network (ANN) were an innovative and highly effective supervised machine learning (ML) algorithm for the early diagnosis and screening of HBV-related HCC. This study aims to identify significant biomarkers and develop a novel genetic model for the efficient diagnosis of HBV-related HCC.\nMethods: Gene Expression Omnibus (GEO) Series (GSE)19665, GSE55092, and GSE121248 were used to identify significant differentially expressed genes (DEGs). The enrichment analysis was performed on Metascape online tool. The RF algorithm and ANN were used to select the potential predictive gene panels and construct an HBV-related HCC diagnostic model. Subsequently, GSE17548, GSE104310, GSE44074, and GSE136247 were used to test the accuracy of the ANN model. Finally, the CIBERSORT algorithm was used to assess the abundance of immune infiltrates in all samples.\nResults: First, 116 genes were identified as DEGs, and the DEGs were particularly enriched in cellular hormone metabolic process, monocarboxylic acid metabolic process, NABA extracellular matrix (ECM) AFFILIATED steroid metabolic process and metabolism of bile acid and bile salt. DNA topoisomerase II alpha (TOP2A), C-type lectin domain family 1 member B (CLEC1B), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), ficolin 2 (FCN2), C-X-C motif chemokine ligand 14 (CXCL14), cyclase associated actin cytoskeleton regulatory protein 2 (CAP2), ficolin 3 (FCN3), kynurenine 3-monooxygenase (KMO) and cadherin related family member 2 (CDHR2) were available to develop an HBV-related HCC diagnostic model. After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833). Finally, the percentage of infiltrating immune cell types [B cells naïve, B cells memory, plasma cells, T cells CD8, T cells CD4 memory resting, T cells regulatory (Tregs), T cells gamma delta, natural killer (NK) cells resting, NK cells activated, Macrophages M0, Dendritic cells activated, Mast cells activated] for hepatitis B-related HCC were significantly different from that of non-cancerous liver tissue with HBV.\nConclusions: A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.", "claims": [{"id": "claim-method", "statement": "The limma R package v.3.5.2 in R software was used to identify DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38482416", "locator": "sections[5] \"Identification of DEGs and enrichment analyses\" paragraph 2", "quote": "The limma R package v.3.5.2 in R software was used to identify DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "(raw data are available at https://cdn.amegroups.cn/static/public/tcr-23-1197-1.xlsx, Table 1)", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38482416", "locator": "sections[4] \"Gene expression data\" paragraph 2", "quote": "(raw data are available at https://cdn.amegroups.cn/static/public/tcr-23-1197-1.xlsx, Table 1)"}], "state": "complete"}, {"id": "claim-target-result", "statement": "A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38482416", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After validation, the diagnostic model showed high sensitivity (88.5%, 90%, 88.5%, 76.5%) and specificity (100%, 81.8%, 89.5%, 72.2%), and the areas under the receiver operating characteristic (ROC) curves showed excellent efficiency (1, 0.927, 0.921, 0.833)."}, {"asset": "source:pmid:38482416", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A novel early diagnostic model of HBV-related HCC was established, and the model showed better efficiency in distinguishing HBV-related HCC from other non-cancerous with HBV individuals.", "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:38482416"], "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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"GSM1329452", "GSM1329453", "GSM1329454", "GSM1329455", "GSM1329456", "GSM1329457", "GSM1329458", "GSM1329459", "GSM1329460"], "sample_count": 140, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Viral Expression and Molecular Profiling in Liver Tissue versus Microdissected Hepatocytes in Hepatitis B Virus - Associated Hepatocellular Carcinoma", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE104310 not downloaded", "asset:data:geo:GSE104310 licence not read", "asset:data:geo:GSE121248 not downloaded", "asset:data:geo:GSE121248 licence not read", "asset:data:geo:GSE136247 not downloaded", "asset:data:geo:GSE136247 licence not read", "asset:data:geo:GSE17548 not downloaded", "asset:data:geo:GSE17548 licence not read", "asset:data:geo:GSE19665 not downloaded", "asset:data:geo:GSE19665 licence not read", "asset:data:geo:GSE44074 not downloaded", "asset:data:geo:GSE44074 licence not read", "asset:data:geo:GSE55092 not downloaded", "asset:data:geo:GSE55092 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The limma R package v.3.5.2 in R software was used to identify DEGs."}}, {"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": "KM"}}, {"id": "method-3", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE19665", "GSE55092", "GSE121248", "GSE17548", "GSE104310", "GSE44074", "GSE136247"], "resolved_accessions": ["GSE19665", "GSE55092", "GSE121248", "GSE17548", "GSE104310", "GSE44074", "GSE136247"], "statements": ["(raw data are available at https://cdn.amegroups.cn/static/public/tcr-23-1197-1.xlsx, Table 1)"], "basis": "The paper names GSE19665, GSE55092, GSE121248, GSE17548, GSE104310, GSE44074, GSE136247 in Gene Expression Omnibus (GEO) database of the National Centre for Biotechnology Information and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38482416", "data:geo:GSE104310"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE104310"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:12:12Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9565, "completion_tokens": 4372, "cached_tokens": 0}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38482416", "type": "paper", "label": "pmid:38482416"}, {"id": "source:pmid:38482416", "type": "manuscript", "label": "PMID 38482416 full text"}, {"id": "data:geo:GSE104310", "type": "dataset", "label": "GEO GSE104310"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38482416", "type": "paper", "label": "pmid:38482416"}, {"id": "source:pmid:38482416", "type": "manuscript", "label": "PMID 38482416 full text"}, {"id": "data:geo:GSE104310", "type": "dataset", "label": "GEO GSE104310"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38482416", "type": "extraction", "used": ["source:pmid:38482416"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE104310", "type": "accession_resolution", "used": ["data:geo:GSE104310"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38482416", "parent_hash": "sha256:338676d24dee37ea91148e04f9bf66f6e8ee4fea360e70c7e455e754817bbcc3", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE104310 licence not read", "asset:data:geo:GSE104310 not downloaded", "asset:data:geo:GSE121248 licence not read", "asset:data:geo:GSE121248 not downloaded", "asset:data:geo:GSE136247 licence not read", "asset:data:geo:GSE136247 not downloaded", "asset:data:geo:GSE17548 licence not read", "asset:data:geo:GSE17548 not downloaded", "asset:data:geo:GSE19665 licence not read", "asset:data:geo:GSE19665 not downloaded", "asset:data:geo:GSE44074 licence not read", "asset:data:geo:GSE44074 not downloaded", "asset:data:geo:GSE55092 licence not read", "asset:data:geo:GSE55092 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The limma R package v.3.5.2 in R software was used to identify DEGs."], "order_basis": "analysis_only", "id": "assay:1"}]}