Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37808522", "kind": "science-record", "revision": 5, "content_hash": "sha256:08410a1e1404fcb8d8d598a26a4fe74bc905ff4947ef0bad1cc19fcb3577b687", "created_at": "2026-11-01T19:02:39Z", "updated_at": "2026-11-01T22:52:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.", "doi": "10.3389/fmolb.2023.1275897", "pmid": "37808522", "authors": ["Shengke Zhang", "Chenglu Jiang", "Lai Jiang", "Haiqing Chen", "Jinbang Huang", "Jieying Zhang", "Rui Wang", "Hao Chi", "Guanhu Yang", "Gang Tian"], "journal": "Frontiers in molecular biosciences", "year": 2023, "abstract": "Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.", "claims": [{"id": "claim-method", "statement": "Differential gene expression analysis was performed on the training set using the R package “Limma” to identify differential genes (DEGs) (|log2FC|>1, FDR<0.05) (Zhao et al., 2022a; Zhao et al., 2022b; Li et al., 2022).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37808522", "locator": "sections[4] \"2.2 Screening and enrichment analysis of differential genes\" paragraph 2", "quote": "Differential gene expression analysis was performed on the training set using the R package “Limma” to identify differential genes (DEGs) (|log2FC|>1, FDR<0.05) (Zhao et al., 2022a; Zhao et al., 2022b; Li et al., 2022)."}], "state": "complete"}, {"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:37808522", "locator": "sections[27] \"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": "The six-feature-gene ANN model achieved a training-set ROC AUC of 0.947 and a validation-set AUC of 0.719 for diagnosing HBV-LC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37808522", "locator": "sections[19] \"3.5 Construction of diagnostic models, validation and construction of column line graphs\" paragraph 2", "quote": "The efficacy of the ANN model was underscored through rigorous training with a designated dataset, culminating in an impressive area under the receiver operating characteristic curve (ROC AUC) of 0.947—a testament to its prowess in prognosticating HBV-LC (Figure 6B)."}, {"asset": "source:pmid:37808522", "locator": "sections[19] \"3.5 Construction of diagnostic models, validation and construction of column line graphs\" paragraph 2", "quote": "Furthermore, the application of the identified feature genes along with their corresponding gene scores to an independent validation dataset yielded an AUC value of 0.719, thereby substantiating the model’s robustness and real-world applicability (Figure 6C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-feature-gene ANN model achieved a training-set ROC AUC of 0.947 and a validation-set AUC of 0.719 for diagnosing HBV-LC.", "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:37808522"], "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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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE114783", "GSE84044", "GSE123932", "GSE121248", "GSE119322"], "resolved_accessions": ["GSE114783", "GSE84044", "GSE123932", "GSE121248", "GSE119322"], "statements": ["The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE114783, GSE84044, GSE123932, GSE121248, GSE119322 in NCBI Gene Expression Omnibus 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:37808522", "data:geo:GSE114783"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE114783"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:55:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13849, "completion_tokens": 1990, "cached_tokens": 264}}, "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:37808522", "type": "paper", "label": "pmid:37808522"}, {"id": "source:pmid:37808522", "type": "manuscript", "label": "PMID 37808522 full text"}, {"id": "data:geo:GSE114783", "type": "dataset", "label": "GEO GSE114783"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37808522", "type": "paper", "label": "pmid:37808522"}, {"id": "source:pmid:37808522", "type": "manuscript", "label": "PMID 37808522 full text"}, {"id": "data:geo:GSE114783", "type": "dataset", "label": "GEO GSE114783"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37808522", "type": "extraction", "used": ["source:pmid:37808522"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE114783", "type": "accession_resolution", "used": ["data:geo:GSE114783"], "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:37808522", "parent_hash": "sha256:36899f670f3410bf525d63f5542c1197b726ae1b764ecd898d1d5de10c181b99", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE114783 licence not read", "asset:data:geo:GSE114783 not downloaded", "asset:data:geo:GSE119322 licence not read", "asset:data:geo:GSE119322 not downloaded", "asset:data:geo:GSE121248 licence not read", "asset:data:geo:GSE121248 not downloaded", "asset:data:geo:GSE123932 licence not read", "asset:data:geo:GSE123932 not downloaded", "asset:data:geo:GSE84044 licence not read", "asset:data:geo:GSE84044 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": ["Differential gene expression analysis was performed on the training set using the R package “Limma” to identify differential genes (DEGs) (|log2FC|>1, FDR<0.05) (Zhao et al., 2022a; Zhao et al., 2022b; Li et al., 2022)."], "order_basis": "analysis_only", "id": "assay:1"}]}