Hepatocellular carcinoma subtypes based on metabolic pathways reveals potential therapeutic targets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36937389", "kind": "science-record", "revision": 3, "content_hash": "sha256:20740a5759a2b2523e83ec50c8d45fbcb89a8eb35f6d9693bbb405967cd7e7bb", "created_at": "2026-11-01T19:01:34Z", "updated_at": "2026-11-01T22:09:05Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hepatocellular carcinoma subtypes based on metabolic pathways reveals potential therapeutic targets.", "doi": "10.3389/fonc.2023.1086604", "pmid": "36937389", "authors": ["Zehua He", "Qingfeng Chen", "Wanrong He", "Junyue Cao", "Shunhan Yao", "Qingqiang Huang", "Yu Zheng"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Introduction: Hepatocellular carcinoma (HCC) is an aggressive malignancy with steadily increasing incidence rates worldwide and poor therapeutic outcomes. Studies show that metabolic reprogramming plays a key role in tumor genesis and progression. In this study, we analyzed the metabolic heterogeneity of epithelial cells in the HCC and screened for potential biomarkers.\nMethods: The hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Based on data intergration and measurement of differences among groups, the metabolic epithelial cell subpopulations were identified. The single-cell metabolic pathway was analyzed and the myeloid subpopulations were identified. Cell-cell interaction analysis and single-cell proliferation analysis were performed. The gene expression profiles of HCC patients were obtained from the GSE14520 dataset of GEO and TCGA-LIHC cohort of the UCSC Xena website. Immune analysis was performed. The differentially expressed genes (DEGs) were identified and functionally annotated. Tumor tissues from HCC patients were probed with anti-ALDOA, anti-CD68, anti-CD163, anti-CD4 and anti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy controls. The epithelial cells were significantly enriched in HCC patients compared to the controls (p = 0.011). The epithelial cells from HCC patients were classified into two metabolism-related subpopulations (MRSs) - pertaining to amino acid metabolism (MRS1) and glycolysis (MRS2). Depending on the abundance of these metabolic subpopulations, the HCC patients were also classified into the MRS1 and MRS2 subtype distinct prognoses and immune infiltration. The MRS2 group had significantly worse clinical outcomes and more inflamed tumor microenvironment (TME), as well as a stronger crosstalk between MRS2 cells and immune subpopulations that resulted in an immunosuppressive TME. We also detected high expression levels of ALDOA in the MRS2 cells and HCC tissues. In the clinical cohort, HCC patients with higher ALDOA expression showed greater enrichment of immunosuppressive cells including M2 macrophages and T regulatory cells.\nDiscussion: The glycolytic subtype of HCC cells with high ALDOA expression is associated with an immunosuppressive TME and predicts worse clinical outcomes, providing new insights into the metabolism and prognosis of HCC.", "claims": [{"id": "claim-method", "statement": "The prognostic factors were identified using the log-rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36937389", "locator": "sections[15] \"Statistical analysis\" paragraph 2", "quote": "The prognostic factors were identified using the log-rank test."}], "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://satijalab.org/seurat, GSE112271; https://satijalab.org/seurat, GSE149614; https://satijalab.org/seurat, GSE151530; https://satijalab.org/seurat, GSE156625.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36937389", "locator": "sections[24] \"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://satijalab.org/seurat, GSE112271; https://satijalab.org/seurat, GSE149614; https://satijalab.org/seurat, GSE151530; https://satijalab.org/seurat, GSE156625."}], "state": "complete"}, {"id": "claim-target-result", "statement": "As shown in Figures 2F, G , higher proportion of MRS1 corresponded to favorable overall survival (OS) (log-rank test, p = 0.00018), whereas the predominance of MRS2 correlated to poor OS (log-rank test, p = 0.0019).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36937389", "locator": "sections[19] \"The metabolic phenotypes of epithelial cells are associated with the clinical outcomes of HCC\" paragraph 2", "quote": "As shown in Figures 2F, G , higher proportion of MRS1 corresponded to favorable overall survival (OS) (log-rank test, p = 0.00018), whereas the predominance of MRS2 correlated to poor OS (log-rank test, p = 0.0019)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "As shown in Figures 2F, G , higher proportion of MRS1 corresponded to favorable overall survival (OS) (log-rank test, p = 0.00018), whereas the predominance of MRS2 correlated to poor OS (log-rank test, p = 0.0019).", "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:36937389"], "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:GSE112271"], "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:GSE112271"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36937389", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2023.1086604", "sha256": "sha256:f01f2073aaeb35c2c4eb2c8c464c005104c85923008042dec927a2b7719390b0", "bytes": 40458, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2023.1086604", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10017446", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE112271", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112271", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE112271", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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Methods: The hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and hea", "ods: The hepatic single-cell RNA sequencing (scRNA-seq) datasets of HCC patients and healthy contro", "ti-FOXP3 antibodies. Results We analyzed the scRNA-seq data from 48 HCC patients and 14 healthy con"], "order_basis": "paper_text_order:450", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["biomarkers. 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