Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36860731", "kind": "science-record", "revision": 2, "content_hash": "sha256:7a198280855d8f912a11911af831da8de53e2d09485be0625c955316e62433ce", "created_at": "2026-11-01T19:01:29Z", "updated_at": "2026-11-01T21:30:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis.", "doi": "10.1155/2023/1847700", "pmid": "36860731", "authors": ["Shimeng Cui", "Minghao Zhang", "Shanshan Bai", "Yanfang Bi", "Shan Cong", "Shi Jin", "Shuang Li", "Hui He", "Jian Zhang"], "journal": "Oxidative medicine and cellular longevity", "year": 2023, "abstract": "Background: The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC.\nMethods: TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA.\nResults: Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis.\nConclusion: The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.", "claims": [{"id": "claim-method", "statement": "We employed the ssGSEA approach to determine each patient's IFNγ score to assess the Th1/IFNγ expression variations in metabolic subtypes and enrichment score of 14 oxidative stress-related biological pathways.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36860731", "locator": "sections[6] \"2.4. Single-Sample Gene Set Enrichment Analysis (ssGSEA)\" paragraph 2", "quote": "We employed the ssGSEA approach to determine each patient's IFNγ score to assess the Th1/IFNγ expression variations in metabolic subtypes and enrichment score of 14 oxidative stress-related biological pathways."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study could be found in GSE14520 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520, in GSE91061 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061, and in GSE14502 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14502.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36860731", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "The datasets analyzed in this study could be found in GSE14520 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520, in GSE91061 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061, and in GSE14502 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14502."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three metabolic HCC subtypes were identified, with MC2 having an unfavorable prognosis and MC1 having a favorable prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36860731", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Three metabolic HCC subtypes were identified, with MC2 having an unfavorable prognosis and MC1 having a favorable prognosis.", "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:36860731"], "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:GSE14502"], "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:GSE14502"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36860731", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2023/1847700", "sha256": 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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE91061, and in GSE14502 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14502.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM362947", "GSM362948", "GSM362949", "GSM362950", "GSM362951", "GSM362952", "GSM362953", "GSM362954", "GSM362955", "GSM362956", "GSM362957", "GSM362958", "GSM362959", "GSM362960", "GSM362961", "GSM362962", "GSM362963", "GSM362964", "GSM362965", "GSM362966", "GSM362967", "GSM362968", "GSM362969", "GSM362970", "GSM362971", "GSM362972", "GSM362973", "GSM362974", "GSM362975", "GSM362976", "GSM362977", "GSM362978", "GSM362979", "GSM362980", "GSM362981", "GSM362982", "GSM362983", "GSM362984", "GSM362985", "GSM362986", "GSM362987", "GSM362988", "GSM362989", "GSM362990", "GSM362991", "GSM362992", "GSM362993", "GSM362994", "GSM362995", "GSM362996", "GSM362997", "GSM362998", "GSM362999", "GSM363000", "GSM363001", "GSM363002", "GSM363003", "GSM363004", "GSM363005", "GSM363006", 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