A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36456877", "kind": "science-record", "revision": 4, "content_hash": "sha256:cae0450a3fb93bbcf910798fdabcc618f3b2aa6eadefead62bff4720339ce1ef", "created_at": "2026-11-01T19:00:56Z", "updated_at": "2026-11-01T22:50:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma.", "doi": "10.1038/s41598-022-25356-2", "pmid": "36456877", "authors": ["Kequan Xu", "Peng Xia", "Pan Liu", "Xiao Zhang"], "journal": "Scientific reports", "year": 2022, "abstract": "Globally, hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Studies have shown that alterations in the tumor immune microenvironment (TIME) play a significant role in the pathogenesis and progression of HCC, and notably, lipid metabolism has been shown to regulate TIME. Therefore, in predicting the prognosis and efficacy of immunotherapy in patients with HCC, lipid metabolism-related prognostic factors are highly relevant. mRNA expression data of HCC were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and gene expression omnibus (GEO) databases. and lipid metabolism-related genes were also obtained from the GSEA databases. Least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox proportional hazards analysis were used to explore lipid metabolism-related prognostic genes and further construct a prognostic signature in the training set, ICGC and GSE54236 were used to validate the accuracy of the signature. qRT-PCR was used to detect the mRNA levels of lipid metabolism-related prognostic genes in HCC tissues and their paired adjacent tissues. Nile red staining was used to demonstrate lipid content in HCC tissues. Immunofluores-cence and ELISA were used to detect immune cells and immune responses in HCC tissues and serum. Six lipid metabolism-related genes (ADH1C, APEX1, ME1, S100A10, ACACA and CYP2C9) were identified as independent prognostic factors, which were used for risk model construction for HCC patients. The areas under the 1-, 2-, and 3-year ROC curves for the TCGA cohort were 0.758, 0.701 and 0.671, respectively. Compared with paired paracancerous tissues, qRT-PCR revealed that APEX1, ME1, S100A10 and ACACA were up-regulated in HCC tissues, whereas ADH1C and CYP2C9 were down-regulated in HCC tissues. Nile red staining indicated that this study showed that both the HCC tissue and serum of patients in the high-risk group exhibited lipid accumulation. Our identified prognostic model comprising six lipid metabolism-related genes could provide survival prediction. Moreover, HCC drug therapy target selection and molecular marker research can be guided by our predictive model.", "claims": [{"id": "claim-method", "statement": "Prognostic models (Risk score = (− 0.000934507 × ADH1C expression) + (0.010025652 × APEX1 expression) + (0.029611698 × ME1 expression) + (0.001505404 × S100A10 expression) + (0.067765234 × ACACA expression) + (− 0.001361529 × CYP2C9 expression).) were constructed using LASSO regression and multivariate Cox regression based on TCGA data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36456877", "locator": "sections[4] \"Differentially expressed genes and prognosis-related genes identified\" paragraph 3", "quote": "Prognostic models (Risk score = (− 0.000934507 × ADH1C expression) + (0.010025652 × APEX1 expression) + (0.029611698 × ME1 expression) + (0.001505404 × S100A10 expression) + (0.067765234 × ACACA expression) + (− 0.001361529 × CYP2C9 expression).) were constructed using LASSO regression and multivariate Cox regression based on TCGA data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36456877", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "The data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed a six-gene lipid metabolism-related prognostic model for HCC that achieved 1-, 2-, and 3-year TCGA ROC AUCs of 0.758, 0.701, and 0.671, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36456877", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Six lipid metabolism-related genes (ADH1C, APEX1, ME1, S100A10, ACACA and CYP2C9) were identified as independent prognostic factors, which were used for risk model construction for HCC patients."}, {"asset": "source:pmid:36456877", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The areas under the 1-, 2-, and 3-year ROC curves for the TCGA cohort were 0.758, 0.701 and 0.671, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed a six-gene lipid metabolism-related prognostic model for HCC that achieved 1-, 2-, and 3-year TCGA ROC AUCs of 0.758, 0.701, and 0.671, respectively.", "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:36456877"], "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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(A) Representative images from multiplex immunofluorescent tissue staining for CD56 (green) and DAPI (b"], "order_basis": "paper_text_order:8601", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Prognostic models (Risk score = (− 0.000934507 × ADH1C expression) + (0.010025652 × APEX1 expression) + (0.029611698 × ME1 expression) + (0.001505404 × S100A10 expression) + (0.067765234 × ACACA expression) + (− 0.001361529 × CYP2C9 expression).) were constructed using LASSO regression and multivariate Cox regression based on TCGA data."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}