System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36195876", "kind": "science-record", "revision": 4, "content_hash": "sha256:2c24ba8af8ff8c1e3b43df20e4464421919db59b72b6bc65dd733694749dffb0", "created_at": "2026-11-01T18:15:30Z", "updated_at": "2026-11-01T22:07:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma.", "doi": "10.1186/s12967-022-03630-1", "pmid": "36195876", "authors": ["Cheng Yan", "Yandie Niu", "Liukai Ma", "Lifang Tian", "Jiahao Ma"], "journal": "Journal of translational medicine", "year": 2022, "abstract": "BACKGROUND: Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported.\nMETHODS: Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC.\nRESULTS: Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells.\nCONCLUSION: In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.", "claims": [{"id": "claim-method", "statement": "Firstly, we performed Lasso cox regression analysis to avoiding overfitting of prognostic risk model variables and built prognostic models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36195876", "locator": "sections[5] \"The construction and validation of a prognostic model based on prognostic cuproptosis-related genes\" paragraph 2", "quote": "Firstly, we performed Lasso cox regression analysis to avoiding overfitting of prognostic risk model variables and built prognostic models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Any data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These are available on The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36195876", "locator": "sections[41] \"Availability of data and materials\" paragraph 2", "quote": "Any data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These are available on The Cancer Genome Atlas (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36195876", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A).", "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:36195876"], "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:GSE76247"], "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:GSE76247"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36195876", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-022-03630-1", "sha256": "sha256:3032e71447a0eaafe789d18a26bce60ec7bde70750fdd7024100830c3ff7bf0e", "bytes": 68586, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-022-03630-1", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9531858", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE76247", "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=GSE76247", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE76247", "version": null, "note": "Any data and R script in this study can be obtained from the corresponding author upon reasonable request. 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