Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33407546", "kind": "science-record", "revision": 7, "content_hash": "sha256:9e4dcf26d1716f86bccf2fc2fe2959428fd2e405c281a74981969719db24b9d0", "created_at": "2026-11-01T15:41:21Z", "updated_at": "2026-11-01T22:02:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes.", "doi": "10.1186/s12967-020-02691-4", "pmid": "33407546", "authors": ["Xinyu Gu", "Jun Guan", "Jia Xu", "Qiuxian Zheng", "Chao Chen", "Qin Yang", "Chunhong Huang", "Gang Wang", "Haibo Zhou", "Zhi Chen", "Haihong Zhu"], "journal": "Journal of translational medicine", "year": 2021, "abstract": "BACKGROUND: Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified.\nMETHODS: A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms.\nRESULTS: Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan-Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures.\nCONCLUSIONS: We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.", "claims": [{"id": "claim-method", "statement": "Time-dependent ROC analyses and Kaplan–Meier log-rank tests were used to evaluate the performance and prognostic ability of the model using verification datasets from the TCGA-LIHC dataset, the entire TCGA-LIHC dataset, the GSE14520 cohort, and the immunotherapy dataset (Imvigor210).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33407546", "locator": "sections[6] \"Validation of the performance and prognostic ability of the five-gene-based model\" paragraph 2", "quote": "Time-dependent ROC analyses and Kaplan–Meier log-rank tests were used to evaluate the performance and prognostic ability of the model using verification datasets from the TCGA-LIHC dataset, the entire TCGA-LIHC dataset, the GSE14520 cohort, and the immunotherapy dataset (Imvigor210)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data in our study are available upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33407546", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "All data in our study are available upon request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33407546"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33407546", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-020-02691-4", "sha256": 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