Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37649103", "kind": "science-record", "revision": 3, "content_hash": "sha256:8b52820314171068cf64c8d128ec9822f8bda3fa64fe948eacdfe492c27a7ecd", "created_at": "2026-11-01T19:02:26Z", "updated_at": "2026-11-01T22:10:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework.", "doi": "10.1186/s40001-023-01300-6", "pmid": "37649103", "authors": ["Qian Feng", "Zhihao Huang", "Lei Song", "Le Wang", "Hongcheng Lu", "Linquan Wu"], "journal": "European journal of medical research", "year": 2023, "abstract": "BACKGROUND: The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients' prognosis and immunotherapeutic response.\nMETHODS: NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA-LIHC data set, GSE14520 data set, GSE76427 data set and ICGC-LIRI-JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.\nRESULTS: With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients' prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS-PD1 blocker, IPS-CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.\nCONCLUSIONS: Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.", "claims": [{"id": "claim-method", "statement": "With a leading average C-index of 0.671 among the 4 cohorts, a combination of LASSO and CoxBoost algorithm was considered as the optimal algorithm to construct prognostic model (Fig. 4).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37649103", "locator": "sections[15] \"Survival analysis and prognostic signature establishment\" paragraph 2", "quote": "With a leading average C-index of 0.671 among the 4 cohorts, a combination of LASSO and CoxBoost algorithm was considered as the optimal algorithm to construct prognostic model (Fig. 4)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37649103", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37649103", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group.", "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:37649103"], "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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