A novel natural killer-related signature to effectively predict prognosis in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37674210", "kind": "science-record", "revision": 3, "content_hash": "sha256:0a50c5af4bb10acb1ace48078edc1ec0546428f3fe19dcaa4183d35874a110b7", "created_at": "2026-11-01T19:02:28Z", "updated_at": "2026-11-01T22:10:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel natural killer-related signature to effectively predict prognosis in hepatocellular carcinoma.", "doi": "10.1186/s12920-023-01638-0", "pmid": "37674210", "authors": ["Deyang Xi", "Jialu Wang", "Yinshuang Yang", "Fang Ji", "Chunyang Li", "Xuebing Yan"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor that poses a significant threat to human health, with 80% of cases being primary HCC. At present, Early diagnosis and predict prognosis of HCC is challenging and the it is characterized by a high degree of invasiveness, both of which negatively impact patient prognosis. Natural killer cells (NK) play an important role in the development, diagnosis and prognosis of malignant tumors. The potential of NK cell-related genes for evaluating the prognosis of patients with hepatocellular carcinoma remains unexplored. This study aims to address this gap by investigating the association between NK cell-related genes and the prognosis of HCC patients, with the goal of developing a reliable model that can provide novel insights into evaluating the immunotherapy response and prognosis of these patients. This work has the potential to significantly advance our understanding of the complex interplay between immune cells and tumors, and may ultimately lead to improved clinical outcomes for HCC patients.\nMETHODS: For this study, we employed transcriptome expression data from the hepatocellular carcinoma cancer genome map (TCGA-LIHC) to develop a model consisting of NK cell-related genes. To construct the NK cell-related signature (NKRLSig), we utilized a combination of univariate COX regression, Area Under Curve (AUC) LASSO COX regression, and multivariate COX regression. To validate the model, we conducted external validation using the GSE14520 cohort.\nRESULTS: We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score. The high-risk group was associated with a poor prognosis, and the risk score had good predictive ability across all clinical subgroups. The risk score and stage were found to be independent prognostic indicators for HCC patients when clinical factors were taken into account. We further created a nomogram incorporating the 5-NKRLSig and clinicopathological characteristics, which revealed that patients in the low-risk group had a better prognosis. Moreover, our analysis of immunotherapy and chemotherapy response indicated that patients in the low-risk group were more responsive to immunotherapy.\nCONCLUSION: The model that we developed not only sheds light on the regulatory mechanism of NK cell-related genes in HCC, but also has the potential to advance our understanding of immunotherapy for HCC. With its strong predictive capacity, our model may prove useful in evaluating the prognosis of patients and guiding clinical decision-making for HCC patients.", "claims": [{"id": "claim-data-access", "statement": "The RNA-seq datasets and clinical datasets are publicly available in the following database: The TCGA-LIHC dataset was downloaded from the GDC portal (https://portal.gdc.cancer.gov/), while the GSE14520 dataset was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo). For further enquiries, please contact the author (Chunyang Li; 13775989791@126.com).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37674210", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The RNA-seq datasets and clinical datasets are publicly available in the following database: The TCGA-LIHC dataset was downloaded from the GDC portal (https://portal.gdc.cancer.gov/), while the GSE14520 dataset was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo). For further enquiries, please contact the author (Chunyang Li; 13775989791@126.com)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37674210", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We developed a prognostic model based on 5-NKRLSig (IL18RAP, CHP1, VAMP2, PIC3R1, PRKCD), which divided patients into high- and low-risk groups based on their risk score.", "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:37674210"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37674210", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-023-01638-0", "sha256": "sha256:12ddfbc3e7d2fd42efa3699ae7903ba3b65b11543390596823a33ec3a73369a3", "bytes": 35277, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-023-01638-0", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10481539", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "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=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "The RNA-seq datasets and clinical datasets are publicly available in the following database: The TCGA-LIHC dataset was downloaded from the GDC portal (https://portal.gdc.cancer.gov/), while the GSE14520 dataset was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo). For further enquiries, please contact the author (Chunyang Li; 13775989791@126.com).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The RNA-seq datasets and clinical datasets are publicly available in the following database: The TCGA-LIHC dataset was downloaded from the GDC portal (https://portal.gdc.cancer.gov/), while the GSE14520 dataset was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo). 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