Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33194067", "kind": "science-record", "revision": 4, "content_hash": "sha256:992a7135956106b7341da76149243ae6350f56b8c897eda72944b89679397e43", "created_at": "2026-11-01T17:34:20Z", "updated_at": "2026-11-01T22:02:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "partial", "title": "Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma.", "doi": null, "pmid": "33194067", "authors": ["Wanli Yang", "Liaoran Niu", "Xinhui Zhao", "Lili Duan", "Yiding Li", "Xiaoqian Wang", "Yujie Zhang", "Wei Zhou", "Jinqiang Liu", "Qingchuan Zhao", "Yu Han", "Daiming Fan", "Liu Hong"], "journal": "American journal of translational research", "year": 2020, "abstract": "OBJECTIVE: This study aimed to identify the novel prognostic gene signature based on autophagy-associated genes (ARGs) in hepatocellular carcinoma (HCC).\nMETHODS: The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model. Kaplan-Meier and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the model. GSE14520 dataset (testing cohort) was used to validate the prognostic risk model in TCGA. A clinical nomogram was established to predict the survival rate of HCC patients.\nRESULTS: Totally 27 differentially expressed ARGs were identified. Three OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these three ARGs, a prognostic prediction model was constructed. HCC patients with high risk score present poorer prognosis than those with low risk score both in TCGA cohort (P=4.478e-04) and testing cohort (P=1.274e-03). Moreover, the risk score curve shows a well feasibility in predicting the patients' survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict survival rate in HCC patients.\nCONCLUSIONS: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.", "claims": [{"id": "claim-method", "statement": "Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33194067", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33194067", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:33194067", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients."}], "state": "complete"}], "gaps": ["doi"]}, "experiment": {"state": "complete", "objective": "The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.", "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:33194067"], "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:33194067", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": null, "sha256": "sha256:81a2515ce2e741ada82c7d234adbb69751b24ae91b4e5ba59294ec33e2168797", "bytes": 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