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pmid:30464626
Evaluation and prediction of hepatocellular carcinoma prognosis based on molecular classification.
Kun Ke · Geng Chen · Zhixiong Cai · Yanbing Huang · Bixing Zhao · Yingchao Wang · Naishun Liao · Xiaolong Liu · Zhenli Li · Jingfeng Liu
Cancer management and research · 2018 doi:10.2147/CMAR.S178579
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Complete file
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We aimed to identify the prognosis-associated molecular subtypes existing in HCC patients and construct an evaluation model based on identified molecular classification.\nMATERIALS AND METHODS: Non-negative matrix factorization consensus clustering was performed using 371 HCC patients from The Cancer Genome Atlas (TCGA) to identify molecular subtypes, based on the expression profile of the survival-associated genes. Signature genes for different subtypes were identified by Significance Analysis of Microarray and Prediction Analysis for Microarrays. Model for subtype discrimination and prognosis evaluation was established using binary logistic regression. The model and its clinical implications were further validated in GSE5436 cohort and Fujian cohort.\nRESULTS: Based on TCGA data, we observed two molecular subtypes with distinct clinical outcomes including significantly different overall survival, tumor differentiation, TNM stage, and vascular invasion (all P<0.05). The existence of these two molecular subtypes was further validated in five other Gene Expression Omnibus datasets. Furthermore, we constructed an evaluation model based on six subtype signature genes, which can discriminate different subtypes with the cutoff of 0.385. Meanwhile, both Cox regression analysis and stratification analysis showed that the calculated continuous prognostic value could also effectively indicate HCC prognosis, regardless of patients' clinical conditions. The prognostic evaluation model was successfully validated in GSE54236 cohort and Fujian cohort.\nCONCLUSION: Two prognostic molecular subtypes existed among HCC patients, which provided promising strategies for overcoming HCC heterogeneity and could be utilized in future clinical application for predicting HCC prognosis.", "claims": [{"id": "claim-method", "statement": "Non-negative matrix factorization (NMF) clustering was performed on the expression profile of identified prognosis-associated genes for TCGA dataset samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30464626", "locator": "sections[4] \"Identification of HCC prognosis subtypes\" paragraph 2", "quote": "Non-negative matrix factorization (NMF) clustering was performed on the expression profile of identified prognosis-associated genes for TCGA dataset samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "TCGA expression data, follow-up, and clinical information of HCC samples were downloaded from the UCSC cancer browser (https://genome-cancer.ucsc.edu).11 Log2(x+1)-transformed RSEM normalized gene expression data (HiSeqV2) of 371 HCC patients were used for subsequent clustering.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30464626", "locator": "sections[3] \"External data source\" paragraph 2", "quote": "TCGA expression data, follow-up, and clinical information of HCC samples were downloaded from the UCSC cancer browser (https://genome-cancer.ucsc.edu).11 Log2(x+1)-transformed RSEM normalized gene expression data (HiSeqV2) of 371 HCC patients were used for subsequent clustering."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:30464626"], "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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