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pmid:31115573
A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma.
Honghong Liu · Ping Zhao · Xueyuan Jin · Yanling Zhao · Yongqian Chen · Tao Yan · Jianjun Wang · Liang Wu · Yongqiang Sun
Molecular medicine reports · 2019 doi:10.3892/mmr.2019.10262
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Complete file
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The aim of the present study was to screen prognosis‑associated long noncoding RNAs (lncRNAs) and construct a risk score system for the disease. The RNA‑sequencing data of patients with HCC (including 100 HCC samples and 26 normal samples) were extracted from The Cancer Genome Atlas (TCGA) database. In addition, GSE55092, GSE19665 and GSE10186 datasets were downloaded from the Gene Expression Omnibus database. Combined with weighted gene co‑expression network analysis, the identification and functional annotation of stable modules was performed. Using the MetaDE package, the consensus differentially expressed RNAs (DE‑RNAs) were analyzed. To construct a risk score system, prognosis‑associated lncRNAs and the optimal lncRNA combination were separately analyzed by survival and penalized packages. Finally, pathway enrichment analysis for the nodes in an lncRNA‑mRNA network was conducted via Gene Set Enrichment Analysis. A total of four stable modules and 3,051 consensus DE‑RNAs were identified. The stable modules were significantly associated with the histological grades of HCC, tumor, node and metastasis stage, pathological stage, recurrence and exposure to radiation therapy. A 9‑lncRNA optimal combination [DiGeorge syndrome critical region gene 9, glucosidase, β, acid 3 (GBA3), HLA complex group 4, N‑acetyltransferase 8B, neighbor of breast cancer 1 gene 2, prostate androgen‑regulated transcript 1, ret finger protein like 1 antisense RNA 1, solute carrier family 22 member 18 antisense and T‑cell leukemia/lymphoma 6] was selected from the 14 prognosis‑associated lncRNAs, and was further supported by the validation dataset, GSE10186. The lncRNA‑mRNA co‑expression network revealed lncRNA GBA3 as a positive regulator of phosphoenolpyruvate carboxykinase 2, an important enzyme in the metabolic pathway of gluconeogenesis. A risk score system was established based on the optimal 9 lncRNAs, which may be valuable for predicting the prognosis of patients with HBV‑positive HCC and improving understanding of mechanisms associated with the pathogenesis of this disease. On the contrary, a larger, independent cohort of patients is required to further validate the risk‑score system.", "claims": [{"id": "claim-method", "statement": "Using Gene Set Enrichment Analysis (http://software.broadinstitute.org/gsea/index.jsp) (37), pathway enrichment analysis was performed to identify lncRNA-associated pathways. The cut-off criterion was set as P<0.05. 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"structure": {"pmcid": "PMC6579967", "section_index": 14, "section_title": "Construction and validation of the risk score system", "row_count": 10, "column_count": 5}, "table": {"label": "Table III.", "caption": "LncRNAs in the optimal lncRNA combination.", "rows": [["LncRNA", "Coefa", "Hazard ratio", "P-value", "Module color"], ["DGCR9", "−0.0308", "0.90", "0.0230", "blue"], ["GBA3", "0.2033", "1.07", "0.0240", "magenta"], ["HCG4", "0.4416", "1.11", "0.0170", "magenta"], ["NAT8B", "0.7662", "1.11", "0.0120", "magenta"], ["NBR2", "−0.5517", "0.72", "0.0068", "yellow"], ["PART1", "0.3786", "1.04", "0.0490", "green"], ["RFPL1S", "0.0590", "1.09", "0.0340", "green"], ["SLC22A18AS", "0.0427", "1.11", "0.0200", "green"], ["TCL6", "1.4731", "1.23", "0.0004", "green"]], "row_count": 10, "column_count": 5, "numeric_cells": 28, "header_rows": 1, "locator": "section[14]/table-wrap[1]#tIII-mmr-20-01-0573"}}], "gaps": ["asset:data:geo:GSE10186 not downloaded", "asset:data:geo:GSE10186 licence not read", "asset:data:geo:GSE19665 not downloaded", "asset:data:geo:GSE19665 licence not read", "asset:data:geo:GSE55092 not downloaded", "asset:data:geo:GSE55092 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}, {"id": "method-2", "family": null, "specific": null, "label": "Gene Set Enrichment Analysis (GSEA)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "reordered", "quote": "Using Gene Set Enrichment Analysis (http://software.broadinstitute.org/gsea/index.jsp) (37), pathway enrichment analysis was performed to identify lncRNA-associated pathways. The cut-off criterion was set as P<0.05. The datasets used during the current study are available from the corresponding author on reasonable request."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pathway_enrichment."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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