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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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title
A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma.
doi
10.3892/mmr.2019.10262
pmid
31115573
authors array · 9 items
[0]
Honghong Liu
[1]
Ping Zhao
[2]
Xueyuan Jin
[3]
Yanling Zhao
[4]
Yongqian Chen
[5]
Tao Yan
[6]
Jianjun Wang
[7]
Liang Wu
[8]
Yongqiang Sun
journal
Molecular medicine reports
year
2019
abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. 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.
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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.
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sections[9] "Analysis of lncRNA-associated pathways" paragraph 2
quote
Using Gene Set Enrichment Analysis (http://software.broadinstitute.org/gsea/index.jsp) (37), pathway enrichment analysis was performed to identify lncRNA-associated pathways.
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sections[9] "Analysis of lncRNA-associated pathways" paragraph 2
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The cut-off criterion was set as P<0.05.
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sections[19] "Availability of data and materials" paragraph 2
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The datasets used during the current study are available from the corresponding author on reasonable request.
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Integrative Transcriptome Analysis Reveals Common Molecular Subtypes of Human Hepatocellular Carcinoma
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Aberrant DNA methylation in hepatitis B and C virus-related hepatocellular carcinoma
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Stabilities of the 10 modules identified in TCGA and the biological functions enriched for the lncRNAs in the modules.
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Stabilities of the 10 modules identified in TCGA and the biological functions enriched for the lncRNAs in the modules.
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Module 3
[1]
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[4]
1
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0.7094
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[0]
Module 4
[1]
green
[2]
264
[3]
255
[4]
9
[5]
26.5495
[6]
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[0]
Module 5
[1]
grey
[2]
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[3]
794
[4]
2
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0.8546
[6]
Response to nutrient levels
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[0]
Module 6
[1]
magenta
[2]
147
[3]
143
[4]
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[5]
26.2491
[6]
Cell cycle
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[0]
Module 7
[1]
pink
[2]
150
[3]
150
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0
[5]
8.0652
[6]
Regulation of cell proliferation
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[0]
Module 8
[1]
red
[2]
233
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232
[4]
1
[5]
0.3724
[6]
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[0]
Module 9
[1]
turquoise
[2]
555
[3]
552
[4]
3
[5]
6.3217
[6]
Ion transport
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[0]
Module 10
[1]
yellow
[2]
286
[3]
283
[4]
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25.7553
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Blood coagulation
locator
section[12]/table-wrap[1]#tII-mmr-20-01-0573
[6] object · 16 fields
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asset:table:pmid:31115573:3
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jats-table
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application/vnd.jats.table+json
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LncRNAs in the optimal lncRNA combination.
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PMC6579967
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14
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Construction and validation of the risk score system
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Table III.
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LncRNAs in the optimal lncRNA combination.
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LncRNA
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GBA3
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1.07
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magenta
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section[14]/table-wrap[1]#tIII-mmr-20-01-0573
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asset:data:geo:GSE55092 licence not read
analysis object · 4 fields
state
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Network / pathway analysis
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pathway_enrich
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edge_list
evidence object · 3 fields
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pathway enrichment
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null
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Gene Set Enrichment Analysis (GSEA)
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evidence object · 3 fields
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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.
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capability_id
capability:computational_analysis
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meta_analysis
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gwas
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Automated Assays Laravel application and simulated adapters
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readiness object · 8 fields
state
complete
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pathway_enrich
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pathway_enrichment.
capability object · 4 fields
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capability_id
capability:computational_analysis
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owned
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
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GSE55092
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GSE19665
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GSE10186
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GSE55092
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GSE19665
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GSE10186
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The datasets used during the current study are available from the corresponding author on reasonable request.
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The paper names GSE55092, GSE19665, GSE10186 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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pathway_enrich
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source:pmid:31115573
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data:geo:GSE10186
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state
partial
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kind
extraction_review
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MiniMax-M3.1-Flash
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minimax
provider_family
minimax
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independence
single_reader
evidence_check object · 2 fields
method
reordered
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verbatim
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32267
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id
extraction
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complete
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MiniMax-M3.1-Flash
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pmid:31115573
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paper
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pmid:31115573
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source:pmid:31115573
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manuscript
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PMID 31115573 full text
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data:geo:GSE10186
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dataset
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GEO GSE10186
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activity-reading-sci-2026-11-01
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paper
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pmid:31115573
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source:pmid:31115573
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PMID 31115573 full text
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data:geo:GSE10186
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GEO GSE10186
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agent:corpus-assay-engine
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lineage object · 4 fields
parent_id
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31115573", "kind": "science-record", "revision": 6, "content_hash": "sha256:3ad16df86526ab70ab4d77ffa9b40c2a75cd63529b935307d4df19d09fa88b5f", "created_at": "2026-11-01T15:40:45Z", "updated_at": "2026-11-01T21:22:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 9‑lncRNA risk score system for predicting the prognosis of patients with hepatitis B virus‑positive hepatocellular carcinoma.", "doi": "10.3892/mmr.2019.10262", "pmid": "31115573", "authors": ["Honghong Liu", "Ping Zhao", "Xueyuan Jin", "Yanling Zhao", "Yongqian Chen", "Tao Yan", "Jianjun Wang", "Liang Wu", "Yongqiang Sun"], "journal": "Molecular medicine reports", "year": 2019, "abstract": "Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and can be induced by hepatitis B virus (HBV) infection. 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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