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pmid:31886163

Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis.

Meng Wang · Licheng Wang · Shusheng Wu · Dongsheng Zhou · Xianming Wang

International journal of genomics · 2019 doi:10.1155/2019/3518378

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:31886163
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science-record
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2026-11-01T15:40:54Z
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2026-11-01T22:44:26Z
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paper object · 10 fields
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title
Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis.
doi
10.1155/2019/3518378
pmid
31886163
authors array · 5 items
[0]
Meng Wang
[1]
Licheng Wang
[2]
Shusheng Wu
[3]
Dongsheng Zhou
[4]
Xianming Wang
journal
International journal of genomics
year
2019
abstract
Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P < 0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.
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The prognostic value of the identified hub genes in HCC was further assessed by the Kaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26]. The analysis included 364 patients, and their KM survival curves were conducted. The log-rank P value for the different survival distribution between the low and high expression group was assessed, and the hazard radio (HR) with 95% confidence interval (95% CI) was calculated and plotted on the webpage.
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sections[8] "2.6. Survival Analysis of DEGs" paragraph 2
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The prognostic value of the identified hub genes in HCC was further assessed by the Kaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26].
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sections[8] "2.6. Survival Analysis of DEGs" paragraph 2
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The analysis included 364 patients, and their KM survival curves were conducted.
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sections[8] "2.6. Survival Analysis of DEGs" paragraph 2
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The log-rank P value for the different survival distribution between the low and high expression group was assessed, and the hazard radio (HR) with 95% confidence interval (95% CI) was calculated and plotted on the webpage.
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The data used to support the findings of this study are included within the article.
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Gene expression profiles of human hepatocellular carcinoma (training set 1)
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Gene expression profiling of Human Hepatocellular Carcinoma tissues
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Expression data from human hepatocellular carcinoma tissues and the correponding non-cancerous tissues
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3.1. Identification of DEGs in HCC
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Upregulated and downregulated DEGs.
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Gene name
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ACSL4, ANLN, ASPM, BUB1, BUB1B, CCNB1, CCNB2, CDC20, CDC6, CDK1, CDKN3, CENPF, CKS2, CRNDE, CTHRC1, DLGAP5, DTL, ECT2, GINS1, GPC3, IGF2BP3, KIAA0101, KIF20A, MAD2L1, NCAPG, NDC80, NUF2, PBK, PEG10, PRC1, RACGAP1, RRM2, SULT1C2, TOP2A, TPX2, TTK, UBE2C, UBE2T
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Functional and pathway enrichment analyses of DEGs in HCC.
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3.2. GO Analysis and Pathway Enrichment Analysis of DEGs in HCC
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Table 2
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Functional and pathway enrichment analyses of DEGs in HCC.
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Description
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Gene name
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GO:0045926
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6.21E-03
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5
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GO:0000922
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7
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[3]
9
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[3]
5
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MT1M, MT1E, MT1H, MT1X, MT1G, MT1F
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Top 5 hub genes in the PPI network.
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3.3. PPI Network Construction and Significant Module Analysis
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Top 5 hub genes in the PPI network.
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quote
The prognostic value of the identified hub genes in HCC was further assessed by the Kaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26]. The analysis included 364 patients, and their KM survival curves were conducted. The log-rank P value for the different survival distribution between the low and high expression group was assessed, and the hazard radio (HR) with 95% confidence interval (95% CI) was calculated and plotted on the webpage.
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The prognostic value of the identified hub genes in HCC was further assessed by the Kaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26]. The analysis included 364 patients, and their KM survival curves were conducted. The log-rank P value for the different survival distribution between the low and high expression group was assessed, and the hazard radio (HR) with 95% confidence interval (95% CI) was calculated and plotted on the webpage.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31886163", "kind": "science-record", "revision": 8, "content_hash": "sha256:7ff8372f1d9a7b831f285bafa28578d4c0c13af19fd5acbaa50abbe91056b1da", "created_at": "2026-11-01T15:40:54Z", "updated_at": "2026-11-01T22:44:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis.", "doi": "10.1155/2019/3518378", "pmid": "31886163", "authors": ["Meng Wang", "Licheng Wang", "Shusheng Wu", "Dongsheng Zhou", "Xianming Wang"], "journal": "International journal of genomics", "year": 2019, "abstract": "Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P < 0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.", "claims": [{"id": "claim-method", "statement": "The prognostic value of the identified hub genes in HCC was further assessed by the Kaplan-Meier plotter (KM plotter, http://www.kmplot.com/analysis/) [26]. 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GO Analysis and Pathway Enrichment Analysis of DEGs in HCC", "row_count": 11, "column_count": 5}, "table": {"label": "Table 2", "caption": "Functional and pathway enrichment analyses of DEGs in HCC.", "rows": [["Term", "Description", "P value", "Count", "Gene name"], ["GO:0045926", "Negative regulation of growth", "9.99E-07", "7", "MT1M, GPC3, MT1E, MT1H, MT1X, MT1G, MT1F"], ["GO:0051301", "Cell division", "1.30E-05", "15", "CDC6, CDK1, NUF2, TPX2, CENPF, NDC80, CDC20, UBE2C, CCNB1, CCNB2, MAD2L1, NCAPG, BUB1, CKS2, BUB1B"], ["GO:0007067", "Mitotic nuclear division", "2.01E-05", "13", "CDK1, CDC6, CCNB2, NUF2, BUB1, TPX2, CENPF, BUB1B, NDC80, ANLN, CDC20, PBK, ASPM"], ["GO:0071276", "Cellular response to cadmium ion", "4.32E-05", "6", "MT1E, CYP1A2, MT1H, MT1X, MT1G, MT1F"], ["GO:0071294", "Cellular response to zinc ion", "8.05E-05", "6", "MT1M, MT1E, MT1H, MT1X, MT1G, MT1F"], ["GO:0007094", "Mitotic spindle assembly checkpoint", "6.21E-03", "5", "MAD2L1, BUB1, CENPF, TTK, BUB1B"], ["GO:0000922", "Spindle pole", "2.80E-02", "7", "CCNB1, CDC6, MAD2L1, PRC1, TPX2, CENPF, CDC20"], ["GO:0007062", "Sister chromatid cohesion", "3.61E-02", "7", "MAD2L1, NUF2, BUB1, CENPF, BUB1B, NDC80, CDC20"], ["KEGG:hsa04110", "Cell cycle", "4.15E-03", "9", "CCNB1, CDK1, CDC6, MAD2L1, CCNB2, BUB1, TTK, BUB1B, CDC20"], ["KEGG:hsa04978", "Mineral absorption", "2.54E-02", "5", "MT1M, MT1E, MT1H, MT1X, MT1G, MT1F"]], "row_count": 11, "column_count": 5, "numeric_cells": 35, "header_rows": 1, "locator": "section[12]/table-wrap[1]#tab2"}}, {"id": "asset:table:pmid:31886163:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:9999f5bbaa9276edc2d48d9a4dc8c02b5336b17dd813872ba7c983e02047665b", "bytes": 309, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#tab3", "version": null, "note": "Top 5 hub genes in the PPI network.", "structure": {"pmcid": "PMC6893264", "section_index": 13, "section_title": "3.3. 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The analysis included 364 patients, and their KM survival curves were conducted. The log-rank P value for the different survival distribution between the low and high expression group was assessed, and the hazard radio (HR) with 95% confidence interval (95% CI) was calculated and plotted on the webpage."}}, {"id": "method-2", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "log-rank"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-4", "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"}}], "targets": [], "gaps": []}, "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": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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