skip to main content
10.1145/3405758.3405762acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicbbtConference Proceedingsconference-collections
research-article

Mining of Gene Modules and Identification of Key Genes in Hepatocellular Carcinoma based on Gene Co-expression Network Analysis

Published: 10 July 2020 Publication History

Abstract

About 80% of liver cancer cases were hepatocellular carcinoma. To explore the pathogenesis of hepatocellular carcinoma, a bioinformatics algorithm based on gene co-expression network analysis was used to study the gene expression data of hepatocellular carcinoma in this paper. The Pearson correlation analysis was used to construct the 2538 genes into a gene co-expression network, and the eigenvector algorithms was used to divide genes into 9 modules. The correlation analysis between gene modules and clinical indicators results showed that the RNA localization (GO: 0006403) related genes changed in four modules. The cell cycle and mitosis processes were related to event module, tRNA transport and multi-organism transport processes were related to T module, Organic biosynthetic process was related to N module and viral transcription process was related to M module. Furthermore, the Disgenet database results showed that 6 key genes was related to liver cancer, such as CASP2, HCFC1, ILF3, NAA40, NCOA6 and SENP1. Among them, the expression of CASP2, ILF3, NAA40 and NCOA6 were negatively correlated with the survival prognosis. Thus, these identified genes may play important roles in the progression of hepatocellular carcinoma and sever as potential biomarker for future diagnosis.

References

[1]
Cho, K., Ro, S. W., Seo, S. H., Jeon, Y., Moon, H., Kim, D. Y., & Kim, S. U. 2019. Genetically Engineered Mouse Models for Liver Cancer. Cancers, 12, 1 (Dec. 2019), E14. Doi=10.3390/cancers12010014
[2]
Calderaro, J., Couchy, G., Imbeaud, S., Amaddeo, G., Letouzé, E., Blanc, J. F., ... Zucman-Rossi, J. 2017. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. Journal of Hepatology, 67, 4 (Oct. 2017), 727--738. Doi=10.1016/j.jhep.2017.05.014
[3]
Wen, Z., Lian, L., Ding, H., Hu, Y., Xiao, Z., Xiong, K., & Yang, Q. 2020. LncRNA ANCR promotes hepatocellular carcinoma metastasis through upregulating HNRNPA1 expression. RNA biology, (Jan. 2020), 1--14. Advance online publication. Doi=10.1080/15476286.2019.1708547
[4]
Dong, Z. R., Sun, D., Yang, Y. F., Zhou, W., Wu, R., Wang, X. W., ... Li, T. 2019. TMPRSS4 Drives Angiogenesis in Hepatocellular Carcinoma by Promoting HB-EGF R@Expression and Proteolytic Cleavage. Hepatology (Baltimore, Md.), (Dec. 2019). Advance online publication. Doi=10.1002/hep.31076
[5]
Butte, A. J., & Kohane, I. S. 1999. Unsupervised knowledge discovery in medical databases using relevance networks. Proceedings. AMIA Symposium, (1999), 711--715.
[6]
Butte, A. J., & Kohane, I. S. 2000. Mutual information relevance networks:functional genomic clustering using pairwise entropy measurements. Pacific Symposium on Biocomputing, (2000), 418--429. Doi=10.1142/9789814447331_0040 PMID 10902190.
[7]
Zhang, B., & Horvath, S. 2005. A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology, 4 (2005), Article17. Doi=10.2202/1544-6115.1128
[8]
Zhai, X., Xue, Q., Liu, Q., Guo, Y., & Chen, Z. 2017. Colon cancer recurrence-associated genes revealed by WGCNA co-expression network analysis. Molecular medicine reports, 16, 5 (Nov. 2017), 6499--6505. Doi=10.3892/mmr.2017.7412
[9]
Xiao, H., Chen, P., Zeng, G., Xu, D., Wang, X., & Zhang, X. 2019. Three novel hub genes and their clinical significance in clear cell renal cell carcinoma. Journal of Cancer, 10, 27 (Nov. 2019), 6779--6791. Doi=10.7150/jca.35223
[10]
Huo, X., Sun, H., Liu, Q., Ma, X., Peng, P., Yu, M., ... Shen, K. 2019. Clinical and Expression Significance of AKT1 by Co-expression Network Analysis in Endometrial Cancer. Frontiers in oncology, 9 (Nov. 2019), 1147. Doi=10.3389/fonc.2019.01147
[11]
Hutter, C., Zenklusen J. C., 2018. The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell, 173, 2 (Apr. 2018), 283--285. Doi=10.1016/j.cell.2018.03.042.
[12]
Chen, J., Wang, X., Hu, B., He, Y., Qian, X., Wang, W. 2018. Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network. PeerJ, 6 (May. 2018), e4692. Doi=10.7717/peerj.4692.
[13]
Chang, Y. M., Lin, H. H., Liu, W. Y., Yu, C. P., Chen, H. J., Wartini, P. P., ... Li, W. H. 2019. Comparative transcriptomics method to infer gene coexpression networks and its applications to maize and rice leaf transcriptomes. Proceedings of the National Academy of Sciences of the United States of America, 116, 8 (Feb. 2019), 3091--3099. Doi=10.1073/pnas.1817621116.
[14]
Newman M. E. 2006. Finding community structure in networks using the eigenvectors of matrices. Physical review. E, Statistical, nonlinear, and soft matter physics, 74, 3 (Sep. 2006), 036104. Doi=10.1103/PhysRevE.74.036104
[15]
Raghavan, U. N., Albert, R., & Kumara, S. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review. E, Statistical, nonlinear, and soft matter physics, 76, 3 (Sep. 2007), 036106. Doi=10.1103/PhysRevE.76.036106
[16]
Rosvall, M., & Bergstrom, C. T. 2007. Maps of information flow reveal community structure in complex networks. arXiv preprint physics.soc-ph/0707.0609.
[17]
Rosvall, M., Axelsson, D., & Bergstrom, C. T. 2009. The map equation. The European Physical Journal Special Topics, 178, 1 (2009), 13--23.
[18]
Brin, S., & Page, L. 1998. The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30, 1-7 (1998), 107--117.
[19]
Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., ... Furlong, L. I. 2017. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research, 45, D1 (Jan. 2017), D833--D839. Doi=10.1093/nar/gkw943
[20]
Anaya, J. 2016. OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Computer Science, 2 (Jun. 2016), e67. Doi=https://doi.org/10.7717/peerj-cs.67
[21]
Zu, J., Gu, Y., Li, Y., Li, C., Zhang, W., Zhang, Y.E., Lee, U.J., Zhang, L., and Long, M. 2019. Topological evolution of coexpression networks by new gene integration maintains the hierarchical and modular structures in human ancestors. Sci China Life Sci, 62 (April. 2019), 594--608. Doi=10.1007/s11427-019-9483-6.

Cited By

View all
  • (2023)Gene Expression and Metadata Based Identification of Key Genes for Hepatocellular Carcinoma Using Machine Learning and Statistical ModelsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2023.332275320:6(3786-3799)Online publication date: Nov-2023
  • (2023)Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learningScientific Reports10.1038/s41598-023-30851-113:1Online publication date: 7-Mar-2023

Index Terms

  1. Mining of Gene Modules and Identification of Key Genes in Hepatocellular Carcinoma based on Gene Co-expression Network Analysis

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICBBT '20: Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology
    May 2020
    163 pages
    ISBN:9781450375719
    DOI:10.1145/3405758
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • NWPU: Northwestern Polytechnical University
    • Universidade Nova de Lisboa

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 July 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Gene co-expression network
    2. Hepatocellular carcinoma
    3. Key genes

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICBBT 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 25 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Gene Expression and Metadata Based Identification of Key Genes for Hepatocellular Carcinoma Using Machine Learning and Statistical ModelsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2023.332275320:6(3786-3799)Online publication date: Nov-2023
    • (2023)Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learningScientific Reports10.1038/s41598-023-30851-113:1Online publication date: 7-Mar-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media