IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0318215.html
   My bibliography  Save this article

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma

Author

Listed:
  • Md Al Mehedi Hasan
  • Md Maniruzzaman
  • Jie Huang
  • Jungpil Shin

Abstract

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis.

Suggested Citation

  • Md Al Mehedi Hasan & Md Maniruzzaman & Jie Huang & Jungpil Shin, 2025. "Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-29, February.
    • Handle: RePEc:plo:pone00:0318215
    DOI: 10.1371/journal.pone.0318215
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0318215
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0318215&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0318215?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hyun Goo Woo & Ji-Hye Choi & Sarah Yoon & Byul A. Jee & Eun Ju Cho & Jeong-Hoon Lee & Su Jong Yu & Jung-Hwan Yoon & Nam-Joon Yi & Kwang-Woong Lee & Kyung-Suk Suh & Yoon Jun Kim, 2017. "Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ji-Hye Choi & Bok-Soon Lee & Jeon Yeob Jang & Yun Sang Lee & Hyo Jeong Kim & Jin Roh & Yoo Seob Shin & Hyun Goo Woo & Chul-Ho Kim, 2023. "Single-cell transcriptome profiling of the stepwise progression of head and neck cancer," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0318215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.