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GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets. © 2024 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Original languageEnglish
Pages (from-to)752-758
JournalComputational and Structural Biotechnology Journal
Volume23
Online published17 Jan 2024
DOIs
Publication statusPublished - Dec 2024

Funding

This work was supported by a grant from Guangdong Basic and Applied Basic Research Foundation (Project No. 2019B030302012), a grant from Shenzhen Science, Technology and Innovation Commission (Project No. 基2020N368), a startup fund (Project No. 4937084), and direct grant (2021.077) from the Chinese University of Hong Kong, grants from the Research Grants Council (Project No. 11103619, 11103921, 14111522, 14104223, C4024-22GF, R4007-23) of the Hong Kong Special Administrative Region, China, awarded to Xin Wang. This work was also partially sponsored by Shenzhen Bay Scholars Program awarded to Xin Wang.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Keywords

  • Cancer subtyping
  • Gastric cancer
  • GCclassifier
  • R package
  • Shiny application

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

RGC Funding Information

  • RGC-funded

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