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<p>Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism–related gene sets, WMRCA + outperformed widely used clustering algorithms—including iCluster, SNF, NMF, CC, and CNMF—achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at <a href="https://github.com/guojunliu7/WMRCA">https://github.com/guojunliu7/WMRCA</a>.</p>

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WMRCA + : a weighted majority rule-based clustering method for cancer subtype prediction using metabolic gene sets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Guojun Liu,
  • Zhaopo Zhu,
  • Yongqiang Xing,
  • Hu Meng,
  • Khyber Shinwari,
  • Ningkun Xiao,
  • Guoqing Liu

摘要

Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism–related gene sets, WMRCA + outperformed widely used clustering algorithms—including iCluster, SNF, NMF, CC, and CNMF—achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA.