NetLnc: A Network-Based Computational Framework to Identify Immune Checkpoint-Related lncRNAs for Immunotherapy Response in Melanoma.

Journal: International journal of molecular sciences
Published Date: May 9, 2025

Abstract

Long non-coding RNAs (lncRNAs) could alter the tumor immune microenvironment and regulate the expression of immune checkpoints (ICPs) by regulating target genes in tumors. However, only a few lncRNAs have precise functions in immunity and potential for predicting ICP inhibitors (ICI) response. Here, we developed a computational multi-step framework that leverages interaction network-based analysis to identify cancer- and immune-context ICP-related lncRNAs (NetLnc). Based on bulk and single-cell RNA sequencing data, these lncRNAs were significantly correlated with immune cell infiltration and immune expression signature. Specific hub ICP-related lncRNAs such as , , and could predict three- and five-year prognosis of melanoma in independent datasets. We also validated that some NetLnc-based predictions could better effectively predict ICI response compared to single molecules using three kinds of machine learning algorithms following independent datasets. Taken together, this study presents the use of a network-based framework to efficiently select ICP-related lncRNAs, which contributes to a comprehensive understanding of lncRNA functions and accelerates the discovery of lncRNA-based biomarkers in ICI treatment.

Authors

  • Qianyi Lu
    Department of Ophthalmology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Wenli Chen
    Department of Rehabilitation Medicine, Zhongda Hospital Southeast University, Nanjing, 210009, China. banlilizhi@163.com.
  • Zhuoru Wang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Chenyu Liu
    Party and Government Office, the Second Affiliated Hospital of Dalian Medical University, Dalian 116027, Liaoning, China.
  • Yue Sun
    Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China.
  • Han Jiang
    Second Affiliated Hospital, Nanchang University, Nanchang, China. jhan3939@sina.com.
  • Caiyu Zhang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
  • Yetong Chang
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
  • Jiajun Zhou
    Key Laboratory for Carbonaceous Waste Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China.
  • Xiaohong Wu
    Department of Oncology, The Fourth People's Hospital of Wuxi, Wuxi, Jiangsu, China.
  • Yue Gao
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Shangwei Ning
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.

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