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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1460607
This article is part of the Research Topic Dissection of Molecular Mechanisms and Novel Biomarkers of Breast Cancer Using Single-Cell Sequencing and Spatial Transcriptome View all 4 articles

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer

Provisionally accepted
Qianshan Hou Qianshan Hou 1Chunzhen Li Chunzhen Li 1Yuhui Chong Yuhui Chong 2Haofeng Yin Haofeng Yin 1Yuchen Guo Yuchen Guo 1Lanjie Yang Lanjie Yang 1TIANLIANG LI TIANLIANG LI 1*Shulei Yin Shulei Yin 1*
  • 1 National Key Laboratory of Immunity and Inflammation, Army Medical University, Shanghai, China
  • 2 School of Pharmacy, Second Military Medical University, Shanghai, China

The final, formatted version of the article will be published soon.

    Background: Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in BRCA. However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via singlecell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, TME analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).Results: Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including tumor microenvironment (TME) landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.An NK-related gene signature for breast cancer 2 Conclusions: We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

    Keywords: breast cancer, Natural killer (Nk) cell, ScRNA-seq, Prognostic signature, Tumor Microenvironment, Immunotherapy

    Received: 06 Jul 2024; Accepted: 07 Oct 2024.

    Copyright: © 2024 Hou, Li, Chong, Yin, Guo, Yang, LI and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    TIANLIANG LI, National Key Laboratory of Immunity and Inflammation, Army Medical University, Shanghai, 050052, China
    Shulei Yin, National Key Laboratory of Immunity and Inflammation, Army Medical University, Shanghai, 050052, China

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