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<p>This study aimed to evaluate the clinical significance and underlying biological mechanisms of LIM kinase 1 (LIMK1) in hepatocellular carcinoma (HCC). Using multi-omics data from TCGA and ICGC cohorts, we analyzed LIMK1 expression and its prognostic value. Clinical validation was performed via immunohistochemistry on tissue microarray specimens. A multivariate Cox model integrating LIMK1 and clinicopathological features was constructed and evaluated using machine learning. Tumor immune microenvironment was profiled using multiple immune deconvolution algorithms. Immunotherapy cohorts and drug sensitivity data were leveraged to assess therapeutic implications. LIMK1 was significantly overexpressed in HCC tissues across all cohorts and correlated with poor overall survival (TCGA HR = 2.26,&#xa0;<i>P</i> &lt; 0.001; ICGC HR = 2.23, <i>P</i> = 0.011; in-house HR = 2.09, <i>P</i> = 0.004). The prognostic Cox model integrating LIMK1 achieved high accuracy (1-year AUC = 0.90)&#xa0;and decision curve analysis showed the potential for clinical decision making. High LIMK1 expression was linked to an immunosuppressive microenvironment, characterized by elevated immunosuppressive cells (MDSCs, M2 macrophages, fibroblasts, and regulatory T cells) and immune checkpoint markers (PDCD1, CTLA4). HCC patients with high LIMK1 expression showed poor responses to immunotherapy but increased sensitivity to chemotherapy agents, including sorafenib, paclitaxel, docetaxel and 5-fluorouracil. In conclusion,&#xa0;LIMK1 serves as a promising biomarker in HCC, stratifying patients by prognosis and therapeutic response.</p>

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LIMK1 is a prognosis and treatment biomarker in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Nan-Fang Jiang,
  • Zhe Zhou,
  • Hai-Ping Zhang

摘要

This study aimed to evaluate the clinical significance and underlying biological mechanisms of LIM kinase 1 (LIMK1) in hepatocellular carcinoma (HCC). Using multi-omics data from TCGA and ICGC cohorts, we analyzed LIMK1 expression and its prognostic value. Clinical validation was performed via immunohistochemistry on tissue microarray specimens. A multivariate Cox model integrating LIMK1 and clinicopathological features was constructed and evaluated using machine learning. Tumor immune microenvironment was profiled using multiple immune deconvolution algorithms. Immunotherapy cohorts and drug sensitivity data were leveraged to assess therapeutic implications. LIMK1 was significantly overexpressed in HCC tissues across all cohorts and correlated with poor overall survival (TCGA HR = 2.26, P < 0.001; ICGC HR = 2.23, P = 0.011; in-house HR = 2.09, P = 0.004). The prognostic Cox model integrating LIMK1 achieved high accuracy (1-year AUC = 0.90) and decision curve analysis showed the potential for clinical decision making. High LIMK1 expression was linked to an immunosuppressive microenvironment, characterized by elevated immunosuppressive cells (MDSCs, M2 macrophages, fibroblasts, and regulatory T cells) and immune checkpoint markers (PDCD1, CTLA4). HCC patients with high LIMK1 expression showed poor responses to immunotherapy but increased sensitivity to chemotherapy agents, including sorafenib, paclitaxel, docetaxel and 5-fluorouracil. In conclusion, LIMK1 serves as a promising biomarker in HCC, stratifying patients by prognosis and therapeutic response.