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<p>Hepatocellular carcinoma (HCC) is a cancer with poor prognosis and high mortality, necessitating the development of various biomarkers to meet clinical treatment needs. Anoikis, a form of programmed cell death, has been shown to be closely related to processes such as cancer proliferation and invasion. However, the role of differentially expressed anoikis-related genes (DEANRGs) in HCC and its control samples remains unclear. Therefore, this study utilized various bioinformatics analysis methods to identify 12 prognostic DEANRGs and constructed a prognostic model for HCC using these genes. This paper thoroughly explores the impact of high- and low-risk groups on participating signaling pathways and the immune infiltration landscape. Subsequently, two reliable HCC subtypes were identified based on the expression levels of DEANRGs. Incorporating scRNA-seq data, cell scores for different cell types were obtained based on the expression levels of DEANRGs using the AUCell algorithm. Finally, several communication pathways between the top-scoring hepatocyte populations and other cell groups were identified. Furthermore, the expression levels of key genes were validated in HCC cell lines (HepG2 and LO2) using qRT-PCR and Western blot. The prognostic DEANRGs obtained in this study can provide references for the treatment and prognostic evaluation of HCC.</p>

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Prognostic significance and immune landscape of anoikis-related genes in hepatocellular carcinoma: a multi-omics analysis of subtypes and cellular communication

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Yingwei Bai,
  • Hailin Yang

摘要

Hepatocellular carcinoma (HCC) is a cancer with poor prognosis and high mortality, necessitating the development of various biomarkers to meet clinical treatment needs. Anoikis, a form of programmed cell death, has been shown to be closely related to processes such as cancer proliferation and invasion. However, the role of differentially expressed anoikis-related genes (DEANRGs) in HCC and its control samples remains unclear. Therefore, this study utilized various bioinformatics analysis methods to identify 12 prognostic DEANRGs and constructed a prognostic model for HCC using these genes. This paper thoroughly explores the impact of high- and low-risk groups on participating signaling pathways and the immune infiltration landscape. Subsequently, two reliable HCC subtypes were identified based on the expression levels of DEANRGs. Incorporating scRNA-seq data, cell scores for different cell types were obtained based on the expression levels of DEANRGs using the AUCell algorithm. Finally, several communication pathways between the top-scoring hepatocyte populations and other cell groups were identified. Furthermore, the expression levels of key genes were validated in HCC cell lines (HepG2 and LO2) using qRT-PCR and Western blot. The prognostic DEANRGs obtained in this study can provide references for the treatment and prognostic evaluation of HCC.