Identification of anoikis-related subtypes and a risk score prognosis model, the association with TME landscapes and therapeutic responses in hepatocellular carcinoma

Bin Jin^1*Xiangyu Zhai²Xinlu Zhang³Yanmei Wu⁴Huaxin Zhou⁵Hao Zhang¹Chongzhong Liu^2*Zili Zhang^6*

• ¹Qilu Hospital, Shandong University, Jinan, China
• ²The Second Hospital of Shandong University, Jinan, Shandong Province, China
• ³Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, China
• ⁴Shandong Maternal and Child Health Hospital, Jinan, Shandong Province, China
• ⁵The Second People’s Hospital of Jinan, Jinan, Shandong Province, China
• ⁶Fourth People’s Hospital of Jinan, Jinan, Shandong Province, China

Anoikis, a novel form of programmed cell death distinct from apoptosis, remains underexplored in its association with malignant tumor progression. This study employs bioinformatics approaches, using hepatocellular carcinoma (HCC) as a model, to investigate the prognostic and clinical implications of anoikis-related gene expression patterns in cancer management. We first analyzed the expression and mutation profiles of 27 known anoikis-related genes (ARGs) in HCC. Unsupervised consensus clustering was applied to stratify HCC patients into distinct anoikis subtypes. Weighted gene coexpression network analysis (WGCNA) was performed to identify hub genes, followed by LASSO regression to construct an anoikis-related risk score prognostic model. The model's correlations with patient prognosis, tumor microenvironment (TME) features, and immunotherapy response were systematically evaluated. Additionally, expression patterns of model genes were explored at single-cell and pan-cancer levels. Finally, in vitro experiments were conducted to preliminarily validate the regulatory roles of model genes in HCC malignant phenotypes. Bioinformatics analysis of public datasets identified two distinct anoikis subtypes in HCC, which stratified patients by prognosis and TME characteristics. The constructed anoikis risk score model demonstrated robust prognostic predictive efficacy and guided therapeutic decision-making, enhancing the clinical utility of subtyping. In vitro experiments revealed that the signature gene TTC26 promoted HCC cell proliferation, migration, and invasion, suggesting its potential as a novel biomarker. The anoikis-based classification and prognostic model established in this study are closely associated with HCC prognosis and TME features, providing mechanistic insights into HCC pathogenesis and informing personalized treatment strategies.