Newly Established Anoikis-Associated Genes Predict the Prognosis of Hepatocellular Carcinoma

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Objective: Anoikis is an anchorage-dependent programmed cell death implicated in multiple pathological processes of cancers; however, the prognostic value of anoikis-related genes (ANRGs) in hepatocellular carcinoma (HCC) remains unclear. Our study aims to develop an ANRGs-based prediction model to improve prognostic assessment in HCC patients. Methods: The RNA-seq profile was performed to estimate the expression of ANRGs in HCC patients. The univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were applied in the model construction to predict the prognosis in terms of differentially expressed ANRGs in the Cancer Genome Atlas (TCGA) training cohort. The TCGA test cohort and the International Cancer Genome Consortium (ICGC)-originated cohort were set to verify the predictive capacity. Nomogram was built on the basis of risk score (RS), gender, age, grade, and T_stage, with the hope of extending the predictive ability of ANRGs to evaluate the HCC prognosis. The expression of differentially expressed ANRGs was assessed in HCC cell lines and orthotopic tumor-bearing mice. Results: Six ANRGs ( ANXA5, BIRC5, BSG, DAP3, SKP2 and CDKN3 ) demonstrated the critical prognostic significance in HCC patients. The prognostic RS model on the basis of these ANRGs was capable of properly predicting 1-, 3-, and 5-year survivals. The Kaplan-Meier results displayed the increased death and decreased survival in the high-risk group. The RS acted as the independent factor for the survival evaluation. Our nomogram model was able to directly reflect the survival probabilities of each patient, which was confirmed through various validations. The transcription and translation of six ANRGs were significantly enhanced in HCC cell lines and tumor tissues. Conclusion: Despite the lack of mechanistic validation, our anoikis-linked RS model serves as a promising tool for predicting HCC prognosis in clinical practice, and provides valuable insights into the decision of individualized therapeutic approaches. Keywords: liver cancer, anoikis, tumor prognosis, risk model, nomogram, validation

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