A hypoxia-linked gene signature for prognosis prediction and evaluating the immune microenvironment in patients with hepatocellular carcinoma

BACKGROUND: Previous research indicates that hypoxia critically affects the initiation and progression of hepatocellular carcinoma (HCC). Nevertheless, the molecular mechanisms responsible for HCC development are poorly understood. Herein, we purposed to build a prognostic model using hypoxia-linked genes to predict patient prognosis and investigate the relationship of hypoxia with immune status in the tumor microenvironment (TME). METHODS: The training cohort included transcriptome along with clinical data abstracted from The Cancer Genome Atlas (TCGA). The validation cohort was abstracted from Gene Expression Omnibus (GEO). Univariate along with multivariate Cox regression were adopted to create the prediction model. We divided all patients into low- and high-risk groups using median risk scores. The estimation power of the prediction model was determined with bioinformatic tools. RESULTS: Six hypoxia-linked genes, HMOX1, TKTL1, TPI1, ENO2, LDHA, and SLC2A1, were employed to create an estimation model. Kaplan-Meier, ROC curve, and risk plot analyses demonstrated that the estimation potential of the risk model was satisfactory. Univariate along with multivariate regression data illustrated that the risk model could independently predict the overall survival (OS). A nomogram integrating the risk signature and clinicopathological characteristics showed a good potential to estimate HCC prognosis. Gene set enrichment analysis (GSEA) revealed that genes associated with cell proliferation and metabolism cascades were abundant in high-risk group. Furthermore, the signature showed a strong ability to distinguish the two groups in terms of immune status. CONCLUSIONS: A prediction model for predicting HCC prognosis using six hypoxia-linked genes was designed in this study, facilitating the diagnosis and treatment of HCC.