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BACKGROUND: Selenium metabolism has been implicated in human health. This study aimed to identify a selenium metabolism regulator-based prognostic signature for hepatocellular carcinoma (HCC) and validate the role of INMT in HCC. METHODS: Transcriptome sequencing data and clinical information related to selenium metabolism regulators in TCGA liver cancer dataset were analysed. Next, a selenium metabolism model was constructed by multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. Then, the potential of this model for predicting the immune landscape of different risk groups was evaluated. Finally, INMT expression was examined in different datasets. After knockdown of INMT, cell proliferation and colony formation assays were conducted. RESULTS: A selenium metabolism model containing INMT and SEPSECS was established and shown to be an independent predictor of prognosis. The survival time of low-risk patients was significantly longer than that of high-risk patients. These two groups had different immune environments. In different datasets, including TCGA, GEO, and our PUMCH dataset, INMT was significantly downregulated in HCC tissues. Moreover, knockdown of INMT significantly promoted HCC cell proliferation. CONCLUSIONS: The current study established a risk signature of selenium metabolism regulators for predicting the prognosis of HCC patients. INMT was identified as a biomarker for poor prognosis of HCC.
Sun et al. (Thu,) studied this question.
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