Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Within-cell polarity is crucial to cell development and function maintenance, and some studies have found that it is closely related to cancer initiation, metastasis, and prognosis. The aim of our research was to find polarity-related biomarkers which improve the treatment and prognosis of HCC. For the knowledge-driven analysis, 189 polarity-related genes (PRGs) were retrieved and curated manually from the molecular signatures database and reviews. Meanwhile, in the data-driven part, genomic datasets and clinical records of HCC was obtained from the cancer genome atlas database. The potential candidates were considered in the respect to differential expression, mutation rate, and prognostic value. Sixty-one PRGs that passed the knowledge and data-driven screening were applied for function analysis and mechanism deduction. Elastic net model combing least absolute shrinkage and selection operator and ridge regression analysis refined the input into a 12-PRG risk model, and its pharmaceutical potency was evaluated. These findings demonstrated that the integration of multi-omics of PRGs can help us in untangling the liver cancer pathogenesis as well as illustrate the underlying mechanisms and therapeutic targets.
Keywords: biomarker; cell polarity; elastic net; hepatocellular carcinoma; overall survival.