Hepatocellular carcinoma (HCC) is a malignant
tumor with high morbidity and mortality worldwide. Many studies have shown that dedicator of cytokinesis 2 (DOCK2) has a crucial role as a prognostic factor in various
cancers. However, the potentiality of DOCK2 in the diagnosis of HCC has not been fully elucidated. In this work, we aimed to investigate the prognostic role of DOCK2 mutation in HCC. The
Cancer Genome Atlas (TCGA) and the International
Cancer Genome Consortium (ICGC) cohorts were utilized to identify the mutation frequency of DOCK2. Then, univariate Cox proportional hazard regression analysis, random forest (RF), and multivariate Cox regression analysis were performed to develop the risk score that was significantly related to DOCK2 mutation. Moreover, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and immune correlation analysis were conducted for an in-depth study of the biological process of DOCK2 mutation involved in HCC. The results revealed that the mutation frequency of DOCK2 was relatively higher than that in non-
cancer control subjects, and patients with DOCK2 mutations had a low survival rate and a poor prognosis compared with the DOCK2-wild group. In addition, the
secretin receptor (SCTR), tetratricopeptide repeat, ankyrin repeat and coiled-coil domain-containing 1 (TANC1), Alkb homolog 7 (ALKBH7), FRAS1-related extracellular matrix 2 (FREM2), and
G protein subunit gamma 4 (GNG4) were found to be the most relevant prognostic genes of DOCK2 mutation, and the risk score based on the five genes played an excellent role in predicting the status of survival,
tumor mutation burden (TMB), and
microsatellite instability (MSI) in DOCK2 mutant patients. In addition, DOCK2 mutation and the risk score were closely related to immune responses. In conclusion, the present study identifies a novel prognostic signature in light of DOCK2 mutation-related genes that shows great prognostic value in HCC patients; and this gene mutation might promote
tumor progression by influencing immune responses. These data may provide valuable insights for future investigations into personalized forecasting methods and also shed light on stratified precision oncology treatment.