Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, with limited treatment options. The goal of this study was to use integrated bioinformatic analysis to find possible biomarkers for prognosis and therapeutic targets for hepatitis B (HBV)-associated HCC. Three microarray datasets (GSE84402, GSE121248, and E-GEOD-19665) from patients with HBV-associated HCC were combined and analyzed. We identified differentially expressed genes (DEGs) and performed pathway enrichment analysis. We constructed protein-protein interaction networks to identify hub genes. We identified a total of 374 DEGs, which included 90 up-regulated and 284 down-regulated genes. Pathway enrichment analysis revealed associations with cell cycle, oocyte meiosis, and the p53 signaling pathway for up-regulated DEGs. Twenty hub genes were identified, and 9 of them (ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1) were validated using the Cancer Genome Atlas data and Kaplan-Meier survival analysis. These genes were significantly associated with a poor prognosis in HCC patients. Our research shows that ZWINT, MELK, DLGAP5, BIRC5, AURKA, HMMR, CDK1, TTK, and MAD2L1 may be useful for predicting how HBV-associated HCC will progress and for finding new ways to treat it. In addition to these further studies are needed to elucidate the functions of the remaining 11 identified hub genes (RRM2, NUSAP1, PBK, CCNB1, CCNB2, BUB1B, NEK2, CENPF, ASPM, TOP2A, and BUB1) in HCC development and progression.