Epidemiological data indicate that in 2022, China reported 368,000 new cases of liver cancer and 317,000 deaths, ranking 4th and 2nd among all malignancies, respectively [1]. Hepatocellular carcinoma (HCC) is the predominant histological subtype of liver cancer. Surgical resection, liver transplantation, and local ablation have improved clinical management, yet postoperative recurrence remains common, with rates approaching 70% and contributing substantially to mortality [2]. Identifying reliable molecular biomarkers capable of predicting recurrence and guiding individualized treatment is therefore of considerable clinical importance. Microchromosome maintenance (MCM) proteins are a highly conserved family of eukaryotic factors that participate in pre-replicative complex assembly as well as the initiation and elongation of DNA replication. The family comprises ten conserved members, including MCM2, MCM4, and MCM10 [3]. Multiple studies have shown that several MCM proteins—particularly MCM2, MCM4, and MCM6—are overexpressed in a variety of malignancies and are associated with patient prognosis and tumor stage [4,5]. With advances in bioinformatics, integrative multi-omics analyses for biomarker discovery and prognostic model development have become a major focus in oncology research. For example, recent bioinformatic work identified MCM2, MCM4, and MCM6 as potential molecular markers in pancreatic cancer [6]. However, studies examining the expression of MCM2, MCM4, and MCM10 in HCC, and their relationship with postoperative recurrence, remain limited. To bridge this gap and provide a clinically actionable tool, this study aims to develop and validate the first nomogram that integrates the expression levels of these three MCM proteins with key clinicopathological factors for the individualized prediction of early postoperative recurrence in HCC. To address this gap, the present study will employ bioinformatic approaches to characterize the expression patterns of MCM2, MCM4, and MCM10 in HCC using publicly available datasets. Clinical data from patients treated at The Second Affiliated Hospital of Zhejiang University School of Medicine (Lin Ping Campus) and The First Affiliated Hospital of Hunan College of TCM will then be integrated to assess the association between these markers and postoperative recurrence. Finally, we will develop a preliminary predictive model. The findings aim to provide insights into the molecular mechanisms of HCC and support early postoperative risk stratification.