Hepatocellular carcinoma (HCC) remains one of the most prevalent and lethal malignancies worldwide, with survival rates still falling short of expectations. Emerging evidence highlights the pivotal roles of both m6A methylation and ferroptosis-related genes (FRGs) in HCC progression. However, the prognostic significance of m6A-modulated FRGs remains largely unexplored. In this study, we developed a novel prognostic signature based on m6A-regulated FRGs, identifying six key genes (VEGFA, FANCD2, ZFP69B, EIF2S1, SLC7A11, and SRXN1) through multivariate and LASSO Cox regression analyses. A high m6A-FRGs score was strongly associated with poor prognosis, and multivariate analysis confirmed it as an independent prognostic factor. Notably, the high-risk group exhibited increased expression of immune checkpoint genes and a higher frequency of gene mutations. Functional assays further demonstrated that silencing ZFP69B significantly suppressed liver cancer cell proliferation, migration, and invasion. Clinical validation in 144 HCC samples revealed that elevated ZFP69B expression correlated with worse patient outcomes. Moreover, qPCR analysis confirmed CLSPN and HNRNPR as downstream targets of ZFP69B. Collectively, our findings establish the m6A-FRGs signature as a powerful prognostic tool for HCC and identify ZFP69B as a promising therapeutic target, warranting further investigation.

I Kappa B Kinase Interacting Protein (IKBIP) has been reported to promote tumor progression in diverse cancers. however, the role of IKBIP in hepatocellula

Background: Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy which predominantly arises in the setting of cirrhosis, and there is lack of models to predict prognosis in cirrhotic HCC. This study aims to develop and validate a prediction model based on the pathomics signature and clinicopathological characteristics to predict the prognosis of HCC with cirrhosis. Methods: In this multicenter, retrospective study, 389 patients were enrolled (training cohort: 268; independent validation cohort: 121). A total of 351 pathomics features were extracted from digital H-E–stained images, and a pathomics signature (PS_HCC) was constructed using a least absolute shrinkage and selection operator Cox regression model. Then two nomograms were established by combining the PS_HCC and clinicopathological characteristics. Further validation was performed in the validation cohort. Results: This study included 389 patients. A 24 feature-based PS_HCC was constructed. A higher PS_HCC was significantly associated with worse OS and DFS in both the training (OS: hazard ratio [HR], 4.341 [95% CI, 3.109–6.062]; DFS: HR, 3.058 [95% CI, 2.223–4.207]) and validation (OS: HR, 4.145 [95% CI, 2.357–7.291]; DFS: HR, 3.395 [95% CI, 2.104–5.479]) cohorts (p < 0.001 for all comparisons). Multivariable analysis revealed that the PS_HCC was an independent factor associated with OS and DFS. Integrating the PS_HCC into pathomics nomograms resulted in better performance for prognosis prediction than the traditional model in both cohorts. Conclusions: The PS_HCC may serve as a reliable surrogate for prognosis, and the nomograms offer promising tools to predict individual outcomes, facilitating personalized management of HCC with cirrhosis.