Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology · 2026
Diagnostic and prognostic value of heme metabolism-related genes in early-stage hepatocellular carcinoma.
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Yang Wenxian, Wang Junbao, Gao Xiaobing, Chen Dexing
Аннотация
BACKGROUND: Hepatocellular carcinoma (HCC) incidence continues to rise globally, with treatment efficacy constrained by the tumor microenvironment's high heterogeneity and challenges in early diagnosis. Heme metabolism plays a crucial role in tumor progression, yet its specific function in the development of early-stage HCC remains poorly understood.
METHODS: This study comprehensively employed univariate and multivariate Cox regression and least absolute shrinkage and selection operator regression models to systematically identify heme metabolism-related genes (HMRGs) significantly associated with early HCC prognosis. Key diagnostic biomarkers were further identified through Support Vector Machine Recursive Feature Elimination and Extreme Gradient Boosting algorithms. Immune microenvironment characteristics were comprehensively analyzed across different prognostic subgroups based on immune infiltration assessment. Additionally, consensus clustering methods revealed molecular subtyping with distinct prognostic features in early-stage HCC.
RESULTS: This study identified four key HMRGs as diagnostic genes for early-stage HCC and constructed a robust diagnostic model based on these findings. Additionally, six HMRGs significantly associated with prognosis were screened. Immune infiltration analysis revealed higher Tregs infiltration and lower NK cells infiltration in the high-risk group. Furthermore, consensus clustering analysis divided early-stage HCC into two molecularly heterogeneous subtypes.
CONCLUSION: By systematically analyzing the molecular functions and immune profiles of HMRGs in early-stage HCC, this study contributes to a deeper understanding of its underlying pathology and lays the groundwork for more tailored treatment strategies.
METHODS: This study comprehensively employed univariate and multivariate Cox regression and least absolute shrinkage and selection operator regression models to systematically identify heme metabolism-related genes (HMRGs) significantly associated with early HCC prognosis. Key diagnostic biomarkers were further identified through Support Vector Machine Recursive Feature Elimination and Extreme Gradient Boosting algorithms. Immune microenvironment characteristics were comprehensively analyzed across different prognostic subgroups based on immune infiltration assessment. Additionally, consensus clustering methods revealed molecular subtyping with distinct prognostic features in early-stage HCC.
RESULTS: This study identified four key HMRGs as diagnostic genes for early-stage HCC and constructed a robust diagnostic model based on these findings. Additionally, six HMRGs significantly associated with prognosis were screened. Immune infiltration analysis revealed higher Tregs infiltration and lower NK cells infiltration in the high-risk group. Furthermore, consensus clustering analysis divided early-stage HCC into two molecularly heterogeneous subtypes.
CONCLUSION: By systematically analyzing the molecular functions and immune profiles of HMRGs in early-stage HCC, this study contributes to a deeper understanding of its underlying pathology and lays the groundwork for more tailored treatment strategies.