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Background <p>Liver hepatocellular carcinoma (LIHC) often has a poor prognosis. Since the relationship between HRas proto-oncogene, GTPase (HRAS) and LIHC has not been elucidated, the aim of this study was to explore the mechanisms by which HRAS is involved in regulating the prognosis of LIHC.</p> Methods <p>We usedThe Cancer Genome Atlas (TCGA) database to characterize differences in HRAS gene expression between LIHC patients and healthy individuals. In addition, we analysed the relationships between HRAS gene expression levels and the clinicopathological characteristics of LIHC patients. Next, we used univariate and multivariate Cox regression analyses to identify prognostic factors. Differentially expressed genes were identified between the low- and high-expression groups, and KEGG and GO analyses and GSEA were performed to study the underlying mechanisms. The effects of high and low HRAS expression on the prognosis of LIHC patients was determined according to CIBERSORT. We subsequently assayed HRAS gene expression at the cellular level, and these data were validated in a tumour xenograft model.</p> Results <p>We established and validated the HRAS gene as a prognostic signature and analysed the relationships between HRAS expression levels and clinicopathological features. Patients were categorized into high and low HRAS gene expression groups. We determined that high HRAS expression is associated with carbon metabolism, the PPAR signalling pathway, and small molecule catabolism in cancer. Furthermore, we conclude that the poor prognosis that results from elevated HRAS expression is associated with immune cell infiltration. We used LASSO + KNN to build an AI classification model that shows good performance in distinguishing liver cancer tissues form normal tissues. Finally, we verified that HRAS is highly expressed in hepatocellular carcinoma cells and promotes tumour growth.</p> Conclusion <p>We identified and validated the role of HRAS in hepatocellular carcinoma to assess hepatocellular carcinoma prognosis. The results of this study can be applied to predict survival, for personalized liver cancer treatment strategies, and provide information for the development of potential targeted therapies and new ideas for liver cancer patient treatment.</p>

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Mechanisms of HRAS regulation of liver hepatocellular carcinoma for prognosis prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月28日

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  • Xingbao Fang,
  • Yan Cai,
  • Zhuoyu Zhao,
  • Shaohua Yang,
  • Zhaojun Li,
  • Xiongbing Peng,
  • Meifang Hang,
  • Peiwan Liu,
  • Yuehong Li

摘要

Background

Liver hepatocellular carcinoma (LIHC) often has a poor prognosis. Since the relationship between HRas proto-oncogene, GTPase (HRAS) and LIHC has not been elucidated, the aim of this study was to explore the mechanisms by which HRAS is involved in regulating the prognosis of LIHC.

Methods

We usedThe Cancer Genome Atlas (TCGA) database to characterize differences in HRAS gene expression between LIHC patients and healthy individuals. In addition, we analysed the relationships between HRAS gene expression levels and the clinicopathological characteristics of LIHC patients. Next, we used univariate and multivariate Cox regression analyses to identify prognostic factors. Differentially expressed genes were identified between the low- and high-expression groups, and KEGG and GO analyses and GSEA were performed to study the underlying mechanisms. The effects of high and low HRAS expression on the prognosis of LIHC patients was determined according to CIBERSORT. We subsequently assayed HRAS gene expression at the cellular level, and these data were validated in a tumour xenograft model.

Results

We established and validated the HRAS gene as a prognostic signature and analysed the relationships between HRAS expression levels and clinicopathological features. Patients were categorized into high and low HRAS gene expression groups. We determined that high HRAS expression is associated with carbon metabolism, the PPAR signalling pathway, and small molecule catabolism in cancer. Furthermore, we conclude that the poor prognosis that results from elevated HRAS expression is associated with immune cell infiltration. We used LASSO + KNN to build an AI classification model that shows good performance in distinguishing liver cancer tissues form normal tissues. Finally, we verified that HRAS is highly expressed in hepatocellular carcinoma cells and promotes tumour growth.

Conclusion

We identified and validated the role of HRAS in hepatocellular carcinoma to assess hepatocellular carcinoma prognosis. The results of this study can be applied to predict survival, for personalized liver cancer treatment strategies, and provide information for the development of potential targeted therapies and new ideas for liver cancer patient treatment.