Predicting hepatocellular carcinoma outcomes and immune therapy response with ATP-dependent chromatin remodeling-related genes, highlighting MORF4L1 as a promising target

被引：0

作者：

Xu, Chao ^{[1
]}

Liang, Litao ^{[1
]}

Liu, Guoqing ^{[2
]}

Feng, Yanzhi ^{[1
]}

Xu, Bin ^{[3
]}

Zhu, Deming ^{[1
]}

Jia, Wenbo ^{[1
]}

Wang, Jinyi ^{[1
]}

Zhao, Wenhu ^{[1
]}

Ling, Xiangyu ^{[1
]}

Zhou, Yongping ^{[4
]}

Ding, Wenzhou ^{[1
]}

Kong, Lianbao ^{[1
]}

机构：

[1] Nanjing Med Univ, Chinese Acad Med Sci, Hepatobiliary Ctr,NHC Key Lab Hepatobiliary Canc, Key Lab Liver Transplantat,Affiliated Hosp 1, Nanjing, Jiangsu, Peoples R China

[2] Nanjing Med Univ, Childrens Hosp, 72 Guangzhou Rd, Nanjing 210008, Jiangsu, Peoples R China

[3] Qingdao Univ, Dept Hepatobiliary & Pancreat Surg, Affiliated Hosp, Qingdao, Peoples R China

[4] Wuxi 2 Peoples Hosp, Dept Hepatobiliary Surg, 68 Zhongshan Rd, Wuxi, Peoples R China

来源：

CANCER CELL INTERNATIONAL | 2025年 / 25卷 / 01期

关键词：

ATP-dependent chromatin remodeling; Hepatocellular carcinoma; Cancer stem cells; Immunotherapy; CELL; STEMNESS;

D O I：

10.1186/s12935-024-03629-2

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

BackgroundHepatocellular carcinoma (HCC) continues to be a major cause of cancer-related death worldwide, primarily due to delays in diagnosis and resistance to existing treatments. Recent research has identified ATP-dependent chromatin remodeling-related genes (ACRRGs) as promising targets for therapeutic intervention across various types of cancer. This development offers potential new avenues for addressing the challenges in HCC management.MethodsThis study integrated bioinformatics analyses and experimental approaches to explore the role of ACRRGs in HCC. We utilized data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), applying machine learning algorithms to develop a prognostic model based on ACRRGs' expression. Experimental validation was conducted using quantitative real-time Polymerase Chain Reaction (qRT-PCR), Western blotting, and functional assays in HCC cell lines and xenograft models.ResultsOur bioinformatics analysis identified four key ACRRGs-MORF4L1, HDAC1, VPS72, and RUVBL2-that serve as prognostic markers for HCC. The developed risk prediction model effectively distinguished between high-risk and low-risk patients, showing significant differences in survival outcomes and predicting responses to immunotherapy in HCC patients. Experimentally, MORF4L1 was demonstrated to enhance cancer stemness by activating the Hedgehog signaling pathway, as supported by both in vitro and in vivo assays.ConclusionACRRGs, particularly MORF4L1, play crucial roles in modulating HCC progression, offering new insights into the molecular mechanisms driving HCC and potential therapeutic targets. Our findings advocate for the inclusion of chromatin remodeling dynamics in the strategic development of precision therapies for HCC.

引用

页数：17