Chen Chen, Peng Rui, Jin Shengjie, Tang Yuhong, Liu Huanxiang, Tu Daoyuan, Su Bingbing, Wang Shunyi, Jiang Guoqing, Cao Jun, Zhang Chi, Bai Dousheng
Department of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Department of Hepatobiliary Surgery, Northern Jiangsu People's Hospital, Yangzhou, China.
Discov Oncol. 2024 Dec 18;15(1):808. doi: 10.1007/s12672-024-01667-w.
Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.
转移是肝细胞癌（HCC）死亡的主要原因。但用于HCC转移的有效生物标志物仍未得到充分探索。在此，我们整合了基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据集，以筛选肝细胞癌转移的候选基因，并通过机器学习构建了一个共识性转移衍生预后特征（MDPS）。基于风险评分，将HCC患者分为高风险组和低风险组。进行了全面分析，以研究包括生存结果、临床特征、免疫细胞浸润以及体外实验等各个方面。我们共同开发了一个基于机器学习的综合程序，用于构建一个包含四个基因（SPP1、TYMS、HMMR和MYCN）的共识性MDPS。我们的研究结果表明，这四个基因可作为HCC有效的预后生物标志物和治疗靶点。此外，体外实验表明，HMMR的过表达加剧了肿瘤进展，包括增殖、迁移和侵袭。
