Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma.

IF 1.4 4区生物学 Q4 GENETICS & HEREDITY

Genetics research Pub Date : 2024-09-11 DOI:10.1155/2024/8217215

Dongqian Li,Qian Bao,Shiqi Ren,Haoxiang Ding,Chengfeng Guo,Kai Gao,Jian Wan,Yao Wang,MingYan Zhu,Yicheng Xiong

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Abstract

Background Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated. Methods This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes. Results In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples. Conclusion The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

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全面分析肝细胞癌中的 Anoikis 机制

背景肝细胞癌（HCC）是导致全球恶性肿瘤死亡的第二大原因，给全球公共卫生造成了沉重负担。无细胞凋亡是一种程序性细胞死亡，是阻止癌细胞向远处器官扩散的屏障，从而限制了癌症的发展。然而，HCC中与anoikis相关基因的作用机制尚待阐明。方法本文的数据（TCGA-HCC）来自癌症基因组图谱（TCGA）数据库。通过单变量 Cox 分析和差异表达分析，确定了对 anoikis 预后有影响的差异基因表达。通过无监督聚类分析，我们根据这些 DEGs 对样本进行了聚类。通过使用最小绝对收缩和选择算子考克斯回归分析（CRA），从 DEGs 中生成了临床预测基因特征。通过估算RNA转录本的相对子集（CIBERSORT）算法确定了免疫细胞类型的比例。外部验证数据（GSE76427）来自基因表达总库（GEO），用于验证临床预后基因特征的性能。Western印迹和免疫组化（IHC）分析证实了风险基因的表达。根据这 23 个 DEGs，样本被分为四个不同的亚组（群 1、群 2、群 3 和群 4）。此外，还利用 ETV4、PBK 和 SLC2A1 构建了临床预测基因特征。基因特征将个体有效地区分为两个风险组，即低风险组和高风险组，前者的存活率明显更高。这些风险基因的表达与多种免疫细胞之间存在显著的相关性。此外，验证队列分析的结果与训练队列分析的结果一致。Western印迹和IHC结果显示，ETV4、PBK和SLC2A1在HCC样本中上调。该特征有望推动 HCC 个性化疗法的发展。

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来源期刊

Genetics research 生物-遗传学

自引率

6.70%

发文量

审稿时长

>12 weeks

期刊介绍： Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.