Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma
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, th...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Genetics Research |
| Online Access: | http://dx.doi.org/10.1155/2024/8217215 |
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| author | Dongqian Li Qian Bao Shiqi Ren Haoxiang Ding Chengfeng Guo Kai Gao Jian Wan Yao Wang MingYan Zhu Yicheng Xiong |
| author_facet | Dongqian Li Qian Bao Shiqi Ren Haoxiang Ding Chengfeng Guo Kai Gao Jian Wan Yao Wang MingYan Zhu Yicheng Xiong |
| author_sort | Dongqian Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-835a2d45a7b249b692ac2ae06f532402 |
| institution | Kabale University |
| issn | 1469-5073 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Genetics Research |
| spelling | doaj-art-835a2d45a7b249b692ac2ae06f5324022025-02-03T00:20:43ZengWileyGenetics Research1469-50732024-01-01202410.1155/2024/8217215Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular CarcinomaDongqian Li0Qian Bao1Shiqi Ren2Haoxiang Ding3Chengfeng Guo4Kai Gao5Jian Wan6Yao Wang7MingYan Zhu8Yicheng Xiong9Department of Hepatobiliary and Pancreatic SurgeryDepartment of Hepatobiliary and Pancreatic SurgeryNantong University Medical SchoolNantong University Medical SchoolNantong University Medical SchoolNantong University Medical SchoolDepartment of Hepatobiliary and Pancreatic SurgeryDepartment of Hepatobiliary and Pancreatic SurgeryDepartment of Hepatobiliary and Pancreatic SurgeryDepartment of Hepatobiliary and Pancreatic SurgeryBackground. 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.http://dx.doi.org/10.1155/2024/8217215 |
| spellingShingle | Dongqian Li Qian Bao Shiqi Ren Haoxiang Ding Chengfeng Guo Kai Gao Jian Wan Yao Wang MingYan Zhu Yicheng Xiong Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma Genetics Research |
| title | Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma |
| title_full | Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma |
| title_fullStr | Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma |
| title_full_unstemmed | Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma |
| title_short | Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma |
| title_sort | comprehensive analysis of the mechanism of anoikis in hepatocellular carcinoma |
| url | http://dx.doi.org/10.1155/2024/8217215 |
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