Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis
Abstract Background Hepatocellular carcinoma (HCC) is characterized by high heterogeneity, and its molecular features and microenvironment complexity contribute to varied patient prognoses. Anoikis, a form of programmed cell death, is considered closely linked to the development and progression of l...
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02770-2 |
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| author | Bing Zhou Jialing Zhang Mingfeng Guo |
| author_facet | Bing Zhou Jialing Zhang Mingfeng Guo |
| author_sort | Bing Zhou |
| collection | DOAJ |
| description | Abstract Background Hepatocellular carcinoma (HCC) is characterized by high heterogeneity, and its molecular features and microenvironment complexity contribute to varied patient prognoses. Anoikis, a form of programmed cell death, is considered closely linked to the development and progression of liver cancer. This study aimed to analyze the cellular heterogeneity of HCC using single-cell sequencing and to construct an anoikis-related prognostic model for predicting patient outcomes. Methods Single-cell sequencing data of HCC patients were obtained from the GEO database. The AUCell algorithm was used to quantify anoikis gene activity across cell types. Cells were grouped based on enrichment scores. Cox regression and 101 machine learning algorithms were employed to identify prognostic genes and construct a risk model, which was validated using TCGA, ICGC, and GEO datasets. Key findings were validated using qPCR on clinical samples, and functional assays using shRNA-mediated knockdown of KPNA2 were conducted in Hep-3B cell lines to assess changes in proliferation and migration. Results Single-cell analysis revealed the cellular heterogeneity of HCC, with particular enrichment of anoikis-related genes in fibroblasts, endothelial cells, and macrophages. The prognostic model, developed using 101 algorithms, demonstrated the highest performance. High-risk patients showed poor prognosis, and the ROC curve analysis showed high accuracy for model predictions. Genetic mutation analysis indicated significant gene amplifications and deletions in the high-risk group. PCR analysis of clinical samples confirmed the expression patterns of key gene KPNA2, and gene knockdown in cell experiments further validated its oncogentic role in HCC. Conclusion This study highlights the anoikis-related cellular heterogeneity in HCC and constructs a machine learning-based prognostic model with high predictive value, enabling effective risk stratification for patients. It offers further support for the potential therapeutic application of the identified genes. |
| format | Article |
| id | doaj-art-48bbc1cd3c7c491cb9a3b68c62931769 |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-48bbc1cd3c7c491cb9a3b68c629317692025-08-20T01:59:57ZengSpringerDiscover Oncology2730-60112025-05-0116111710.1007/s12672-025-02770-2Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysisBing Zhou0Jialing Zhang1Mingfeng Guo2Department of Hepatobiliary Surgery, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical UniversityDepartment of Gastroenterology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical UniversityDepartment of EICU, The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical UniversityAbstract Background Hepatocellular carcinoma (HCC) is characterized by high heterogeneity, and its molecular features and microenvironment complexity contribute to varied patient prognoses. Anoikis, a form of programmed cell death, is considered closely linked to the development and progression of liver cancer. This study aimed to analyze the cellular heterogeneity of HCC using single-cell sequencing and to construct an anoikis-related prognostic model for predicting patient outcomes. Methods Single-cell sequencing data of HCC patients were obtained from the GEO database. The AUCell algorithm was used to quantify anoikis gene activity across cell types. Cells were grouped based on enrichment scores. Cox regression and 101 machine learning algorithms were employed to identify prognostic genes and construct a risk model, which was validated using TCGA, ICGC, and GEO datasets. Key findings were validated using qPCR on clinical samples, and functional assays using shRNA-mediated knockdown of KPNA2 were conducted in Hep-3B cell lines to assess changes in proliferation and migration. Results Single-cell analysis revealed the cellular heterogeneity of HCC, with particular enrichment of anoikis-related genes in fibroblasts, endothelial cells, and macrophages. The prognostic model, developed using 101 algorithms, demonstrated the highest performance. High-risk patients showed poor prognosis, and the ROC curve analysis showed high accuracy for model predictions. Genetic mutation analysis indicated significant gene amplifications and deletions in the high-risk group. PCR analysis of clinical samples confirmed the expression patterns of key gene KPNA2, and gene knockdown in cell experiments further validated its oncogentic role in HCC. Conclusion This study highlights the anoikis-related cellular heterogeneity in HCC and constructs a machine learning-based prognostic model with high predictive value, enabling effective risk stratification for patients. It offers further support for the potential therapeutic application of the identified genes.https://doi.org/10.1007/s12672-025-02770-2Disease progressionBiomarkersTumor microenvironmentNext-generation sequencing technologiesSingle-cell RNA sequencingSolid tumors |
| spellingShingle | Bing Zhou Jialing Zhang Mingfeng Guo Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis Discover Oncology Disease progression Biomarkers Tumor microenvironment Next-generation sequencing technologies Single-cell RNA sequencing Solid tumors |
| title | Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis |
| title_full | Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis |
| title_fullStr | Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis |
| title_full_unstemmed | Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis |
| title_short | Anoikis in hepatocellular carcinoma: genetic alterations and immune microenvironment insights from single-cell and computational analysis |
| title_sort | anoikis in hepatocellular carcinoma genetic alterations and immune microenvironment insights from single cell and computational analysis |
| topic | Disease progression Biomarkers Tumor microenvironment Next-generation sequencing technologies Single-cell RNA sequencing Solid tumors |
| url | https://doi.org/10.1007/s12672-025-02770-2 |
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