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Unraveling anoikis in glioblastoma: insights from single-cell sequencing and prognostic modeling
Qikai Tang1,2, Chenfeng Ma1, Jiaheng Xie3
1Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, Jiangsu, People's Republic of China.
Background:
Despite advances, Glioblastoma (GBM) treatment remains challenging due to its rapid progression and resistance to therapies.
Objectives:
This study aimed to investigate the role of anoikis-a mechanism by which cells evade programmed cell death upon detachment from the extracellular matrix-in GBM progression and prognosis.
Methods:
Utilizing single-cell sequencing and bulk-transcriptome sequencing data from TCGA, GEO, and CGGA databases, we performed comprehensive bioinformatics analyses. We identified anoikis-related genes, constructed a prognostic model using 101 machine learning algorithms, and validated its clinical utility across multiple cohorts.Finally, we also verified the expression of model genes and the function of key gene in clinical samples and cell lines.
Results:
Single-cell sequencing revealed heterogeneous expression of anoikis-related genes across distinct cell populations within GBM. MES-like Malignant cells and Myeloids exhibited higher enrichment of these genes, implicating their role in anoikis resistance and tumor aggressiveness. The prognostic model, based on identified genes, effectively stratified patients into high-risk and low-risk groups, demonstrating significant differences in survival outcomes. Mutation and tumor microenvironment analyses highlighted distinct genetic landscapes and immune cell infiltration patterns associated with different risk groups. SLC43A3 emerged as a key gene, showing significant upregulation in tumor tissues and correlating with poor prognosis in GBM.
Conclusion:
This study provides insights into the molecular mechanisms of anoikis resistance in GBM, underscoring its critical role in tumor progression and patient prognosis. The developed prognostic model offers a promising tool for personalized treatment strategies and warrants further exploration of targeted therapies to improve outcomes for GBM patients.
Insights
This study reveals how Glioblastoma (GBM) cells resist anoikis, a cell death process, contributing to tumor aggressiveness. A new prognostic model using anoikis-related genes aids in stratifying GBM patients for personalized treatment.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Glioblastoma (GBM) presents significant therapeutic challenges due to its aggressive nature and treatment resistance.
- Understanding mechanisms of anoikis resistance is crucial for improving GBM patient outcomes.
Purpose of the Study:
- To investigate the role of anoikis, a programmed cell death evasion mechanism, in Glioblastoma (GBM) progression and prognosis.
- To develop a prognostic model for GBM based on anoikis-related genes.
Main Methods:
- Comprehensive bioinformatics analyses were performed on single-cell and bulk-transcriptome sequencing data from TCGA, GEO, and CGGA databases.
- Anoikis-related genes were identified, and a prognostic model was constructed using 101 machine learning algorithms.
- Gene expression, clinical samples, cell lines, mutation data, and tumor microenvironment were analyzed for validation.
Main Results:
- Heterogeneous expression of anoikis-related genes was observed across GBM cell populations, with higher enrichment in MES-like Malignant cells and Myeloids.
- The developed prognostic model effectively stratified GBM patients into high-risk and low-risk groups with distinct survival outcomes.
- SLC43A3 was identified as a key gene upregulated in tumors, correlating with poor GBM prognosis and altered tumor microenvironment.
Conclusions:
- Anoikis resistance plays a critical role in Glioblastoma progression and patient prognosis.
- The developed prognostic model shows promise for personalized treatment strategies in GBM.
- Further research into targeted therapies focusing on anoikis mechanisms is warranted to improve GBM patient outcomes.
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