ArticleCell cycle (Georgetown, Tex.)2022
Identification of a five-gene signature deriving from the vacuolar ATPase (V-ATPase) sub-classifies gliomas and decides prognoses and immune microenvironment alterations.
Article in Cell cycle (Georgetown, Tex.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 21 citations in OpenAlex.
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- Antitumor and immunomodulatory activities of diphyllin and its derivatives.Bioorganic & medicinal chemistry · 2025Review
- Exploring the underlying molecular connections: a bioinformatics approach to link melanoma and Parkinson's disease.Frontiers in genetics · 2025Article
- Deciphering MOSPD1's impact on breast cancer progression and therapeutic response.Biology direct · 2024Article
- Construction of oxidative phosphorylation-related prognostic risk score model in uveal melanoma.BMC ophthalmology · 2024Article
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- Vacuolar Proton-Translocating ATPase May Take Part in the Drug Resistance Phenotype of Glioma Stem Cells.International journal of molecular sciences · 2024Article
- A transcriptome-wide association study identified susceptibility genes for hepatocellular carcinoma in East Asia.Gastroenterology report · 2024Article
- Translational insights into the hormetic potential of carbon dioxide: from physiological mechanisms to innovative adjunct therapeutic potential for cancer.Frontiers in physiology · 2024Review
- Plasma proteins and psoriatic arthritis: a proteome-wide Mendelian randomization study.Frontiers in immunology · 2024Article
- Proton pump inhibitors display anti-tumour potential in glioma.Cell proliferation · 2023Review
- Establishment and validation of a novel integrin-based prognostic gene signature that sub-classifies gliomas and effectively predicts immunosuppressive microenvironment.Cell cycle (Georgetown, Tex.) · 2023Article
- Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
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Abstract
Aberrant expression of coding genes of the V-ATPase subunits has been reported in glioma patients that can activate oncogenic pathways and result in worse prognosis. However, the predictive effect of a single gene is not specific or sensitive enough. In this study, by using a series of bioinformatics analyses, we identified five coding genes (ATP6V1C2, ATP6V1G2, TCIRG1, ATP6AP1 and ATP6AP2) of the V-ATPase that were related to glioma patient prognosis. Based on the expression of these genes, glioma patients were sub-classified into different prognosis clusters, of which C1 cluster performed better prognosis; however, C2 cluster showed more malignant phenotypes with oncogenic and immune-related pathway activation. The single-cell RNA-seq data revealed that ATP6AP1, ATP6AP2, ATP6V1G2 and TCIRG1 might be cell-type potential markers. Copy number variation and DNA promoter methylation potentially regulate these five gene expressions. A risk score model consisted of these five genes effectively predicted glioma prognosis and was fully validated by six independent datasets. The risk scores also showed a positive correlation with immune checkpoint expression. Importantly, glioma patients with high-risk scores presented resistance to traditional treatment. We also revealed that more inhibitory immune cells infiltration and higher rates of "non-response" to immune checkpoint blockade (ICB) treatment in the high-risk score group. In conclusion, our study identified a five-gene signature from the V-ATPase that could sub-classify gliomas into different phenotypes and their abnormal expression was regulated by distinct mechanisms and accompanied with immune microenvironment alterations potentially act as a biomarker for ICB treatment.
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