Abstract
Background
The importance of molecular diagnostics is increasingly emphasized in the 2021 WHO guidelines for gliomas. There is considerable variability in molecular features and prognosis among glioma patients with the same pathological WHO grade.
Methods
mRNA data and clinical information of human glioma patients were obtained from TCGA and CGGA databases, while expression profiles and TMZ resistance phenotypes of human glioma stem cells were acquired from the GEO database. Differentially expressed genes were identified across distinct WHO grades. Unsupervised clustering was performed on glioma patients based on DEG expression profiles. The Boruta algorithm was employed to identify feature genes for distinct molecular subtypes, and PCA was used to reduce the dimensionality of the feature gene expression data. Grade scores for each sample were calculated and correlated with patients' clinical molecular pathological features and immune microenvironment. Gene set enrichment analysis identified grade score-related functional pathways. Weighted gene co-expression network analysis identified grade score-associated biomarkers. The impact of the hub gene on malignant glioma behavior was validated through in vitro experiments, including CCK-8, EdU, colony formation, Transwell, wound healing, and immunofluorescence assays.
Results
A total of 672 and 687 samples were screened from TCGA and CGGA databases, respectively, along with 6 control, 24 low-grade, and 40 glioblastoma samples from our hospital. Two robust gene clusters were identified based on the expression profiles of 4,476 DEGs among grades 2, 3, and 4 tissues, revealing distinct prognoses. The grade scores exhibited significant heterogeneity across different WHO grade samples, representing diverse immune microenvironments. Grade scores served as independent risk factors for predicting patient prognosis, with higher sensitivity than traditional biomarkers. KIF20A, identified as a grade score-related biomarker, was independently associated with glioma prognosis. Exclusively expressed in tumor cells, KIF20A knockdown significantly inhibited tumor growth, invasion, and EMT biological behavior in glioma cells. Furthermore, KIF20A could serve as a biological marker for predicting patient response to TMZ treatment.
Conclusion
The grade scoring system enhances our understanding of the glioma tumor microenvironment. KIF20A, a novel biomarker for predicting TMZ treatment efficiency, influences malignant tumor behavior by affecting the EMT biological behavior of glioma cells.
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Data availability
Publicly available datasets were analyzed in this study. This data can be found below: https://www.cancer.gov/; http://www.cgga.org.cn/; http://tisch.comp-genomics.org/gallery/.
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Acknowledgements
We gratefully acknowledge The Cancer Genome Atlas pilot project, Chinese Glioma Genome Atlas and Tumor Immune Single-cell Hub 2, which made the genomic data and clinical data available.
Funding
This work was funded by the Beijing Municipal Natural Science Foundation (7202150) and the National High Level Hospital Clinical Research Funding (2022-PUMCH-A-019) for Yu Wang, and by the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-113), the Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program (2019ZLH101) and the Beijing Municipal Natural Science Foundation (19JCZDJC64200[Z]) for Wenbin Ma.
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LGY and ST contributed to the conception and design of this study. YNW, WM and YW contributed to the analysis and interpretation of data. All authors read and approved the final manuscript.
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Ethics approval was obtained from the ethics committee at Peking Union Medical College Hospital (2022-PUMCH-B-113). Informed consent was obtained from all subjects and/or their legal guardians. All experiments were performed in accordance with relevant guidelines and regulations.
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432_2023_4898_MOESM1_ESM.tif
Figure S1. Prognosis and clinical features of grade-related gene clusters in CGGA-gliomas. (A) Consensus clustering matrix for glioma samples with k = 2. (B) CDF of consensus clustering for k = 2 to k = 9. (C) Relative changes in the area under the CDF curve by cluster number. (D) Principal component analysis (PCA) of glioma samples with K=2. (E) Survival analysis of clusters A and B in CGGA datasets. (F) Heatmap illustrating clinicopathological features of the two gene clusters (TIF 5147 KB)
432_2023_4898_MOESM2_ESM.tif
Figure S2. Expression and prognostic significance of hub genes in gliomas in the CGGA database. (A) The mRNA expression of hub genes among different WHO grade gliomas in CGGA cohort. (B) Forest plot of univariate and multivariate Cox regression analysis (TIF 2252 KB)
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Ye, L., Tong, S., Wang, Y. et al. Grade scoring system reveals distinct molecular subtypes and identifies KIF20A as a novel biomarker for predicting temozolomide treatment efficiency in gliomas. J Cancer Res Clin Oncol 149, 9857–9876 (2023). https://doi.org/10.1007/s00432-023-04898-6
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DOI: https://doi.org/10.1007/s00432-023-04898-6