Open medicine (Warsaw, Poland) · 2026
A novel prognostic model based on Golgi apparatus-related genes predicts patient survival in glioblastoma.
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Wang Hongliu, Liao Zhigang, Jin Lihua, Dong Youkun, Cui Hanjiang, Xing Xihong, Lu Feng, Jiang Kedong, Hu Xiaoli, Bo Xiaofen, Ni Li
Аннотация
OBJECTIVES: Glioblastoma (GBM) is a highly aggressive brain cancer. Golgi dysfunction plays a role in tumorigenesis.
METHODS: This study analyzed the role of Golgi apparatus-related genes (GARGs) in GBM using bioinformatics. Prognostic genes were identified using differential expression, Cox, and Lasso analyses. A prognostic model and nomogram were constructed based on gene expression and clinical characteristics. Immune infiltration and drug sensitivity analyses were performed in different risk groups. Reverse transcription quantitative PCR (RT-qPCR) was employed to explore the expression of prognostic genes.
RESULTS: A total of 5 prognostic genes (CD14, FMOD, LGR6, SLC16A13, and KDELR2) of GBM were identified, and on the basis of the prognostic genes, a prognostic model was built. There was a significant survival difference between patients in the high and low risk groups for GBM. Receiver operating characteristic (ROC) curve indicated that the predictive effect of the prognostic model in the training set was moderate, with an AUC value of around 0.7. The nomogram showed that risk score had a better predictive effect than age. Drug sensitivity analysis demonstrated that sorafenib was more sensitive in the low-risk group of GBM. RT-qPCR results revealed that compared with the control group, the expression levels of CD14 and SLC16A13 were significantly downregulated in GBM cell lines, while FMOD and LGR6 were markedly upregulated. The expression of KDELR2 exhibited obvious cell line-dependent variation.
CONCLUSIONS: In summary, this study establishes a Golgi apparatus-related gene signature that not only offers prognostic insights but may also informs tailored therapeutic strategies for GBM patients.
METHODS: This study analyzed the role of Golgi apparatus-related genes (GARGs) in GBM using bioinformatics. Prognostic genes were identified using differential expression, Cox, and Lasso analyses. A prognostic model and nomogram were constructed based on gene expression and clinical characteristics. Immune infiltration and drug sensitivity analyses were performed in different risk groups. Reverse transcription quantitative PCR (RT-qPCR) was employed to explore the expression of prognostic genes.
RESULTS: A total of 5 prognostic genes (CD14, FMOD, LGR6, SLC16A13, and KDELR2) of GBM were identified, and on the basis of the prognostic genes, a prognostic model was built. There was a significant survival difference between patients in the high and low risk groups for GBM. Receiver operating characteristic (ROC) curve indicated that the predictive effect of the prognostic model in the training set was moderate, with an AUC value of around 0.7. The nomogram showed that risk score had a better predictive effect than age. Drug sensitivity analysis demonstrated that sorafenib was more sensitive in the low-risk group of GBM. RT-qPCR results revealed that compared with the control group, the expression levels of CD14 and SLC16A13 were significantly downregulated in GBM cell lines, while FMOD and LGR6 were markedly upregulated. The expression of KDELR2 exhibited obvious cell line-dependent variation.
CONCLUSIONS: In summary, this study establishes a Golgi apparatus-related gene signature that not only offers prognostic insights but may also informs tailored therapeutic strategies for GBM patients.