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. 2023 Jun 16;23(1):551.
doi: 10.1186/s12885-023-11001-2.

Molecular and clinical characterization of PTRF in glioma via 1,022 samples

Si Sun #  1   2 , Changlin Yang #  1 , Kuanyu Wang #  1 , Ruoyu Huang  1 , Ke-Nan Zhang  1 , Yanwei Liu  3 , Zhi Cao  4 , Zheng Zhao  5   6 , Tao Jiang  7   8   9   10   11   12
Affiliations

Molecular and clinical characterization of PTRF in glioma via 1,022 samples

Si Sun et al. BMC Cancer. .

Abstract

Polymerase I and transcript release factor (PTRF) plays a role in the regulation of gene expression and the release of RNA transcripts during transcription, which have been associated with various human diseases. However, the role of PTRF in glioma remains unclear. In this study, RNA sequencing (RNA-seq) data (n = 1022 cases) and whole-exome sequencing (WES) data (n = 286 cases) were used to characterize the PTRF expression features. Gene ontology (GO) functional enrichment analysis was used to assess the biological implication of changes in PTRF expression. As a result, the expression of PTRF was associated with malignant progression in gliomas. Meanwhile, somatic mutational profiles and copy number variations (CNV) revealed the glioma subtypes classified by PTRF expression showed distinct genomic alteration. Furthermore, GO functional enrichment analysis suggested that PTRF expression was associated with cell migration and angiogenesis, particularly during an immune response. Survival analysis confirmed that a high expression of PTRF is associated with a poor prognosis. In summary, PTRF may be a valuable factor for the diagnosis and treatment target of glioma.

Keywords: Glioma; Immune response; PTRF; Prognosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
PTRF expression pattern in glioma. A and G WHO grade; B and H histology; C and (I) 2016 WHO classification; D and J IDH mutation status in LGGs and GBMs; E and K MGMT promoter methylation status; F and L TCGA subtype. A-F for CGGA dataset and G-L for TCGA dataset. M Traced lines indicate the PTRF expression changes between primary and paired recurrent glioma. N Representative immunohistochemistry (IHC) of PTRF in low-grade glioma and high-grade glioma tissues. O Dot plots of immunohistochemistry in LGG and GBM
Fig. 2
Fig. 2
Mutational profile of glioma with high and low expression of PTRF
Fig. 3
Fig. 3
Gene ontology analysis of PTRF in glioma. A The gene set enrichment analysis of PTRF expression associated genes in CGGA dataset. B The gene set enrichment analysis of PTRF expression associated genes in TCGA dataset. C The heat map shows the expression pattern of PTRF expression associated genes in CGGA dataset. D The heat map shows the expression pattern of PTRF expression associated genes in TCGA dataset
Fig. 4
Fig. 4
PTRF-related immune genes in CGGA and TCGA databases. A Correlogram of PTRF and Immune Checkpoints in CGGA dataset. B Correlogram of PTRF and Immune Checkpoints in TCGA dataset
Fig. 5
Fig. 5
PTRF-related systemic immunosuppression and neutrophils in CGGA and TCGA databases. A-B PTRF-related systemic immunosuppression in CGGA and TCGA databases. C-F PTRF-related neutrophils in CGGA and TCGA databases
Fig. 6
Fig. 6
PTRF is highly expressed in microvascular proliferation (MVP) regions and MES tumor cells. A Expression of PTRF in different histological regions of GBM in Ivy dataset. B The single-cell data showed that PTRF was highly expressed in tumor cells. C Expression of PTRF in different glioma cellular states
Fig. 7
Fig. 7
Survival analysis for PTRF in glioma patients. A Kaplan–Meier survival analysis of all grades of glioma patients in CGGA dataset based on PTRF expression. B Kaplan–Meier survival analysis of LGG patients in CGGA dataset based on PTRF expression. C Kaplan–Meier survival analysis of GBM patients in CGGA dataset based on PTRF expression. D Kaplan–Meier survival analysis of all grades of glioma patients in TCGA dataset based on PTRF expression. E Kaplan–Meier survival analysis of LGG patients in TCGA dataset based on PTRF expression. F Kaplan–Meier survival analysis of GBM patients in TCGA dataset based on PTRF expression
Fig. 8
Fig. 8
Nomogram for predicting 1-, 3- and 5-year mortality in association with PTRF expression and clinical data. A Nomogram of all grades of glioma patients in CGGA dataset. B Nomogram of GBM patients in CGGA dataset. C The calibration curve for predicting patient survival of all grades of glioma patients in CGGA dataset. D The calibration curve for predicting patient survival of GBM patients in CGGA dataset
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