Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes

Junsheng Li; Jia Wang; Dongjing Liu; Chuming Tao; Jizong Zhao; Wen Wang

doi:10.3389/fimmu.2022.1018942

Establishment and validation of a novel prognostic model for lower-grade glioma based on senescence-related genes

Front Immunol. 2022 Oct 21:13:1018942. doi: 10.3389/fimmu.2022.1018942. eCollection 2022.

Authors

Junsheng Li^{1

2

3

4

5}, Jia Wang^{1

2

3

4

5}, Dongjing Liu^{1

2

3

4

5}, Chuming Tao⁶, Jizong Zhao^{1

2

3

4

5

7}, Wen Wang^{1

2

3

4

5}

Affiliations

¹ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² China National Clinical Research Center for Neurological Diseases, Beijing, China.
³ Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.
⁴ Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.
⁵ Beijing Translational Engineering Center for 3D Printer in Clinical Neuroscience, Beijing, China.
⁶ Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
⁷ Savaid Medical School, University of the Chinese Academy of Sciences, Beijing, China.

Abstract

Objective: Increasing studies have indicated that senescence was associated with tumorigenesis and progression. Lower-grade glioma (LGG) presented a less invasive nature, however, its treatment efficacy and prognosis prediction remained challenging due to the intrinsic heterogeneity. Therefore, we established a senescence-related signature and investigated its prognostic role in LGGs.

Methods: The gene expression data and clinicopathologic features were from The Cancer Genome Atlas (TCGA) database. The experimentally validated senescence genes (SnGs) from the CellAge database were obtained. Then LASSO regression has been performed to build a prognostic model. Cox regression and Kaplan-Meier survival curves were performed to investigate the prognostic value of the SnG-risk score. A nomogram model has been constructed for outcome prediction. Immunological analyses were further performed. Data from the Chinese Glioma Genome Atlas (CGGA), Repository of Molecular Brain Neoplasia Data (REMBRANDT), and GSE16011 were used for validation.

Results: The 6-SnG signature has been established. The results showed SnG-risk score could be considered as an independent predictor for LGG patients (HR=2.763, 95%CI=1.660-4.599, P<0.001). The high SnG-risk score indicated a worse outcome in LGG (P<0.001). Immune analysis showed a positive correlation between the SnG-risk score and immune infiltration level, and the expression of immune checkpoints. The CGGA datasets confirmed the prognostic role of the SnG-risk score. And Kaplan-Meier analyses in the additional datasets (CGGA, REMBRANDT, and GSE16011) validated the prognostic role of the SnG-signature (P<0.001 for all).

Conclusion: The SnG-related prognostic model could predict the survival of LGG accurately. This study proposed a novel indicator for predicting the prognosis of LGG and provided potential therapeutic targets.

Keywords: biomarker; lower-grade glioma; prognostic model; senescence; signature.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain Neoplasms* / pathology
Glioma* / pathology
Humans
Kaplan-Meier Estimate
Prognosis