New Autophagy-Ferroptosis Gene Signature Predicts Survival in Glioma

Overview

Journal Front Cell Dev Biol

Specialty Cell Biology

Date 2021 Dec 6

PMID 34869322

Citations 3

Authors

Liwei Zhou

Zhengye Jiang

Zhongjie Shi

Wenpeng Zhao

Zhenwei Lu

Yuanyuan Xie

Bingchang Zhang

Hanwen Lu

Guowei Tan

Zhanxiang Wang

Affiliations

Soon will be listed here.

Abstract

Ferroptosis plays an important role in glioma and significantly affects the prognosis, but the specific mechanism has not yet been elucidated. Recent studies suggest that autophagy regulates the process of ferroptosis. This study aimed to find potential autophagy-ferroptosis genes and explore the prognostic significance in glioma. Ferroptosis and autophagy genes were obtained from two online databases (zhounan.org/ferrdb and autophagy.lu/). The RNAseq data and clinical information were obtained from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Univariate, multivariate, lasso and Cox regression analysis screened out prognosis-related genes, and a risk model was constructed. Receiver operating characteristic (ROC) curve analysis evaluated the predictive efficiency of the model. Finally, a nomogram was constructed to more accurately predict the prognosis of glioma. We developed a Venn diagram showing 23 autophagy-ferroptosis genes. A total of 660 cases (including RNA sequences and complete clinical information) from two different cohorts (training group = 413, verification group = 247) of the CGGA database was acquired. Cohorts were screened to include five prognosis-related genes (, , , , ). Kaplan-Meier curves showed that the risk model was a good prognostic indicator ( < 0.001). ROC analysis showed good efficacy of the risk model. Multivariate Cox analysis also revealed that the risk model was suitable for clinical factors related to prognosis, including type of disease (primary, recurrence), grade (III-IV), age, temozolomide treatment, and 1p19q state. Using the five prognosis-related genes and the risk score, we constructed a nomogram assessed by C-index (0.7205) and a calibration plot that could more accurately predict glioma prognosis. Using a current database of autophagy and ferroptosis genes, we confirmed the prognostic significance of autophagy-ferroptosis genes in glioma, and we constructed a prognostic model to help guide treatment for high grade glioma in the future.

Citing Articles

Machine learning unveils immune-related signature in multicenter glioma studies.

Yang S, Wang X, Huan R, Deng M, Kong Z, Xiong Y iScience. 2024; 27(4):109317.

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Ferroptosis Modulation: Potential Therapeutic Target for Glioblastoma Treatment.

de Souza I, Ramalho M, Guedes C, Osawa I, Seregni Monteiro L, Gomes L Int J Mol Sci. 2022; 23(13).

PMID: 35805884 PMC: 9266903. DOI: 10.3390/ijms23136879.

A Novel Prognostic Signature Based on Glioma Essential Ferroptosis-Related Genes Predicts Clinical Outcomes and Indicates Treatment in Glioma.

Yun D, Wang X, Wang W, Ren X, Li J, Wang X Front Oncol. 2022; 12:897702.

PMID: 35756689 PMC: 9232254. DOI: 10.3389/fonc.2022.897702.

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