Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Novel Anoikis-Related Gene Signature Predicts Prognosis in Patients with Low-Grade Glioma and Reveals Immune Infiltration

Songyun Zhao
ORCID logo ,
Hao Chi
ORCID logo ,
Wei Ji
,
Qisheng He
,
Guichuan Lai
ORCID logo ,
Gaoge Peng
ORCID logo ,
Xiaoyu Zhao
,
Chao Cheng
* ORCID logo ,
Weiyi Huang
Version 1 : Received: 21 September 2022 / Approved: 22 September 2022 / Online: 22 September 2022 (10:38:43 CEST)

A peer-reviewed article of this Preprint also exists.

Zhao, S.; Chi, H.; Ji, W.; He, Q.; Lai, G.; Peng, G.; Zhao, X.; Cheng, C. A Bioinformatics-Based Analysis of an Anoikis-Related Gene Signature Predicts the Prognosis of Patients with Low-Grade Gliomas. Brain Sci. 2022, 12, 1349. Zhao, S.; Chi, H.; Ji, W.; He, Q.; Lai, G.; Peng, G.; Zhao, X.; Cheng, C. A Bioinformatics-Based Analysis of an Anoikis-Related Gene Signature Predicts the Prognosis of Patients with Low-Grade Gliomas. Brain Sci. 2022, 12, 1349.

Abstract

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study，anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA cohort and investigated the differences in clinical features, mutational landscape, immune cell infiltration, etc. between different risk groups. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher immune cell infiltration, tumor mutation load, immune checkpoint gene expression, and ICB treatment response. Functional analysis showed that these high- and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and DCA analysis showed that the model could enable patients to benefit from clinical treatment strategies.

Keywords

anoikis; Low-grade glioma; signature; prognosis; immune microenvironment

Subject

Medicine and Pharmacology, Oncology and Oncogenics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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