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. 2022 Jan 31;9:812422. doi: 10.3389/fcell.2021.812422

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Copyright © 2022 Cai, Liang, Zhan, Zeng, Lin, Xu, Xue, Xu, Chai, Mao, Song, Han, Xiao, Song and Zhang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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FRGPI was developed based on FRGs for glioma. (A) Differential gene expression heat map, where different colors represent expression trends in different tissues. Due to the large number of differential genes, the 50 up-regulated genes and 50 down-regulated genes with the largest differential changes are shown here. (B) Correlation of weighted gene correlation network analysis (WGCNA) modules with ferroptosis-related subtypes and immuneScore. (C) Upset plot for DEGs between the two ferroptosis-related subtypes, DEGs between the two ferroptosis-related subgroups, FRGs, IRGs and genes in blue module. IRGs, immune-related genes. (D) FRGPI, survival outcome and HMOX1, TFRC, JUN and SOCS1 expression profiles of each sample are shown. (E) Principal component analysis (PCA) plot of glioma samples based on HMOX1, TFRC, JUN and SOCS1 expression profiles. (F) Kaplan–Meier analysis of glioma patients with low or high FRGPI. (G) ROC curves predicted prognostic value of FRGPI in glioma patients.