Background The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC).

Methods The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis.

Results Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression.

Conclusions The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

L.M. and Y.L. contributed to manuscript preparation. L.Z. and S.T. contributed to the study design, manuscript writing, and data analysis. H.G. and J.S. contributed to clinical data collection. J.S., S.T., H.Y., X.W., and Z.J. contributed to molecular experiments and data statistics analysis. All authors read and approved the final manuscript.

Not applicable.

Not applicable.

The raw data, supporting the conclusions of this manuscript, will be made available by contacting the corresponding author.

This article does not contain any studies with human participants performed by any of the authors. The data from TCGA are publicly available. Thus, the present study was exempted from the approval of local ethics committees. The current research follows the TCGA data access policies and publication guidelines. All data submitted to the TCGA database have been ethically approved. The TCGA data citation guidelines and licenses have been followed.

*The authors contribute to the manuscript equally.

Article published online:
30 October 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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