A survival model for prognostic prediction based on ferroptosis-associated genes and the association with immune infiltration in lung squamous cell carcinoma

PLoS One. 2023 Mar 16;18(3):e0282888. doi: 10.1371/journal.pone.0282888. eCollection 2023.

Abstract

Lung squamous cell carcinoma (LUSC) is the primary pathological type of lung cancer with a less favorable prognosis. This study attempts to construct a ferroptosis-associated signature associated with overall survival (OS) that can predict the prognosis of LUSC and explore its relationship with immune infiltration. A 5 ferroptosis-associated gene model was constructed by LASSO-penalized regression analysis to predict the prognosis of patients with LUSC in the TCGA database and validated in the GEO and TCGA databases. Patients were stratified into high-risk and low-risk groups by the median value of the risk scores, and the former prognosis was significantly worse (P<0.001). Additionally, we found a certain association between the two risk groups and immune infiltration through CIBERSORT. Meanwhile, the differentially expressed genes (DEGs) between normal and tumor tissue were used to perform functional analysis, which showed a significant association with leukocyte transendothelial migration pathways in the TCGA cohort. In addition, immune cell infiltration analysis confirmed that M2 macrophages were significantly highly expressed in the high-risk group. Overall, the model successfully established by ferroptosis-associated genes suggests that ferroptosis may be related to immune infiltration in LUSC.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carcinoma, Non-Small-Cell Lung*
  • Carcinoma, Squamous Cell* / genetics
  • Ferroptosis* / genetics
  • Humans
  • Lung
  • Lung Neoplasms* / genetics
  • Prognosis

Grants and funding

This research was funded by Guizhou Provincial Health Commission, grant number: gzwjkj2020-1-034, Science and Technology Department of Guizhou Province, grant number: ZK [2021]-452, Dr Zunyi Medical University, grant number: [2017] No.19, Dr Zunyi Medical University, grant number: [2015] No.51 and Zunyi Medical University School-level Education Reform, grant number: XJJG2021-45. All the funders above provided the cost of software training, learning materials, and the service charges for software problems. The financial support provided by each funder is limited, so the successful completion of this study cannot do without the support of all the above funders.