Identification of a Pyroptosis-Related Gene Signature for Predicting the Immune Status and Prognosis in Lung Adenocarcinoma

Zetian Gong; Qifan Li; Jian Yang; Pengpeng Zhang; Wei Sun; Qianhe Ren; Junjie Tang; Wei Wang; Hui Gong; Jun Li

doi:10.3389/fbioe.2022.852734

Identification of a Pyroptosis-Related Gene Signature for Predicting the Immune Status and Prognosis in Lung Adenocarcinoma

Front Bioeng Biotechnol. 2022 May 12:10:852734. doi: 10.3389/fbioe.2022.852734. eCollection 2022.

Authors

Zetian Gong¹, Qifan Li², Jian Yang², Pengpeng Zhang¹, Wei Sun¹, Qianhe Ren¹, Junjie Tang¹, Wei Wang¹, Hui Gong³, Jun Li¹

Affiliations

¹ Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
² Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
³ Department of Trauma Center, Affiliated Hospital of Nantong University, Nantong, China.

Abstract

Background: Pyroptosis is a form of programmed cell death triggered by the rupture of cell membranes and the release of inflammatory substances; it is essential in the occurrence and development of cancer. A considerable number of studies have revealed that pyroptosis is closely associated to the biological process of several cancers. However, the role of pyroptosis in lung adenocarcinoma (LUAD) remains elusive. The purpose of this study was to explore the prognostic role of pyroptosis-related genes (PRGs) and their relationship with the tumor immune microenvironment (TIME) in LUAD. Methods: Gene expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prognostic PRG signature was established in the training set and verified in the validation sets. Functional enrichment and immune microenvironment analyses related to PRGs were performed and a nomogram based on the risk score and clinical characteristics was established. What is more, quantitative real-time PCR (qRT-PCR) analysis was applied in order to verify the potential biomarkers for LUAD. Results: A prognostic signature based on five PRGs was constructed to separate LUAD patients into two risk groups. Patients in the high-risk group had worse prognoses than those in the low-risk group. The signature was identified as independent via Cox regression analyses and obtained the largest area under the curve (AUC = 0.677) in the receiver operating characteristic (ROC). Functional enrichment and immune microenvironment analyses demonstrated that the immune status was significantly different in the two subgroups and that immunotherapy may be effective for the high-risk group. Furthermore, qRT-PCR analysis verified that serum PRKACA and GPX4 could serve as diagnostic biomarkers for LUAD. Conclusion: Overall, a risk signature based on five PRGs was generated, providing a novel perspective on the determinants of prognosis and survival in LUAD, as well as a basis for the development of individualized regimes.

Keywords: GPX4; PRKACA; TCGA; bioinformatics analysis; immune checkpoint genes; lung adenocarcinoma; pyroptosis.