Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Chaofan Guo; Xin Yang; Lijun Li

doi:10.2147/JIR.S440425

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

J Inflamm Res. 2024 Jan 20:17:417-445. doi: 10.2147/JIR.S440425. eCollection 2024.

Authors

Chaofan Guo^#^{1

2}, Xin Yang^#³, Lijun Li^{1

2}

Affiliations

¹ Department of Orthopedics, Shanxi Provincial People's Hospital, Taiyuan, Shanxi Province, People's Republic of China.
² Department of Spine Surgery, Fifth Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People's Republic of China.
³ Department of Neurosurgery, Chongqing Fourth People's Hospital, Chongqing, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and methods: Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results: The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion: The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

Keywords: TARGET; drug sensitivity; immune microenvironment; nomogram model.

Grants and funding

This work is supported by Science and Technology Innovation Talent Team Project of Shanxi Province (Grant No. 202204051002035). The funders had no role in study design, data collection or analysis, preparation of the manuscript, or the decision to publish.