Subtype and prognostic analysis of immunogenic cell death-related gene signature in prostate cancer

Zhen Kang; Jiang-Bo Sun; Fei Lin; Xu-Yun Huang; Qi Huang; Dong-Ning Chen; Qing-Shui Zheng; Xue-Yi Xue; Ning Xu; Yong Wei

doi:10.3389/fonc.2023.1160972

Subtype and prognostic analysis of immunogenic cell death-related gene signature in prostate cancer

Front Oncol. 2023 Jun 6:13:1160972. doi: 10.3389/fonc.2023.1160972. eCollection 2023.

Authors

Zhen Kang^{1

2}, Jiang-Bo Sun^{1

2}, Fei Lin^{1

2}, Xu-Yun Huang^{1

2}, Qi Huang^{1

2}, Dong-Ning Chen^{1

2}, Qing-Shui Zheng^{1

2}, Xue-Yi Xue^{1

2

3}, Ning Xu^{1

2

3}, Yong Wei^{1

2}

Affiliations

¹ Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
² Department of Urology, National Region Medical Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
³ Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

Abstract

Background: Immunogenic cell death (ICD) plays a vital role in tumor progression and immune response. However, the integrative role of ICD-related genes and subtypes in the tumor microenvironment (TME) in prostate cancer (PCa) remains unknown.

Materials and methods: The sample data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Memorial Sloan Kettering Cancer Center (MSKCC) prostate cancer-related databases. We first divided the subtypes based on ICD genes from 901 PCa patients and then identified the prognosis- related genes (PRGs) between different ICD subtypes. Subsequently, all the patients were randomly split into the training and test groups. We developed a risk signature in the training set by least absolute shrinkage and selection operator (LASSO)-Cox regression. Following this, we verified this prognostic signature in both the training test and external test sets. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, the artificial neural network (ANN) and fundamental experiment study were constructed to verify the accuracy of the prognostic signature.

Results: We identified two ICD clusters with immunological features and three gene clusters composed of PRGs. Additionally, we demonstrated that the risk signature can be used to evaluate tumor immune cell infiltration, prognostic status, and an immune checkpoint inhibitor. The low-risk group, which has a high overlap with group C of the gene cluster, is characterized by high ICD levels, immunocompetence, and favorable survival probability. Furthermore, the tumor progression genes selected by the ANN also exhibit potential associations with risk signature genes.

Conclusion: This study identified individuals with high ICD levels in prostate cancer who may have more abundant immune infiltration and revealed the potential effects of risk signature on the TME, immune checkpoint inhibitor, and prognosis of PCa.

Keywords: cancer subtype; immune infiltration; immunogenic cell death; prognostic signature; prostate cancer.

Grants and funding

This study was supported by Science and Technology Innovation Joint Fund project of Fujian province (Grant number: 2021Y9126), the First Affiliated Hospital of Fujian Medical University, Class B talent research project (Grant number: YJCRC-B-XN2022), and the Foundation of Fujian Provincial Department of Finance (BPB-2020XXY).