A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer

Jian Xiong; Junyuan Chen; Zhongming Guo; Chaoyue Zhang; Li Yuan; Kefei Gao

doi:10.3389/fonc.2023.1224071

A novel machine learning-based programmed cell death-related clinical diagnostic and prognostic model associated with immune infiltration in endometrial cancer

Front Oncol. 2023 Jul 18:13:1224071. doi: 10.3389/fonc.2023.1224071. eCollection 2023.

Authors

Jian Xiong¹, Junyuan Chen², Zhongming Guo³, Chaoyue Zhang², Li Yuan³, Kefei Gao¹

Affiliations

¹ Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
² China Medical University, Shenyang, China.
³ Department of Pathology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.

Abstract

Background: To explore the underlying mechanism of programmed cell death (PCD)-related genes in patients with endometrial cancer (EC) and establish a prognostic model.

Methods: The RNA sequencing data (RNAseq), single nucleotide variation (SNV) data, and corresponding clinical data were downloaded from TCGA. The prognostic PCD-related genes were screened and subjected to consensus clustering analysis. The two clusters were compared by weighted correlation network analysis (WGCNA), immune infiltration analysis, and other analyses. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct the PCD-related prognostic model. The biological significance of the PCD-related gene signature was evaluated through various bioinformatics methods.

Results: We identified 43 PCD-related genes that were significantly related to prognoses of EC patients, and classified them into two clusters via consistent clustering analysis. Patients in cluster B had higher tumor purity, higher T stage, and worse prognoses compared to those in cluster A. The latter generally showed higher immune infiltration. A prognostic model was constructed using 11 genes (GZMA, ASNS, GLS, PRKAA2, VLDLR, PRDX6, PSAT1, CDKN2A, SIRT3, TNFRSF1A, LRPPRC), and exhibited good diagnostic performance. Patients with high-risk scores were older, and had higher stage and grade tumors, along with worse prognoses. The frequency of mutations in PCD-related genes was correlated with the risk score. LRPPRC, an adverse prognostic gene in EC, was strongly correlated with proliferation-related genes and multiple PCD-related genes. LRPPRC expression was higher in patients with higher clinical staging and in the deceased patients. In addition, a positive correlation was observed between LRPPRC and infiltration of multiple immune cell types.

Conclusion: We identified a PCD-related gene signature that can predict the prognosis of EC patients and offer potential targets for therapeutic interventions.

Keywords: LASSO; cell assay; cell death; endometrial cancer; immune infiltration; signature.

Grants and funding

This study was funded by the Research Foundation of Guangzhou Women and Children’s Medical Center for Clinical Doctor (2020RC003, 2021BS044), the Science and Technology Program of Guangzhou, China (2023A04J1244), the Plan on enhancing scientific research in GMU (02-410-2302169XM), and the Administration of Traditional Chinese Medicine of Guangdong Province (20201299).