JOURNAL ARTICLE

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

Abstract

Background Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. Methods RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). Results We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.

Keywords:
Proportional hazards model Immune system Gene Lung cancer Gene signature Biology Oncology Adenocarcinoma Survival analysis Computational biology Gene expression Cancer Medicine Internal medicine Immunology Genetics

Metrics

25
Cited By
8.43
FWCI (Field Weighted Citation Impact)
61
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Ferroptosis and cancer prognosis
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
RNA modifications and cancer
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology
Cancer-related molecular mechanisms research
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Cancer Research
© 2026 ScienceGate Book Chapters — All rights reserved.