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

Frontiers in Immunology

Immunology

Chenggui Zhang¹  Ke Yin²  Yanjun Wei³  Liang Li⁴  Haizeng Qu⁵  Xiaoqing Ma⁶  Zhe Yang⁷  Renya Zeng⁷  Fengge Zhou⁷  Wentao Zhang⁷  Yuanliu Nie⁸  Ye Wang⁸ 

[1] Department of Orthopedics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China;Department of Pathology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China;Department of Radiation Oncology, Weifang People's Hospital, Weifang, China;Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Shandong, China;Radiotherapy Department, Dongming People’s Hospital, Heze, Shandong, China;Radiotherapy and Minimally Invasive Group I, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, China;Tumor Research and Therapy Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China;Tumor Research and Therapy Center, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China;

关键词: cuproptosis;    immune;    LUAD;    prognosis;    signature;   

DOI  :  10.3389/fimmu.2023.1179742

 received in 2023-03-04, accepted in 2023-07-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundCuproptosis 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.MethodsRNA 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).ResultsWe 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.

【 授权许可】

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Copyright © 2023 Zhang, Qu, Ma, Li, Wei, Wang, Zeng, Nie, Zhang, Yin, Zhou and Yang